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2023[ to top ]

Magnussen, B.M., Stern, C., Sick, B.: Utilizing Continuous Kernels for Processing Irregularly and Inconsistently Sampled Data With Position-Dependent Features In: International Conference on Autonomic and Autonomous Systems (ICAS). bll 49–53. ThinkMind (2023).
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Continuous Kernels have been a recent development in convolutional neural networks. Such kernels are used to process data sampled at different resolutions as well as irregularly and inconsistently sampled data. Convolutional neural networks have the property of translational invariance (e.g., features are detected regardless of their position in the measurement domain), which is unsuitable for certain types of data, where the position of detected features is relevant. However, the capabilities of continuous kernels to process irregularly sampled data are still desired. This article introduces a novel method utilizing continuous kernels for detecting global features at absolute positions in the data domain. Through a use case in processing multiple spatially resolved reflection spectroscopy data, which is sampled irregularly and inconsistently, we show that the proposed method is capable of processing such data natively without additional preprocessing as is needed using comparable methods. In addition, we show that the proposed method is able to achieve a higher prediction accuracy than a comparable network on a dataset with position-dependent features. Furthermore, a higher robustness to missing data compared to a benchmark network using data interpolation is observed, which allows the network to adapt to sensors with individual failed components without the need for retraining.
@inproceedings{magnussen2023utilizing, abstract = {Continuous Kernels have been a recent development in convolutional neural networks. Such kernels are used to process data sampled at different resolutions as well as irregularly and inconsistently sampled data. Convolutional neural networks have the property of translational invariance (e.g., features are detected regardless of their position in the measurement domain), which is unsuitable for certain types of data, where the position of detected features is relevant. However, the capabilities of continuous kernels to process irregularly sampled data are still desired. This article introduces a novel method utilizing continuous kernels for detecting global features at absolute positions in the data domain. Through a use case in processing multiple spatially resolved reflection spectroscopy data, which is sampled irregularly and inconsistently, we show that the proposed method is capable of processing such data natively without additional preprocessing as is needed using comparable methods. In addition, we show that the proposed method is able to achieve a higher prediction accuracy than a comparable network on a dataset with position-dependent features. Furthermore, a higher robustness to missing data compared to a benchmark network using data interpolation is observed, which allows the network to adapt to sensors with individual failed components without the need for retraining.}, author = {Magnussen, Birk Martin and Stern, Claudius and Sick, Bernhard}, booktitle = {International Conference on Autonomic and Autonomous Systems (ICAS)}, keywords = {itegpub}, pages = {49–53}, publisher = {ThinkMind}, title = {Utilizing Continuous Kernels for Processing Irregularly and Inconsistently Sampled Data With Position-Dependent Features}, year = 2023 }
%0 Conference Paper %1 magnussen2023utilizing %A Magnussen, Birk Martin %A Stern, Claudius %A Sick, Bernhard %B International Conference on Autonomic and Autonomous Systems (ICAS) %D 2023 %I ThinkMind %P 49--53 %T Utilizing Continuous Kernels for Processing Irregularly and Inconsistently Sampled Data With Position-Dependent Features %U http://www.thinkmind.org/index.php?view=article&articleid=icas_2023_1_90_20043 %X Continuous Kernels have been a recent development in convolutional neural networks. Such kernels are used to process data sampled at different resolutions as well as irregularly and inconsistently sampled data. Convolutional neural networks have the property of translational invariance (e.g., features are detected regardless of their position in the measurement domain), which is unsuitable for certain types of data, where the position of detected features is relevant. However, the capabilities of continuous kernels to process irregularly sampled data are still desired. This article introduces a novel method utilizing continuous kernels for detecting global features at absolute positions in the data domain. Through a use case in processing multiple spatially resolved reflection spectroscopy data, which is sampled irregularly and inconsistently, we show that the proposed method is capable of processing such data natively without additional preprocessing as is needed using comparable methods. In addition, we show that the proposed method is able to achieve a higher prediction accuracy than a comparable network on a dataset with position-dependent features. Furthermore, a higher robustness to missing data compared to a benchmark network using data interpolation is observed, which allows the network to adapt to sensors with individual failed components without the need for retraining.
Hetzel, M., Reichert, H., Reitberger, G., Doll, K., Sick, B., Fuchs, E.: The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset In: IEEE Intelligent Vehicles Symposium (IV). IEEE (2023).
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@inproceedings{hetzel2023imptc, author = {Hetzel, Manuel and Reichert, Hannes and Reitberger, Günther and Doll, Konrad and Sick, Bernhard and Fuchs, Erich}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, keywords = {itegpub}, note = {(accepted)}, publisher = {IEEE}, title = {The {IMPTC} Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset}, year = 2023 }
%0 Conference Paper %1 hetzel2023imptc %A Hetzel, Manuel %A Reichert, Hannes %A Reitberger, Günther %A Doll, Konrad %A Sick, Bernhard %A Fuchs, Erich %B IEEE Intelligent Vehicles Symposium (IV) %D 2023 %I IEEE %T The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset
Reichert, H., Hetzel, M., Schreck, S., Doll, K., Sick, B.: Sensor Equivariance by LiDAR Projection Images In: IEEE Intelligent Vehicles Symposium (IV). IEEE (2023).
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In this work, we propose an extension of conventional image data by an additional channel in which the associated projection properties are encoded. This addresses the issue of sensor-dependent object representation in projection-based sensors, such as LiDAR, which can lead to distorted physical and geometric properties due to variations in sensor resolution and field of view. To that end, we propose an architecture for processing this data in an instance segmentation framework. We focus specifically on LiDAR as a key sensor modality for machine vision tasks and highly automated driving (HAD). Through an experimental setup in a controlled synthetic environment, we identify a bias on sensor resolution and field of view and demonstrate that our proposed method can reduce said bias for the task of LiDAR instance segmentation. Furthermore, we define our method such that it can be applied to other projection-based sensors, such as cameras. To promote transparency, we make our code and dataset publicly available. This method shows the potential to improve performance and robustness in various machine vision tasks that utilize projection-based sensors.
@inproceedings{reichert2023sensor, abstract = {In this work, we propose an extension of conventional image data by an additional channel in which the associated projection properties are encoded. This addresses the issue of sensor-dependent object representation in projection-based sensors, such as LiDAR, which can lead to distorted physical and geometric properties due to variations in sensor resolution and field of view. To that end, we propose an architecture for processing this data in an instance segmentation framework. We focus specifically on LiDAR as a key sensor modality for machine vision tasks and highly automated driving (HAD). Through an experimental setup in a controlled synthetic environment, we identify a bias on sensor resolution and field of view and demonstrate that our proposed method can reduce said bias for the task of LiDAR instance segmentation. Furthermore, we define our method such that it can be applied to other projection-based sensors, such as cameras. To promote transparency, we make our code and dataset publicly available. This method shows the potential to improve performance and robustness in various machine vision tasks that utilize projection-based sensors.}, author = {Reichert, Hannes and Hetzel, Manuel and Schreck, Steven and Doll, Konrad and Sick, Bernhard}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, keywords = {itegpub}, note = {(accepted)}, publisher = {IEEE}, title = {Sensor Equivariance by {LiDAR} Projection Images}, year = 2023 }
%0 Conference Paper %1 reichert2023sensor %A Reichert, Hannes %A Hetzel, Manuel %A Schreck, Steven %A Doll, Konrad %A Sick, Bernhard %B IEEE Intelligent Vehicles Symposium (IV) %D 2023 %I IEEE %T Sensor Equivariance by LiDAR Projection Images %X In this work, we propose an extension of conventional image data by an additional channel in which the associated projection properties are encoded. This addresses the issue of sensor-dependent object representation in projection-based sensors, such as LiDAR, which can lead to distorted physical and geometric properties due to variations in sensor resolution and field of view. To that end, we propose an architecture for processing this data in an instance segmentation framework. We focus specifically on LiDAR as a key sensor modality for machine vision tasks and highly automated driving (HAD). Through an experimental setup in a controlled synthetic environment, we identify a bias on sensor resolution and field of view and demonstrate that our proposed method can reduce said bias for the task of LiDAR instance segmentation. Furthermore, we define our method such that it can be applied to other projection-based sensors, such as cameras. To promote transparency, we make our code and dataset publicly available. This method shows the potential to improve performance and robustness in various machine vision tasks that utilize projection-based sensors.
Heidecker, F., El-Khateeb, A., Sick, B.: Sampling-based Uncertainty Estimation for an Instance Segmentation Network arXiv e-prints. arXiv:2305.14977 (2023).
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The examination of uncertainty in the predictions of machine learning (ML) models is receiving increasing attention. One uncertainty modeling technique used for this purpose is Monte-Carlo (MC)-Dropout, where repeated predictions are generated for a single input. Therefore, clustering is required to describe the resulting uncertainty, but only through efficient clustering is it possible to describe the uncertainty from the model attached to each object. This article uses Bayesian Gaussian Mixture (BGM) to solve this problem. In addition, we investigate different values for the dropout rate and other techniques, such as focal loss and calibration, which we integrate into the Mask-RCNN model to obtain the most accurate uncertainty approximation of each instance and showcase it graphically.
@article{heidecker2023sampling, abstract = {The examination of uncertainty in the predictions of machine learning (ML) models is receiving increasing attention. One uncertainty modeling technique used for this purpose is Monte-Carlo (MC)-Dropout, where repeated predictions are generated for a single input. Therefore, clustering is required to describe the resulting uncertainty, but only through efficient clustering is it possible to describe the uncertainty from the model attached to each object. This article uses Bayesian Gaussian Mixture (BGM) to solve this problem. In addition, we investigate different values for the dropout rate and other techniques, such as focal loss and calibration, which we integrate into the Mask-RCNN model to obtain the most accurate uncertainty approximation of each instance and showcase it graphically.}, author = {Heidecker, Florian and El-Khateeb, Ahmad and Sick, Bernhard}, journal = {arXiv e-prints}, keywords = {itegpub}, pages = {arXiv:2305.14977}, title = {Sampling-based Uncertainty Estimation for an Instance Segmentation Network}, year = 2023 }
%0 Journal Article %1 heidecker2023sampling %A Heidecker, Florian %A El-Khateeb, Ahmad %A Sick, Bernhard %D 2023 %J arXiv e-prints %P arXiv:2305.14977 %T Sampling-based Uncertainty Estimation for an Instance Segmentation Network %U https://arxiv.org/abs/2305.14977 %X The examination of uncertainty in the predictions of machine learning (ML) models is receiving increasing attention. One uncertainty modeling technique used for this purpose is Monte-Carlo (MC)-Dropout, where repeated predictions are generated for a single input. Therefore, clustering is required to describe the resulting uncertainty, but only through efficient clustering is it possible to describe the uncertainty from the model attached to each object. This article uses Bayesian Gaussian Mixture (BGM) to solve this problem. In addition, we investigate different values for the dropout rate and other techniques, such as focal loss and calibration, which we integrate into the Mask-RCNN model to obtain the most accurate uncertainty approximation of each instance and showcase it graphically.
Meier, D., Ragunathan, R., Degener, S., Liehr, A., Vollmer, M., Niendorf, T., Sick, B.: Reconstruction of incomplete X-ray diffraction pole figures of oligocrystalline materials using deep learning Scientific Reports. 13, 5410 (2023). https://doi.org/10.1038/s41598-023-31580-1.
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X‑ray diffraction crystallography allows non‑destructive examination of crystal structures. Furthermore, it has low requirements regarding surface preparation, especially compared to electron backscatter diffraction. However, up to now, X‑ray diffraction has been highly time‑ consuming in standard laboratory conditions since intensities on multiple lattice planes have to be recorded by rotating and tilting. Furthermore, examining oligocrystalline materials is challenging due to the limited number of diffraction spots. Moreover, commonly used evaluation methods for crystallographic orientation analysis need multiple lattice planes for a reliable pole figure reconstruction. In this article, we propose a deep‑learning‑based method for oligocrystalline specimens, i.e., specimens with up to three grains of arbitrary crystal orientations. Our approach allows faster experimentation due to accurate reconstructions of pole figure regions, which we did not probe experimentally. In contrast to other methods, the pole figure is reconstructed based on only a single incomplete pole figure. To speed up the development of our proposed method and for usage in other machine learning algorithms, we introduce a GPU‑based simulation for data generation. Furthermore, we present a pole widths standardization technique using a custom deep learning architecture that makes algorithms more robust against influences from the experiment setup and material.
@article{meier2023reconstruction, abstract = {X‑ray diffraction crystallography allows non‑destructive examination of crystal structures. Furthermore, it has low requirements regarding surface preparation, especially compared to electron backscatter diffraction. However, up to now, X‑ray diffraction has been highly time‑ consuming in standard laboratory conditions since intensities on multiple lattice planes have to be recorded by rotating and tilting. Furthermore, examining oligocrystalline materials is challenging due to the limited number of diffraction spots. Moreover, commonly used evaluation methods for crystallographic orientation analysis need multiple lattice planes for a reliable pole figure reconstruction. In this article, we propose a deep‑learning‑based method for oligocrystalline specimens, i.e., specimens with up to three grains of arbitrary crystal orientations. Our approach allows faster experimentation due to accurate reconstructions of pole figure regions, which we did not probe experimentally. In contrast to other methods, the pole figure is reconstructed based on only a single incomplete pole figure. To speed up the development of our proposed method and for usage in other machine learning algorithms, we introduce a GPU‑based simulation for data generation. Furthermore, we present a pole widths standardization technique using a custom deep learning architecture that makes algorithms more robust against influences from the experiment setup and material.}, author = {Meier, David and Ragunathan, Rishan and Degener, Sebastian and Liehr, Alexander and Vollmer, Malte and Niendorf, Thomas and Sick, Bernhard}, journal = {Scientific Reports}, keywords = {itegpub}, number = 1, pages = 5410, publisher = {Springer Nature}, title = {Reconstruction of incomplete {X}-ray diffraction pole figures of oligocrystalline materials using deep learning}, volume = 13, year = 2023 }
%0 Journal Article %1 meier2023reconstruction %A Meier, David %A Ragunathan, Rishan %A Degener, Sebastian %A Liehr, Alexander %A Vollmer, Malte %A Niendorf, Thomas %A Sick, Bernhard %D 2023 %I Springer Nature %J Scientific Reports %N 1 %P 5410 %R 10.1038/s41598-023-31580-1 %T Reconstruction of incomplete X-ray diffraction pole figures of oligocrystalline materials using deep learning %U https://www.nature.com/articles/s41598-023-31580-1 %V 13 %X X‑ray diffraction crystallography allows non‑destructive examination of crystal structures. Furthermore, it has low requirements regarding surface preparation, especially compared to electron backscatter diffraction. However, up to now, X‑ray diffraction has been highly time‑ consuming in standard laboratory conditions since intensities on multiple lattice planes have to be recorded by rotating and tilting. Furthermore, examining oligocrystalline materials is challenging due to the limited number of diffraction spots. Moreover, commonly used evaluation methods for crystallographic orientation analysis need multiple lattice planes for a reliable pole figure reconstruction. In this article, we propose a deep‑learning‑based method for oligocrystalline specimens, i.e., specimens with up to three grains of arbitrary crystal orientations. Our approach allows faster experimentation due to accurate reconstructions of pole figure regions, which we did not probe experimentally. In contrast to other methods, the pole figure is reconstructed based on only a single incomplete pole figure. To speed up the development of our proposed method and for usage in other machine learning algorithms, we introduce a GPU‑based simulation for data generation. Furthermore, we present a pole widths standardization technique using a custom deep learning architecture that makes algorithms more robust against influences from the experiment setup and material.
Beinert, D., Holzhüter, C., Thomas, J., Vogt, S.: Power flow forecasts at transmission grid nodes using Graph Neural Networks Energy and AI. 14, 100262 (2023). https://doi.org/10.1016/j.egyai.2023.100262.
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The increasing share of renewable energy in the electricity grid and progressing changes in power consumption have led to fluctuating, and weather-dependent power flows. To ensure grid stability, grid operators rely on power forecasts which are crucial for grid calculations and planning. In this paper, a Multi-Task Learning approach is combined with a Graph Neural Network (GNN) to predict vertical power flows at transformers connecting high and extra-high voltage levels. The proposed method accounts for local differences in power flow characteristics by using an Embedding Multi-Task Learning approach. The use of a Bayesian embedding to capture the latent node characteristics allows to share the weights across all transformers in the subsequent node-invariant GNN while still allowing the individual behavioral patterns of the transformers to be distinguished. At the same time, dependencies between transformers are considered by the GNN architecture which can learn relationships between different transformers and thus take into account that power flows in an electricity network are not independent from each other. The effectiveness of the proposed method is demonstrated through evaluation on two real-world data sets provided by two of four German Transmission System Operators, comprising large portions of the operated German transmission grid. The results show that the proposed Multi-Task Graph Neural Network is a suitable representation learner for electricity networks with a clear advantage provided by the preceding embedding layer. It is able to capture interconnections between correlated transformers and indeed improves the performance in power flow prediction compared to standard Neural Networks. A sign test shows that the proposed model reduces the test RMSE on both data sets compared to the benchmark models significantly.
@article{beinert2023power, abstract = {The increasing share of renewable energy in the electricity grid and progressing changes in power consumption have led to fluctuating, and weather-dependent power flows. To ensure grid stability, grid operators rely on power forecasts which are crucial for grid calculations and planning. In this paper, a Multi-Task Learning approach is combined with a Graph Neural Network (GNN) to predict vertical power flows at transformers connecting high and extra-high voltage levels. The proposed method accounts for local differences in power flow characteristics by using an Embedding Multi-Task Learning approach. The use of a Bayesian embedding to capture the latent node characteristics allows to share the weights across all transformers in the subsequent node-invariant GNN while still allowing the individual behavioral patterns of the transformers to be distinguished. At the same time, dependencies between transformers are considered by the GNN architecture which can learn relationships between different transformers and thus take into account that power flows in an electricity network are not independent from each other. The effectiveness of the proposed method is demonstrated through evaluation on two real-world data sets provided by two of four German Transmission System Operators, comprising large portions of the operated German transmission grid. The results show that the proposed Multi-Task Graph Neural Network is a suitable representation learner for electricity networks with a clear advantage provided by the preceding embedding layer. It is able to capture interconnections between correlated transformers and indeed improves the performance in power flow prediction compared to standard Neural Networks. A sign test shows that the proposed model reduces the test RMSE on both data sets compared to the benchmark models significantly.}, author = {Beinert, Dominik and Holzhüter, Clara and Thomas, Josephine and Vogt, Stephan}, journal = {Energy and AI}, keywords = {itegpub}, number = 1, pages = 100262, publisher = {Elsevier}, title = {Power flow forecasts at transmission grid nodes using Graph Neural Networks}, volume = 14, year = 2023 }
%0 Journal Article %1 beinert2023power %A Beinert, Dominik %A Holzhüter, Clara %A Thomas, Josephine %A Vogt, Stephan %D 2023 %I Elsevier %J Energy and AI %N 1 %P 100262 %R 10.1016/j.egyai.2023.100262 %T Power flow forecasts at transmission grid nodes using Graph Neural Networks %U https://www.sciencedirect.com/science/article/pii/S2666546823000344?via%3Dihub %V 14 %X The increasing share of renewable energy in the electricity grid and progressing changes in power consumption have led to fluctuating, and weather-dependent power flows. To ensure grid stability, grid operators rely on power forecasts which are crucial for grid calculations and planning. In this paper, a Multi-Task Learning approach is combined with a Graph Neural Network (GNN) to predict vertical power flows at transformers connecting high and extra-high voltage levels. The proposed method accounts for local differences in power flow characteristics by using an Embedding Multi-Task Learning approach. The use of a Bayesian embedding to capture the latent node characteristics allows to share the weights across all transformers in the subsequent node-invariant GNN while still allowing the individual behavioral patterns of the transformers to be distinguished. At the same time, dependencies between transformers are considered by the GNN architecture which can learn relationships between different transformers and thus take into account that power flows in an electricity network are not independent from each other. The effectiveness of the proposed method is demonstrated through evaluation on two real-world data sets provided by two of four German Transmission System Operators, comprising large portions of the operated German transmission grid. The results show that the proposed Multi-Task Graph Neural Network is a suitable representation learner for electricity networks with a clear advantage provided by the preceding embedding layer. It is able to capture interconnections between correlated transformers and indeed improves the performance in power flow prediction compared to standard Neural Networks. A sign test shows that the proposed model reduces the test RMSE on both data sets compared to the benchmark models significantly.
Thomas, J., Moallemy-Oureh, A., Beddar-Wiesing, S., Holzhüter, C.: Graph Neural Networks Designed for Different Graph Types: A Survey Transactions on Machine Learning Research. (2023).
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Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. For this purpose, they can be defined as many different types which suitably reflect the individual contexts of the represented problem. To address cutting-edge problems based on graph data, the research field of Graph Neural Networks (GNNs) has emerged. Despite the field’s youth and the speed at which new models are developed, many recent surveys have been published to keep track of them. Nevertheless, it has not yet been gathered which GNN can process what kind of graph types. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static and dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover, we distinguish between GNN models for discrete-time or continuous-time dynamic graphs and group the models according to their architecture. We find that there are still graph types that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.
@article{thomas2023graph, abstract = {Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. For this purpose, they can be defined as many different types which suitably reflect the individual contexts of the represented problem. To address cutting-edge problems based on graph data, the research field of Graph Neural Networks (GNNs) has emerged. Despite the field’s youth and the speed at which new models are developed, many recent surveys have been published to keep track of them. Nevertheless, it has not yet been gathered which GNN can process what kind of graph types. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static and dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover, we distinguish between GNN models for discrete-time or continuous-time dynamic graphs and group the models according to their architecture. We find that there are still graph types that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.}, author = {Thomas, Josephine and Moallemy-Oureh, Alice and Beddar-Wiesing, Silvia and Holzhüter, Clara}, journal = {Transactions on Machine Learning Research}, keywords = {itegpub}, title = {Graph Neural Networks Designed for Different Graph Types: A Survey}, year = 2023 }
%0 Journal Article %1 thomas2023graph %A Thomas, Josephine %A Moallemy-Oureh, Alice %A Beddar-Wiesing, Silvia %A Holzhüter, Clara %D 2023 %J Transactions on Machine Learning Research %T Graph Neural Networks Designed for Different Graph Types: A Survey %U https://openreview.net/pdf?id=h4BYtZ79uy %X Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. For this purpose, they can be defined as many different types which suitably reflect the individual contexts of the represented problem. To address cutting-edge problems based on graph data, the research field of Graph Neural Networks (GNNs) has emerged. Despite the field’s youth and the speed at which new models are developed, many recent surveys have been published to keep track of them. Nevertheless, it has not yet been gathered which GNN can process what kind of graph types. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static and dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover, we distinguish between GNN models for discrete-time or continuous-time dynamic graphs and group the models according to their architecture. We find that there are still graph types that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.
Eider, M., Sick, B., Berl, A.: Context-aware recommendations for extended electric vehicle battery lifetime Sustainable Computing: Informatics and Systems (SUSCOM). 37, 100845 (2023). https://doi.org/10.1016/j.suscom.2022.100845.
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Electric vehicles are a means of reducing CO2 emissions in transportation. However, the sustainability of electric vehicle batteries is affected by battery health degradation, which decreases their overall lifetime. This results in a substantial amount of depleted batteries due to replacements. Users have a major impact on battery health degradation through their actions while handling electric vehicles, such as the use of fast charging. To mitigate this problem, this article presents a methodology to generate user guidance for batteryfriendly actions in the upcoming use. Therefore, we first identify general recommendations from related work and combine them with the vehicle context in order to define context-aware recommendations in the form of if–then rules. These context-aware recommendations are then used to generate user advice. Second, the article covers how to predict the vehicle context in order to determine necessary recommendations. Third, a prescriptive recommendation system architecture is proposed, which takes vehicle context information, and produces user guidance. Finally, we test the proposed architecture using fuzzy logic as decision system. Overall, the architecture provides satisfactory user advice
@article{eider2023context, abstract = {Electric vehicles are a means of reducing CO2 emissions in transportation. However, the sustainability of electric vehicle batteries is affected by battery health degradation, which decreases their overall lifetime. This results in a substantial amount of depleted batteries due to replacements. Users have a major impact on battery health degradation through their actions while handling electric vehicles, such as the use of fast charging. To mitigate this problem, this article presents a methodology to generate user guidance for batteryfriendly actions in the upcoming use. Therefore, we first identify general recommendations from related work and combine them with the vehicle context in order to define context-aware recommendations in the form of if–then rules. These context-aware recommendations are then used to generate user advice. Second, the article covers how to predict the vehicle context in order to determine necessary recommendations. Third, a prescriptive recommendation system architecture is proposed, which takes vehicle context information, and produces user guidance. Finally, we test the proposed architecture using fuzzy logic as decision system. Overall, the architecture provides satisfactory user advice}, author = {Eider, Markus and Sick, Bernhard and Berl, Andreas}, journal = {Sustainable Computing: Informatics and Systems (SUSCOM)}, keywords = {itegpub}, pages = 100845, publisher = {Elsevier}, title = {Context-aware recommendations for extended electric vehicle battery lifetime}, volume = 37, year = 2023 }
%0 Journal Article %1 eider2023context %A Eider, Markus %A Sick, Bernhard %A Berl, Andreas %D 2023 %I Elsevier %J Sustainable Computing: Informatics and Systems (SUSCOM) %P 100845 %R 10.1016/j.suscom.2022.100845 %T Context-aware recommendations for extended electric vehicle battery lifetime %V 37 %X Electric vehicles are a means of reducing CO2 emissions in transportation. However, the sustainability of electric vehicle batteries is affected by battery health degradation, which decreases their overall lifetime. This results in a substantial amount of depleted batteries due to replacements. Users have a major impact on battery health degradation through their actions while handling electric vehicles, such as the use of fast charging. To mitigate this problem, this article presents a methodology to generate user guidance for batteryfriendly actions in the upcoming use. Therefore, we first identify general recommendations from related work and combine them with the vehicle context in order to define context-aware recommendations in the form of if–then rules. These context-aware recommendations are then used to generate user advice. Second, the article covers how to predict the vehicle context in order to determine necessary recommendations. Third, a prescriptive recommendation system architecture is proposed, which takes vehicle context information, and produces user guidance. Finally, we test the proposed architecture using fuzzy logic as decision system. Overall, the architecture provides satisfactory user advice

2022[ to top ]

Beddar-Wiesing, S., D’Inverno, G.A., Graziani, C., Lachi, V., Moallemy-Oureh, A., Scarselli, F., Thomas, J.: Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs arXiv e-prints. arXiv:2210.03990 (2022).
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Graph Neural Networks (GNNs) are a large class of relational models for graph processing. Recent theoretical studies on the expressive power of GNNs have focused on two issues. On the one hand, it has been proven that GNNs are as powerful as the Weisfeiler-Lehman test (1-WL) in their ability to distinguish graphs. Moreover, it has been shown that the equivalence enforced by 1-WL equals unfolding equivalence. On the other hand, GNNs turned out to be universal approximators on graphs modulo the constraints enforced by 1-WL/unfolding equivalence. However, these results only apply to Static Undirected Homogeneous Graphs with node attributes. In contrast, real-life applications often involve a variety of graph properties, such as, e.g., dynamics or node and edge attributes. In this paper, we conduct a theoretical analysis of the expressive power of GNNs for these two graph types that are particularly of interest. Dynamic graphs are widely used in modern applications, and its theoretical analysis requires new approaches. The attributed type acts as a standard form for all graph types since it has been shown that all graph types can be transformed without loss to Static Undirected Homogeneous Graphs with attributes on nodes and edges (SAUHG). The study considers generic GNN models and proposes appropriate 1-WL tests for those domains. Then, the results on the expressive power of GNNs are extended by proving that GNNs have the same capability as the 1-WL test in distinguishing dynamic and attributed graphs, the 1-WL equivalence equals unfolding equivalence and that GNNs are universal approximators modulo 1-WL/unfolding equivalence. Moreover, the proof of the approximation capability holds for SAUHGs, which include most of those used in practical applications, and it is constructive in nature allowing to deduce hints on the architecture of GNNs that can achieve the desired accuracy.
@article{beddarwiesing2022weisfeiler, abstract = {Graph Neural Networks (GNNs) are a large class of relational models for graph processing. Recent theoretical studies on the expressive power of GNNs have focused on two issues. On the one hand, it has been proven that GNNs are as powerful as the Weisfeiler-Lehman test (1-WL) in their ability to distinguish graphs. Moreover, it has been shown that the equivalence enforced by 1-WL equals unfolding equivalence. On the other hand, GNNs turned out to be universal approximators on graphs modulo the constraints enforced by 1-WL/unfolding equivalence. However, these results only apply to Static Undirected Homogeneous Graphs with node attributes. In contrast, real-life applications often involve a variety of graph properties, such as, e.g., dynamics or node and edge attributes. In this paper, we conduct a theoretical analysis of the expressive power of GNNs for these two graph types that are particularly of interest. Dynamic graphs are widely used in modern applications, and its theoretical analysis requires new approaches. The attributed type acts as a standard form for all graph types since it has been shown that all graph types can be transformed without loss to Static Undirected Homogeneous Graphs with attributes on nodes and edges (SAUHG). The study considers generic GNN models and proposes appropriate 1-WL tests for those domains. Then, the results on the expressive power of GNNs are extended by proving that GNNs have the same capability as the 1-WL test in distinguishing dynamic and attributed graphs, the 1-WL equivalence equals unfolding equivalence and that GNNs are universal approximators modulo 1-WL/unfolding equivalence. Moreover, the proof of the approximation capability holds for SAUHGs, which include most of those used in practical applications, and it is constructive in nature allowing to deduce hints on the architecture of GNNs that can achieve the desired accuracy.}, author = {Beddar-Wiesing, Silvia and D'Inverno, Giuseppe Alessio and Graziani, Caterina and Lachi, Veronica and Moallemy-Oureh, Alice and Scarselli, Franco and Thomas, Josephine}, journal = {arXiv e-prints}, keywords = {itegpub}, pages = {arXiv:2210.03990}, title = {{W}eisfeiler-{L}ehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs}, year = 2022 }
%0 Journal Article %1 beddarwiesing2022weisfeiler %A Beddar-Wiesing, Silvia %A D'Inverno, Giuseppe Alessio %A Graziani, Caterina %A Lachi, Veronica %A Moallemy-Oureh, Alice %A Scarselli, Franco %A Thomas, Josephine %D 2022 %J arXiv e-prints %P arXiv:2210.03990 %T Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs %U https://arxiv.org/abs/2210.03990 %X Graph Neural Networks (GNNs) are a large class of relational models for graph processing. Recent theoretical studies on the expressive power of GNNs have focused on two issues. On the one hand, it has been proven that GNNs are as powerful as the Weisfeiler-Lehman test (1-WL) in their ability to distinguish graphs. Moreover, it has been shown that the equivalence enforced by 1-WL equals unfolding equivalence. On the other hand, GNNs turned out to be universal approximators on graphs modulo the constraints enforced by 1-WL/unfolding equivalence. However, these results only apply to Static Undirected Homogeneous Graphs with node attributes. In contrast, real-life applications often involve a variety of graph properties, such as, e.g., dynamics or node and edge attributes. In this paper, we conduct a theoretical analysis of the expressive power of GNNs for these two graph types that are particularly of interest. Dynamic graphs are widely used in modern applications, and its theoretical analysis requires new approaches. The attributed type acts as a standard form for all graph types since it has been shown that all graph types can be transformed without loss to Static Undirected Homogeneous Graphs with attributes on nodes and edges (SAUHG). The study considers generic GNN models and proposes appropriate 1-WL tests for those domains. Then, the results on the expressive power of GNNs are extended by proving that GNNs have the same capability as the 1-WL test in distinguishing dynamic and attributed graphs, the 1-WL equivalence equals unfolding equivalence and that GNNs are universal approximators modulo 1-WL/unfolding equivalence. Moreover, the proof of the approximation capability holds for SAUHGs, which include most of those used in practical applications, and it is constructive in nature allowing to deduce hints on the architecture of GNNs that can achieve the desired accuracy.
Nivarthi, C.P., Vogt, S., Sick, B.: Unified Autoencoder with Task Embeddings for Multi-Task Learning in Renewable Power Forecasting In: International Conference on Machine Learning and Applications (ICMLA). bll 1530–1536. IEEE (2022). https://doi.org/10.1109/ICMLA55696.2022.00240.
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Renewable power generation forecasts using machine learning are typically implemented as single-task learning models, where a separate model is trained for each photovoltaic or wind park. In recent years, transfer learning is gaining popularity in these systems, as it can be used to transfer the knowledge gained from source parks to a target park. However, for transferring the knowledge to a target park, there is a need to determine the most similar source park(s) among the existing parks. This similarity determination using historical power measurements is challenging when the target park has limited to no historical data samples. Therefore, we propose a simple multi-task learning architecture that initially learns a common representation of input weather features among the tasks, using a Unified Autoencoder (UAE) and then learns the task specific information utilizing a Task Embedding layer in a Neural Network (TENN). This proposed architecture, UAE-TENN, can be easily extended to new parks with or without historical data. An elaborate performance comparison of single and multi-task learning models is performed on six photovoltaic and wind farm datasets comprising a total of 529 parks. UAE-TENN significantly improves the performance of power forecasting by 10 to 19% for photovoltaic parks and 5 to 22% for wind parks compared to the baseline models. Even in the zero-shot learning scenario, when there is no historical data, we successfully demonstrate that the UAE-TENN improves the forecast accuracy for a new park by 19% for photovoltaic parks.
@inproceedings{nivarthi2022unified, abstract = {Renewable power generation forecasts using machine learning are typically implemented as single-task learning models, where a separate model is trained for each photovoltaic or wind park. In recent years, transfer learning is gaining popularity in these systems, as it can be used to transfer the knowledge gained from source parks to a target park. However, for transferring the knowledge to a target park, there is a need to determine the most similar source park(s) among the existing parks. This similarity determination using historical power measurements is challenging when the target park has limited to no historical data samples. Therefore, we propose a simple multi-task learning architecture that initially learns a common representation of input weather features among the tasks, using a Unified Autoencoder (UAE) and then learns the task specific information utilizing a Task Embedding layer in a Neural Network (TENN). This proposed architecture, UAE-TENN, can be easily extended to new parks with or without historical data. An elaborate performance comparison of single and multi-task learning models is performed on six photovoltaic and wind farm datasets comprising a total of 529 parks. UAE-TENN significantly improves the performance of power forecasting by 10 to 19% for photovoltaic parks and 5 to 22% for wind parks compared to the baseline models. Even in the zero-shot learning scenario, when there is no historical data, we successfully demonstrate that the UAE-TENN improves the forecast accuracy for a new park by 19% for photovoltaic parks.}, author = {Nivarthi, Chandana Priya and Vogt, Stephan and Sick, Bernhard}, booktitle = {International Conference on Machine Learning and Applications (ICMLA)}, keywords = {itegpub}, pages = {1530–1536}, publisher = {IEEE}, title = {Unified Autoencoder with Task Embeddings for Multi-Task Learning in Renewable Power Forecasting}, year = 2022 }
%0 Conference Paper %1 nivarthi2022unified %A Nivarthi, Chandana Priya %A Vogt, Stephan %A Sick, Bernhard %B International Conference on Machine Learning and Applications (ICMLA) %D 2022 %I IEEE %P 1530--1536 %R 10.1109/ICMLA55696.2022.00240 %T Unified Autoencoder with Task Embeddings for Multi-Task Learning in Renewable Power Forecasting %U https://ieeexplore.ieee.org/document/10068974 %X Renewable power generation forecasts using machine learning are typically implemented as single-task learning models, where a separate model is trained for each photovoltaic or wind park. In recent years, transfer learning is gaining popularity in these systems, as it can be used to transfer the knowledge gained from source parks to a target park. However, for transferring the knowledge to a target park, there is a need to determine the most similar source park(s) among the existing parks. This similarity determination using historical power measurements is challenging when the target park has limited to no historical data samples. Therefore, we propose a simple multi-task learning architecture that initially learns a common representation of input weather features among the tasks, using a Unified Autoencoder (UAE) and then learns the task specific information utilizing a Task Embedding layer in a Neural Network (TENN). This proposed architecture, UAE-TENN, can be easily extended to new parks with or without historical data. An elaborate performance comparison of single and multi-task learning models is performed on six photovoltaic and wind farm datasets comprising a total of 529 parks. UAE-TENN significantly improves the performance of power forecasting by 10 to 19% for photovoltaic parks and 5 to 22% for wind parks compared to the baseline models. Even in the zero-shot learning scenario, when there is no historical data, we successfully demonstrate that the UAE-TENN improves the forecast accuracy for a new park by 19% for photovoltaic parks.
Nivarthi, C.P.: Transfer Learning as an Essential Tool for Digital Twins in Renewable Energy Systems In: Tomforde, S. en Krupitzer, C. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2021. bll 47–59. kassel university press (2022).
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Transfer learning (TL), the next frontier in machine learning (ML), has gained much popularity in recent years, due to the various challenges faced in ML, like the requirement of vast amounts of training data, expensive and time-consuming labelling processes for data samples, and long training duration for models. TL is useful in tackling these problems, as it focuses on transferring knowledge from previously solved tasks to new tasks. Digital twins and other intelligent systems need to utilise TL to use the previously gained knowledge and solve new tasks in a more self-reliant way, and to incrementally increase their knowledge base. Therefore, in this article, the critical challenges in power forecasting and anomaly detection in the context of renewable energy systems are identified, and a potential TL framework to meet these challenges is proposed. This article also proposes a feature embedding approach to handle the missing sensors data. The proposed TL methods help to make a system more autonomous in the context of organic computing.
@incollection{nivarthi2022transfer, abstract = {Transfer learning (TL), the next frontier in machine learning (ML), has gained much popularity in recent years, due to the various challenges faced in ML, like the requirement of vast amounts of training data, expensive and time-consuming labelling processes for data samples, and long training duration for models. TL is useful in tackling these problems, as it focuses on transferring knowledge from previously solved tasks to new tasks. Digital twins and other intelligent systems need to utilise TL to use the previously gained knowledge and solve new tasks in a more self-reliant way, and to incrementally increase their knowledge base. Therefore, in this article, the critical challenges in power forecasting and anomaly detection in the context of renewable energy systems are identified, and a potential TL framework to meet these challenges is proposed. This article also proposes a feature embedding approach to handle the missing sensors data. The proposed TL methods help to make a system more autonomous in the context of organic computing.}, author = {Nivarthi, Chandana Priya}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2021}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {itegpub}, pages = {47–59}, publisher = {kassel university press}, title = {Transfer Learning as an Essential Tool for Digital Twins in Renewable Energy Systems}, year = 2022 }
%0 Book Section %1 nivarthi2022transfer %A Nivarthi, Chandana Priya %B Organic Computing -- Doctoral Dissertation Colloquium 2021 %D 2022 %E Tomforde, Sven %E Krupitzer, Christian %I kassel university press %P 47--59 %T Transfer Learning as an Essential Tool for Digital Twins in Renewable Energy Systems %X Transfer learning (TL), the next frontier in machine learning (ML), has gained much popularity in recent years, due to the various challenges faced in ML, like the requirement of vast amounts of training data, expensive and time-consuming labelling processes for data samples, and long training duration for models. TL is useful in tackling these problems, as it focuses on transferring knowledge from previously solved tasks to new tasks. Digital twins and other intelligent systems need to utilise TL to use the previously gained knowledge and solve new tasks in a more self-reliant way, and to incrementally increase their knowledge base. Therefore, in this article, the critical challenges in power forecasting and anomaly detection in the context of renewable energy systems are identified, and a potential TL framework to meet these challenges is proposed. This article also proposes a feature embedding approach to handle the missing sensors data. The proposed TL methods help to make a system more autonomous in the context of organic computing.
Huhnstock, R., Reginka, M., Sonntag, C., Merkel, M., Dingel, K., Sick, B., Vogel, M., Ehresmann, A.: Three-dimensional close-to-substrate trajectories of magnetic microparticles in dynamically changing magnetic field landscapes Scientific Reports. 12, 1–10 (2022). https://doi.org/10.1038/s41598-022-25391-z.
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The transport of magnetic particles (MPs) by dynamic magnetic field landscapes (MFLs) using magnetically patterned substrates is promising for the development of Lab-on-a-chip (LOC) systems. The inherent close-to-substrate MP motion is sensitive to changing particle–substrate interactions. Thus, the detection of a modified particle–substrate separation distance caused by surface binding of an analyte is expected to be a promising probe in analytics and diagnostics. Here, we present an essential prerequisite for such an application, namely the label-free quantitative experimental determination of the three-dimensional trajectories of superparamagnetic particles (SPPs) transported by a dynamically changing MFL. The evaluation of defocused SPP images from optical bright-field microscopy revealed a “hopping”-like motion of the magnetic particles, previously predicted by theory, additionally allowing a quantification of maximum jump heights. As our findings pave the way towards precise determination of particle–substrate separations, they bear deep implications for future LOC detection schemes using only optical microscopy.
@article{huhnstock2022three, abstract = {The transport of magnetic particles (MPs) by dynamic magnetic field landscapes (MFLs) using magnetically patterned substrates is promising for the development of Lab-on-a-chip (LOC) systems. The inherent close-to-substrate MP motion is sensitive to changing particle–substrate interactions. Thus, the detection of a modified particle–substrate separation distance caused by surface binding of an analyte is expected to be a promising probe in analytics and diagnostics. Here, we present an essential prerequisite for such an application, namely the label-free quantitative experimental determination of the three-dimensional trajectories of superparamagnetic particles (SPPs) transported by a dynamically changing MFL. The evaluation of defocused SPP images from optical bright-field microscopy revealed a “hopping”-like motion of the magnetic particles, previously predicted by theory, additionally allowing a quantification of maximum jump heights. As our findings pave the way towards precise determination of particle–substrate separations, they bear deep implications for future LOC detection schemes using only optical microscopy.}, author = {Huhnstock, Rico and Reginka, Meike and Sonntag, Claudius and Merkel, Maximilian and Dingel, Kristina and Sick, Bernhard and Vogel, Michael and Ehresmann, Arno}, journal = {Scientific Reports}, keywords = {itegpub}, number = 1, pages = {1–10}, publisher = {Springer Nature}, title = {Three-dimensional close-to-substrate trajectories of magnetic microparticles in dynamically changing magnetic field landscapes}, volume = 12, year = 2022 }
%0 Journal Article %1 huhnstock2022three %A Huhnstock, Rico %A Reginka, Meike %A Sonntag, Claudius %A Merkel, Maximilian %A Dingel, Kristina %A Sick, Bernhard %A Vogel, Michael %A Ehresmann, Arno %D 2022 %I Springer Nature %J Scientific Reports %N 1 %P 1--10 %R 10.1038/s41598-022-25391-z %T Three-dimensional close-to-substrate trajectories of magnetic microparticles in dynamically changing magnetic field landscapes %U https://www.nature.com/articles/s41598-022-25391-z %V 12 %X The transport of magnetic particles (MPs) by dynamic magnetic field landscapes (MFLs) using magnetically patterned substrates is promising for the development of Lab-on-a-chip (LOC) systems. The inherent close-to-substrate MP motion is sensitive to changing particle–substrate interactions. Thus, the detection of a modified particle–substrate separation distance caused by surface binding of an analyte is expected to be a promising probe in analytics and diagnostics. Here, we present an essential prerequisite for such an application, namely the label-free quantitative experimental determination of the three-dimensional trajectories of superparamagnetic particles (SPPs) transported by a dynamically changing MFL. The evaluation of defocused SPP images from optical bright-field microscopy revealed a “hopping”-like motion of the magnetic particles, previously predicted by theory, additionally allowing a quantification of maximum jump heights. As our findings pave the way towards precise determination of particle–substrate separations, they bear deep implications for future LOC detection schemes using only optical microscopy.
Loeser, I., Braun, M., Gruhl, C., Menke, J.-H., Sick, B., Tomforde, S.: The Vision of Self-Management in Cognitive Organic Power Distribution Systems Energies. 15, 881 (2022). https://doi.org/https://doi.org/10.3390/en15030881.
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Due to the ongoing trend towards a decarbonisation of energy use, the power system is expected to become the backbone of all energy sectors and thus the fundamental critical infrastructure. High penetration with distributed energy resources demands the coordination of a large number of prosumers, partly controlled by home energy management systems (HEMS), to be designed in such a way that the power system’s operational limits are not violated. On the grid level, distribution management systems (DMS) seek to keep the power system in the normal operational state. On the prosumer level, distributed HEMS optimise the internal power flows by setpoint specification of batteries, photovoltaic generators, or flexible loads. The vision of the ODiS (Organic Distribution System) initiative is to develop an architecture to operate a distribution grid reliably, with high resiliency, and fully autonomously by developing “organic” HEMS and DMS which possess multiple self-x capabilities, collectively referred to as self-management. Thus, ODiS seeks answers to the following question: How can we create the most appropriate models, techniques, and algorithms to develop novel kinds of self-configuring, self-organising, self-healing, and self-optimising DMS that are integrally coupled with the distributed HEMS? In this concept paper, the vision of ODiS is presented in detail based on a thorough review of the state of the art.
@article{loeser2022vision, abstract = {Due to the ongoing trend towards a decarbonisation of energy use, the power system is expected to become the backbone of all energy sectors and thus the fundamental critical infrastructure. High penetration with distributed energy resources demands the coordination of a large number of prosumers, partly controlled by home energy management systems (HEMS), to be designed in such a way that the power system’s operational limits are not violated. On the grid level, distribution management systems (DMS) seek to keep the power system in the normal operational state. On the prosumer level, distributed HEMS optimise the internal power flows by setpoint specification of batteries, photovoltaic generators, or flexible loads. The vision of the ODiS (Organic Distribution System) initiative is to develop an architecture to operate a distribution grid reliably, with high resiliency, and fully autonomously by developing “organic” HEMS and DMS which possess multiple self-x capabilities, collectively referred to as self-management. Thus, ODiS seeks answers to the following question: How can we create the most appropriate models, techniques, and algorithms to develop novel kinds of self-configuring, self-organising, self-healing, and self-optimising DMS that are integrally coupled with the distributed HEMS? In this concept paper, the vision of ODiS is presented in detail based on a thorough review of the state of the art.}, author = {Loeser, Inga and Braun, Martin and Gruhl, Christian and Menke, Jan-Hendrik and Sick, Bernhard and Tomforde, Sven}, journal = {Energies}, keywords = {self-healing}, number = 3, pages = 881, publisher = {MDPI}, title = {The Vision of Self-Management in Cognitive Organic Power Distribution Systems}, volume = 15, year = 2022 }
%0 Journal Article %1 loeser2022vision %A Loeser, Inga %A Braun, Martin %A Gruhl, Christian %A Menke, Jan-Hendrik %A Sick, Bernhard %A Tomforde, Sven %D 2022 %I MDPI %J Energies %N 3 %P 881 %R https://doi.org/10.3390/en15030881 %T The Vision of Self-Management in Cognitive Organic Power Distribution Systems %U https://www.mdpi.com/1996-1073/15/3/881 %V 15 %X Due to the ongoing trend towards a decarbonisation of energy use, the power system is expected to become the backbone of all energy sectors and thus the fundamental critical infrastructure. High penetration with distributed energy resources demands the coordination of a large number of prosumers, partly controlled by home energy management systems (HEMS), to be designed in such a way that the power system’s operational limits are not violated. On the grid level, distribution management systems (DMS) seek to keep the power system in the normal operational state. On the prosumer level, distributed HEMS optimise the internal power flows by setpoint specification of batteries, photovoltaic generators, or flexible loads. The vision of the ODiS (Organic Distribution System) initiative is to develop an architecture to operate a distribution grid reliably, with high resiliency, and fully autonomously by developing “organic” HEMS and DMS which possess multiple self-x capabilities, collectively referred to as self-management. Thus, ODiS seeks answers to the following question: How can we create the most appropriate models, techniques, and algorithms to develop novel kinds of self-configuring, self-organising, self-healing, and self-optimising DMS that are integrally coupled with the distributed HEMS? In this concept paper, the vision of ODiS is presented in detail based on a thorough review of the state of the art.
Beddar-Wiesing, S.: Student Research Abstract: Using Local Activity Encoding for Dynamic Graph Pooling in Stuctural-Dynamic Graphs In: ACM/SIGAPP Symposium on Applied Computing (SAC). bll 604–609. ACM (2022). https://doi.org/10.1145/3477314.3506969.
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Graphs have recently become more popular for modeling real-world as well as theoretical problems, and several Machine Learning algorithms for learning on them have developed. For graphs that change over time, however, research is still at an early stage. This article proposes a framework DynHeatmaP that generates a constant-size graph sequence representation of graphs whose structure changes over time (structural-dynamic, SDG) in linear time. The framework uses a representation of additions and deletions of nodes and edges (events) to perform dynamic graph pooling. Our algorithm translates the dynamic changes of the nodes and edges to a heatmap representation of the current node activity, regarding additions and deletions in the neighborhood, respectively. The created activity heatmap is utilized as probabilities of the nodes to be sampled in the subsequent pooling step. In addition, the neighborhood of selected nodes has been sampled such that the ratio of additions and deletions is represented accordingly. The obtained downsampled graph sequence can then be used representatively as input to a GNN for learning on the original stream. Focussing on the active regions in a graph, the fast pooling process can reduce the training time significantly and addresses the problem of unbalanced datasets in real-world applications modeled as sparse graphs.
@inproceedings{beddarwiesing2022student, abstract = {Graphs have recently become more popular for modeling real-world as well as theoretical problems, and several Machine Learning algorithms for learning on them have developed. For graphs that change over time, however, research is still at an early stage. This article proposes a framework DynHeatmaP that generates a constant-size graph sequence representation of graphs whose structure changes over time (structural-dynamic, SDG) in linear time. The framework uses a representation of additions and deletions of nodes and edges (events) to perform dynamic graph pooling. Our algorithm translates the dynamic changes of the nodes and edges to a heatmap representation of the current node activity, regarding additions and deletions in the neighborhood, respectively. The created activity heatmap is utilized as probabilities of the nodes to be sampled in the subsequent pooling step. In addition, the neighborhood of selected nodes has been sampled such that the ratio of additions and deletions is represented accordingly. The obtained downsampled graph sequence can then be used representatively as input to a GNN for learning on the original stream. Focussing on the active regions in a graph, the fast pooling process can reduce the training time significantly and addresses the problem of unbalanced datasets in real-world applications modeled as sparse graphs.}, author = {Beddar-Wiesing, Silvia}, booktitle = {ACM/SIGAPP Symposium on Applied Computing (SAC)}, keywords = {itegpub}, pages = {604–609}, publisher = {ACM}, title = {Student Research Abstract: Using Local Activity Encoding for Dynamic Graph Pooling in Stuctural-Dynamic Graphs}, year = 2022 }
%0 Conference Paper %1 beddarwiesing2022student %A Beddar-Wiesing, Silvia %B ACM/SIGAPP Symposium on Applied Computing (SAC) %D 2022 %I ACM %P 604--609 %R 10.1145/3477314.3506969 %T Student Research Abstract: Using Local Activity Encoding for Dynamic Graph Pooling in Stuctural-Dynamic Graphs %U https://dl.acm.org/doi/abs/10.1145/3477314.3506969 %X Graphs have recently become more popular for modeling real-world as well as theoretical problems, and several Machine Learning algorithms for learning on them have developed. For graphs that change over time, however, research is still at an early stage. This article proposes a framework DynHeatmaP that generates a constant-size graph sequence representation of graphs whose structure changes over time (structural-dynamic, SDG) in linear time. The framework uses a representation of additions and deletions of nodes and edges (events) to perform dynamic graph pooling. Our algorithm translates the dynamic changes of the nodes and edges to a heatmap representation of the current node activity, regarding additions and deletions in the neighborhood, respectively. The created activity heatmap is utilized as probabilities of the nodes to be sampled in the subsequent pooling step. In addition, the neighborhood of selected nodes has been sampled such that the ratio of additions and deletions is represented accordingly. The obtained downsampled graph sequence can then be used representatively as input to a GNN for learning on the original stream. Focussing on the active regions in a graph, the fast pooling process can reduce the training time significantly and addresses the problem of unbalanced datasets in real-world applications modeled as sparse graphs.
Moallemy-Oureh, A.: Student Research Abstract: Continuous-Time Generative Graph Neural Network for Attributed Dynamic Graphs In: ACM/SIGAPP Symposium on Applied Computing (SAC). bll 600–603. ACM (2022). https://doi.org/10.1145/3477314.3508018.
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The history of neural networks dates back to the early 1940s and has not only evolved rapidly over time but they are now amongst one of the most popular and powerful machine learning techniques. In this area, graph representation learning (GRL) using graph neural networks (GNNs) has emerged during the early 90s and developed into an impactful approach for modeling graph-structured real-world data, such as social or biological networks. There are a variety of successful applications of GRL in computational neuroscience, chemistry, mathematics, and so on. While temporal changes (dynamics) play an essential role in many real-world applications, most of the literature on GNNs and GRL, deals with static graphs. Since the few GNN models on dynamic graphs only consider exceptional cases of dynamics (i.e., attribute-dynamic graphs in discrete-time representation or structure-dynamic graphs in continuous-time representation), we aim to present a novel GNN model that can handle attribute-dynamic graphs in continuous time. This model learns any kind of graph information embedding, performs node/edge attribute forecasts, and allows for attribute-dynamic graph generation in both discrete and continuous-time.
@inproceedings{moallemyoureh2022student, abstract = {The history of neural networks dates back to the early 1940s and has not only evolved rapidly over time but they are now amongst one of the most popular and powerful machine learning techniques. In this area, graph representation learning (GRL) using graph neural networks (GNNs) has emerged during the early 90s and developed into an impactful approach for modeling graph-structured real-world data, such as social or biological networks. There are a variety of successful applications of GRL in computational neuroscience, chemistry, mathematics, and so on. While temporal changes (dynamics) play an essential role in many real-world applications, most of the literature on GNNs and GRL, deals with static graphs. Since the few GNN models on dynamic graphs only consider exceptional cases of dynamics (i.e., attribute-dynamic graphs in discrete-time representation or structure-dynamic graphs in continuous-time representation), we aim to present a novel GNN model that can handle attribute-dynamic graphs in continuous time. This model learns any kind of graph information embedding, performs node/edge attribute forecasts, and allows for attribute-dynamic graph generation in both discrete and continuous-time.}, author = {Moallemy-Oureh, Alice}, booktitle = {ACM/SIGAPP Symposium on Applied Computing (SAC)}, keywords = {itegpub}, pages = {600–603}, publisher = {ACM}, title = {Student Research Abstract: Continuous-Time Generative Graph Neural Network for Attributed Dynamic Graphs}, year = 2022 }
%0 Conference Paper %1 moallemyoureh2022student %A Moallemy-Oureh, Alice %B ACM/SIGAPP Symposium on Applied Computing (SAC) %D 2022 %I ACM %P 600--603 %R 10.1145/3477314.3508018 %T Student Research Abstract: Continuous-Time Generative Graph Neural Network for Attributed Dynamic Graphs %U https://dl.acm.org/doi/abs/10.1145/3477314.3508018 %X The history of neural networks dates back to the early 1940s and has not only evolved rapidly over time but they are now amongst one of the most popular and powerful machine learning techniques. In this area, graph representation learning (GRL) using graph neural networks (GNNs) has emerged during the early 90s and developed into an impactful approach for modeling graph-structured real-world data, such as social or biological networks. There are a variety of successful applications of GRL in computational neuroscience, chemistry, mathematics, and so on. While temporal changes (dynamics) play an essential role in many real-world applications, most of the literature on GNNs and GRL, deals with static graphs. Since the few GNN models on dynamic graphs only consider exceptional cases of dynamics (i.e., attribute-dynamic graphs in discrete-time representation or structure-dynamic graphs in continuous-time representation), we aim to present a novel GNN model that can handle attribute-dynamic graphs in continuous time. This model learns any kind of graph information embedding, performs node/edge attribute forecasts, and allows for attribute-dynamic graph generation in both discrete and continuous-time.
Pham, T.: Stream-Based Active Learning in Changing Environments under Verification Latency In: Tomforde, S. en Krupitzer, C. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2021. bll 152–164. kassel university press (2022).
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Labeling data often requires human interaction, which is slow and costly depending on the required expertise. Stream-based active learning reduces the cost of labeling for data stream mining by only querying the labels for instances that improve the classifier the most. However, state-of-the-art stream-based active learning strategies assume that verification latency, i.e., the time between a query and the availability of the queried label, is negligible. In reality, this is usually not the case as the experts who provide such labels are not available all the time, and the labeling process itself requires time. This article investigates the impact of verification latency within a stream-based active learning scenario for changing data streams. We focus on two different types of changes that occur over time, i.e., changes in the number of features and changes in the distribution of instances and/or labels. In particular, we identify challenges that occur due to verification latency and propose ideas to overcome these problems within a stream-based active learning strategy.
@incollection{pham2022stream-based, abstract = {Labeling data often requires human interaction, which is slow and costly depending on the required expertise. Stream-based active learning reduces the cost of labeling for data stream mining by only querying the labels for instances that improve the classifier the most. However, state-of-the-art stream-based active learning strategies assume that verification latency, i.e., the time between a query and the availability of the queried label, is negligible. In reality, this is usually not the case as the experts who provide such labels are not available all the time, and the labeling process itself requires time. This article investigates the impact of verification latency within a stream-based active learning scenario for changing data streams. We focus on two different types of changes that occur over time, i.e., changes in the number of features and changes in the distribution of instances and/or labels. In particular, we identify challenges that occur due to verification latency and propose ideas to overcome these problems within a stream-based active learning strategy.}, author = {Pham, Tuan}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2021}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {itegpub}, pages = {152–164}, publisher = {kassel university press}, title = {Stream-Based Active Learning in Changing Environments under Verification Latency}, year = 2022 }
%0 Book Section %1 pham2022stream-based %A Pham, Tuan %B Organic Computing -- Doctoral Dissertation Colloquium 2021 %D 2022 %E Tomforde, Sven %E Krupitzer, Christian %I kassel university press %P 152--164 %T Stream-Based Active Learning in Changing Environments under Verification Latency %X Labeling data often requires human interaction, which is slow and costly depending on the required expertise. Stream-based active learning reduces the cost of labeling for data stream mining by only querying the labels for instances that improve the classifier the most. However, state-of-the-art stream-based active learning strategies assume that verification latency, i.e., the time between a query and the availability of the queried label, is negligible. In reality, this is usually not the case as the experts who provide such labels are not available all the time, and the labeling process itself requires time. This article investigates the impact of verification latency within a stream-based active learning scenario for changing data streams. We focus on two different types of changes that occur over time, i.e., changes in the number of features and changes in the distribution of instances and/or labels. In particular, we identify challenges that occur due to verification latency and propose ideas to overcome these problems within a stream-based active learning strategy.
Rösch, K., Heidecker, F., Truetsch, J., Kowol, K., Schicktanz, C., Bieshaar, M., Sick, B., Stiller, C.: Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving In: IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (IEEE CIVTS), IEEE SSCI. bll 86–93. IEEE (2022). https://doi.org/10.1109/SSCI51031.2022.10022241.
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Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users’ movement and behavior patterns. Based on these predictions — and additional contextual information such as the course of the road, (traffic) rules, and interaction with other road users — the highly automated vehicle (HAV) must be able to reliably and safely perform the task assigned to it, e.g., moving from point A to B. Ideally, the HAV moves safely through its environment, just as we would expect a human driver to do. However, if unusual trajectories occur, so-called trajectory corner cases, a human driver can usually cope well, but an HAV can quickly get into trouble. In the definition of trajectory corner cases, which we provide in this work, we will consider the relevance of unusual trajectories with respect to the task at hand. Based on this, we will also present a taxonomy of different trajectory corner cases. The categorization of corner cases into the taxonomy will be shown with examples and is done by cause and required data sources. To illustrate the complexity between the machine learning (ML) model and the corner case cause, we present a general processing chain underlying the taxonomy.
@inproceedings{rosch2022space, abstract = {Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users’ movement and behavior patterns. Based on these predictions — and additional contextual information such as the course of the road, (traffic) rules, and interaction with other road users — the highly automated vehicle (HAV) must be able to reliably and safely perform the task assigned to it, e.g., moving from point A to B. Ideally, the HAV moves safely through its environment, just as we would expect a human driver to do. However, if unusual trajectories occur, so-called trajectory corner cases, a human driver can usually cope well, but an HAV can quickly get into trouble. In the definition of trajectory corner cases, which we provide in this work, we will consider the relevance of unusual trajectories with respect to the task at hand. Based on this, we will also present a taxonomy of different trajectory corner cases. The categorization of corner cases into the taxonomy will be shown with examples and is done by cause and required data sources. To illustrate the complexity between the machine learning (ML) model and the corner case cause, we present a general processing chain underlying the taxonomy.}, author = {Rösch, Kevin and Heidecker, Florian and Truetsch, Julian and Kowol, Kamil and Schicktanz, Clemens and Bieshaar, Maarten and Sick, Bernhard and Stiller, Christoph}, booktitle = {IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (IEEE CIVTS), IEEE SSCI}, keywords = {itegpub}, pages = {86–93}, publisher = {IEEE}, title = {Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving}, year = 2022 }
%0 Conference Paper %1 rosch2022space %A Rösch, Kevin %A Heidecker, Florian %A Truetsch, Julian %A Kowol, Kamil %A Schicktanz, Clemens %A Bieshaar, Maarten %A Sick, Bernhard %A Stiller, Christoph %B IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (IEEE CIVTS), IEEE SSCI %D 2022 %I IEEE %P 86--93 %R 10.1109/SSCI51031.2022.10022241 %T Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving %U https://ieeexplore.ieee.org/document/10022241 %X Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users’ movement and behavior patterns. Based on these predictions — and additional contextual information such as the course of the road, (traffic) rules, and interaction with other road users — the highly automated vehicle (HAV) must be able to reliably and safely perform the task assigned to it, e.g., moving from point A to B. Ideally, the HAV moves safely through its environment, just as we would expect a human driver to do. However, if unusual trajectories occur, so-called trajectory corner cases, a human driver can usually cope well, but an HAV can quickly get into trouble. In the definition of trajectory corner cases, which we provide in this work, we will consider the relevance of unusual trajectories with respect to the task at hand. Based on this, we will also present a taxonomy of different trajectory corner cases. The categorization of corner cases into the taxonomy will be shown with examples and is done by cause and required data sources. To illustrate the complexity between the machine learning (ML) model and the corner case cause, we present a general processing chain underlying the taxonomy.
Draude, C., Gruhl, C., Hornung, G., Kropf, J., Lamla, J., Leimeister, J.M., Sick, B., Stumme, G.: Social Machines Informatik Spektrum. 45, 38–42 (2022). https://doi.org/https://doi.org/10.1007/s00287-021-01421-4.
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Social Machines sind ein Paradigma für die Gestaltung soziotechnischer Systeme, die unter Verwendung von Web- und Plattformlösungen das Potenzial digitaler Technologien mit der Eigenlogik sozialer Interaktion, Organisation und Struk- turbildung auf neue Weise zusammenführen. Im Folgenden diskutieren wir das Paradigma der Social Machine aus den Perspektiven der Informatik, der Wirtschaftsinformatik, der Soziologie und des Rechts, um Orientierungspunkte für seine Gestaltung zu identifizieren. Der Begriff ist in der Literatur jedoch bisher nicht abschließend definiert sondern nur durch Beispiele illustriert. In diesem Artikel stellen wir zunächst die folgende Definition zur Diskussion: Social Machines sind soziotechnische Sys- teme, in denen die Prozesse sozialer Interaktion hybrid zwischen menschlichen und maschinellen Akteuren ablaufen und teilweise algorithmisiert sind. Im Anschluss beleuchten wir drei aktuelle, sich gegenseitig bedingende Entwicklungen von Social Machines: die immer stärkere Verschmelzung von Sozialität und Maschine, die Vermessung von Nutzeraktivitäten als Grundstoff gesellschaftlichen Zusammenhalts und die zunehmende Algorithmisierung gesellschaftlicher Prozesse. Ab- schließend diskutieren wir, dass eine teilhabeorientierte, demokratischen Werten folgende Gestaltung von Social Machines die Perspektiven der Nutzungsakzeptanz, der gesellschaftlichen Akzeptabilität und der nachhaltigen Wirtschaftlichkeit adressieren und umsetzen muss.
@article{draude2022social, abstract = {Social Machines sind ein Paradigma für die Gestaltung soziotechnischer Systeme, die unter Verwendung von Web- und Plattformlösungen das Potenzial digitaler Technologien mit der Eigenlogik sozialer Interaktion, Organisation und Struk- turbildung auf neue Weise zusammenführen. Im Folgenden diskutieren wir das Paradigma der Social Machine aus den Perspektiven der Informatik, der Wirtschaftsinformatik, der Soziologie und des Rechts, um Orientierungspunkte für seine Gestaltung zu identifizieren. Der Begriff ist in der Literatur jedoch bisher nicht abschließend definiert sondern nur durch Beispiele illustriert. In diesem Artikel stellen wir zunächst die folgende Definition zur Diskussion: Social Machines sind soziotechnische Sys- teme, in denen die Prozesse sozialer Interaktion hybrid zwischen menschlichen und maschinellen Akteuren ablaufen und teilweise algorithmisiert sind. Im Anschluss beleuchten wir drei aktuelle, sich gegenseitig bedingende Entwicklungen von Social Machines: die immer stärkere Verschmelzung von Sozialität und Maschine, die Vermessung von Nutzeraktivitäten als Grundstoff gesellschaftlichen Zusammenhalts und die zunehmende Algorithmisierung gesellschaftlicher Prozesse. Ab- schließend diskutieren wir, dass eine teilhabeorientierte, demokratischen Werten folgende Gestaltung von Social Machines die Perspektiven der Nutzungsakzeptanz, der gesellschaftlichen Akzeptabilität und der nachhaltigen Wirtschaftlichkeit adressieren und umsetzen muss.}, author = {Draude, Claude and Gruhl, Christian and Hornung, Gerrit and Kropf, Jonathan and Lamla, Jörn and Leimeister, Jan Marco and Sick, Bernhard and Stumme, Gerd}, journal = {Informatik Spektrum}, keywords = {itegpub}, number = 1, pages = {38–42}, publisher = {Springer}, title = {Social Machines}, volume = 45, year = 2022 }
%0 Journal Article %1 draude2022social %A Draude, Claude %A Gruhl, Christian %A Hornung, Gerrit %A Kropf, Jonathan %A Lamla, Jörn %A Leimeister, Jan Marco %A Sick, Bernhard %A Stumme, Gerd %D 2022 %I Springer %J Informatik Spektrum %N 1 %P 38--42 %R https://doi.org/10.1007/s00287-021-01421-4 %T Social Machines %V 45 %X Social Machines sind ein Paradigma für die Gestaltung soziotechnischer Systeme, die unter Verwendung von Web- und Plattformlösungen das Potenzial digitaler Technologien mit der Eigenlogik sozialer Interaktion, Organisation und Struk- turbildung auf neue Weise zusammenführen. Im Folgenden diskutieren wir das Paradigma der Social Machine aus den Perspektiven der Informatik, der Wirtschaftsinformatik, der Soziologie und des Rechts, um Orientierungspunkte für seine Gestaltung zu identifizieren. Der Begriff ist in der Literatur jedoch bisher nicht abschließend definiert sondern nur durch Beispiele illustriert. In diesem Artikel stellen wir zunächst die folgende Definition zur Diskussion: Social Machines sind soziotechnische Sys- teme, in denen die Prozesse sozialer Interaktion hybrid zwischen menschlichen und maschinellen Akteuren ablaufen und teilweise algorithmisiert sind. Im Anschluss beleuchten wir drei aktuelle, sich gegenseitig bedingende Entwicklungen von Social Machines: die immer stärkere Verschmelzung von Sozialität und Maschine, die Vermessung von Nutzeraktivitäten als Grundstoff gesellschaftlichen Zusammenhalts und die zunehmende Algorithmisierung gesellschaftlicher Prozesse. Ab- schließend diskutieren wir, dass eine teilhabeorientierte, demokratischen Werten folgende Gestaltung von Social Machines die Perspektiven der Nutzungsakzeptanz, der gesellschaftlichen Akzeptabilität und der nachhaltigen Wirtschaftlichkeit adressieren und umsetzen muss.
Gruhl, C., Tomforde, S., Sick, B.: Self-Aware Microsystems In: Workshop on Self-Improving System Integration (SISSY), ACSOS. bll 126–127. IEEE (2022). https://doi.org/10.1109/ACSOSC56246.2022.00045.
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Besides infrastructure-based solutions, small-scale microsystems are increasingly connected and interoperating with others in changing environments. Following the same motivation as for self-adaptive systems, this demands novel attempts to counter complexity and provide autonomous decision freedom that paves the path towards resilient and flexible behaviour. In this article, we outline a vision for research on self-aware microsystems (SAM) that aims at providing technical solutions that are especially relevant for initiatives such as self-improving system integration.
@inproceedings{gruhl2022self, abstract = {Besides infrastructure-based solutions, small-scale microsystems are increasingly connected and interoperating with others in changing environments. Following the same motivation as for self-adaptive systems, this demands novel attempts to counter complexity and provide autonomous decision freedom that paves the path towards resilient and flexible behaviour. In this article, we outline a vision for research on self-aware microsystems (SAM) that aims at providing technical solutions that are especially relevant for initiatives such as self-improving system integration.}, author = {Gruhl, Christian and Tomforde, Sven and Sick, Bernhard}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS}, keywords = {itegpub}, pages = {126–127}, publisher = {IEEE}, title = {Self-Aware Microsystems}, year = 2022 }
%0 Conference Paper %1 gruhl2022self %A Gruhl, Christian %A Tomforde, Sven %A Sick, Bernhard %B Workshop on Self-Improving System Integration (SISSY), ACSOS %D 2022 %I IEEE %P 126--127 %R 10.1109/ACSOSC56246.2022.00045 %T Self-Aware Microsystems %U https://ieeexplore.ieee.org/abstract/document/9934876/ %X Besides infrastructure-based solutions, small-scale microsystems are increasingly connected and interoperating with others in changing environments. Following the same motivation as for self-adaptive systems, this demands novel attempts to counter complexity and provide autonomous decision freedom that paves the path towards resilient and flexible behaviour. In this article, we outline a vision for research on self-aware microsystems (SAM) that aims at providing technical solutions that are especially relevant for initiatives such as self-improving system integration.
Hassouna, M.: Self-Adaptive Charging Management in Electric Vehicle Infrastructures based on Reinforcement Learning In: Organic Computing -- Doctoral Dissertation Colloquium 2022. kassel university press (2022).
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Electromobility plays an increasingly important role in the energy transition. Electric vehicle charging poses challenges to the electricity grid due to high peak demand situations. Therefore, it is crucial to leverage the vehicle's flexibility when charging can be delayed. Conventional charging management systems cannot adapt to frequently changing conditions such as fluctuating renewable energy output, electricity prices and user behavior. In this article, we investigate the application of reinforcement learning approaches for self-adaptive charging management. In particular, we identify challenges regarding realistic environments and adaption to varying topologies and connections among charging stations. We describe related approaches and propose ideas and planned experiments to overcome these problems by utilizing generative models and graph neural networks.
@incollection{hassouna2022self, abstract = {Electromobility plays an increasingly important role in the energy transition. Electric vehicle charging poses challenges to the electricity grid due to high peak demand situations. Therefore, it is crucial to leverage the vehicle's flexibility when charging can be delayed. Conventional charging management systems cannot adapt to frequently changing conditions such as fluctuating renewable energy output, electricity prices and user behavior. In this article, we investigate the application of reinforcement learning approaches for self-adaptive charging management. In particular, we identify challenges regarding realistic environments and adaption to varying topologies and connections among charging stations. We describe related approaches and propose ideas and planned experiments to overcome these problems by utilizing generative models and graph neural networks.}, author = {Hassouna, Mohamed}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2022}, keywords = {itegpub}, note = {(accepted)}, publisher = {kassel university press}, title = {Self-Adaptive Charging Management in Electric Vehicle Infrastructures based on Reinforcement Learning}, year = 2022 }
%0 Book Section %1 hassouna2022self %A Hassouna, Mohamed %B Organic Computing -- Doctoral Dissertation Colloquium 2022 %D 2022 %I kassel university press %T Self-Adaptive Charging Management in Electric Vehicle Infrastructures based on Reinforcement Learning %X Electromobility plays an increasingly important role in the energy transition. Electric vehicle charging poses challenges to the electricity grid due to high peak demand situations. Therefore, it is crucial to leverage the vehicle's flexibility when charging can be delayed. Conventional charging management systems cannot adapt to frequently changing conditions such as fluctuating renewable energy output, electricity prices and user behavior. In this article, we investigate the application of reinforcement learning approaches for self-adaptive charging management. In particular, we identify challenges regarding realistic environments and adaption to varying topologies and connections among charging stations. We describe related approaches and propose ideas and planned experiments to overcome these problems by utilizing generative models and graph neural networks.
Krupitzer, C., Gruhl, C., Sick, B., Tomforde, S.: Proactive hybrid learning and optimisation in self-adaptive systems: The swarm-fleet infrastructure scenario Information and Software Technology. 145, 106826 (2022). https://doi.org/10.1016/j.infsof.2022.106826.
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Context: Smart and adaptive Systems, such as self-adaptive and self-organising (SASO) systems, typically consist of a large set of highly autonomous and heterogeneous subsystems that are able to adapt their behaviour to the requirements of ever-changing, dynamic environments. Their successful operation is based on appropriate modelling of the internal and external conditions. Objective: The control problem for establishing a near-to-optimal coordinated behaviour of systems with multiple, potentially conflicting objectives is either approached in a distributed (i.e., fully autonomous by the autonomous subsystems) or in a centralised way (i.e. one instance controlling the optimisation and planning process). In the distributed approach, selfish behaviour and being limited to local knowledge may lead to sub-optimal system behaviour, while the centralised approach ignores the autonomy and the coordination efforts of parts of the system. Method: In this article, we present a concept for a hybrid (i.e., integrating a central optimisation with a distributed decision-making process) system management that combines local reinforcement learning and self-awareness mechanisms of fully autonomous subsystems with external system-wide planning and optimisation of adaptation freedom that steers the behaviour dynamically by issuing plans and guidelines augmented with incentivisation schemes. Results: This work addresses the inherent uncertainty of the dynamic system behaviour, the local autonomous and context-aware learning of subsystems, and proactive control based on adaptiveness. We provide the ‘swarm-fleet infrastructure’—a self-organised taxi service established by autonomous, privately-owned cars—as a testbed for structured comparison of systems. Conclusion: The ‘swarm-fleet infrastructure’ supports the advantages of a proactive hybrid self-adaptive and self-organising system operation. Further, we provide a system model to combine the system-wide optimisation while ensuring local decision-making through reinforcement learning for individualised configurations.
@article{krupitzer2022proactive, abstract = {Context: Smart and adaptive Systems, such as self-adaptive and self-organising (SASO) systems, typically consist of a large set of highly autonomous and heterogeneous subsystems that are able to adapt their behaviour to the requirements of ever-changing, dynamic environments. Their successful operation is based on appropriate modelling of the internal and external conditions. Objective: The control problem for establishing a near-to-optimal coordinated behaviour of systems with multiple, potentially conflicting objectives is either approached in a distributed (i.e., fully autonomous by the autonomous subsystems) or in a centralised way (i.e. one instance controlling the optimisation and planning process). In the distributed approach, selfish behaviour and being limited to local knowledge may lead to sub-optimal system behaviour, while the centralised approach ignores the autonomy and the coordination efforts of parts of the system. Method: In this article, we present a concept for a hybrid (i.e., integrating a central optimisation with a distributed decision-making process) system management that combines local reinforcement learning and self-awareness mechanisms of fully autonomous subsystems with external system-wide planning and optimisation of adaptation freedom that steers the behaviour dynamically by issuing plans and guidelines augmented with incentivisation schemes. Results: This work addresses the inherent uncertainty of the dynamic system behaviour, the local autonomous and context-aware learning of subsystems, and proactive control based on adaptiveness. We provide the ‘swarm-fleet infrastructure’—a self-organised taxi service established by autonomous, privately-owned cars—as a testbed for structured comparison of systems. Conclusion: The ‘swarm-fleet infrastructure’ supports the advantages of a proactive hybrid self-adaptive and self-organising system operation. Further, we provide a system model to combine the system-wide optimisation while ensuring local decision-making through reinforcement learning for individualised configurations.}, author = {Krupitzer, Christian and Gruhl, Christian and Sick, Bernhard and Tomforde, Sven}, journal = {Information and Software Technology}, keywords = {itegpub}, pages = 106826, publisher = {Elsevier}, title = {Proactive hybrid learning and optimisation in self-adaptive systems: The swarm-fleet infrastructure scenario}, volume = 145, year = 2022 }
%0 Journal Article %1 krupitzer2022proactive %A Krupitzer, Christian %A Gruhl, Christian %A Sick, Bernhard %A Tomforde, Sven %D 2022 %I Elsevier %J Information and Software Technology %P 106826 %R 10.1016/j.infsof.2022.106826 %T Proactive hybrid learning and optimisation in self-adaptive systems: The swarm-fleet infrastructure scenario %U https://www.sciencedirect.com/science/article/abs/pii/S0950584922000052 %V 145 %X Context: Smart and adaptive Systems, such as self-adaptive and self-organising (SASO) systems, typically consist of a large set of highly autonomous and heterogeneous subsystems that are able to adapt their behaviour to the requirements of ever-changing, dynamic environments. Their successful operation is based on appropriate modelling of the internal and external conditions. Objective: The control problem for establishing a near-to-optimal coordinated behaviour of systems with multiple, potentially conflicting objectives is either approached in a distributed (i.e., fully autonomous by the autonomous subsystems) or in a centralised way (i.e. one instance controlling the optimisation and planning process). In the distributed approach, selfish behaviour and being limited to local knowledge may lead to sub-optimal system behaviour, while the centralised approach ignores the autonomy and the coordination efforts of parts of the system. Method: In this article, we present a concept for a hybrid (i.e., integrating a central optimisation with a distributed decision-making process) system management that combines local reinforcement learning and self-awareness mechanisms of fully autonomous subsystems with external system-wide planning and optimisation of adaptation freedom that steers the behaviour dynamically by issuing plans and guidelines augmented with incentivisation schemes. Results: This work addresses the inherent uncertainty of the dynamic system behaviour, the local autonomous and context-aware learning of subsystems, and proactive control based on adaptiveness. We provide the ‘swarm-fleet infrastructure’—a self-organised taxi service established by autonomous, privately-owned cars—as a testbed for structured comparison of systems. Conclusion: The ‘swarm-fleet infrastructure’ supports the advantages of a proactive hybrid self-adaptive and self-organising system operation. Further, we provide a system model to combine the system-wide optimisation while ensuring local decision-making through reinforcement learning for individualised configurations.
Decke, J., Engelhardt, A., Rauch, L., Degener, S., Sajjadifar, S., Scharifi, E., Steinhoff, K., Niendorf, T., Sick, B.: Predicting flow stress behavior of an AA7075 alloy using machine learning methods Crystals. 9, 1–19 (2022). https://doi.org/10.3390/cryst12091281.
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This work focuses on the prediction of hot deformation behavior of thermo-mechanically processed precipitation hardenable aluminum alloy AA7075. Data available are focusing on a novel hot forming process at different tool temperatures ranging from 24°C to 350°C to set different cooling rates after solution-heat-treatment. Isothermal uniaxial tensile tests in the temperature range from 200°C to 400°C and at strain rates ranging from 0.001 s^-1 to 0.1 s^-1 were carried out on four different material conditions. The present paper mainly focuses on a comparative study of modeling techniques based on Machine Learning (ML) and the well-known Zerilli-Armstrong model (Z-A) as an empirically based reference. Work focused on predicting single data points of curves that the model was trained on. Due to the novel way data were split with respect to training and testing data, it becomes possible to predict entire stress-strain curves which leads to a reduction in the number of required laboratory experiments, finally saving costs and time in future experiments. While all investigated ML methods showed a higher performance than the Z-A model, the extreme Gradient Boosting model (XGB) showed the superior results, i.e., highest error reduction of 91% with respect to the Mean Squared Error.
@article{decke2022predicting, abstract = {This work focuses on the prediction of hot deformation behavior of thermo-mechanically processed precipitation hardenable aluminum alloy AA7075. Data available are focusing on a novel hot forming process at different tool temperatures ranging from 24°C to 350°C to set different cooling rates after solution-heat-treatment. Isothermal uniaxial tensile tests in the temperature range from 200°C to 400°C and at strain rates ranging from 0.001 s^{-1} to 0.1 s^{-1} were carried out on four different material conditions. The present paper mainly focuses on a comparative study of modeling techniques based on Machine Learning (ML) and the well-known Zerilli-Armstrong model (Z-A) as an empirically based reference. Work focused on predicting single data points of curves that the model was trained on. Due to the novel way data were split with respect to training and testing data, it becomes possible to predict entire stress-strain curves which leads to a reduction in the number of required laboratory experiments, finally saving costs and time in future experiments. While all investigated ML methods showed a higher performance than the Z-A model, the extreme Gradient Boosting model (XGB) showed the superior results, i.e., highest error reduction of 91% with respect to the Mean Squared Error.}, author = {Decke, Jens and Engelhardt, Anna and Rauch, Lukas and Degener, Sebastian and Sajjadifar, Seyedvahid and Scharifi, Emad and Steinhoff, Kurt and Niendorf, Thomas and Sick, Bernhard}, journal = {Crystals}, keywords = {itegpub}, number = 12, pages = {1–19}, publisher = {MDPI}, title = {Predicting flow stress behavior of an AA7075 alloy using machine learning methods}, volume = 9, year = 2022 }
%0 Journal Article %1 decke2022predicting %A Decke, Jens %A Engelhardt, Anna %A Rauch, Lukas %A Degener, Sebastian %A Sajjadifar, Seyedvahid %A Scharifi, Emad %A Steinhoff, Kurt %A Niendorf, Thomas %A Sick, Bernhard %D 2022 %I MDPI %J Crystals %N 12 %P 1--19 %R 10.3390/cryst12091281 %T Predicting flow stress behavior of an AA7075 alloy using machine learning methods %U https://www.mdpi.com/2073-4352/12/9/1281 %V 9 %X This work focuses on the prediction of hot deformation behavior of thermo-mechanically processed precipitation hardenable aluminum alloy AA7075. Data available are focusing on a novel hot forming process at different tool temperatures ranging from 24°C to 350°C to set different cooling rates after solution-heat-treatment. Isothermal uniaxial tensile tests in the temperature range from 200°C to 400°C and at strain rates ranging from 0.001 s^-1 to 0.1 s^-1 were carried out on four different material conditions. The present paper mainly focuses on a comparative study of modeling techniques based on Machine Learning (ML) and the well-known Zerilli-Armstrong model (Z-A) as an empirically based reference. Work focused on predicting single data points of curves that the model was trained on. Due to the novel way data were split with respect to training and testing data, it becomes possible to predict entire stress-strain curves which leads to a reduction in the number of required laboratory experiments, finally saving costs and time in future experiments. While all investigated ML methods showed a higher performance than the Z-A model, the extreme Gradient Boosting model (XGB) showed the superior results, i.e., highest error reduction of 91% with respect to the Mean Squared Error.
Kress, V., Jeske, F., Zernetsch, S., Doll, K., Sick, B.: Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users Trajectories IEEE Transactions on Intelligent Vehicles. 8, 2592–2603 (2022). https://doi.org/10.1109/TIV.2022.3149624.
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In this article, an approach for probabilistic trajectory forecasting of vulnerable road users (VRUs) is presented, taking into consideration past movements and the surrounding environment. Past movements are represented by 3D poses reflecting the posture and movements of individual body parts. The surrounding environment is modeled in the form of semantic maps showing, e.g., the course of streets, sidewalks, and the occurrence of obstacles. Forecasts are generated in grids discretizing the space and in the form of arbitrary discrete probability distributions. The distributions are evaluated for their reliability, sharpness, and positional accuracy. We compare our method with two approaches providing forecasts in the form of continuous probability distributions, and we discuss their respective advantages and disadvantages. We thereby investigate the impact of poses and semantic maps. Using a technique we refer to as spatial label smoothing, our approach is able to achieve reliable forecasts. Overall, the 3D poses have a positive impact on the forecasts. The semantic maps facilitate the adaptation of the probability distributions to the individual situation and prevent forecasts of trajectories leading through obstacles. Our method is evaluated on a dataset recorded in inner-city traffic using a research vehicle. The dataset has been made publicly available.
@article{kress2022pose, abstract = {In this article, an approach for probabilistic trajectory forecasting of vulnerable road users (VRUs) is presented, taking into consideration past movements and the surrounding environment. Past movements are represented by 3D poses reflecting the posture and movements of individual body parts. The surrounding environment is modeled in the form of semantic maps showing, e.g., the course of streets, sidewalks, and the occurrence of obstacles. Forecasts are generated in grids discretizing the space and in the form of arbitrary discrete probability distributions. The distributions are evaluated for their reliability, sharpness, and positional accuracy. We compare our method with two approaches providing forecasts in the form of continuous probability distributions, and we discuss their respective advantages and disadvantages. We thereby investigate the impact of poses and semantic maps. Using a technique we refer to as spatial label smoothing, our approach is able to achieve reliable forecasts. Overall, the 3D poses have a positive impact on the forecasts. The semantic maps facilitate the adaptation of the probability distributions to the individual situation and prevent forecasts of trajectories leading through obstacles. Our method is evaluated on a dataset recorded in inner-city traffic using a research vehicle. The dataset has been made publicly available.}, author = {Kress, Viktor and Jeske, Fabian and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, journal = {IEEE Transactions on Intelligent Vehicles}, keywords = {itegpub}, number = 3, pages = {2592–2603}, publisher = {IEEE}, title = {Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users Trajectories}, volume = 8, year = 2022 }
%0 Journal Article %1 kress2022pose %A Kress, Viktor %A Jeske, Fabian %A Zernetsch, Stefan %A Doll, Konrad %A Sick, Bernhard %D 2022 %I IEEE %J IEEE Transactions on Intelligent Vehicles %N 3 %P 2592--2603 %R 10.1109/TIV.2022.3149624 %T Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users Trajectories %V 8 %X In this article, an approach for probabilistic trajectory forecasting of vulnerable road users (VRUs) is presented, taking into consideration past movements and the surrounding environment. Past movements are represented by 3D poses reflecting the posture and movements of individual body parts. The surrounding environment is modeled in the form of semantic maps showing, e.g., the course of streets, sidewalks, and the occurrence of obstacles. Forecasts are generated in grids discretizing the space and in the form of arbitrary discrete probability distributions. The distributions are evaluated for their reliability, sharpness, and positional accuracy. We compare our method with two approaches providing forecasts in the form of continuous probability distributions, and we discuss their respective advantages and disadvantages. We thereby investigate the impact of poses and semantic maps. Using a technique we refer to as spatial label smoothing, our approach is able to achieve reliable forecasts. Overall, the 3D poses have a positive impact on the forecasts. The semantic maps facilitate the adaptation of the probability distributions to the individual situation and prevent forecasts of trajectories leading through obstacles. Our method is evaluated on a dataset recorded in inner-city traffic using a research vehicle. The dataset has been made publicly available.
Meier, D., Ramirez, L.V., Völker, J., Viefhaus, J., Sick, B., Hartmann, G.: Optimizing a superconducting radio-frequency gun using deep reinforcement learning Physical Review Accelerators and Beams. 25, 104604 (2022). https://doi.org/10.1103/PhysRevAccelBeams.25.104604.
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Superconducting photoelectron injectors are promising for generating highly brilliant pulsed electron beams with high repetition rates and low emittances. Experiments such as ultrafast electron diffraction, experiments at the Terahertz scale, and energy recovery linac applications require such properties. However, optimizing the beam properties is challenging due to the high number of possible machine parameter combinations. This article shows the successful automated optimization of beam properties utilizing an already existing simulation model. To reduce the required computation time, we replace the costly simulation with a faster approximation with a neural network. For optimization, we propose a reinforcement learning approach leveraging the simple computation of the derivative of the approximation. We prove that our approach outperforms standard optimization methods for the required function evaluations given a defined minimum accuracy.
@article{meier2022optimizing, abstract = {Superconducting photoelectron injectors are promising for generating highly brilliant pulsed electron beams with high repetition rates and low emittances. Experiments such as ultrafast electron diffraction, experiments at the Terahertz scale, and energy recovery linac applications require such properties. However, optimizing the beam properties is challenging due to the high number of possible machine parameter combinations. This article shows the successful automated optimization of beam properties utilizing an already existing simulation model. To reduce the required computation time, we replace the costly simulation with a faster approximation with a neural network. For optimization, we propose a reinforcement learning approach leveraging the simple computation of the derivative of the approximation. We prove that our approach outperforms standard optimization methods for the required function evaluations given a defined minimum accuracy.}, author = {Meier, David and Ramirez, Luis Vera and Völker, Jens and Viefhaus, Jens and Sick, Bernhard and Hartmann, Gregor}, journal = {Physical Review Accelerators and Beams}, keywords = {SimulationTechniques}, number = 10, pages = 104604, publisher = {American Physical Society}, title = {Optimizing a superconducting radio-frequency gun using deep reinforcement learning}, volume = 25, year = 2022 }
%0 Journal Article %1 meier2022optimizing %A Meier, David %A Ramirez, Luis Vera %A Völker, Jens %A Viefhaus, Jens %A Sick, Bernhard %A Hartmann, Gregor %D 2022 %I American Physical Society %J Physical Review Accelerators and Beams %N 10 %P 104604 %R 10.1103/PhysRevAccelBeams.25.104604 %T Optimizing a superconducting radio-frequency gun using deep reinforcement learning %U https://journals.aps.org/prab/abstract/10.1103/PhysRevAccelBeams.25.104604 %V 25 %X Superconducting photoelectron injectors are promising for generating highly brilliant pulsed electron beams with high repetition rates and low emittances. Experiments such as ultrafast electron diffraction, experiments at the Terahertz scale, and energy recovery linac applications require such properties. However, optimizing the beam properties is challenging due to the high number of possible machine parameter combinations. This article shows the successful automated optimization of beam properties utilizing an already existing simulation model. To reduce the required computation time, we replace the costly simulation with a faster approximation with a neural network. For optimization, we propose a reinforcement learning approach leveraging the simple computation of the derivative of the approximation. We prove that our approach outperforms standard optimization methods for the required function evaluations given a defined minimum accuracy.
Decke, J., Schmeißing, J., Botache, D., Bieshaar, M., Sick, B., Gruhl, C.: NDNET: A Unified Framework for Anomaly and Novelty Detection In: International Conference on Architecture of Computing Systems (ARCS). bll 197–210. Springer (2022). https://doi.org/10.1007/978-3-031-21867-5_13.
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We introduce NDNET (https://novelty-detection.net/p/ndnet), an anomaly and novelty detection library that implements various detection algorithms adjusted for online processing of data streams. The intention of this library is threefold: 1) Make experimentation with different anomaly and novelty detection algorithms simple. 2) Support the development of new novelty detection approaches by providing the mCANDIES framework. 3) Provide fundamentals to analyze and evaluate novelty detection algorithms on data streams. The library is freely available and developed as open-source software.
@inproceedings{decke2022ndnet, abstract = {We introduce NDNET (https://novelty-detection.net/p/ndnet), an anomaly and novelty detection library that implements various detection algorithms adjusted for online processing of data streams. The intention of this library is threefold: 1) Make experimentation with different anomaly and novelty detection algorithms simple. 2) Support the development of new novelty detection approaches by providing the mCANDIES framework. 3) Provide fundamentals to analyze and evaluate novelty detection algorithms on data streams. The library is freely available and developed as open-source software.}, author = {Decke, Jens and Schmeißing, Jörn and Botache, Diego and Bieshaar, Maarten and Sick, Bernhard and Gruhl, Christian}, booktitle = {International Conference on Architecture of Computing Systems (ARCS)}, keywords = {itegpub}, pages = {197–210}, publisher = {Springer}, title = {{NDNET}: {A} Unified Framework for Anomaly and Novelty Detection}, year = 2022 }
%0 Conference Paper %1 decke2022ndnet %A Decke, Jens %A Schmeißing, Jörn %A Botache, Diego %A Bieshaar, Maarten %A Sick, Bernhard %A Gruhl, Christian %B International Conference on Architecture of Computing Systems (ARCS) %D 2022 %I Springer %P 197--210 %R 10.1007/978-3-031-21867-5_13 %T NDNET: A Unified Framework for Anomaly and Novelty Detection %X We introduce NDNET (https://novelty-detection.net/p/ndnet), an anomaly and novelty detection library that implements various detection algorithms adjusted for online processing of data streams. The intention of this library is threefold: 1) Make experimentation with different anomaly and novelty detection algorithms simple. 2) Support the development of new novelty detection approaches by providing the mCANDIES framework. 3) Provide fundamentals to analyze and evaluate novelty detection algorithms on data streams. The library is freely available and developed as open-source software.
Ali, M.W.: Heterogeneous Multi-Source Deep Adaptive Knowledge-Aware Learning for E-Mobility In: IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C). bll 57–60. IEEE (2022). https://doi.org/10.1109/ACSOSC56246.2022.00030.
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Electromobility (e-mobility) reduces global fossil fuel-based energy demand and promotes the usage of renewable energy sources. Different types of electric vehicle (EVs) production have increased recently due to their need. However, there is no fixed charging behavior, and demand exists. Uncontrolled EV charging may lead to a harmful impact on the power grid. Besides, most renewable energy sources depend highly on the weather and location. Thus, analyzing, forecasting, and adapting EV charging demand is essential to prevent power outages. Deep Learning (DL) models are a de facto standard technology for energy forecasting in many scenarios. Still, there is a lack of real-world data in the e-mobility domain to train a DL model from scratch. Besides, DL models have explainability issues, which can hinder decision-making. This article proposes a novel framework for addressing data scarcity issues, enabling continuous adaptation to change, and improving explainability.
@inproceedings{ali2022heterogeneous, abstract = {Electromobility (e-mobility) reduces global fossil fuel-based energy demand and promotes the usage of renewable energy sources. Different types of electric vehicle (EVs) production have increased recently due to their need. However, there is no fixed charging behavior, and demand exists. Uncontrolled EV charging may lead to a harmful impact on the power grid. Besides, most renewable energy sources depend highly on the weather and location. Thus, analyzing, forecasting, and adapting EV charging demand is essential to prevent power outages. Deep Learning (DL) models are a de facto standard technology for energy forecasting in many scenarios. Still, there is a lack of real-world data in the e-mobility domain to train a DL model from scratch. Besides, DL models have explainability issues, which can hinder decision-making. This article proposes a novel framework for addressing data scarcity issues, enabling continuous adaptation to change, and improving explainability.}, author = {Ali, Mohammad Wazed}, booktitle = {IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)}, keywords = {itegpub}, pages = {57–60}, publisher = {IEEE}, title = {Heterogeneous Multi-Source Deep Adaptive Knowledge-Aware Learning for E-Mobility}, year = 2022 }
%0 Conference Paper %1 ali2022heterogeneous %A Ali, Mohammad Wazed %B IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C) %D 2022 %I IEEE %P 57--60 %R 10.1109/ACSOSC56246.2022.00030 %T Heterogeneous Multi-Source Deep Adaptive Knowledge-Aware Learning for E-Mobility %U https://ieeexplore.ieee.org/document/9935088 %X Electromobility (e-mobility) reduces global fossil fuel-based energy demand and promotes the usage of renewable energy sources. Different types of electric vehicle (EVs) production have increased recently due to their need. However, there is no fixed charging behavior, and demand exists. Uncontrolled EV charging may lead to a harmful impact on the power grid. Besides, most renewable energy sources depend highly on the weather and location. Thus, analyzing, forecasting, and adapting EV charging demand is essential to prevent power outages. Deep Learning (DL) models are a de facto standard technology for energy forecasting in many scenarios. Still, there is a lack of real-world data in the e-mobility domain to train a DL model from scratch. Besides, DL models have explainability issues, which can hinder decision-making. This article proposes a novel framework for addressing data scarcity issues, enabling continuous adaptation to change, and improving explainability.
Thomas, J.M., Moallemy-Oureh, A., Beddar-Wiesing, S., Holzhüter, C.: Graph Neural Networks Designed for Different Graph Types: A Survey arXiv e-prints. arXiv:2204.03080 (2022).
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Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. Based on this, the young research field of Graph Neural Networks (GNNs) has emerged. Despite the youth of the field and the speed in which new models are developed, many good surveys have been published in the last years. Nevertheless, an overview on which graph types can be modeled by GNNs is missing. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static as well as on dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover in the dynamic case, we separate the models in discrete-time and continuous-time dynamic graphs based on their architecture. While ordering the existing GNN models, we find, that there are still graph types, that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.
@article{thomas2022graph, abstract = {Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. Based on this, the young research field of Graph Neural Networks (GNNs) has emerged. Despite the youth of the field and the speed in which new models are developed, many good surveys have been published in the last years. Nevertheless, an overview on which graph types can be modeled by GNNs is missing. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static as well as on dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover in the dynamic case, we separate the models in discrete-time and continuous-time dynamic graphs based on their architecture. While ordering the existing GNN models, we find, that there are still graph types, that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.}, author = {Thomas, Josephine M. and Moallemy-Oureh, Alice and Beddar-Wiesing, Silvia and Holzhüter, Clara}, journal = {arXiv e-prints}, keywords = {itegpub}, pages = {arXiv:2204.03080}, title = {Graph Neural Networks Designed for Different Graph Types: A Survey}, year = 2022 }
%0 Journal Article %1 thomas2022graph %A Thomas, Josephine M. %A Moallemy-Oureh, Alice %A Beddar-Wiesing, Silvia %A Holzhüter, Clara %D 2022 %J arXiv e-prints %P arXiv:2204.03080 %T Graph Neural Networks Designed for Different Graph Types: A Survey %U https://arxiv.org/abs/2204.03080 %X Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. Based on this, the young research field of Graph Neural Networks (GNNs) has emerged. Despite the youth of the field and the speed in which new models are developed, many good surveys have been published in the last years. Nevertheless, an overview on which graph types can be modeled by GNNs is missing. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static as well as on dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover in the dynamic case, we separate the models in discrete-time and continuous-time dynamic graphs based on their architecture. While ordering the existing GNN models, we find, that there are still graph types, that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.
Westmeier, T., Botache, D., Bieshaar, M., Sick, B.: Generating Synthetic Time Series for Machine-Learning-Empowered Monitoring of Electric Motor Test Benches In: IEEE International Conference on Data Science and Advanced Analytics (DSAA). bll 513–522. IEEE (2022). https://doi.org/10.1109/DSAA54385.2022.10032385.
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The development of new electric traction machines is a time-consuming process as it involves intensive testing on motor test benches. Machine-Learning-empowered monitoring offers the opportunity to anticipate costly failures early and hence reducing development time. However, machine learning (ML) for process monitoring requires large amounts of training data, especially as the targeted fault states are scarce and yet diverse in their appearances. Therefore, we propose to use synthetic time series data to leverage the high cost of acquiring training data from experiments in real test benches. In this article, we present a novel scheme to generate synthetic data based on a sub-dimensional time series representation. We introduce a highly flexible model by mapping the data to a latent representation and approximating the latent data distribution by a Gaussian Mixture Model. In addition, we propose the Frechet InceptionTime Distance (FITD) as a new distance measure to evaluate the generated data. It allows extracting characteristics at different scales by using multiple kernel sizes. In this way, we ensure that the synthesized data contains characteristics similar to those present in the real data. In our experiment, we train two types of fault detectors, one based on real data of a motor test bench and the other based on synthetic data. We also consider employing fault-aware conditional architectures to generate training data for different fault types explicitly. Our final results show that using synthesized data in the training process increases the performance in terms of classification accuracy score (CAS) up to 29%.
@inproceedings{westmeier2022generating, abstract = {The development of new electric traction machines is a time-consuming process as it involves intensive testing on motor test benches. Machine-Learning-empowered monitoring offers the opportunity to anticipate costly failures early and hence reducing development time. However, machine learning (ML) for process monitoring requires large amounts of training data, especially as the targeted fault states are scarce and yet diverse in their appearances. Therefore, we propose to use synthetic time series data to leverage the high cost of acquiring training data from experiments in real test benches. In this article, we present a novel scheme to generate synthetic data based on a sub-dimensional time series representation. We introduce a highly flexible model by mapping the data to a latent representation and approximating the latent data distribution by a Gaussian Mixture Model. In addition, we propose the Frechet InceptionTime Distance (FITD) as a new distance measure to evaluate the generated data. It allows extracting characteristics at different scales by using multiple kernel sizes. In this way, we ensure that the synthesized data contains characteristics similar to those present in the real data. In our experiment, we train two types of fault detectors, one based on real data of a motor test bench and the other based on synthetic data. We also consider employing fault-aware conditional architectures to generate training data for different fault types explicitly. Our final results show that using synthesized data in the training process increases the performance in terms of classification accuracy score (CAS) up to 29%.}, author = {Westmeier, Tobias and Botache, Diego and Bieshaar, Maarten and Sick, Bernhard}, booktitle = {IEEE International Conference on Data Science and Advanced Analytics (DSAA)}, keywords = {Variational-Autoencoder}, pages = {513–522}, publisher = {IEEE}, title = {Generating Synthetic Time Series for Machine-Learning-Empowered Monitoring of Electric Motor Test Benches}, year = 2022 }
%0 Conference Paper %1 westmeier2022generating %A Westmeier, Tobias %A Botache, Diego %A Bieshaar, Maarten %A Sick, Bernhard %B IEEE International Conference on Data Science and Advanced Analytics (DSAA) %D 2022 %I IEEE %P 513--522 %R 10.1109/DSAA54385.2022.10032385 %T Generating Synthetic Time Series for Machine-Learning-Empowered Monitoring of Electric Motor Test Benches %U https://ieeexplore.ieee.org/document/10032385 %X The development of new electric traction machines is a time-consuming process as it involves intensive testing on motor test benches. Machine-Learning-empowered monitoring offers the opportunity to anticipate costly failures early and hence reducing development time. However, machine learning (ML) for process monitoring requires large amounts of training data, especially as the targeted fault states are scarce and yet diverse in their appearances. Therefore, we propose to use synthetic time series data to leverage the high cost of acquiring training data from experiments in real test benches. In this article, we present a novel scheme to generate synthetic data based on a sub-dimensional time series representation. We introduce a highly flexible model by mapping the data to a latent representation and approximating the latent data distribution by a Gaussian Mixture Model. In addition, we propose the Frechet InceptionTime Distance (FITD) as a new distance measure to evaluate the generated data. It allows extracting characteristics at different scales by using multiple kernel sizes. In this way, we ensure that the synthesized data contains characteristics similar to those present in the real data. In our experiment, we train two types of fault detectors, one based on real data of a motor test bench and the other based on synthetic data. We also consider employing fault-aware conditional architectures to generate training data for different fault types explicitly. Our final results show that using synthesized data in the training process increases the performance in terms of classification accuracy score (CAS) up to 29%.
Moallemy-Oureh, A., Beddar-Wiesing, S., Nather, R., Thomas, J.M.: FDGNN: Fully Dynamic Graph Neural Network arXiv e-prints. arXiv:2206.03469 (2022).
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Dynamic Graph Neural Networks recently became more and more important as graphs from many scientific fields, ranging from mathematics, biology, social sciences, and physics to computer science, are dynamic by nature. While temporal changes (dynamics) play an essential role in many real-world applications, most of the models in the literature on Graph Neural Networks (GNN) process static graphs. The few GNN models on dynamic graphs only consider exceptional cases of dynamics, e.g., node attribute-dynamic graphs or structure-dynamic graphs limited to additions or changes to the graph's edges, etc. Therefore, we present a novel Fully Dynamic Graph Neural Network (FDGNN) that can handle fully-dynamic graphs in continuous time. The proposed method provides a node and an edge embedding that includes their activity to address added and deleted nodes or edges, and possible attributes. Furthermore, the embeddings specify Temporal Point Processes for each event to encode the distributions of the structure- and attribute-related incoming graph events. In addition, our model can be updated efficiently by considering single events for local retraining.
@article{moallemyoureh2022fdgnn, abstract = {Dynamic Graph Neural Networks recently became more and more important as graphs from many scientific fields, ranging from mathematics, biology, social sciences, and physics to computer science, are dynamic by nature. While temporal changes (dynamics) play an essential role in many real-world applications, most of the models in the literature on Graph Neural Networks (GNN) process static graphs. The few GNN models on dynamic graphs only consider exceptional cases of dynamics, e.g., node attribute-dynamic graphs or structure-dynamic graphs limited to additions or changes to the graph's edges, etc. Therefore, we present a novel Fully Dynamic Graph Neural Network (FDGNN) that can handle fully-dynamic graphs in continuous time. The proposed method provides a node and an edge embedding that includes their activity to address added and deleted nodes or edges, and possible attributes. Furthermore, the embeddings specify Temporal Point Processes for each event to encode the distributions of the structure- and attribute-related incoming graph events. In addition, our model can be updated efficiently by considering single events for local retraining.}, author = {Moallemy-Oureh, Alice and Beddar-Wiesing, Silcia and Nather, Rüdiger and Thomas, Josephine M.}, journal = {arXiv e-prints}, keywords = {itegpub}, pages = {arXiv:2206.03469}, title = {FDGNN: Fully Dynamic Graph Neural Network}, year = 2022 }
%0 Journal Article %1 moallemyoureh2022fdgnn %A Moallemy-Oureh, Alice %A Beddar-Wiesing, Silcia %A Nather, Rüdiger %A Thomas, Josephine M. %D 2022 %J arXiv e-prints %P arXiv:2206.03469 %T FDGNN: Fully Dynamic Graph Neural Network %U https://arxiv.org/abs/2206.03469 %X Dynamic Graph Neural Networks recently became more and more important as graphs from many scientific fields, ranging from mathematics, biology, social sciences, and physics to computer science, are dynamic by nature. While temporal changes (dynamics) play an essential role in many real-world applications, most of the models in the literature on Graph Neural Networks (GNN) process static graphs. The few GNN models on dynamic graphs only consider exceptional cases of dynamics, e.g., node attribute-dynamic graphs or structure-dynamic graphs limited to additions or changes to the graph's edges, etc. Therefore, we present a novel Fully Dynamic Graph Neural Network (FDGNN) that can handle fully-dynamic graphs in continuous time. The proposed method provides a node and an edge embedding that includes their activity to address added and deleted nodes or edges, and possible attributes. Furthermore, the embeddings specify Temporal Point Processes for each event to encode the distributions of the structure- and attribute-related incoming graph events. In addition, our model can be updated efficiently by considering single events for local retraining.
Herde, M., Huang, Z., Huseljic, D., Kottke, D., Vogt, S., Sick, B.: Fast Bayesian Updates for Deep Learning with a Use Case in Active Learning arXiv e-prints. arXiv:2210.06112 (2022).
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Retraining deep neural networks when new data arrives is typically computationally expensive. Moreover, certain applications do not allow such costly retraining due to time or computational constraints. Fast Bayesian updates are a possible solution to this issue. Therefore, we propose a Bayesian update based on Monte-Carlo samples and a last-layer Laplace approximation for different Bayesian neural network types, i.e., Dropout, Ensemble, and Spectral Normalized Neural Gaussian Process (SNGP). In a large-scale evaluation study, we show that our updates combined with SNGP represent a fast and competitive alternative to costly retraining. As a use case, we combine the Bayesian updates for SNGP with different sequential query strategies to exemplarily demonstrate their improved selection performance in active learning.
@article{herde2022fast, abstract = {Retraining deep neural networks when new data arrives is typically computationally expensive. Moreover, certain applications do not allow such costly retraining due to time or computational constraints. Fast Bayesian updates are a possible solution to this issue. Therefore, we propose a Bayesian update based on Monte-Carlo samples and a last-layer Laplace approximation for different Bayesian neural network types, i.e., Dropout, Ensemble, and Spectral Normalized Neural Gaussian Process (SNGP). In a large-scale evaluation study, we show that our updates combined with SNGP represent a fast and competitive alternative to costly retraining. As a use case, we combine the Bayesian updates for SNGP with different sequential query strategies to exemplarily demonstrate their improved selection performance in active learning.}, author = {Herde, Marek and Huang, Zhixin and Huseljic, Denis and Kottke, Daniel and Vogt, Stephan and Sick, Bernhard}, journal = {arXiv e-prints}, keywords = {itegpub}, pages = {arXiv:2210.06112}, title = {Fast Bayesian Updates for Deep Learning with a Use Case in Active Learning}, year = 2022 }
%0 Journal Article %1 herde2022fast %A Herde, Marek %A Huang, Zhixin %A Huseljic, Denis %A Kottke, Daniel %A Vogt, Stephan %A Sick, Bernhard %D 2022 %J arXiv e-prints %P arXiv:2210.06112 %T Fast Bayesian Updates for Deep Learning with a Use Case in Active Learning %U https://arxiv.org/abs/2210.06112 %X Retraining deep neural networks when new data arrives is typically computationally expensive. Moreover, certain applications do not allow such costly retraining due to time or computational constraints. Fast Bayesian updates are a possible solution to this issue. Therefore, we propose a Bayesian update based on Monte-Carlo samples and a last-layer Laplace approximation for different Bayesian neural network types, i.e., Dropout, Ensemble, and Spectral Normalized Neural Gaussian Process (SNGP). In a large-scale evaluation study, we show that our updates combined with SNGP represent a fast and competitive alternative to costly retraining. As a use case, we combine the Bayesian updates for SNGP with different sequential query strategies to exemplarily demonstrate their improved selection performance in active learning.
Rauch, L., Huseljic, D., Sick, B.: Enhancing Active Learning with Weak Supervision and Transfer Learning by Leveraging Information and Knowledge Sources In: Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. bll 27–42 (2022).
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One of the major limitations of deploying a machine learning model is the availability of labeled training data and the resulting expensive annotation process. Although active learning (AL) methods may reduce the annotation cost by actively selecting the most-useful instances, a costly human annotator usually provides the labels. Therefore, even with AL, we still consider the annotation process to be time-consuming and expensive. Besides human annotators, though, companies often have a vast amount of information and knowledge sources available that can generate low-cost labels (e.g., a black-box model) or improve the learning process (e.g., a pre-trained model). We present a novel approach that enhances AL with weak supervision (WS) and transfer learning (TL) to reduce the annotation cost by leveraging these sources. Specifically, we consider a black-box model like a rule-based system as an error-prone and weakly-supervised annotator that inexpensively provides labels. We estimate its performance with an annotator model to decide whether a human annotation is required. Additionally, we utilize unlabeled internal and external data by transferring knowledge from a pre-trained model to the AL cycle. We sequentially investigate the impact of WS and TL on annotation cost and model performance in an AL cycle through a use case. Our evaluation shows that our approach can reduce annotation cost by 51% while achieving nearly identical model performance compared to a traditional AL approach.
@inproceedings{rauch2022enhancing, abstract = {One of the major limitations of deploying a machine learning model is the availability of labeled training data and the resulting expensive annotation process. Although active learning (AL) methods may reduce the annotation cost by actively selecting the most-useful instances, a costly human annotator usually provides the labels. Therefore, even with AL, we still consider the annotation process to be time-consuming and expensive. Besides human annotators, though, companies often have a vast amount of information and knowledge sources available that can generate low-cost labels (e.g., a black-box model) or improve the learning process (e.g., a pre-trained model). We present a novel approach that enhances AL with weak supervision (WS) and transfer learning (TL) to reduce the annotation cost by leveraging these sources. Specifically, we consider a black-box model like a rule-based system as an error-prone and weakly-supervised annotator that inexpensively provides labels. We estimate its performance with an annotator model to decide whether a human annotation is required. Additionally, we utilize unlabeled internal and external data by transferring knowledge from a pre-trained model to the AL cycle. We sequentially investigate the impact of WS and TL on annotation cost and model performance in an AL cycle through a use case. Our evaluation shows that our approach can reduce annotation cost by 51% while achieving nearly identical model performance compared to a traditional AL approach.}, author = {Rauch, Lukas and Huseljic, Denis and Sick, Bernhard}, booktitle = {Workshop on Interactive Adaptive Learning (IAL), ECML PKDD}, keywords = {itegpub}, pages = {27–42}, title = {Enhancing Active Learning with Weak Supervision and Transfer Learning by Leveraging Information and Knowledge Sources}, year = 2022 }
%0 Conference Paper %1 rauch2022enhancing %A Rauch, Lukas %A Huseljic, Denis %A Sick, Bernhard %B Workshop on Interactive Adaptive Learning (IAL), ECML PKDD %D 2022 %P 27--42 %T Enhancing Active Learning with Weak Supervision and Transfer Learning by Leveraging Information and Knowledge Sources %U http://ceur-ws.org/Vol-3259/ialatecml_paper3.pdf %X One of the major limitations of deploying a machine learning model is the availability of labeled training data and the resulting expensive annotation process. Although active learning (AL) methods may reduce the annotation cost by actively selecting the most-useful instances, a costly human annotator usually provides the labels. Therefore, even with AL, we still consider the annotation process to be time-consuming and expensive. Besides human annotators, though, companies often have a vast amount of information and knowledge sources available that can generate low-cost labels (e.g., a black-box model) or improve the learning process (e.g., a pre-trained model). We present a novel approach that enhances AL with weak supervision (WS) and transfer learning (TL) to reduce the annotation cost by leveraging these sources. Specifically, we consider a black-box model like a rule-based system as an error-prone and weakly-supervised annotator that inexpensively provides labels. We estimate its performance with an annotator model to decide whether a human annotation is required. Additionally, we utilize unlabeled internal and external data by transferring knowledge from a pre-trained model to the AL cycle. We sequentially investigate the impact of WS and TL on annotation cost and model performance in an AL cycle through a use case. Our evaluation shows that our approach can reduce annotation cost by 51% while achieving nearly identical model performance compared to a traditional AL approach.
Englhardt, A., Trittenbach, H., Kottke, D., Sick, B., Böhm, K.: Efficient SVDD sampling with approximation guarantees for the decision boundary Machine Learning. 111, 1349–1375 (2022). https://doi.org/10.1007/s10994-022-06149-0.
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Support Vector Data Description (SVDD) is a popular one-class classifier for anomaly and novelty detection. But despite its effectiveness, SVDD does not scale well with data size. To avoid prohibitive training times, sampling methods select small subsets of the training data on which SVDD trains a decision boundary hopefully equivalent to the one obtained on the full data set. According to the literature, a good sample should therefore contain so-called boundary observations that SVDD would select as support vectors on the full data set. However, non-boundary observations also are essential to not fragment contiguous inlier regions and avoid poor classification accuracy. Other aspects, such as selecting a sufficiently representative sample, are important as well. But existing sampling methods largely overlook them, resulting in poor classification accuracy. In this article, we study how to select a sample considering these points. Our approach is to frame SVDD sampling as an optimization problem, where constraints guarantee that sampling indeed approximates the original decision boundary. We then propose RAPID, an efficient algorithm to solve this optimization problem. RAPID does not require any tuning of parameters, is easy to implement and scales well to large data sets. We evaluate our approach on real-world and synthetic data. Our evaluation is the most comprehensive one for SVDD sampling so far. Our results show that RAPID outperforms its competitors in classification accuracy, in sample size, and in runtime.
@article{englhardt2022efficient, abstract = {Support Vector Data Description (SVDD) is a popular one-class classifier for anomaly and novelty detection. But despite its effectiveness, SVDD does not scale well with data size. To avoid prohibitive training times, sampling methods select small subsets of the training data on which SVDD trains a decision boundary hopefully equivalent to the one obtained on the full data set. According to the literature, a good sample should therefore contain so-called boundary observations that SVDD would select as support vectors on the full data set. However, non-boundary observations also are essential to not fragment contiguous inlier regions and avoid poor classification accuracy. Other aspects, such as selecting a sufficiently representative sample, are important as well. But existing sampling methods largely overlook them, resulting in poor classification accuracy. In this article, we study how to select a sample considering these points. Our approach is to frame SVDD sampling as an optimization problem, where constraints guarantee that sampling indeed approximates the original decision boundary. We then propose RAPID, an efficient algorithm to solve this optimization problem. RAPID does not require any tuning of parameters, is easy to implement and scales well to large data sets. We evaluate our approach on real-world and synthetic data. Our evaluation is the most comprehensive one for SVDD sampling so far. Our results show that RAPID outperforms its competitors in classification accuracy, in sample size, and in runtime.}, author = {Englhardt, Adrian and Trittenbach, Holger and Kottke, Daniel and Sick, Bernhard and Böhm, Klemens}, journal = {Machine Learning}, keywords = {itegpub}, number = 4, pages = {1349–1375}, publisher = {Springer}, title = {Efficient {SVDD} sampling with approximation guarantees for the decision boundary}, volume = 111, year = 2022 }
%0 Journal Article %1 englhardt2022efficient %A Englhardt, Adrian %A Trittenbach, Holger %A Kottke, Daniel %A Sick, Bernhard %A Böhm, Klemens %D 2022 %I Springer %J Machine Learning %N 4 %P 1349--1375 %R 10.1007/s10994-022-06149-0 %T Efficient SVDD sampling with approximation guarantees for the decision boundary %U https://link.springer.com/article/10.1007/s10994-022-06149-0 %V 111 %X Support Vector Data Description (SVDD) is a popular one-class classifier for anomaly and novelty detection. But despite its effectiveness, SVDD does not scale well with data size. To avoid prohibitive training times, sampling methods select small subsets of the training data on which SVDD trains a decision boundary hopefully equivalent to the one obtained on the full data set. According to the literature, a good sample should therefore contain so-called boundary observations that SVDD would select as support vectors on the full data set. However, non-boundary observations also are essential to not fragment contiguous inlier regions and avoid poor classification accuracy. Other aspects, such as selecting a sufficiently representative sample, are important as well. But existing sampling methods largely overlook them, resulting in poor classification accuracy. In this article, we study how to select a sample considering these points. Our approach is to frame SVDD sampling as an optimization problem, where constraints guarantee that sampling indeed approximates the original decision boundary. We then propose RAPID, an efficient algorithm to solve this optimization problem. RAPID does not require any tuning of parameters, is easy to implement and scales well to large data sets. We evaluate our approach on real-world and synthetic data. Our evaluation is the most comprehensive one for SVDD sampling so far. Our results show that RAPID outperforms its competitors in classification accuracy, in sample size, and in runtime.
Heidecker, F.: Detecting Corner Case in the Context of Highly Automated Driving In: Tomforde, S. en Krupitzer, C. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2021. bll 60–73. kassel university press (2022).
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This article introduces a research proposal that aims to deﬁne, detect, and evaluate corner cases in the context of machine learning (ML) systems and highly automated driving. Corner cases are scenarios or data in a dataset that are recorded only very rarely and therefore only available in small quantities or not at all in the dataset. However, corner cases are very important for model training, as they are needed to improve the model performance in these rare scenarios. On the other hand, in safety- and security-critical applications, such as highly automated driving, corner case data is extremely important for testing and verifying the ML system. The research proposal addresses these challenges and focuses on the deﬁnition and categorization of corner cases. Based on this, ML methods for corner case detection are developed, focusing on object detection for automated driving and trajectory data for intention detection of pedestrians and cyclists. Finally, the research proposal addresses corner case metrics and the evaluation of the developed detection models.
@incollection{florianheidecker2022detecting, abstract = {This article introduces a research proposal that aims to deﬁne, detect, and evaluate corner cases in the context of machine learning (ML) systems and highly automated driving. Corner cases are scenarios or data in a dataset that are recorded only very rarely and therefore only available in small quantities or not at all in the dataset. However, corner cases are very important for model training, as they are needed to improve the model performance in these rare scenarios. On the other hand, in safety- and security-critical applications, such as highly automated driving, corner case data is extremely important for testing and verifying the ML system. The research proposal addresses these challenges and focuses on the deﬁnition and categorization of corner cases. Based on this, ML methods for corner case detection are developed, focusing on object detection for automated driving and trajectory data for intention detection of pedestrians and cyclists. Finally, the research proposal addresses corner case metrics and the evaluation of the developed detection models.}, author = {Heidecker, Florian}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2021}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {itegpub}, pages = {60–73}, publisher = {kassel university press}, title = {Detecting Corner Case in the Context of Highly Automated Driving}, year = 2022 }
%0 Book Section %1 florianheidecker2022detecting %A Heidecker, Florian %B Organic Computing -- Doctoral Dissertation Colloquium 2021 %D 2022 %E Tomforde, Sven %E Krupitzer, Christian %I kassel university press %P 60--73 %T Detecting Corner Case in the Context of Highly Automated Driving %X This article introduces a research proposal that aims to deﬁne, detect, and evaluate corner cases in the context of machine learning (ML) systems and highly automated driving. Corner cases are scenarios or data in a dataset that are recorded only very rarely and therefore only available in small quantities or not at all in the dataset. However, corner cases are very important for model training, as they are needed to improve the model performance in these rare scenarios. On the other hand, in safety- and security-critical applications, such as highly automated driving, corner case data is extremely important for testing and verifying the ML system. The research proposal addresses these challenges and focuses on the deﬁnition and categorization of corner cases. Based on this, ML methods for corner case detection are developed, focusing on object detection for automated driving and trajectory data for intention detection of pedestrians and cyclists. Finally, the research proposal addresses corner case metrics and the evaluation of the developed detection models.
He, Y., Huang, Z., Sick, B.: Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning In: Workshop on Interactive Machine Learning Workshop (IMLW), AAAI. bll 1–6 (2022).
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Continual learning can enable neural networks to evolve by learning new tasks sequentially in task-changing scenarios. However, two general challenges should be overcome in further research before we apply this technique to real-world applications. Firstly, newly collected novelties from the data stream in applications could contain anomalies that are meaningless for continual learning. Instead of viewing them as a new task for updating, we have to filter out such anomalies to reduce the disturbance of extremely high-entropy data for the progression of convergence. Secondly, fewer efforts have been put into research regarding the explainability of continual learning, which leads to a lack of transparency and credibility of the updated neural networks. Elaborated explanations about the process and result of continual learning can help experts in judgment and making decisions. Therefore, we propose the conceptual design of an explainability module with experts in the loop based on techniques, such as dimension reduction, visualization, and evaluation strategies. This work aims to overcome the mentioned challenges by sufficiently explaining and visualizing the identified anomalies and the updated neural network. With the help of this module, experts can be more confident in decision-making regarding anomaly filtering, dynamic adjustment of hyperparameters, data backup, etc.
@inproceedings{he2022design, abstract = {Continual learning can enable neural networks to evolve by learning new tasks sequentially in task-changing scenarios. However, two general challenges should be overcome in further research before we apply this technique to real-world applications. Firstly, newly collected novelties from the data stream in applications could contain anomalies that are meaningless for continual learning. Instead of viewing them as a new task for updating, we have to filter out such anomalies to reduce the disturbance of extremely high-entropy data for the progression of convergence. Secondly, fewer efforts have been put into research regarding the explainability of continual learning, which leads to a lack of transparency and credibility of the updated neural networks. Elaborated explanations about the process and result of continual learning can help experts in judgment and making decisions. Therefore, we propose the conceptual design of an explainability module with experts in the loop based on techniques, such as dimension reduction, visualization, and evaluation strategies. This work aims to overcome the mentioned challenges by sufficiently explaining and visualizing the identified anomalies and the updated neural network. With the help of this module, experts can be more confident in decision-making regarding anomaly filtering, dynamic adjustment of hyperparameters, data backup, etc.}, author = {He, Yujiang and Huang, Zhixin and Sick, Bernhard}, booktitle = {Workshop on Interactive Machine Learning Workshop (IMLW), AAAI}, keywords = {itegpub}, pages = {1–6}, title = {Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning}, year = 2022 }
%0 Conference Paper %1 he2022design %A He, Yujiang %A Huang, Zhixin %A Sick, Bernhard %B Workshop on Interactive Machine Learning Workshop (IMLW), AAAI %D 2022 %P 1--6 %T Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning %U https://arxiv.org/abs/2202.06781 %X Continual learning can enable neural networks to evolve by learning new tasks sequentially in task-changing scenarios. However, two general challenges should be overcome in further research before we apply this technique to real-world applications. Firstly, newly collected novelties from the data stream in applications could contain anomalies that are meaningless for continual learning. Instead of viewing them as a new task for updating, we have to filter out such anomalies to reduce the disturbance of extremely high-entropy data for the progression of convergence. Secondly, fewer efforts have been put into research regarding the explainability of continual learning, which leads to a lack of transparency and credibility of the updated neural networks. Elaborated explanations about the process and result of continual learning can help experts in judgment and making decisions. Therefore, we propose the conceptual design of an explainability module with experts in the loop based on techniques, such as dimension reduction, visualization, and evaluation strategies. This work aims to overcome the mentioned challenges by sufficiently explaining and visualizing the identified anomalies and the updated neural network. With the help of this module, experts can be more confident in decision-making regarding anomaly filtering, dynamic adjustment of hyperparameters, data backup, etc.
Ali, M.W.: Deep Adaptive Knowledge-Aware Learning for E-Mobility In: Organic Computing - Doctoral Dissertation Colloquium 2022. kassel university press (2022).
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Electromobility can reduce global energy demand by reducing the dependence on fossil fuels. Many different types of electric vehicles (EVs) have been produced recently. They are in high demand, and EV usage is also rising. However, there is no fixed charging behavior or energy demand for these different types of EV users. Moreover, most renewable energy sources are stochastic and highly dependent on the weather. Thus, it is essential that EV charging demand is controlled and adapted in advance to prevent catastrophic failures such as blackouts. Deep Learning (DL) models are standard technology for power forecasting in many scenarios. However, there is a lack of real-world heterogeneous multi-source data in the e-mobility domain to train a deep learning model from scratch. Moreover, DL models lack model explainability. Therefore, EV stakeholders can benefit from a framework that coordinates deep transfer learning, interactive machine learning, and heterogeneous graph representation learning (to ensure self-explainability). This article aims to develop a novel framework to solve e-mobility-related tasks such as forecasting EV loads efficiently in a resource-constrained environment (data, computing power, etc.).
@incollection{ali2022deep, abstract = {Electromobility can reduce global energy demand by reducing the dependence on fossil fuels. Many different types of electric vehicles (EVs) have been produced recently. They are in high demand, and EV usage is also rising. However, there is no fixed charging behavior or energy demand for these different types of EV users. Moreover, most renewable energy sources are stochastic and highly dependent on the weather. Thus, it is essential that EV charging demand is controlled and adapted in advance to prevent catastrophic failures such as blackouts. Deep Learning (DL) models are standard technology for power forecasting in many scenarios. However, there is a lack of real-world heterogeneous multi-source data in the e-mobility domain to train a deep learning model from scratch. Moreover, DL models lack model explainability. Therefore, EV stakeholders can benefit from a framework that coordinates deep transfer learning, interactive machine learning, and heterogeneous graph representation learning (to ensure self-explainability). This article aims to develop a novel framework to solve e-mobility-related tasks such as forecasting EV loads efficiently in a resource-constrained environment (data, computing power, etc.).}, author = {Ali, Mohammad Wazed}, booktitle = {Organic Computing - Doctoral Dissertation Colloquium 2022}, keywords = {itegpub}, note = {(accepted)}, publisher = {kassel university press}, title = {Deep Adaptive Knowledge-Aware Learning for E-Mobility}, year = 2022 }
%0 Book Section %1 ali2022deep %A Ali, Mohammad Wazed %B Organic Computing - Doctoral Dissertation Colloquium 2022 %D 2022 %I kassel university press %T Deep Adaptive Knowledge-Aware Learning for E-Mobility %X Electromobility can reduce global energy demand by reducing the dependence on fossil fuels. Many different types of electric vehicles (EVs) have been produced recently. They are in high demand, and EV usage is also rising. However, there is no fixed charging behavior or energy demand for these different types of EV users. Moreover, most renewable energy sources are stochastic and highly dependent on the weather. Thus, it is essential that EV charging demand is controlled and adapted in advance to prevent catastrophic failures such as blackouts. Deep Learning (DL) models are standard technology for power forecasting in many scenarios. However, there is a lack of real-world heterogeneous multi-source data in the e-mobility domain to train a deep learning model from scratch. Moreover, DL models lack model explainability. Therefore, EV stakeholders can benefit from a framework that coordinates deep transfer learning, interactive machine learning, and heterogeneous graph representation learning (to ensure self-explainability). This article aims to develop a novel framework to solve e-mobility-related tasks such as forecasting EV loads efficiently in a resource-constrained environment (data, computing power, etc.).
Dingel, K., Otto, T., Marder, L., Funke, L., Held, A., Savio, S., Hans, A., Hartmann, G., Meier, D., Viefhaus, J., Sick, B., Ehresmann, A., Ilchen, M., Helml, W.: Artificial intelligence for online characterization of ultrashort X‑ray free‑electron laser pulses Scientific Reports. 12, 1–14 (2022). https://doi.org/10.1038/s41598-022-21646-x.
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X-ray free-electron lasers (XFELs) as the world’s brightest light sources provide ultrashort X-ray pulses with a duration typically in the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena such as localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes has been, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time–energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence techniques, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics even at high-repetition-rate XFELs, thus enhancing and refining their scientific accessibility in all related disciplines.
@article{dingel2022artificial, abstract = {X-ray free-electron lasers (XFELs) as the world’s brightest light sources provide ultrashort X-ray pulses with a duration typically in the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena such as localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes has been, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time–energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence techniques, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics even at high-repetition-rate XFELs, thus enhancing and refining their scientific accessibility in all related disciplines.}, author = {Dingel, Kristina and Otto, Thorsten and Marder, Lutz and Funke, Lars and Held, Arne and Savio, Sara and Hans, Andreas and Hartmann, Gregor and Meier, David and Viefhaus, Jens and Sick, Bernhard and Ehresmann, Arno and Ilchen, Markus and Helml, Wolfram}, journal = {Scientific Reports}, keywords = {itegpub}, number = 1, pages = {1–14}, publisher = {Nature Publishing Group}, title = {Artificial intelligence for online characterization of ultrashort X‑ray free‑electron laser pulses}, volume = 12, year = 2022 }
%0 Journal Article %1 dingel2022artificial %A Dingel, Kristina %A Otto, Thorsten %A Marder, Lutz %A Funke, Lars %A Held, Arne %A Savio, Sara %A Hans, Andreas %A Hartmann, Gregor %A Meier, David %A Viefhaus, Jens %A Sick, Bernhard %A Ehresmann, Arno %A Ilchen, Markus %A Helml, Wolfram %D 2022 %I Nature Publishing Group %J Scientific Reports %N 1 %P 1--14 %R 10.1038/s41598-022-21646-x %T Artificial intelligence for online characterization of ultrashort X‑ray free‑electron laser pulses %U https://www.nature.com/articles/s41598-022-21646-x %V 12 %X X-ray free-electron lasers (XFELs) as the world’s brightest light sources provide ultrashort X-ray pulses with a duration typically in the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena such as localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes has been, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time–energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence techniques, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics even at high-repetition-rate XFELs, thus enhancing and refining their scientific accessibility in all related disciplines.
He, Y.: Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts In: Tomforde, S. en Krupitzer, C. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2021. bll 125–140. kassel university press (2022).
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Compared with traditional deep learning techniques, continual learning enables deep neural networks to learn continually and adaptively. Deep neural networks have to learn unseen tasks and overcome forgetting the knowledge obtained from previously learned tasks as the amount of data keeps increasing in applications. This article proposes two continual learning application scenarios, i.e., the target-domain incremental scenario and the data-domain incremental scenario, to describe the potential challenges in this context. Based on our previous work regarding the CLeaR framework, which is short for continual learning for regression tasks, models will be enabled to extend themselves and to learn data successively. Research topics are related, but not limited, to developing continual deep learning algorithms, strategies for non-stationarity detection in data streams, explainable and visualizable artificial intelligence, etc. Moreover, the framework- and algorithm-related hyperparameters should be dynamically updated in applications. Forecasting experiments will be conducted based on power generation and consumption data collected from real-world applications. A series of comprehensive evaluation metrics and visualization tools are applied to access the experimental results. The proposed framework is expected to be generally applied to other constantly changing scenarios.
@incollection{he2022adaptive, abstract = {Compared with traditional deep learning techniques, continual learning enables deep neural networks to learn continually and adaptively. Deep neural networks have to learn unseen tasks and overcome forgetting the knowledge obtained from previously learned tasks as the amount of data keeps increasing in applications. This article proposes two continual learning application scenarios, i.e., the target-domain incremental scenario and the data-domain incremental scenario, to describe the potential challenges in this context. Based on our previous work regarding the CLeaR framework, which is short for continual learning for regression tasks, models will be enabled to extend themselves and to learn data successively. Research topics are related, but not limited, to developing continual deep learning algorithms, strategies for non-stationarity detection in data streams, explainable and visualizable artificial intelligence, etc. Moreover, the framework- and algorithm-related hyperparameters should be dynamically updated in applications. Forecasting experiments will be conducted based on power generation and consumption data collected from real-world applications. A series of comprehensive evaluation metrics and visualization tools are applied to access the experimental results. The proposed framework is expected to be generally applied to other constantly changing scenarios.}, author = {He, Yujiang}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2021}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {itegpub}, pages = {125–140}, publisher = {kassel university press}, title = {Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts}, year = 2022 }
%0 Book Section %1 he2022adaptive %A He, Yujiang %B Organic Computing -- Doctoral Dissertation Colloquium 2021 %D 2022 %E Tomforde, Sven %E Krupitzer, Christian %I kassel university press %P 125--140 %T Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts %X Compared with traditional deep learning techniques, continual learning enables deep neural networks to learn continually and adaptively. Deep neural networks have to learn unseen tasks and overcome forgetting the knowledge obtained from previously learned tasks as the amount of data keeps increasing in applications. This article proposes two continual learning application scenarios, i.e., the target-domain incremental scenario and the data-domain incremental scenario, to describe the potential challenges in this context. Based on our previous work regarding the CLeaR framework, which is short for continual learning for regression tasks, models will be enabled to extend themselves and to learn data successively. Research topics are related, but not limited, to developing continual deep learning algorithms, strategies for non-stationarity detection in data streams, explainable and visualizable artificial intelligence, etc. Moreover, the framework- and algorithm-related hyperparameters should be dynamically updated in applications. Forecasting experiments will be conducted based on power generation and consumption data collected from real-world applications. A series of comprehensive evaluation metrics and visualization tools are applied to access the experimental results. The proposed framework is expected to be generally applied to other constantly changing scenarios.
Dingel, K.: Actively Controlling and Redesigning Experiments using the Application Case of Free-Electron Laser Pulse Characterization In: Tomforde, S. en Krupitzer, C. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2021. bll 86–98. kassel university press (2022).
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Experiments are the elective tool for developing new technologies, testing hypotheses, and designing new materials in many scientific fields. Thereby, it is state-of-the-art to create experiments traditionally, starting with a hypothesis leading to the product or relevant data after several experiment cycles. There are some techniques such as automation, parallelization, or inverse design to accelerate experiments. Particularly interesting, however, is the approach to integrate immediate data analysis into the experiment cycle. By analyzing experiment output data on-site using, e.g., neural networks, one can make inferences regarding the experiment's current settings at run-time and furthermore improve them. In the following research project, the goal is to develop and incorporate a data analysis stage into an experimental setup, which can be actively adapted and designed on-site. To demonstrate the developed method, the experimental design of a self-amplification of spontaneous emission pulse characterization experiment at a free-electron laser will be used to show to what extent the experiment can be adapted and optimized.
@incollection{dingel2022actively, abstract = {Experiments are the elective tool for developing new technologies, testing hypotheses, and designing new materials in many scientific fields. Thereby, it is state-of-the-art to create experiments traditionally, starting with a hypothesis leading to the product or relevant data after several experiment cycles. There are some techniques such as automation, parallelization, or inverse design to accelerate experiments. Particularly interesting, however, is the approach to integrate immediate data analysis into the experiment cycle. By analyzing experiment output data on-site using, e.g., neural networks, one can make inferences regarding the experiment's current settings at run-time and furthermore improve them. In the following research project, the goal is to develop and incorporate a data analysis stage into an experimental setup, which can be actively adapted and designed on-site. To demonstrate the developed method, the experimental design of a self-amplification of spontaneous emission pulse characterization experiment at a free-electron laser will be used to show to what extent the experiment can be adapted and optimized.}, author = {Dingel, Kristina}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2021}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {itegpub}, pages = {86–98}, publisher = {kassel university press}, title = {Actively Controlling and Redesigning Experiments using the Application Case of Free-Electron Laser Pulse Characterization}, year = 2022 }
%0 Book Section %1 dingel2022actively %A Dingel, Kristina %B Organic Computing -- Doctoral Dissertation Colloquium 2021 %D 2022 %E Tomforde, Sven %E Krupitzer, Christian %I kassel university press %P 86--98 %T Actively Controlling and Redesigning Experiments using the Application Case of Free-Electron Laser Pulse Characterization %X Experiments are the elective tool for developing new technologies, testing hypotheses, and designing new materials in many scientific fields. Thereby, it is state-of-the-art to create experiments traditionally, starting with a hypothesis leading to the product or relevant data after several experiment cycles. There are some techniques such as automation, parallelization, or inverse design to accelerate experiments. Particularly interesting, however, is the approach to integrate immediate data analysis into the experiment cycle. By analyzing experiment output data on-site using, e.g., neural networks, one can make inferences regarding the experiment's current settings at run-time and furthermore improve them. In the following research project, the goal is to develop and incorporate a data analysis stage into an experimental setup, which can be actively adapted and designed on-site. To demonstrate the developed method, the experimental design of a self-amplification of spontaneous emission pulse characterization experiment at a free-electron laser will be used to show to what extent the experiment can be adapted and optimized.
Huang, Z.: Active Learning in Multivariate Time Series Anomaly Detection In: Tomforde, S. en Krupitzer, C. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2021. bll 113–124. kassel university press (2022).
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Multivariate time series anomaly detection is an important research field of industry, science, and finances. In practical applications, unlabeled data is abundant, and labeled data is rare. Experts’ experience plays a key role in data labeling, while experts often have limited time, are expensive, and are concerned primarily with finding the potential anomalies. Active learning, which has been widely used to select the most informative data for labeling through experts, can improve training with less labeling effort. However, most active learning algorithms have not been evaluated on datasets with imbalanced class frequencies, such as anomaly detection. Although some semi-supervised and active learning methods were proposed to handle univariate time series data, not much work has been done on multivariate time series classification. Therefore, this article identifies critical challenges and proposes potential solutions for active learning in multivariate time series anomaly detection. We combine all possible solutions into a framework that conforms to the scope of the observation/control system in organic computing.
@incollection{huang2022active, abstract = {Multivariate time series anomaly detection is an important research field of industry, science, and finances. In practical applications, unlabeled data is abundant, and labeled data is rare. Experts’ experience plays a key role in data labeling, while experts often have limited time, are expensive, and are concerned primarily with finding the potential anomalies. Active learning, which has been widely used to select the most informative data for labeling through experts, can improve training with less labeling effort. However, most active learning algorithms have not been evaluated on datasets with imbalanced class frequencies, such as anomaly detection. Although some semi-supervised and active learning methods were proposed to handle univariate time series data, not much work has been done on multivariate time series classification. Therefore, this article identifies critical challenges and proposes potential solutions for active learning in multivariate time series anomaly detection. We combine all possible solutions into a framework that conforms to the scope of the observation/control system in organic computing.}, author = {Huang, Zhixin}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2021}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {itegpub}, pages = {113–124}, publisher = {kassel university press}, title = {Active Learning in Multivariate Time Series Anomaly Detection}, year = 2022 }
%0 Book Section %1 huang2022active %A Huang, Zhixin %B Organic Computing -- Doctoral Dissertation Colloquium 2021 %D 2022 %E Tomforde, Sven %E Krupitzer, Christian %I kassel university press %P 113--124 %T Active Learning in Multivariate Time Series Anomaly Detection %X Multivariate time series anomaly detection is an important research field of industry, science, and finances. In practical applications, unlabeled data is abundant, and labeled data is rare. Experts’ experience plays a key role in data labeling, while experts often have limited time, are expensive, and are concerned primarily with finding the potential anomalies. Active learning, which has been widely used to select the most informative data for labeling through experts, can improve training with less labeling effort. However, most active learning algorithms have not been evaluated on datasets with imbalanced class frequencies, such as anomaly detection. Although some semi-supervised and active learning methods were proposed to handle univariate time series data, not much work has been done on multivariate time series classification. Therefore, this article identifies critical challenges and proposes potential solutions for active learning in multivariate time series anomaly detection. We combine all possible solutions into a framework that conforms to the scope of the observation/control system in organic computing.
Kottke, D., Sandrock, C., Krempl, G., Sick, B.: A Stopping Criterion for Transductive Active Learning In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD). bll 468–484. Springer (2022). https://doi.org/10.1007/978-3-031-26412-2_29.
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In transductive active learning, the goal is to determine the correct labels for an unlabeled, known dataset. Therefore, we can either ask an oracle to provide the right label at some cost or use the prediction of a classifier which we train on the labels acquired so far. In contrast, the commonly used (inductive) active learning aims to select instances for labeling out of the unlabeled set to create a generalized classifier, which will be deployed on unknown data. This article formally defines the transductive setting and shows that it requires new solutions. Additionally, we formalize the theoretically cost-optimal stopping point for the transductive scenario. Building upon the probabilistic active learning framework, we propose a new transductive selection strategy that includes a stopping criterion and show its superiority.
@inproceedings{kottke2022stopping, abstract = {In transductive active learning, the goal is to determine the correct labels for an unlabeled, known dataset. Therefore, we can either ask an oracle to provide the right label at some cost or use the prediction of a classifier which we train on the labels acquired so far. In contrast, the commonly used (inductive) active learning aims to select instances for labeling out of the unlabeled set to create a generalized classifier, which will be deployed on unknown data. This article formally defines the transductive setting and shows that it requires new solutions. Additionally, we formalize the theoretically cost-optimal stopping point for the transductive scenario. Building upon the probabilistic active learning framework, we propose a new transductive selection strategy that includes a stopping criterion and show its superiority.}, author = {Kottke, Daniel and Sandrock, Christoph and Krempl, Georg and Sick, Bernhard}, booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD)}, keywords = {itegpub}, pages = {468–484}, publisher = {Springer}, title = {A Stopping Criterion for Transductive Active Learning}, year = 2022 }
%0 Conference Paper %1 kottke2022stopping %A Kottke, Daniel %A Sandrock, Christoph %A Krempl, Georg %A Sick, Bernhard %B European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD) %D 2022 %I Springer %P 468--484 %R 10.1007/978-3-031-26412-2_29 %T A Stopping Criterion for Transductive Active Learning %U https://link.springer.com/chapter/10.1007/978-3-031-26412-2_29 %X In transductive active learning, the goal is to determine the correct labels for an unlabeled, known dataset. Therefore, we can either ask an oracle to provide the right label at some cost or use the prediction of a classifier which we train on the labels acquired so far. In contrast, the commonly used (inductive) active learning aims to select instances for labeling out of the unlabeled set to create a generalized classifier, which will be deployed on unknown data. This article formally defines the transductive setting and shows that it requires new solutions. Additionally, we formalize the theoretically cost-optimal stopping point for the transductive scenario. Building upon the probabilistic active learning framework, we propose a new transductive selection strategy that includes a stopping criterion and show its superiority.
Huseljic, D., Herde, M., Muejde, M., Sick, B.: A Review of Uncertainty Calibration in Pretrained Object Detectors arXiv e-prints. arXiv:2210.02935 (2022).
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In the field of deep learning based computer vision, the development of deep object detection has led to unique paradigms (e.g., two-stage or set-based) and architectures (e.g., Faster-RCNN or DETR) which enable outstanding performance on challenging benchmark datasets. Despite this, the trained object detectors typically do not reliably assess uncertainty regarding their own knowledge, and the quality of their probabilistic predictions is usually poor. As these are often used to make subsequent decisions, such inaccurate probabilistic predictions must be avoided. In this work, we investigate the uncertainty calibration properties of different pretrained object detection architectures in a multi-class setting. We propose a framework to ensure a fair, unbiased, and repeatable evaluation and conduct detailed analyses assessing the calibration under distributional changes (e.g., distributional shift and application to out-of-distribution data). Furthermore, by investigating the influence of different detector paradigms, post-processing steps, and suitable choices of metrics, we deliver novel insights into why poor detector calibration emerges. Based on these insights, we are able to improve the calibration of a detector by simply finetuning its last layer.
@article{huseljic2022review, abstract = {In the field of deep learning based computer vision, the development of deep object detection has led to unique paradigms (e.g., two-stage or set-based) and architectures (e.g., Faster-RCNN or DETR) which enable outstanding performance on challenging benchmark datasets. Despite this, the trained object detectors typically do not reliably assess uncertainty regarding their own knowledge, and the quality of their probabilistic predictions is usually poor. As these are often used to make subsequent decisions, such inaccurate probabilistic predictions must be avoided. In this work, we investigate the uncertainty calibration properties of different pretrained object detection architectures in a multi-class setting. We propose a framework to ensure a fair, unbiased, and repeatable evaluation and conduct detailed analyses assessing the calibration under distributional changes (e.g., distributional shift and application to out-of-distribution data). Furthermore, by investigating the influence of different detector paradigms, post-processing steps, and suitable choices of metrics, we deliver novel insights into why poor detector calibration emerges. Based on these insights, we are able to improve the calibration of a detector by simply finetuning its last layer.}, author = {Huseljic, Denis and Herde, Marek and Muejde, Mehmet and Sick, Bernhard}, journal = {arXiv e-prints}, keywords = {itegpub}, pages = {arXiv:2210.02935}, title = {A Review of Uncertainty Calibration in Pretrained Object Detectors}, year = 2022 }
%0 Journal Article %1 huseljic2022review %A Huseljic, Denis %A Herde, Marek %A Muejde, Mehmet %A Sick, Bernhard %D 2022 %J arXiv e-prints %P arXiv:2210.02935 %T A Review of Uncertainty Calibration in Pretrained Object Detectors %U https://arxiv.org/abs/2210.02935 %X In the field of deep learning based computer vision, the development of deep object detection has led to unique paradigms (e.g., two-stage or set-based) and architectures (e.g., Faster-RCNN or DETR) which enable outstanding performance on challenging benchmark datasets. Despite this, the trained object detectors typically do not reliably assess uncertainty regarding their own knowledge, and the quality of their probabilistic predictions is usually poor. As these are often used to make subsequent decisions, such inaccurate probabilistic predictions must be avoided. In this work, we investigate the uncertainty calibration properties of different pretrained object detection architectures in a multi-class setting. We propose a framework to ensure a fair, unbiased, and repeatable evaluation and conduct detailed analyses assessing the calibration under distributional changes (e.g., distributional shift and application to out-of-distribution data). Furthermore, by investigating the influence of different detector paradigms, post-processing steps, and suitable choices of metrics, we deliver novel insights into why poor detector calibration emerges. Based on these insights, we are able to improve the calibration of a detector by simply finetuning its last layer.
Schneegans, J., Bieshaar, M., Sick, B.: A Practical Evaluation of Active Learning Approaches for Object Detection In: Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. bll 49–67 (2022).
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Deep learning models are trained most successfully in a supervised manner, which requires vast amounts of annotated data. With active learning, the annotation process can be made much more efficient by intelligently selecting the most valuable batches of samples to annotate and train on. Those samples are selected based on their utility regarding the training algorithm. In this work, we examine a wide range of such selection criteria for the task of object detection as performed by the widely applied Faster R-CNN model. We focus on the large and diverse BDD100K autonomous driving dataset, paying special attention to the model’s sub-tasks and the dataset’s meta information. Furthermore, we distinguish between approaches that select samples based on aleatoric or epistemic uncertainty. A selection of evaluation measures that cover specific error sources and the overall model performance suggests that there is little difference between the individual active learning approaches, even in regards to their specialized focus on different model parts and sub-tasks. We conclude with a detailed discussion of the implied mechanisms regarding the active learning approaches that seem to affect model performances.
@inproceedings{schneegans2022practical, abstract = {Deep learning models are trained most successfully in a supervised manner, which requires vast amounts of annotated data. With active learning, the annotation process can be made much more efficient by intelligently selecting the most valuable batches of samples to annotate and train on. Those samples are selected based on their utility regarding the training algorithm. In this work, we examine a wide range of such selection criteria for the task of object detection as performed by the widely applied Faster R-CNN model. We focus on the large and diverse BDD100K autonomous driving dataset, paying special attention to the model’s sub-tasks and the dataset’s meta information. Furthermore, we distinguish between approaches that select samples based on aleatoric or epistemic uncertainty. A selection of evaluation measures that cover specific error sources and the overall model performance suggests that there is little difference between the individual active learning approaches, even in regards to their specialized focus on different model parts and sub-tasks. We conclude with a detailed discussion of the implied mechanisms regarding the active learning approaches that seem to affect model performances.}, author = {Schneegans, Jan and Bieshaar, Maarten and Sick, Bernhard}, booktitle = {Workshop on Interactive Adaptive Learning (IAL), ECML PKDD}, keywords = {itegpub}, pages = {49–67}, title = {A Practical Evaluation of Active Learning Approaches for Object Detection}, year = 2022 }
%0 Conference Paper %1 schneegans2022practical %A Schneegans, Jan %A Bieshaar, Maarten %A Sick, Bernhard %B Workshop on Interactive Adaptive Learning (IAL), ECML PKDD %D 2022 %P 49--67 %T A Practical Evaluation of Active Learning Approaches for Object Detection %U http://ceur-ws.org/Vol-3259/ialatecml_paper5.pdf %X Deep learning models are trained most successfully in a supervised manner, which requires vast amounts of annotated data. With active learning, the annotation process can be made much more efficient by intelligently selecting the most valuable batches of samples to annotate and train on. Those samples are selected based on their utility regarding the training algorithm. In this work, we examine a wide range of such selection criteria for the task of object detection as performed by the widely applied Faster R-CNN model. We focus on the large and diverse BDD100K autonomous driving dataset, paying special attention to the model’s sub-tasks and the dataset’s meta information. Furthermore, we distinguish between approaches that select samples based on aleatoric or epistemic uncertainty. A selection of evaluation measures that cover specific error sources and the overall model performance suggests that there is little difference between the individual active learning approaches, even in regards to their specialized focus on different model parts and sub-tasks. We conclude with a detailed discussion of the implied mechanisms regarding the active learning approaches that seem to affect model performances.
Zernetsch, S., Reichert, H., Kress, V., Doll, K., Sick, B.: A Holistic View on Probabilistic Trajectory Forecasting -- Case Study: Cyclist Intention Detection In: IEEE Intelligent Vehicles Symposium (IV). bll 265–272. IEEE (2022). https://doi.org/10.1109/IV51971.2022.9827220.
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This article presents a holistic approach for probabilistic cyclist intention detection. By combining probabilities for the current cyclist motion states with a probabilistic ensemble trajectory forecast, we are able to generate reliable estimates of cyclists’ future positions. The probabilities are used as weights in the probabilistic ensemble trajectory forecast. The ensemble consists of specialized models, which produce individual forecasts in the form of Gaussian distributions under the assumption of a certain motion state of the cyclist (e.g. cyclist is starting). By weighting the specialized models, we create forecasts in the form of Gaussian mixtures that define regions within which the cyclists will reside with a certain probability. To evaluate our method, we rate the reliability, sharpness, and positional accuracy of our forecasted distributions. We compare our method to a widely used unimodal approach which produces forecasts in the form of Gaussian distributions and show that our method is able to produce more reliable and sharper outputs while retaining comparable positional accuracy. By comparing two different methods for basic movement detection, one based on the past cyclist trajectory and one using past image sequences, we demonstrate that the results can be further improved by incorporating video information. Our methods are evaluated using a dataset created at a public intersection. The code and the dataset are publicly available.
@inproceedings{zernetsch2022holistic, abstract = {This article presents a holistic approach for probabilistic cyclist intention detection. By combining probabilities for the current cyclist motion states with a probabilistic ensemble trajectory forecast, we are able to generate reliable estimates of cyclists’ future positions. The probabilities are used as weights in the probabilistic ensemble trajectory forecast. The ensemble consists of specialized models, which produce individual forecasts in the form of Gaussian distributions under the assumption of a certain motion state of the cyclist (e.g. cyclist is starting). By weighting the specialized models, we create forecasts in the form of Gaussian mixtures that define regions within which the cyclists will reside with a certain probability. To evaluate our method, we rate the reliability, sharpness, and positional accuracy of our forecasted distributions. We compare our method to a widely used unimodal approach which produces forecasts in the form of Gaussian distributions and show that our method is able to produce more reliable and sharper outputs while retaining comparable positional accuracy. By comparing two different methods for basic movement detection, one based on the past cyclist trajectory and one using past image sequences, we demonstrate that the results can be further improved by incorporating video information. Our methods are evaluated using a dataset created at a public intersection. The code and the dataset are publicly available.}, author = {Zernetsch, Stefan and Reichert, Hannes and Kress, Viktor and Doll, Konrad and Sick, Bernhard}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, keywords = {itegpub}, pages = {265–272}, publisher = {IEEE}, title = {A Holistic View on Probabilistic Trajectory Forecasting – Case Study: Cyclist Intention Detection}, year = 2022 }
%0 Conference Paper %1 zernetsch2022holistic %A Zernetsch, Stefan %A Reichert, Hannes %A Kress, Viktor %A Doll, Konrad %A Sick, Bernhard %B IEEE Intelligent Vehicles Symposium (IV) %D 2022 %I IEEE %P 265--272 %R 10.1109/IV51971.2022.9827220 %T A Holistic View on Probabilistic Trajectory Forecasting -- Case Study: Cyclist Intention Detection %U https://ieeexplore.ieee.org/document/9827220 %X This article presents a holistic approach for probabilistic cyclist intention detection. By combining probabilities for the current cyclist motion states with a probabilistic ensemble trajectory forecast, we are able to generate reliable estimates of cyclists’ future positions. The probabilities are used as weights in the probabilistic ensemble trajectory forecast. The ensemble consists of specialized models, which produce individual forecasts in the form of Gaussian distributions under the assumption of a certain motion state of the cyclist (e.g. cyclist is starting). By weighting the specialized models, we create forecasts in the form of Gaussian mixtures that define regions within which the cyclists will reside with a certain probability. To evaluate our method, we rate the reliability, sharpness, and positional accuracy of our forecasted distributions. We compare our method to a widely used unimodal approach which produces forecasts in the form of Gaussian distributions and show that our method is able to produce more reliable and sharper outputs while retaining comparable positional accuracy. By comparing two different methods for basic movement detection, one based on the past cyclist trajectory and one using past image sequences, we demonstrate that the results can be further improved by incorporating video information. Our methods are evaluated using a dataset created at a public intersection. The code and the dataset are publicly available.
Herde, M., Huseljic, D., Mitrovic, J., Granitzer, M., Sick, B.: A Concept for Automated Polarized Web Content Annotation based on Multimodal Active Learning In: Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. bll 1–6 (2022).
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Active learning (AL) techniques hardly cope with complex annotations tasks, where, for example, annotations might express relationships across data modalities. As a use case, we consider the task of automatically detecting and reporting multimodal, polarized web content (PWC). Samples of this content type emerge dynamically, covering a broad spectrum of topics. Thus, training machine learning systems for detecting PWC is challenging, particularly if it needs to be done with minimum annotation cost. In this article, we propose the concept of multimodal AL for complex annotations in the context of PWC detection and formulate the resulting challenges as questions for future research.
@inproceedings{herde2022concept, abstract = {Active learning (AL) techniques hardly cope with complex annotations tasks, where, for example, annotations might express relationships across data modalities. As a use case, we consider the task of automatically detecting and reporting multimodal, polarized web content (PWC). Samples of this content type emerge dynamically, covering a broad spectrum of topics. Thus, training machine learning systems for detecting PWC is challenging, particularly if it needs to be done with minimum annotation cost. In this article, we propose the concept of multimodal AL for complex annotations in the context of PWC detection and formulate the resulting challenges as questions for future research.}, author = {Herde, Marek and Huseljic, Denis and Mitrovic, Jelena and Granitzer, Michael and Sick, Bernhard}, booktitle = {Workshop on Interactive Adaptive Learning (IAL), ECML PKDD}, keywords = {itegpub}, pages = {1–6}, title = {A Concept for Automated Polarized Web Content Annotation based on Multimodal Active Learning}, year = 2022 }
%0 Conference Paper %1 herde2022concept %A Herde, Marek %A Huseljic, Denis %A Mitrovic, Jelena %A Granitzer, Michael %A Sick, Bernhard %B Workshop on Interactive Adaptive Learning (IAL), ECML PKDD %D 2022 %P 1--6 %T A Concept for Automated Polarized Web Content Annotation based on Multimodal Active Learning %U http://ceur-ws.org/Vol-3259/ialatecml_paper1.pdf %X Active learning (AL) techniques hardly cope with complex annotations tasks, where, for example, annotations might express relationships across data modalities. As a use case, we consider the task of automatically detecting and reporting multimodal, polarized web content (PWC). Samples of this content type emerge dynamically, covering a broad spectrum of topics. Thus, training machine learning systems for detecting PWC is challenging, particularly if it needs to be done with minimum annotation cost. In this article, we propose the concept of multimodal AL for complex annotations in the context of PWC detection and formulate the resulting challenges as questions for future research.

2021[ to top ]

Huang, Z., He, Y., Vogt, S., Sick, B.: Uncertainty and Utility Sampling with Pre-Clustering In: Workshop on Interactive Adaptive Learning (IAL), ECML PKDD (2021).
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@inproceedings{huang2021uncertainty, author = {Huang, Zhixin and He, Yujiang and Vogt, Stephan and Sick, Bernhard}, booktitle = {Workshop on Interactive Adaptive Learning (IAL), ECML PKDD}, keywords = {itegpub}, note = {(accepted)}, title = {Uncertainty and Utility Sampling with Pre-Clustering}, year = 2021 }
%0 Conference Paper %1 huang2021uncertainty %A Huang, Zhixin %A He, Yujiang %A Vogt, Stephan %A Sick, Bernhard %B Workshop on Interactive Adaptive Learning (IAL), ECML PKDD %D 2021 %T Uncertainty and Utility Sampling with Pre-Clustering
Reginka, M., Hoang, H., Efendi, Özge, Merkel, M., Huhnstock, R., Holzinger, D., Dingel, K., Sick, B., Bertinetti, D., Herberg, F., Ehresmann, A.: Transport efficiency of biofunctionalized magnetic particles tailored by surfactant concentration Langmuir. 37, 8498–8507 (2021). https://doi.org/10.1021/acs.langmuir.1c00900.
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@article{reginka2021transport, author = {{Reginka}, Meike and {Hoang}, Hai and {Efendi}, {\"O}zge and {Merkel}, Maximilan and {Huhnstock}, Rico and {Holzinger}, Dennis and {Dingel}, Kristina and {Sick}, Bernhard and {Bertinetti}, Daniela and {Herberg}, Friedrich and {Ehresmann}, Arno}, journal = {Langmuir}, keywords = {itegpub}, number = 28, pages = {8498–8507}, publisher = {ACS}, title = {Transport efficiency of biofunctionalized magnetic particles tailored by surfactant concentration}, volume = 37, year = 2021 }
%0 Journal Article %1 reginka2021transport %A Reginka, Meike %A Hoang, Hai %A Efendi, Özge %A Merkel, Maximilan %A Huhnstock, Rico %A Holzinger, Dennis %A Dingel, Kristina %A Sick, Bernhard %A Bertinetti, Daniela %A Herberg, Friedrich %A Ehresmann, Arno %D 2021 %I ACS %J Langmuir %N 28 %P 8498--8507 %R 10.1021/acs.langmuir.1c00900 %T Transport efficiency of biofunctionalized magnetic particles tailored by surfactant concentration %U https://pubs.acs.org/doi/abs/10.1021/acs.langmuir.1c00900 %V 37
Huhnstock, R., Reginka, M., Tomita, A., Merkel, M., Dingel, K., Holzinger, D., Sick, B., Vogel, M., Ehresmann, A.: Translatory and rotatory motion of exchange-bias capped Janus particles controlled by dynamic magnetic field landscapes Scientific Reports. 11, 21794 (2021). https://doi.org/10.1038/s41598-021-01351-x.
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Magnetic Janus particles (MJPs), fabricated by covering a non-magnetic spherical particle with a hemispherical magnetic in-plane exchange-bias layer system cap, display an onion magnetization state for comparably large diameters of a few microns. In this work, the motion characteristics of these MJPs will be investigated when they are steered by a magnetic field landscape over prototypical parallel-stripe domains, dynamically varied by superposed external magnetic field pulse sequences, in an aqueous medium. We demonstrate, that due to the engineered magnetization state in the hemispherical cap, a comparably fast, directed particle transport and particle rotation can be induced. Additionally, by modifying the frequency of the applied pulse sequence and the strengths of the individual field components, we observe a possible separation between a combined or an individual occurrence of these two types of motion. Our findings bear importance for lab-on-a-chip systems, where particle immobilization on a surface via analyte bridges shall be used for low concentration analyte detection and a particle rotation over a defined position of a substrate may dramatically increase the immobilization (and therefore analyte detection) probability.
@article{huhnstock2021translatory, abstract = {Magnetic Janus particles (MJPs), fabricated by covering a non-magnetic spherical particle with a hemispherical magnetic in-plane exchange-bias layer system cap, display an onion magnetization state for comparably large diameters of a few microns. In this work, the motion characteristics of these MJPs will be investigated when they are steered by a magnetic field landscape over prototypical parallel-stripe domains, dynamically varied by superposed external magnetic field pulse sequences, in an aqueous medium. We demonstrate, that due to the engineered magnetization state in the hemispherical cap, a comparably fast, directed particle transport and particle rotation can be induced. Additionally, by modifying the frequency of the applied pulse sequence and the strengths of the individual field components, we observe a possible separation between a combined or an individual occurrence of these two types of motion. Our findings bear importance for lab-on-a-chip systems, where particle immobilization on a surface via analyte bridges shall be used for low concentration analyte detection and a particle rotation over a defined position of a substrate may dramatically increase the immobilization (and therefore analyte detection) probability.}, author = {Huhnstock, Rico and Reginka, Meike and Tomita, Andreea and Merkel, Maximilian and Dingel, Kristina and Holzinger, Dennis and Sick, Bernhard and Vogel, Michael and Ehresmann, Arno}, journal = {Scientific Reports}, keywords = {itegpub}, number = 1, pages = 21794, publisher = {Nature Publishing Group}, title = {Translatory and rotatory motion of exchange-bias capped Janus particles controlled by dynamic magnetic field landscapes}, volume = 11, year = 2021 }
%0 Journal Article %1 huhnstock2021translatory %A Huhnstock, Rico %A Reginka, Meike %A Tomita, Andreea %A Merkel, Maximilian %A Dingel, Kristina %A Holzinger, Dennis %A Sick, Bernhard %A Vogel, Michael %A Ehresmann, Arno %D 2021 %I Nature Publishing Group %J Scientific Reports %N 1 %P 21794 %R 10.1038/s41598-021-01351-x %T Translatory and rotatory motion of exchange-bias capped Janus particles controlled by dynamic magnetic field landscapes %U https://www.nature.com/articles/s41598-021-01351-x %V 11 %X Magnetic Janus particles (MJPs), fabricated by covering a non-magnetic spherical particle with a hemispherical magnetic in-plane exchange-bias layer system cap, display an onion magnetization state for comparably large diameters of a few microns. In this work, the motion characteristics of these MJPs will be investigated when they are steered by a magnetic field landscape over prototypical parallel-stripe domains, dynamically varied by superposed external magnetic field pulse sequences, in an aqueous medium. We demonstrate, that due to the engineered magnetization state in the hemispherical cap, a comparably fast, directed particle transport and particle rotation can be induced. Additionally, by modifying the frequency of the applied pulse sequence and the strengths of the individual field components, we observe a possible separation between a combined or an individual occurrence of these two types of motion. Our findings bear importance for lab-on-a-chip systems, where particle immobilization on a surface via analyte bridges shall be used for low concentration analyte detection and a particle rotation over a defined position of a substrate may dramatically increase the immobilization (and therefore analyte detection) probability. %@ 0123456789
König, I., Bachmann, M., Bieshaar, M., Schindler, S., Lambrecht, F., David, K., Sick, B., Hornung, G., Sommer, C.: Traffic Safety in Future Cities by Using a Safety Approach Based on AI and Wireless Communications In: ITG-Symposium on Mobile Communication - Technologies and Applications. bll 1–6. , Osnabrück, Germany (2021).
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@inproceedings{koenig2021traffic, address = {Osnabrück, Germany}, author = {K\"onig, I. and Bachmann, M. and Bieshaar, M. and Schindler, S. and Lambrecht, F. and David, K. and Sick, B. and Hornung, G. and Sommer, C.}, booktitle = {ITG-Symposium on Mobile Communication - Technologies and Applications}, keywords = {itegpub}, pages = {1–6}, title = {Traffic Safety in Future Cities by Using a Safety Approach Based on AI and Wireless Communications}, year = 2021 }
%0 Conference Paper %1 koenig2021traffic %A König, I. %A Bachmann, M. %A Bieshaar, M. %A Schindler, S. %A Lambrecht, F. %A David, K. %A Sick, B. %A Hornung, G. %A Sommer, C. %B ITG-Symposium on Mobile Communication - Technologies and Applications %C Osnabrück, Germany %D 2021 %P 1--6 %T Traffic Safety in Future Cities by Using a Safety Approach Based on AI and Wireless Communications
Reichert, H., Lang, L., Rösch, K., Bogdoll, D., Doll, K., Sick, B., Reuss, H.-C., Stiller, C., Zöllner, J.M.: Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems In: IEEE International Smart Cities Conference (ISC2). bll 1–4. IEEE (2021). https://doi.org/10.1109/isc253183.2021.9562912.
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@inproceedings{reichert2021towards, author = {Reichert, Hannes and Lang, Lukas and Rösch, Kevin and Bogdoll, Daniel and Doll, Konrad and Sick, Bernhard and Reuss, Hans-Christian and Stiller, Christoph and Zöllner, J. Marius}, booktitle = {IEEE International Smart Cities Conference (ISC2)}, keywords = {itegpub}, pages = {1–4}, publisher = {IEEE}, title = {Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems}, year = 2021 }
%0 Conference Paper %1 reichert2021towards %A Reichert, Hannes %A Lang, Lukas %A Rösch, Kevin %A Bogdoll, Daniel %A Doll, Konrad %A Sick, Bernhard %A Reuss, Hans-Christian %A Stiller, Christoph %A Zöllner, J. Marius %B IEEE International Smart Cities Conference (ISC2) %D 2021 %I IEEE %P 1--4 %R 10.1109/isc253183.2021.9562912 %T Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems
Botache, D., Bethke, F., Hardieck, M., Bieshaar, M., Brabetz, L., Ayeb, M., Zipf, P., Sick, B.: Towards Highly Automated Machine-Learning-Empowered Monitoring of Motor Test Stands In: IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS). bll 120–130. IEEE, Washington, DC, USA (2021). https://doi.org/10.1109/acsos52086.2021.00031.
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@inproceedings{botache2021towards, address = {Washington, DC, USA}, author = {Botache, Diego and Bethke, Florian and Hardieck, Martin and Bieshaar, Maarten and Brabetz, Ludwig and Ayeb, Mohamed and Zipf, Peter and Sick, Bernhard}, booktitle = {IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)}, keywords = {itegpub}, pages = {120–130}, publisher = {IEEE}, title = {Towards Highly Automated Machine-Learning-Empowered Monitoring of Motor Test Stands}, year = 2021 }
%0 Conference Paper %1 botache2021towards %A Botache, Diego %A Bethke, Florian %A Hardieck, Martin %A Bieshaar, Maarten %A Brabetz, Ludwig %A Ayeb, Mohamed %A Zipf, Peter %A Sick, Bernhard %B IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) %C Washington, DC, USA %D 2021 %I IEEE %P 120--130 %R 10.1109/acsos52086.2021.00031 %T Towards Highly Automated Machine-Learning-Empowered Monitoring of Motor Test Stands
Heidecker, F., Hannan, A., Bieshaar, M., Sick, B.: Towards Corner Case Detection by Modeling the Uncertainty of Instance Segmentation Networks In: Workshop on Integrated Artificial Intelligence in Data Science, ICPR. bll 361–374. IEEE, Milan, Italy (2021). https://doi.org/10.1007/978-3-030-68799-1_26.
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@inproceedings{heidecker2021towards, address = {Milan, Italy}, author = {Heidecker, Florian and Hannan, Abdul and Bieshaar, Maarten and Sick, Bernhard}, booktitle = {Workshop on Integrated Artificial Intelligence in Data Science, ICPR}, keywords = {itegpub}, pages = {361–374}, publisher = {IEEE}, title = {Towards Corner Case Detection by Modeling the Uncertainty of Instance Segmentation Networks}, year = 2021 }
%0 Conference Paper %1 heidecker2021towards %A Heidecker, Florian %A Hannan, Abdul %A Bieshaar, Maarten %A Sick, Bernhard %B Workshop on Integrated Artificial Intelligence in Data Science, ICPR %C Milan, Italy %D 2021 %I IEEE %P 361--374 %R 10.1007/978-3-030-68799-1_26 %T Towards Corner Case Detection by Modeling the Uncertainty of Instance Segmentation Networks
Kottke, D., Herde, M., Sandrock, C., Huseljic, D., Krempl, G., Sick, B.: Toward optimal probabilistic active learning using a Bayesian approach Machine Learning. 110, 1199–1231 (2021). https://doi.org/10.1007/s10994-021-05986-9.
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Gathering labeled data to train well-performing machine learning models is one of the critical challenges in many applications. Active learning aims at reducing the labeling costs by an efficient and effective allocation of costly labeling resources. In this article, we propose a decision-theoretic selection strategy that (1) directly optimizes the gain in misclassification error, and (2) uses a Bayesian approach by introducing a conjugate prior distribution to determine the class posterior to deal with uncertainties. By reformulating existing selection strategies within our proposed model, we can explain which aspects are not covered in current state-of-the-art and why this leads to the superior performance of our approach. Extensive experiments on a large variety of datasets and different kernels validate our claims.
@article{kottke2021optimal, abstract = {Gathering labeled data to train well-performing machine learning models is one of the critical challenges in many applications. Active learning aims at reducing the labeling costs by an efficient and effective allocation of costly labeling resources. In this article, we propose a decision-theoretic selection strategy that (1) directly optimizes the gain in misclassification error, and (2) uses a Bayesian approach by introducing a conjugate prior distribution to determine the class posterior to deal with uncertainties. By reformulating existing selection strategies within our proposed model, we can explain which aspects are not covered in current state-of-the-art and why this leads to the superior performance of our approach. Extensive experiments on a large variety of datasets and different kernels validate our claims.}, author = {Kottke, Daniel and Herde, Marek and Sandrock, Christoph and Huseljic, Denis and Krempl, Georg and Sick, Bernhard}, journal = {Machine Learning}, keywords = {itegpub}, number = 6, pages = {1199–1231}, publisher = {Springer}, title = {Toward optimal probabilistic active learning using a Bayesian approach}, volume = 110, year = 2021 }
%0 Journal Article %1 kottke2021optimal %A Kottke, Daniel %A Herde, Marek %A Sandrock, Christoph %A Huseljic, Denis %A Krempl, Georg %A Sick, Bernhard %D 2021 %I Springer %J Machine Learning %N 6 %P 1199--1231 %R 10.1007/s10994-021-05986-9 %T Toward optimal probabilistic active learning using a Bayesian approach %V 110 %X Gathering labeled data to train well-performing machine learning models is one of the critical challenges in many applications. Active learning aims at reducing the labeling costs by an efficient and effective allocation of costly labeling resources. In this article, we propose a decision-theoretic selection strategy that (1) directly optimizes the gain in misclassification error, and (2) uses a Bayesian approach by introducing a conjugate prior distribution to determine the class posterior to deal with uncertainties. By reformulating existing selection strategies within our proposed model, we can explain which aspects are not covered in current state-of-the-art and why this leads to the superior performance of our approach. Extensive experiments on a large variety of datasets and different kernels validate our claims.
He, Y., Huang, Z., Sick, B.: Toward Application of Continuous Power Forecasts in a Regional Flexibility Market In: International Joint Conference on Neural Networks (IJCNN). bll 1–8. IEEE (2021). https://doi.org/10.1109/ijcnn52387.2021.9533626.
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@inproceedings{he2021application, author = {He, Yujiang and Huang, Zhixin and Sick, Bernhard}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {itegpub}, pages = {1–8}, publisher = {IEEE}, title = {Toward Application of Continuous Power Forecasts in a Regional Flexibility Market}, year = 2021 }
%0 Conference Paper %1 he2021application %A He, Yujiang %A Huang, Zhixin %A Sick, Bernhard %B International Joint Conference on Neural Networks (IJCNN) %D 2021 %I IEEE %P 1--8 %R 10.1109/ijcnn52387.2021.9533626 %T Toward Application of Continuous Power Forecasts in a Regional Flexibility Market
Dingel, K., Otto, T., Marder, L., Funke, L., Held, A., Savio, S., Hans, A., Hartmann, G., Meier, D., Viefhaus, J., Sick, B., Ehresmann, A., Ilchen, M., Helml, W.: Toward AI-enhanced online-characterization and shaping of ultrashort X-ray free-electron laser pulses arXiv e-prints. arXiv:2108.13979 (2021).
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X-ray free-electron lasers (XFELs) as the world`s most brilliant light sources provide ultrashort X-ray pulses with durations typically on the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena like localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes was, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time-energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence algorithms, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics at XFELs, thus enhancing and refining their scientific access in all related disciplines.
@article{dingel2021ai, abstract = {X-ray free-electron lasers (XFELs) as the world`s most brilliant light sources provide ultrashort X-ray pulses with durations typically on the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena like localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes was, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time-energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence algorithms, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics at XFELs, thus enhancing and refining their scientific access in all related disciplines.}, author = {{Dingel}, Kristina and {Otto}, Thorsten and {Marder}, Lutz and {Funke}, Lars and {Held}, Arne and {Savio}, Sara and {Hans}, Andreas and {Hartmann}, Gregor and {Meier}, David and {Viefhaus}, Jens and {Sick}, Bernhard and {Ehresmann}, Arno and {Ilchen}, Markus and {Helml}, Wolfram}, journal = {arXiv e-prints}, keywords = {Accelerator}, pages = {arXiv:2108.13979}, title = {{Toward AI-enhanced online-characterization and shaping of ultrashort X-ray free-electron laser pulses}}, year = 2021 }
%0 Journal Article %1 dingel2021ai %A Dingel, Kristina %A Otto, Thorsten %A Marder, Lutz %A Funke, Lars %A Held, Arne %A Savio, Sara %A Hans, Andreas %A Hartmann, Gregor %A Meier, David %A Viefhaus, Jens %A Sick, Bernhard %A Ehresmann, Arno %A Ilchen, Markus %A Helml, Wolfram %D 2021 %J arXiv e-prints %P arXiv:2108.13979 %T Toward AI-enhanced online-characterization and shaping of ultrashort X-ray free-electron laser pulses %X X-ray free-electron lasers (XFELs) as the world`s most brilliant light sources provide ultrashort X-ray pulses with durations typically on the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena like localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes was, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time-energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence algorithms, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics at XFELs, thus enhancing and refining their scientific access in all related disciplines.
Gruhl, C., Hannan, A., Huang, Z., Nivarthi, C., Vogt, S.: The Problem with Real-World Novelty Detection -- Issues in Multivariate Probabilistic Models In: Workshop on Self-Improving System Integration (SISSY), ACSOS. bll 204–209. IEEE (2021). https://doi.org/10.1109/acsos-c52956.2021.00055.
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@inproceedings{gruhl2021problem, author = {Gruhl, Christian and Hannan, Abdul and Huang, Zhixin and Nivarthi, Chandana and Vogt, Stephan}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS}, keywords = {itegpub}, pages = {204–209}, publisher = {IEEE}, title = {The Problem with Real-World Novelty Detection – Issues in Multivariate Probabilistic Models}, year = 2021 }
%0 Conference Paper %1 gruhl2021problem %A Gruhl, Christian %A Hannan, Abdul %A Huang, Zhixin %A Nivarthi, Chandana %A Vogt, Stephan %B Workshop on Self-Improving System Integration (SISSY), ACSOS %D 2021 %I IEEE %P 204--209 %R 10.1109/acsos-c52956.2021.00055 %T The Problem with Real-World Novelty Detection -- Issues in Multivariate Probabilistic Models
Schreiber, J., Vogt, S., Sick, B.: Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time Series Forecast In: European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD): Applied Data Science Track. bll 118–134. Springer (2021). https://doi.org/10.1007/978-3-030-86514-6_8.
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@inproceedings{schreiber2021task, author = {Schreiber, Jens and Vogt, Stephan and Sick, Bernhard}, booktitle = {European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD): Applied Data Science Track}, keywords = {itegpub}, pages = {118–134}, publisher = {Springer}, title = {Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time Series Forecast}, year = 2021 }
%0 Conference Paper %1 schreiber2021task %A Schreiber, Jens %A Vogt, Stephan %A Sick, Bernhard %B European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD): Applied Data Science Track %D 2021 %I Springer %P 118--134 %R 10.1007/978-3-030-86514-6_8 %T Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time Series Forecast
Pham, T., Kottke, D., Krempl, G., Sick, B.: Stream-Based Active Learning for Sliding Windows Under Verification Latency Machine Learning. (2021). https://doi.org/10.1007/s10994-021-06099-z.
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@article{pham2021stream, author = {Pham, Tuan and Kottke, Daniel and Krempl, Georg and Sick, Bernhard}, journal = {Machine Learning}, keywords = {itegpub}, note = {(accepted)}, publisher = {Springer}, title = {Stream-Based Active Learning for Sliding Windows Under Verification Latency}, year = 2021 }
%0 Journal Article %1 pham2021stream %A Pham, Tuan %A Kottke, Daniel %A Krempl, Georg %A Sick, Bernhard %D 2021 %I Springer %J Machine Learning %R 10.1007/s10994-021-06099-z %T Stream-Based Active Learning for Sliding Windows Under Verification Latency
Krempl, G., Kottke, D., Pham, T.: Statistical Analysis of Pairwise Connectivity In: International Conference on Discovery Science (DS). bll 138–148. Springer (2021). https://doi.org/10.1007/978-3-030-88942-5_11.
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@inproceedings{krempl2021statistical, author = {Krempl, Georg and Kottke, Daniel and Pham, Tuan}, booktitle = {International Conference on Discovery Science (DS)}, keywords = {itegpub}, pages = {138–148}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Statistical Analysis of Pairwise Connectivity}, year = 2021 }
%0 Conference Paper %1 krempl2021statistical %A Krempl, Georg %A Kottke, Daniel %A Pham, Tuan %B International Conference on Discovery Science (DS) %D 2021 %I Springer %P 138--148 %R 10.1007/978-3-030-88942-5_11 %T Statistical Analysis of Pairwise Connectivity
Hetzel, M., Reichert, H., Doll, K., Sick, B.: Smart Infrastructure: A Research Junction In: IEEE International Smart Cities Conference (ISC2). IEEE (2021). https://doi.org/10.1109/isc253183.2021.9562809.
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@inproceedings{hetzel2021smart, author = {Hetzel, Manuel and Reichert, Hannes and Doll, Konrad and Sick, Bernhard}, booktitle = {IEEE International Smart Cities Conference (ISC2)}, keywords = {itegpub}, note = 1, publisher = {IEEE}, title = {Smart Infrastructure: A Research Junction}, year = 2021 }
%0 Conference Paper %1 hetzel2021smart %A Hetzel, Manuel %A Reichert, Hannes %A Doll, Konrad %A Sick, Bernhard %B IEEE International Smart Cities Conference (ISC2) %D 2021 %I IEEE %R 10.1109/isc253183.2021.9562809 %T Smart Infrastructure: A Research Junction
Huseljic, D., Sick, B., Herde, M., Kottke, D.: Separation of Aleatoric and Epistemic Uncertainty in Deterministic Deep Neural Networks In: International Conference on Pattern Recognition (ICPR). bll 9172–9179. IEEE (2021). https://doi.org/10.1109/icpr48806.2021.9412616.
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@inproceedings{huseljic2021separation, author = {Huseljic, Denis and Sick, Bernhard and Herde, Marek and Kottke, Daniel}, booktitle = {International Conference on Pattern Recognition (ICPR)}, keywords = {itegpub}, pages = {9172–9179}, publisher = {IEEE}, title = {Separation of Aleatoric and Epistemic Uncertainty in Deterministic Deep Neural Networks}, year = 2021 }
%0 Conference Paper %1 huseljic2021separation %A Huseljic, Denis %A Sick, Bernhard %A Herde, Marek %A Kottke, Daniel %B International Conference on Pattern Recognition (ICPR) %D 2021 %I IEEE %P 9172--9179 %R 10.1109/icpr48806.2021.9412616 %T Separation of Aleatoric and Epistemic Uncertainty in Deterministic Deep Neural Networks
Bellman, K., Botev, J., Diaconescu, A., Esterle, L., Gruhl, C., Landauer, C., Lewis, P.R., Nelson, P.R., Pournaras, E., Stein, A., Tomforde, S.: Self-improving system integration: Mastering continuous change Future Generation Computer Systems. 117, 29–46 (2021). https://doi.org/10.1016/j.future.2020.11.019.
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The research initiative “self-improving system integration” (SISSY) was established with the goal to master the ever-changing demands of system organisation in the presence of autonomous subsystems, evolving architectures, and highly-dynamic open environments. It aims to move integration-related decisions from design-time to run-time, implying a further shift of expertise and responsibility from human engineers to autonomous systems. This introduces a qualitative shift from existing self-adaptive and self-organising systems, moving from self-adaptation based on predefined variation types, towards more open contexts involving novel autonomous subsystems, collaborative behaviours, and emerging goals. In this article, we revisit existing SISSY research efforts and establish a corresponding terminology focusing on how SISSY relates to the broad field of integration sciences. We then investigate SISSY-related research efforts and derive a taxonomy of SISSY technology. This is concluded by establishing a research road-map for developing operational self-improving self-integrating systems.
@article{bellman2021self, abstract = {The research initiative “self-improving system integration” (SISSY) was established with the goal to master the ever-changing demands of system organisation in the presence of autonomous subsystems, evolving architectures, and highly-dynamic open environments. It aims to move integration-related decisions from design-time to run-time, implying a further shift of expertise and responsibility from human engineers to autonomous systems. This introduces a qualitative shift from existing self-adaptive and self-organising systems, moving from self-adaptation based on predefined variation types, towards more open contexts involving novel autonomous subsystems, collaborative behaviours, and emerging goals. In this article, we revisit existing SISSY research efforts and establish a corresponding terminology focusing on how SISSY relates to the broad field of integration sciences. We then investigate SISSY-related research efforts and derive a taxonomy of SISSY technology. This is concluded by establishing a research road-map for developing operational self-improving self-integrating systems.}, author = {Bellman, Kirstie and Botev, Jean and Diaconescu, Ada and Esterle, Lukas and Gruhl, Christian and Landauer, Christopher and Lewis, Peter R. and Nelson, Phyllis R. and Pournaras, Evangelos and Stein, Anthony and Tomforde, Sven}, journal = {Future Generation Computer Systems}, keywords = {System}, pages = {29–46}, publisher = {Elsevier}, title = {Self-improving system integration: Mastering continuous change}, volume = 117, year = 2021 }
%0 Journal Article %1 bellman2021self %A Bellman, Kirstie %A Botev, Jean %A Diaconescu, Ada %A Esterle, Lukas %A Gruhl, Christian %A Landauer, Christopher %A Lewis, Peter R. %A Nelson, Phyllis R. %A Pournaras, Evangelos %A Stein, Anthony %A Tomforde, Sven %D 2021 %I Elsevier %J Future Generation Computer Systems %P 29--46 %R 10.1016/j.future.2020.11.019 %T Self-improving system integration: Mastering continuous change %U http://www.sciencedirect.com/science/article/pii/S0167739X20330430 %V 117 %X The research initiative “self-improving system integration” (SISSY) was established with the goal to master the ever-changing demands of system organisation in the presence of autonomous subsystems, evolving architectures, and highly-dynamic open environments. It aims to move integration-related decisions from design-time to run-time, implying a further shift of expertise and responsibility from human engineers to autonomous systems. This introduces a qualitative shift from existing self-adaptive and self-organising systems, moving from self-adaptation based on predefined variation types, towards more open contexts involving novel autonomous subsystems, collaborative behaviours, and emerging goals. In this article, we revisit existing SISSY research efforts and establish a corresponding terminology focusing on how SISSY relates to the broad field of integration sciences. We then investigate SISSY-related research efforts and derive a taxonomy of SISSY technology. This is concluded by establishing a research road-map for developing operational self-improving self-integrating systems.
Kottke, D., Herde, M., Minh, T.P., Benz, A., Mergard, P., Roghman, A., Sandrock, C., Sick, B.: scikit-activeml: A Library and Toolbox for Active Learning Algorithms Preprints. 2021030194 (2021).
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Machine learning applications often need large amounts of training data to perform well. Whereas unlabeled data can be easily gathered, the labeling process is difficult, time-consuming, or expensive in most applications. Active learning can help solve this problem by querying labels for those data points that will improve the performance the most. Thereby, the goal is that the learning algorithm performs sufficiently well with fewer labels. We provide a library called scikit-activeml that covers the most relevant query strategies and implements tools to work with partially labeled data. It is programmed in Python and builds on top of scikit-learn.
@article{kottke2021scikit, abstract = {Machine learning applications often need large amounts of training data to perform well. Whereas unlabeled data can be easily gathered, the labeling process is difficult, time-consuming, or expensive in most applications. Active learning can help solve this problem by querying labels for those data points that will improve the performance the most. Thereby, the goal is that the learning algorithm performs sufficiently well with fewer labels. We provide a library called scikit-activeml that covers the most relevant query strategies and implements tools to work with partially labeled data. It is programmed in Python and builds on top of scikit-learn.}, author = {Kottke, Daniel and Herde, Marek and Minh, Tuan Pham and Benz, Alexander and Mergard, Pascal and Roghman, Atal and Sandrock, Christoph and Sick, Bernhard}, journal = {Preprints}, keywords = {itegpub}, pages = 2021030194, title = {{scikit-activeml}: {A} Library and Toolbox for Active Learning Algorithms}, year = 2021 }
%0 Journal Article %1 kottke2021scikit %A Kottke, Daniel %A Herde, Marek %A Minh, Tuan Pham %A Benz, Alexander %A Mergard, Pascal %A Roghman, Atal %A Sandrock, Christoph %A Sick, Bernhard %D 2021 %J Preprints %P 2021030194 %T scikit-activeml: A Library and Toolbox for Active Learning Algorithms %U https://www.preprints.org/manuscript/202103.0194/ %X Machine learning applications often need large amounts of training data to perform well. Whereas unlabeled data can be easily gathered, the labeling process is difficult, time-consuming, or expensive in most applications. Active learning can help solve this problem by querying labels for those data points that will improve the performance the most. Thereby, the goal is that the learning algorithm performs sufficiently well with fewer labels. We provide a library called scikit-activeml that covers the most relevant query strategies and implements tools to work with partially labeled data. It is programmed in Python and builds on top of scikit-learn.
Schneegans, J., Eilbrecht, J., Zernetsch, S., Bieshaar, M., Doll, K., Stursberg, O., Sick, B.: Probabilistic VRU Trajectory Forecasting for Model-Predictive Planning -- A Case Study: Overtaking Cyclists In: Workshop From Benchmarking Behavior Prediction to Socially Compatible Behavior Generation in Autonomous Driving, IV (2021).
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This work considers (deep) artiﬁcial feed-forward neural networks as parametric approximators in optimal control of discrete-time switched linear systems with controlled switching. The proposed approach is based on approximate dynamic programming and allows the fast computation of (sub-)optimal discrete and continuous control inputs, either by approximating the optimal cost-to-go functions or by approximating the optimal discrete and continuous input policies. An important property of the approach is the satisfaction of polytopic state and input constraints, which is crucial for ensuring safety, as required in many control applications. A numeric example is provided for illustration and evaluation of the approaches.
@inproceedings{schneegans2021probabilistic, abstract = {This work considers (deep) artiﬁcial feed-forward neural networks as parametric approximators in optimal control of discrete-time switched linear systems with controlled switching. The proposed approach is based on approximate dynamic programming and allows the fast computation of (sub-)optimal discrete and continuous control inputs, either by approximating the optimal cost-to-go functions or by approximating the optimal discrete and continuous input policies. An important property of the approach is the satisfaction of polytopic state and input constraints, which is crucial for ensuring safety, as required in many control applications. A numeric example is provided for illustration and evaluation of the approaches.}, author = {Schneegans, Jan and Eilbrecht, Jan and Zernetsch, Stefan and Bieshaar, Maarten and Doll, Konrad and Stursberg, Olaf and Sick, Bernhard}, booktitle = {Workshop From Benchmarking Behavior Prediction to Socially Compatible Behavior Generation in Autonomous Driving, IV}, keywords = {itegpub}, note = {(accepted)}, title = {Probabilistic VRU Trajectory Forecasting for Model-Predictive Planning – A Case Study: Overtaking Cyclists}, year = 2021 }
%0 Conference Paper %1 schneegans2021probabilistic %A Schneegans, Jan %A Eilbrecht, Jan %A Zernetsch, Stefan %A Bieshaar, Maarten %A Doll, Konrad %A Stursberg, Olaf %A Sick, Bernhard %B Workshop From Benchmarking Behavior Prediction to Socially Compatible Behavior Generation in Autonomous Driving, IV %D 2021 %T Probabilistic VRU Trajectory Forecasting for Model-Predictive Planning -- A Case Study: Overtaking Cyclists %X This work considers (deep) artiﬁcial feed-forward neural networks as parametric approximators in optimal control of discrete-time switched linear systems with controlled switching. The proposed approach is based on approximate dynamic programming and allows the fast computation of (sub-)optimal discrete and continuous control inputs, either by approximating the optimal cost-to-go functions or by approximating the optimal discrete and continuous input policies. An important property of the approach is the satisfaction of polytopic state and input constraints, which is crucial for ensuring safety, as required in many control applications. A numeric example is provided for illustration and evaluation of the approaches.
Kress, V., Zernetsch, S., Doll, K., Sick, B.: Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks In: Workshop on Integrated Artificial Intelligence in Data Science, ICPR. bll 57–71. Springer (2021). https://doi.org/10.1007/978-3-030-68763-2_5.
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@inproceedings{kress2021pose, author = {Kress, Viktor and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, booktitle = {Workshop on Integrated Artificial Intelligence in Data Science, ICPR}, keywords = {itegpub}, pages = {57–71}, publisher = {Springer}, title = {Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks}, year = 2021 }
%0 Conference Paper %1 kress2021pose %A Kress, Viktor %A Zernetsch, Stefan %A Doll, Konrad %A Sick, Bernhard %B Workshop on Integrated Artificial Intelligence in Data Science, ICPR %D 2021 %I Springer %P 57--71 %R 10.1007/978-3-030-68763-2_5 %T Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks
Möller, F., Botache, D., Huseljic, D., Heidecker, F., Bieshaar, M., Sick, B.: Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders In: Workshop on Safe Artificial Intelligence for Automated Driving (SAIAD), CVPR. bll 1–10 (2021).
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@inproceedings{moeller2021out, author = {Möller, Felix and Botache, Diego and Huseljic, Denis and Heidecker, Florian and Bieshaar, Maarten and Sick, Bernhard}, booktitle = {Workshop on Safe Artificial Intelligence for Automated Driving (SAIAD), CVPR}, keywords = {itegpub}, pages = {1–10}, title = {{Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders}}, year = 2021 }
%0 Conference Paper %1 moeller2021out %A Möller, Felix %A Botache, Diego %A Huseljic, Denis %A Heidecker, Florian %A Bieshaar, Maarten %A Sick, Bernhard %B Workshop on Safe Artificial Intelligence for Automated Driving (SAIAD), CVPR %D 2021 %P 1--10 %T Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders %U https://openaccess.thecvf.com/content/CVPR2021W/SAIAD/papers/Moller_Out-of-Distribution_Detection_and_Generation_Using_Soft_Brownian_Offset_Sampling_and_CVPRW_2021_paper.pdf
Gruhl, C., Tomforde, S.: OHODIN -- Online Anomaly Detection for Data Streams In: Workshop on Self-Improving System Integration (SISSY), ACSOS. bll 193–197. IEEE (2021). https://doi.org/10.1109/acsos-c52956.2021.00046.
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@inproceedings{gruhl2021ohodin, author = {Gruhl, Christian and Tomforde, Sven}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS}, keywords = {itegpub}, pages = {193–197}, publisher = {IEEE}, title = {OHODIN – Online Anomaly Detection for Data Streams}, year = 2021 }
%0 Conference Paper %1 gruhl2021ohodin %A Gruhl, Christian %A Tomforde, Sven %B Workshop on Self-Improving System Integration (SISSY), ACSOS %D 2021 %I IEEE %P 193--197 %R 10.1109/acsos-c52956.2021.00046 %T OHODIN -- Online Anomaly Detection for Data Streams
Scheiner, N., Kraus, F., Appenrodt, N., Dickmann, J., Sick, B.: Object Detection For Automotive Radar Point Clouds -- A Comparison AI Perspectives. 3, 6 (2021). https://doi.org/10.1186/s42467-021-00012-z.
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@article{scheiner2021object, author = {Scheiner, Nicolas and Kraus, Florian and Appenrodt, Nils and Dickmann, Jürgen and Sick, Bernhard}, journal = {AI Perspectives}, keywords = {itegpub}, number = 1, pages = 6, publisher = {Springer}, title = {Object Detection For Automotive Radar Point Clouds – A Comparison}, volume = 3, year = 2021 }
%0 Journal Article %1 scheiner2021object %A Scheiner, Nicolas %A Kraus, Florian %A Appenrodt, Nils %A Dickmann, Jürgen %A Sick, Bernhard %D 2021 %I Springer %J AI Perspectives %N 1 %P 6 %R 10.1186/s42467-021-00012-z %T Object Detection For Automotive Radar Point Clouds -- A Comparison %V 3
Gruhl, C., Sick, B., Tomforde, S.: Novelty detection in continuously changing environments Future Generation Computer Systems. 114, 138–154 (2021). https://doi.org/10.1016/j.future.2020.07.037.
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Self-improving system integration (SISSY) aims at mastering the challenges of system organisation decisions for subsystems with highly dynamic behaviours. This is achieved by increased decision freedom of these subsystems, i.e., by moving the corresponding design decisions from design-time to run-time and from engineers to the systems themselves. As a result, a more robust and efficient constellation of the overall system organisation is maintained even under the presence of continuous change and disturbed conditions. Appropriate integration decisions require a solid foundation of assessing the current conditions and their dynamics using identifying triggers for a necessary evaluation of their own integration status. With this article, we present mCANDIES, a modular novelty detection technique especially suited for SISSY systems that fulfils exactly these identification tasks. mCANDIES tackles an anomaly or novelty detection problem in a modular way by combining the advantages of different detection approaches by exploiting locality in different regions of the input space — depending on, e.g. the density or location of expected observations. This is augmented with information about changes in the underlying model of the observations, i.e., mCANDIES identifies concept drifts. The corresponding information can then be used to trigger online learning behaviour. We analyse the behaviour of mCANDIES on artificial and real-world data sets and compare it to other techniques from the state of the art.
@article{gruhl2021novelty, abstract = {Self-improving system integration (SISSY) aims at mastering the challenges of system organisation decisions for subsystems with highly dynamic behaviours. This is achieved by increased decision freedom of these subsystems, i.e., by moving the corresponding design decisions from design-time to run-time and from engineers to the systems themselves. As a result, a more robust and efficient constellation of the overall system organisation is maintained even under the presence of continuous change and disturbed conditions. Appropriate integration decisions require a solid foundation of assessing the current conditions and their dynamics using identifying triggers for a necessary evaluation of their own integration status. With this article, we present mCANDIES, a modular novelty detection technique especially suited for SISSY systems that fulfils exactly these identification tasks. mCANDIES tackles an anomaly or novelty detection problem in a modular way by combining the advantages of different detection approaches by exploiting locality in different regions of the input space — depending on, e.g. the density or location of expected observations. This is augmented with information about changes in the underlying model of the observations, i.e., mCANDIES identifies concept drifts. The corresponding information can then be used to trigger online learning behaviour. We analyse the behaviour of mCANDIES on artificial and real-world data sets and compare it to other techniques from the state of the art.}, author = {Gruhl, Christian and Sick, Bernhard and Tomforde, Sven}, journal = {Future Generation Computer Systems}, keywords = {itegpub}, pages = {138–154}, publisher = {Elsevier}, title = {Novelty detection in continuously changing environments}, volume = 114, year = 2021 }
%0 Journal Article %1 gruhl2021novelty %A Gruhl, Christian %A Sick, Bernhard %A Tomforde, Sven %D 2021 %I Elsevier %J Future Generation Computer Systems %P 138--154 %R 10.1016/j.future.2020.07.037 %T Novelty detection in continuously changing environments %V 114 %X Self-improving system integration (SISSY) aims at mastering the challenges of system organisation decisions for subsystems with highly dynamic behaviours. This is achieved by increased decision freedom of these subsystems, i.e., by moving the corresponding design decisions from design-time to run-time and from engineers to the systems themselves. As a result, a more robust and efficient constellation of the overall system organisation is maintained even under the presence of continuous change and disturbed conditions. Appropriate integration decisions require a solid foundation of assessing the current conditions and their dynamics using identifying triggers for a necessary evaluation of their own integration status. With this article, we present mCANDIES, a modular novelty detection technique especially suited for SISSY systems that fulfils exactly these identification tasks. mCANDIES tackles an anomaly or novelty detection problem in a modular way by combining the advantages of different detection approaches by exploiting locality in different regions of the input space — depending on, e.g. the density or location of expected observations. This is augmented with information about changes in the underlying model of the observations, i.e., mCANDIES identifies concept drifts. The corresponding information can then be used to trigger online learning behaviour. We analyse the behaviour of mCANDIES on artificial and real-world data sets and compare it to other techniques from the state of the art.
Heidecker, F., Gruhl, C., Sick, B.: Novelty based Driver Identification on RR Intervals from ECG Data In: Workshop on Integrated Artificial Intelligence in Data Science, ICPR. bll 407–421. IEEE, Milan, Italy (2021). https://doi.org/10.1007/978-3-030-68799-1_29.
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@inproceedings{heidecker2021novelty, address = {Milan, Italy}, author = {Heidecker, Florian and Gruhl, Christian and Sick, Bernhard}, booktitle = {Workshop on Integrated Artificial Intelligence in Data Science, ICPR}, keywords = {itegpub}, pages = {407–421}, publisher = {IEEE}, title = {Novelty based Driver Identification on RR Intervals from ECG Data}, year = 2021 }
%0 Conference Paper %1 heidecker2021novelty %A Heidecker, Florian %A Gruhl, Christian %A Sick, Bernhard %B Workshop on Integrated Artificial Intelligence in Data Science, ICPR %C Milan, Italy %D 2021 %I IEEE %P 407--421 %R 10.1007/978-3-030-68799-1_29 %T Novelty based Driver Identification on RR Intervals from ECG Data
Herde, M., Kottke, D., Huseljic, D., Sick, B.: Multi-annotator Probabilistic Active Learning In: International Conference on Pattern Recognition (ICPR). bll 10281–10288. IEEE (2021). https://doi.org/10.1109/icpr48806.2021.9412298.
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@inproceedings{herde2021multi, author = {Herde, Marek and Kottke, Daniel and Huseljic, Denis and Sick, Bernhard}, booktitle = {International Conference on Pattern Recognition (ICPR)}, keywords = {itegpub}, pages = {10281–10288}, publisher = {IEEE}, title = {Multi-annotator Probabilistic Active Learning}, year = 2021 }
%0 Conference Paper %1 herde2021multi %A Herde, Marek %A Kottke, Daniel %A Huseljic, Denis %A Sick, Bernhard %B International Conference on Pattern Recognition (ICPR) %D 2021 %I IEEE %P 10281--10288 %R 10.1109/icpr48806.2021.9412298 %T Multi-annotator Probabilistic Active Learning
Haase-Schütz, C., Stal, R., Hertlein, H., Sick, B.: Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning In: International Conference on Pattern Recognition (ICPR). bll 9483–9490. IEEE (2021). https://doi.org/10.1109/icpr48806.2021.9411918.
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@inproceedings{haaseschuetz2021iterative, author = {Haase-Schütz, Christian and Stal, Rainer and Hertlein, Heinz and Sick, Bernhard}, booktitle = {International Conference on Pattern Recognition (ICPR)}, keywords = {itegpub}, pages = {9483–9490}, publisher = {IEEE}, title = {Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning}, year = 2021 }
%0 Conference Paper %1 haaseschuetz2021iterative %A Haase-Schütz, Christian %A Stal, Rainer %A Hertlein, Heinz %A Sick, Bernhard %B International Conference on Pattern Recognition (ICPR) %D 2021 %I IEEE %P 9483--9490 %R 10.1109/icpr48806.2021.9411918 %T Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning
Schneegans, J., Bieshaar, M., Heidecker, F., Sick, B.: Intelligent and Interactive Video Annotation for Instance Segmentation using Siamese Neural Networks In: Workshop on Integrated Artificial Intelligence in Data Science, ICPR. bll 375–389. IEEE, Milan, Italy (2021). https://doi.org/10.1007/978-3-030-68799-1_27.
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@inproceedings{schneegans2021intelligent, address = {Milan, Italy}, author = {Schneegans, Jan and Bieshaar, Maarten and Heidecker, Florian and Sick, Bernhard}, booktitle = {Workshop on Integrated Artificial Intelligence in Data Science, ICPR}, keywords = {itegpub}, pages = {375–389}, publisher = {IEEE}, title = {Intelligent and Interactive Video Annotation for Instance Segmentation using Siamese Neural Networks}, year = 2021 }
%0 Conference Paper %1 schneegans2021intelligent %A Schneegans, Jan %A Bieshaar, Maarten %A Heidecker, Florian %A Sick, Bernhard %B Workshop on Integrated Artificial Intelligence in Data Science, ICPR %C Milan, Italy %D 2021 %I IEEE %P 375--389 %R 10.1007/978-3-030-68799-1_27 %T Intelligent and Interactive Video Annotation for Instance Segmentation using Siamese Neural Networks
Zernetsch, S., Schreck, S., Kress, V., Doll, K., Sick, B.: Image Sequence Based Cyclist Action Recognition Using Multi-Stream 3D Convolution In: International Conference on Pattern Recognition (ICPR). bll 2620–2626. IEEE (2021). https://doi.org/10.1109/icpr48806.2021.9413233.
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@inproceedings{zernetsch2021image, author = {Zernetsch, Stefan and Schreck, Steven and Kress, Viktor and Doll, Konrad and Sick, Bernhard}, booktitle = {International Conference on Pattern Recognition (ICPR)}, keywords = {itegpub}, pages = {2620–2626}, publisher = {IEEE}, title = {Image Sequence Based Cyclist Action Recognition Using Multi-Stream 3D Convolution}, year = 2021 }
%0 Conference Paper %1 zernetsch2021image %A Zernetsch, Stefan %A Schreck, Steven %A Kress, Viktor %A Doll, Konrad %A Sick, Bernhard %B International Conference on Pattern Recognition (ICPR) %D 2021 %I IEEE %P 2620--2626 %R 10.1109/icpr48806.2021.9413233 %T Image Sequence Based Cyclist Action Recognition Using Multi-Stream 3D Convolution
Schreiber, J., Sick, B.: Emerging Relation Network and Task Embedding for Multi-Task Regression Problems In: International Conference on Pattern Recognition (ICPR). bll 2663–2670. IEEE (2021). https://doi.org/10.1109/icpr48806.2021.9412476.
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@inproceedings{schreiber2021emerging, author = {Schreiber, Jens and Sick, Bernhard}, booktitle = {International Conference on Pattern Recognition (ICPR)}, keywords = {itegpub}, pages = {2663–2670}, publisher = {IEEE}, title = {Emerging Relation Network and Task Embedding for Multi-Task Regression Problems}, year = 2021 }
%0 Conference Paper %1 schreiber2021emerging %A Schreiber, Jens %A Sick, Bernhard %B International Conference on Pattern Recognition (ICPR) %D 2021 %I IEEE %P 2663--2670 %R 10.1109/icpr48806.2021.9412476 %T Emerging Relation Network and Task Embedding for Multi-Task Regression Problems
Al-Falouji, G., Gruhl, C., Tomforde, S.: Digital Shadows in Self-Improving System Integration: A Concept Using Generative Modelling In: Workshop on Self-Improving System Integration (SISSY), ACSOS. bll 166–171. IEEE (2021). https://doi.org/10.1109/acsos-c52956.2021.00047.
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@inproceedings{alfalouji2021digital, author = {Al-Falouji, Ghassan and Gruhl, Christian and Tomforde, Sven}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS}, keywords = {itegpub}, pages = {166–171}, publisher = {IEEE}, title = {Digital Shadows in Self-Improving System Integration: A Concept Using Generative Modelling}, year = 2021 }
%0 Conference Paper %1 alfalouji2021digital %A Al-Falouji, Ghassan %A Gruhl, Christian %A Tomforde, Sven %B Workshop on Self-Improving System Integration (SISSY), ACSOS %D 2021 %I IEEE %P 166--171 %R 10.1109/acsos-c52956.2021.00047 %T Digital Shadows in Self-Improving System Integration: A Concept Using Generative Modelling
Bogdoll, D., Breitenstein, J., Heidecker, F., Bieshaar, M., Sick, B., Fingscheidt, T., Zöllner, J.M.: Description of Corner Cases in Automated Driving: Goals and Challenges In: Embedded and Real-World Computer Vision in Autonomous Driving (ERCVAD), ICCV. bll 1023–1028,. IEEE (2021). https://doi.org/10.1109/iccvw54120.2021.00119.
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@inproceedings{bogdoll2021description, author = {Bogdoll, Daniel and Breitenstein, Jasmin and Heidecker, Florian and Bieshaar, Maarten and Sick, Bernhard and Fingscheidt, Tim and Zöllner, J. Marius}, booktitle = {Embedded and Real-World Computer Vision in Autonomous Driving (ERCVAD), ICCV}, keywords = {itegpub}, pages = {1023–1028,}, publisher = {IEEE}, title = {Description of Corner Cases in Automated Driving: Goals and Challenges}, year = 2021 }
%0 Conference Paper %1 bogdoll2021description %A Bogdoll, Daniel %A Breitenstein, Jasmin %A Heidecker, Florian %A Bieshaar, Maarten %A Sick, Bernhard %A Fingscheidt, Tim %A Zöllner, J. Marius %B Embedded and Real-World Computer Vision in Autonomous Driving (ERCVAD), ICCV %D 2021 %I IEEE %P 1023--1028, %R 10.1109/iccvw54120.2021.00119 %T Description of Corner Cases in Automated Driving: Goals and Challenges
Zernetsch, S., Trupp, O., Kress, V., Doll, K., Sick, B.: Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information In: IEEE International Smart Cities Conference (ISC2). bll 1–7. IEEE (2021). https://doi.org/10.1109/isc253183.2021.9562857.
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@inproceedings{zernetsch2021cyclist, author = {Zernetsch, Stefan and Trupp, Oliver and Kress, Viktor and Doll, Konrad and Sick, Bernhard}, booktitle = {IEEE International Smart Cities Conference (ISC2)}, keywords = {itegpub}, pages = {1–7}, publisher = {IEEE}, title = {Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information}, year = 2021 }
%0 Conference Paper %1 zernetsch2021cyclist %A Zernetsch, Stefan %A Trupp, Oliver %A Kress, Viktor %A Doll, Konrad %A Sick, Bernhard %B IEEE International Smart Cities Conference (ISC2) %D 2021 %I IEEE %P 1--7 %R 10.1109/isc253183.2021.9562857 %T Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information
Bieshaar, M., Zernetsch, S., Riepe, K., Doll, K., Sick, B.: Cyclist Motion State Forecasting -- Going beyond Detection In: IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, Orlando, FL, USA (2021).
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@inproceedings{bieshaar2021cyclist, address = {Orlando, FL, USA}, author = {Bieshaar, M. and Zernetsch, S. and Riepe, K. and Doll, K. and Sick, B.}, booktitle = {IEEE Symposium Series on Computational Intelligence (SSCI)}, keywords = {itegpub}, note = {(accepted)}, publisher = {IEEE}, title = {Cyclist Motion State Forecasting – Going beyond Detection}, year = 2021 }
%0 Conference Paper %1 bieshaar2021cyclist %A Bieshaar, M. %A Zernetsch, S. %A Riepe, K. %A Doll, K. %A Sick, B. %B IEEE Symposium Series on Computational Intelligence (SSCI) %C Orlando, FL, USA %D 2021 %I IEEE %T Cyclist Motion State Forecasting -- Going beyond Detection
Bieshaar, M.: Cooperative intention detection using machine learning. Advanced cyclist protection in the context of automated driving, (2021). https://doi.org/10.17170/kobra-202101263056.
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@phdthesis{bieshaar2021cooperative, author = {Bieshaar, Maarten}, keywords = {imported}, publisher = {kassel university press}, school = {University of Kassel}, title = {Cooperative intention detection using machine learning. Advanced cyclist protection in the context of automated driving}, year = 2021 }
%0 Thesis %1 bieshaar2021cooperative %A Bieshaar, Maarten %D 2021 %I kassel university press %R 10.17170/kobra-202101263056 %T Cooperative intention detection using machine learning. Advanced cyclist protection in the context of automated driving
He, Y., Sick, B.: CLeaR: An adaptive continual learning framework for regression tasks AI Perspectives. 3, 2 (2020). https://doi.org/10.1186/s42467-021-00009-8.
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Catastrophic forgetting means that a trained neural network model gradually forgets the previously learned tasks when being retrained on new tasks. Overcoming the forgetting problem is a major problem in machine learning. Numerous continual learning algorithms are very successful in incremental learning of classification tasks, where new samples with their labels appear frequently. However, there is currently no research that addresses the catastrophic forgetting problem in regression tasks as far as we know. This problem has emerged as one of the primary constraints in some applications, such as renewable energy forecasts. This article clarifies problem-related definitions and proposes a new methodological framework that can forecast targets and update itself by means of continual learning. The framework consists of forecasting neural networks and buffers, which store newly collected data from a non-stationary data stream in an application. The changed probability distribution of the data stream, which the framework has identified, will be learned sequentially. The framework is called CLeaR (Continual Learning for Regression Tasks), where components can be flexibly customized for a specific application scenario. We design two sets of experiments to evaluate the CLeaR framework concerning fitting error (training), prediction error (test), and forgetting ratio. The first one is based on an artificial time series to explore how hyperparameters affect the CLeaR framework. The second one is designed with data collected from European wind farms to evaluate the CLeaR framework's performance in a real-world application. The experimental results demonstrate that the CLeaR framework can continually acquire knowledge in the data stream and improve the prediction accuracy. The article concludes with further research issues arising from requirements to extend the framework.
@article{he2021clear, abstract = {Catastrophic forgetting means that a trained neural network model gradually forgets the previously learned tasks when being retrained on new tasks. Overcoming the forgetting problem is a major problem in machine learning. Numerous continual learning algorithms are very successful in incremental learning of classification tasks, where new samples with their labels appear frequently. However, there is currently no research that addresses the catastrophic forgetting problem in regression tasks as far as we know. This problem has emerged as one of the primary constraints in some applications, such as renewable energy forecasts. This article clarifies problem-related definitions and proposes a new methodological framework that can forecast targets and update itself by means of continual learning. The framework consists of forecasting neural networks and buffers, which store newly collected data from a non-stationary data stream in an application. The changed probability distribution of the data stream, which the framework has identified, will be learned sequentially. The framework is called CLeaR (Continual Learning for Regression Tasks), where components can be flexibly customized for a specific application scenario. We design two sets of experiments to evaluate the CLeaR framework concerning fitting error (training), prediction error (test), and forgetting ratio. The first one is based on an artificial time series to explore how hyperparameters affect the CLeaR framework. The second one is designed with data collected from European wind farms to evaluate the CLeaR framework's performance in a real-world application. The experimental results demonstrate that the CLeaR framework can continually acquire knowledge in the data stream and improve the prediction accuracy. The article concludes with further research issues arising from requirements to extend the framework.}, author = {He, Yujiang and Sick, Bernhard}, journal = {AI Perspectives}, keywords = {itegpub}, number = 1, pages = 2, publisher = {Springer}, title = {CLeaR: An adaptive continual learning framework for regression tasks}, volume = 3, year = 2021 }
%0 Journal Article %1 he2021clear %A He, Yujiang %A Sick, Bernhard %D 2021 %I Springer %J AI Perspectives %N 1 %P 2 %R 10.1186/s42467-021-00009-8 %T CLeaR: An adaptive continual learning framework for regression tasks %U https://doi.org/10.1186/s42467-021-00009-8 %V 3 %X Catastrophic forgetting means that a trained neural network model gradually forgets the previously learned tasks when being retrained on new tasks. Overcoming the forgetting problem is a major problem in machine learning. Numerous continual learning algorithms are very successful in incremental learning of classification tasks, where new samples with their labels appear frequently. However, there is currently no research that addresses the catastrophic forgetting problem in regression tasks as far as we know. This problem has emerged as one of the primary constraints in some applications, such as renewable energy forecasts. This article clarifies problem-related definitions and proposes a new methodological framework that can forecast targets and update itself by means of continual learning. The framework consists of forecasting neural networks and buffers, which store newly collected data from a non-stationary data stream in an application. The changed probability distribution of the data stream, which the framework has identified, will be learned sequentially. The framework is called CLeaR (Continual Learning for Regression Tasks), where components can be flexibly customized for a specific application scenario. We design two sets of experiments to evaluate the CLeaR framework concerning fitting error (training), prediction error (test), and forgetting ratio. The first one is based on an artificial time series to explore how hyperparameters affect the CLeaR framework. The second one is designed with data collected from European wind farms to evaluate the CLeaR framework's performance in a real-world application. The experimental results demonstrate that the CLeaR framework can continually acquire knowledge in the data stream and improve the prediction accuracy. The article concludes with further research issues arising from requirements to extend the framework.
Hannan, A., Gruhl, C., Sick, B.: Anomaly based Resilient Network Intrusion Detection using Inferential Autoencoders In: IEEE International Conference on Cyber Security and Resilience (CSR). bll 1–7. IEEE (2021). https://doi.org/10.1109/csr51186.2021.9527980.
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@inproceedings{hannan2021anomaly, author = {Hannan, Abdul and Gruhl, Christian and Sick, Bernhard}, booktitle = {IEEE International Conference on Cyber Security and Resilience (CSR)}, keywords = {itegpub}, pages = {1–7}, publisher = {IEEE}, title = {{Anomaly based Resilient Network Intrusion Detection using Inferential Autoencoders}}, year = 2021 }
%0 Conference Paper %1 hannan2021anomaly %A Hannan, Abdul %A Gruhl, Christian %A Sick, Bernhard %B IEEE International Conference on Cyber Security and Resilience (CSR) %D 2021 %I IEEE %P 1--7 %R 10.1109/csr51186.2021.9527980 %T Anomaly based Resilient Network Intrusion Detection using Inferential Autoencoders
Heidecker, F., Breitenstein, J., Rösch, K., Löhdefink, J., Bieshaar, M., Stiller, C., Fingscheidt, T., Sick, B.: An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving In: IEEE Intelligent Vehicles Symposium (IV). bll 644–651. IEEE, Nagoya, Japan (2021). https://doi.org/10.1109/iv48863.2021.9575933.
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@inproceedings{heidecker2021application, address = {Nagoya, Japan}, author = {Heidecker, Florian and Breitenstein, Jasmin and Rösch, Kevin and Löhdefink, Jonas and Bieshaar, Maarten and Stiller, Christoph and Fingscheidt, Tim and Sick, Bernhard}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, keywords = {itegpub}, pages = {644–651}, publisher = {IEEE}, title = {{An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving}}, year = 2021 }
%0 Conference Paper %1 heidecker2021application %A Heidecker, Florian %A Breitenstein, Jasmin %A Rösch, Kevin %A Löhdefink, Jonas %A Bieshaar, Maarten %A Stiller, Christoph %A Fingscheidt, Tim %A Sick, Bernhard %B IEEE Intelligent Vehicles Symposium (IV) %C Nagoya, Japan %D 2021 %I IEEE %P 644--651 %R 10.1109/iv48863.2021.9575933 %T An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving
Dingel, K., Liehr, A., Vogel, M., Degener, S., Meier, D., Niendorf, T., Ehresmann, A., Sick, B.: AI - Based On The Fly Design of Experiments in Physics and Engineering In: Workshop on Self-Improving System Integration (SISSY), ACSOS. bll 150–153. IEEE (2021). https://doi.org/10.1109/ACSOS-C52956.2021.00048.
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@inproceedings{dingel2021aia, author = {Dingel, Kristina and Liehr, Alexander and Vogel, Michael and Degener, Sebastian and Meier, David and Niendorf, Thomas and Ehresmann, Arno and Sick, Bernhard}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS}, keywords = {itegpub}, pages = {150–153}, publisher = {IEEE}, title = {AI - Based On The Fly Design of Experiments in Physics and Engineering}, year = 2021 }
%0 Conference Paper %1 dingel2021aia %A Dingel, Kristina %A Liehr, Alexander %A Vogel, Michael %A Degener, Sebastian %A Meier, David %A Niendorf, Thomas %A Ehresmann, Arno %A Sick, Bernhard %B Workshop on Self-Improving System Integration (SISSY), ACSOS %D 2021 %I IEEE %P 150--153 %R 10.1109/ACSOS-C52956.2021.00048 %T AI - Based On The Fly Design of Experiments in Physics and Engineering
Dingel, K., Huhnstock, R., Knie, A., Ehresmann, A., Sick, B.: AdaPT: Adaptable particle tracking for spherical microparticles in lab on chip systems Computer Physics Communications. 262, 107859 (2021). https://doi.org/10.1016/j.cpc.2021.107859.
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Due to its rising importance in science and technology in recent years, particle tracking in videos presents itself as a tool for successfully acquiring new knowledge in the field of life sciences and physics. Accordingly, different particle tracking methods for various scenarios have been developed. In this article, we present a particle tracking application implemented in Python for, in particular, spherical magnetic particles, including superparamagnetic beads and Janus particles. In the following, we distinguish between two sub-steps in particle tracking, namely the localization of particles in single images and the linking of the extracted particle positions of the subsequent frames into trajectories. We provide an intensity-based localization technique to detect particles and two linking algorithms, which apply either frame-by-frame linking or linear assignment problem solving. Beyond that, we offer helpful tools to preprocess images automatically as well as estimate parameters required for the localization algorithm by utilizing machine learning. As an extra, we have implemented a technique to estimate the current spatial orientation of Janus particles within the x-y-plane. Our framework is readily extendable and easy-to-use as we offer a graphical user interface and a command-line tool. Various output options, such as data frames and videos, ensure further analysis that can be automated.
@article{dingel2021adapt, abstract = {Due to its rising importance in science and technology in recent years, particle tracking in videos presents itself as a tool for successfully acquiring new knowledge in the field of life sciences and physics. Accordingly, different particle tracking methods for various scenarios have been developed. In this article, we present a particle tracking application implemented in Python for, in particular, spherical magnetic particles, including superparamagnetic beads and Janus particles. In the following, we distinguish between two sub-steps in particle tracking, namely the localization of particles in single images and the linking of the extracted particle positions of the subsequent frames into trajectories. We provide an intensity-based localization technique to detect particles and two linking algorithms, which apply either frame-by-frame linking or linear assignment problem solving. Beyond that, we offer helpful tools to preprocess images automatically as well as estimate parameters required for the localization algorithm by utilizing machine learning. As an extra, we have implemented a technique to estimate the current spatial orientation of Janus particles within the x-y-plane. Our framework is readily extendable and easy-to-use as we offer a graphical user interface and a command-line tool. Various output options, such as data frames and videos, ensure further analysis that can be automated.}, author = {Dingel, Kristina and Huhnstock, Rico and Knie, Andr{\'{e}} and Ehresmann, Arno and Sick, Bernhard}, journal = {Computer Physics Communications}, keywords = {itegpub}, pages = 107859, publisher = {Elsevier}, title = {{AdaPT: Adaptable particle tracking for spherical microparticles in lab on chip systems}}, volume = 262, year = 2021 }
%0 Journal Article %1 dingel2021adapt %A Dingel, Kristina %A Huhnstock, Rico %A Knie, André %A Ehresmann, Arno %A Sick, Bernhard %D 2021 %I Elsevier %J Computer Physics Communications %P 107859 %R 10.1016/j.cpc.2021.107859 %T AdaPT: Adaptable particle tracking for spherical microparticles in lab on chip systems %V 262 %X Due to its rising importance in science and technology in recent years, particle tracking in videos presents itself as a tool for successfully acquiring new knowledge in the field of life sciences and physics. Accordingly, different particle tracking methods for various scenarios have been developed. In this article, we present a particle tracking application implemented in Python for, in particular, spherical magnetic particles, including superparamagnetic beads and Janus particles. In the following, we distinguish between two sub-steps in particle tracking, namely the localization of particles in single images and the linking of the extracted particle positions of the subsequent frames into trajectories. We provide an intensity-based localization technique to detect particles and two linking algorithms, which apply either frame-by-frame linking or linear assignment problem solving. Beyond that, we offer helpful tools to preprocess images automatically as well as estimate parameters required for the localization algorithm by utilizing machine learning. As an extra, we have implemented a technique to estimate the current spatial orientation of Janus particles within the x-y-plane. Our framework is readily extendable and easy-to-use as we offer a graphical user interface and a command-line tool. Various output options, such as data frames and videos, ensure further analysis that can be automated.
Reuse, M., Simon, M., Sick, B.: About the Ambiguity of Data Augmentation for 3D Object Detection in Autonomous Driving In: Embedded and Real-World Computer Vision in Autonomous Driving (ERCVAD), ICCV. bll 979–987. IEEE (2021). https://doi.org/10.1109/ICCVW54120.2021.00114.
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@inproceedings{reuse2021ambiguity, author = {Reuse, Matthias and Simon, Martin and Sick, Bernhard}, booktitle = {Embedded and Real-World Computer Vision in Autonomous Driving (ERCVAD), ICCV}, keywords = {itegpub}, pages = {979–987}, publisher = {IEEE}, title = {About the Ambiguity of Data Augmentation for 3D Object Detection in Autonomous Driving}, year = 2021 }
%0 Conference Paper %1 reuse2021ambiguity %A Reuse, Matthias %A Simon, Martin %A Sick, Bernhard %B Embedded and Real-World Computer Vision in Autonomous Driving (ERCVAD), ICCV %D 2021 %I IEEE %P 979--987 %R 10.1109/ICCVW54120.2021.00114 %T About the Ambiguity of Data Augmentation for 3D Object Detection in Autonomous Driving
Herde, M., Huseljic, D., Sick, B., Calma, A.: A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification IEEE Access. 9, 166970–166989 (2021). https://doi.org/10.1109/ACCESS.2021.3135514.
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Pool-based active learning (AL) aims to optimize the annotation process (i.e., labeling) as the acquisition of annotations is often time-consuming and therefore expensive. For this purpose, an AL strategy queries annotations intelligently from annotators to train a high-performance classification model at a low annotation cost. Traditional AL strategies operate in an idealized framework. They assume a single, omniscient annotator who never gets tired and charges uniformly regardless of query difficulty. However, in real-world applications, we often face human annotators, e.g., crowd or in-house workers, who make annotation mistakes and can be reluctant to respond if tired or faced with complex queries. Recently, many novel AL strategies have been proposed to address these issues. They differ in at least one of the following three central aspects from traditional AL: 1) modeling of (multiple) human annotators whose performances can be affected by various factors, such as missing expertise; 2) generalization of the interaction with human annotators through different query and annotation types, such as asking an annotator for feedback on an inferred classification rule; 3) consideration of complex cost schemes regarding annotations and misclassification. This survey provides an overview of these AL strategies and refers to them as real-world AL. Therefore, we introduce a general real-world AL strategy as part of a learning cycle and use its elements, e.g., the query and annotator selection algorithm, to categorize about 60 real-world AL strategies. Finally, we outline possible directions for future research in the field of AL.
@article{herde2021survey, abstract = {Pool-based active learning (AL) aims to optimize the annotation process (i.e., labeling) as the acquisition of annotations is often time-consuming and therefore expensive. For this purpose, an AL strategy queries annotations intelligently from annotators to train a high-performance classification model at a low annotation cost. Traditional AL strategies operate in an idealized framework. They assume a single, omniscient annotator who never gets tired and charges uniformly regardless of query difficulty. However, in real-world applications, we often face human annotators, e.g., crowd or in-house workers, who make annotation mistakes and can be reluctant to respond if tired or faced with complex queries. Recently, many novel AL strategies have been proposed to address these issues. They differ in at least one of the following three central aspects from traditional AL: 1) modeling of (multiple) human annotators whose performances can be affected by various factors, such as missing expertise; 2) generalization of the interaction with human annotators through different query and annotation types, such as asking an annotator for feedback on an inferred classification rule; 3) consideration of complex cost schemes regarding annotations and misclassification. This survey provides an overview of these AL strategies and refers to them as real-world AL. Therefore, we introduce a general real-world AL strategy as part of a learning cycle and use its elements, e.g., the query and annotator selection algorithm, to categorize about 60 real-world AL strategies. Finally, we outline possible directions for future research in the field of AL.}, author = {Herde, Marek and Huseljic, Denis and Sick, Bernhard and Calma, Adrian}, journal = {IEEE Access}, keywords = {classification}, pages = {166970–166989}, publisher = {IEEE}, title = {A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification}, volume = 9, year = 2021 }
%0 Journal Article %1 herde2021survey %A Herde, Marek %A Huseljic, Denis %A Sick, Bernhard %A Calma, Adrian %D 2021 %I IEEE %J IEEE Access %P 166970--166989 %R 10.1109/ACCESS.2021.3135514 %T A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification %U https://ieeexplore.ieee.org/abstract/document/9650877 %V 9 %X Pool-based active learning (AL) aims to optimize the annotation process (i.e., labeling) as the acquisition of annotations is often time-consuming and therefore expensive. For this purpose, an AL strategy queries annotations intelligently from annotators to train a high-performance classification model at a low annotation cost. Traditional AL strategies operate in an idealized framework. They assume a single, omniscient annotator who never gets tired and charges uniformly regardless of query difficulty. However, in real-world applications, we often face human annotators, e.g., crowd or in-house workers, who make annotation mistakes and can be reluctant to respond if tired or faced with complex queries. Recently, many novel AL strategies have been proposed to address these issues. They differ in at least one of the following three central aspects from traditional AL: 1) modeling of (multiple) human annotators whose performances can be affected by various factors, such as missing expertise; 2) generalization of the interaction with human annotators through different query and annotation types, such as asking an annotator for feedback on an inferred classification rule; 3) consideration of complex cost schemes regarding annotations and misclassification. This survey provides an overview of these AL strategies and refers to them as real-world AL. Therefore, we introduce a general real-world AL strategy as part of a learning cycle and use its elements, e.g., the query and annotator selection algorithm, to categorize about 60 real-world AL strategies. Finally, we outline possible directions for future research in the field of AL.
Thomas, J.M., Beddar-Wiesing, S., Moallemy-Oureh, A., Nather, R.: A Note on the Modeling Power of Different Graph Types arXiv e-prints. arXiv:2109.10708 (2021).
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Graphs can have different properties that lead to several graph types and may allow for a varying representation of diverse information. In order to clarify the modeling power of graphs, we introduce a partial order on the most common graph types based on an expressivity relation. The expressivity relation quantifies how many properties a graph type can encode compared to another type. Additionally, we show that all attributed graph types are equally expressive and have the same modeling power.
@article{thomas2021note, abstract = {Graphs can have different properties that lead to several graph types and may allow for a varying representation of diverse information. In order to clarify the modeling power of graphs, we introduce a partial order on the most common graph types based on an expressivity relation. The expressivity relation quantifies how many properties a graph type can encode compared to another type. Additionally, we show that all attributed graph types are equally expressive and have the same modeling power.}, author = {Thomas, Josephine M. and Beddar-Wiesing, Silvia and Moallemy-Oureh, Alice and Nather, Rüdiger}, journal = {arXiv e-prints}, keywords = {itegpub}, pages = {arXiv:2109.10708}, title = {A Note on the Modeling Power of Different Graph Types}, year = 2021 }
%0 Journal Article %1 thomas2021note %A Thomas, Josephine M. %A Beddar-Wiesing, Silvia %A Moallemy-Oureh, Alice %A Nather, Rüdiger %D 2021 %J arXiv e-prints %P arXiv:2109.10708 %T A Note on the Modeling Power of Different Graph Types %U https://arxiv.org/abs/2109.10708 %X Graphs can have different properties that lead to several graph types and may allow for a varying representation of diverse information. In order to clarify the modeling power of graphs, we introduce a partial order on the most common graph types based on an expressivity relation. The expressivity relation quantifies how many properties a graph type can encode compared to another type. Additionally, we show that all attributed graph types are equally expressive and have the same modeling power.
Kottke, D.: A Holistic, Decision-Theoretic Framework for Pool-Based Active Learning, (2021). https://doi.org/10.17170/kobra-202109244802.
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@phdthesis{kottke2021holistic, author = {Kottke, Daniel}, keywords = {imported}, publisher = {kassel university press}, school = {University of Kassel}, title = {A Holistic, Decision-Theoretic Framework for Pool-Based Active Learning}, year = 2021 }
%0 Thesis %1 kottke2021holistic %A Kottke, Daniel %D 2021 %I kassel university press %R 10.17170/kobra-202109244802 %T A Holistic, Decision-Theoretic Framework for Pool-Based Active Learning
Bieshaar, M., Herde, M., Huselijc, D., Sick, B.: A Concept for Highly Automated Pre-Labeling via Cross-Domain Label Transfer for Perception in Autonomous Driving In: Workshop on Interactive Adaptive Learning (IAL), ECML PKDD (2021).
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@inproceedings{bieshaar2021concept, author = {Bieshaar, Maarten and Herde, Marek and Huselijc, Denis and Sick, Bernhard}, booktitle = {Workshop on Interactive Adaptive Learning (IAL), ECML PKDD}, keywords = {itegpub}, note = {(accepted)}, title = {A Concept for Highly Automated Pre-Labeling via Cross-Domain Label Transfer for Perception in Autonomous Driving}, year = 2021 }
%0 Conference Paper %1 bieshaar2021concept %A Bieshaar, Maarten %A Herde, Marek %A Huselijc, Denis %A Sick, Bernhard %B Workshop on Interactive Adaptive Learning (IAL), ECML PKDD %D 2021 %T A Concept for Highly Automated Pre-Labeling via Cross-Domain Label Transfer for Perception in Autonomous Driving

2020[ to top ]

Kottke, D., Herde, M., Sandrock, C., Huseljic, D., Krempl, G., Sick, B.: Toward Optimal Probabilistic Active Learning Using a Bayesian Approach arXiv e-prints. arXiv:2006.01732 (2020).
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@article{kottke2020optimal, author = {Kottke, Daniel and Herde, Marek and Sandrock, Christoph and Huseljic, Denis and Krempl, Georg and Sick, Bernhard}, journal = {arXiv e-prints}, keywords = {Machine}, pages = {arXiv:2006.01732}, title = {{Toward Optimal Probabilistic Active Learning Using a Bayesian Approach}}, year = 2020 }
%0 Journal Article %1 kottke2020optimal %A Kottke, Daniel %A Herde, Marek %A Sandrock, Christoph %A Huseljic, Denis %A Krempl, Georg %A Sick, Bernhard %D 2020 %J arXiv e-prints %P arXiv:2006.01732 %T Toward Optimal Probabilistic Active Learning Using a Bayesian Approach
Scheiner, N., Kraus, F., Wei, F., Phan, B., Mannan, F., Appenrodt, N., Ritter, W., Dickmann, J., Dietmayer, K., Sick, B., Heide, F.: Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2020). https://doi.org/10.1109/cvpr42600.2020.00214.
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@inproceedings{scheiner2020seeing, author = {Scheiner, Nicolas and Kraus, Florian and Wei, Fangyin and Phan, Buu and Mannan, Fahim and Appenrodt, Nils and Ritter, Werner and Dickmann, Jurgen and Dietmayer, Klaus and Sick, Bernhard and Heide, Felix}, booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, keywords = {itegpub}, publisher = {IEEE}, title = {Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar}, year = 2020 }
%0 Conference Paper %1 scheiner2020seeing %A Scheiner, Nicolas %A Kraus, Florian %A Wei, Fangyin %A Phan, Buu %A Mannan, Fahim %A Appenrodt, Nils %A Ritter, Werner %A Dickmann, Jurgen %A Dietmayer, Klaus %A Sick, Bernhard %A Heide, Felix %B IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) %D 2020 %I IEEE %R 10.1109/cvpr42600.2020.00214 %T Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar
Henze, J., Schreiber, J., Sick, B.: Representation Learning in Power Time Series Forecasting In: Pedrycz, W. en Chen, S.-M. (reds) Deep Learning: Algorithms and Applications. bll 67–101. Springer (2020). https://doi.org/10.1007/978-3-030-31760-7_3.
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@incollection{henze2020representation, author = {Henze, Janosch and Schreiber, Jens and Sick, Bernhard}, booktitle = {Deep Learning: Algorithms and Applications}, editor = {Pedrycz, Witold and Chen, Shyi-Ming}, keywords = {imported}, pages = {67–101}, publisher = {Springer}, title = {Representation Learning in Power Time Series Forecasting}, year = 2020 }
%0 Book Section %1 henze2020representation %A Henze, Janosch %A Schreiber, Jens %A Sick, Bernhard %B Deep Learning: Algorithms and Applications %D 2020 %E Pedrycz, Witold %E Chen, Shyi-Ming %I Springer %P 67--101 %R 10.1007/978-3-030-31760-7_3 %T Representation Learning in Power Time Series Forecasting %U https://doi.org/10.1007/978-3-030-31760-7_3 %@ 978-3-030-31760-7
Meier, D., Hartmann, G., Völker, J., Viefhaus, J., Sick, B.: Reconstruction of offsets of an electron gun using deep learning and an optimization algorithm In: Advances in Computational Methods for X-Ray Optics V. bll 71–77. SPIE (2020). https://doi.org/10.1117/12.2568001.
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@inproceedings{meier2020reconstruction, author = {Meier, David and Hartmann, Gregor and Völker, Jens and Viefhaus, Jens and Sick, Bernhard}, booktitle = {Advances in Computational Methods for X-Ray Optics V}, keywords = {itegpub}, pages = {71–77}, publisher = {SPIE}, title = {Reconstruction of offsets of an electron gun using deep learning and an optimization algorithm}, year = 2020 }
%0 Conference Paper %1 meier2020reconstruction %A Meier, David %A Hartmann, Gregor %A Völker, Jens %A Viefhaus, Jens %A Sick, Bernhard %B Advances in Computational Methods for X-Ray Optics V %D 2020 %I SPIE %P 71--77 %R 10.1117/12.2568001 %T Reconstruction of offsets of an electron gun using deep learning and an optimization algorithm %U https://doi.org/10.1117/12.2568001
Bieshaar, M., Schreiber, J., Vogt, S., Gensler, A., Sick, B.: Quantile Surfaces -- Generalizing Quantile Regression to Multivariate Targets arXiv e-prints. arXiv:2010.05898 (2020).
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@article{bieshaar2020quantile, author = {{Bieshaar}, Maarten and {Schreiber}, Jens and {Vogt}, Stephan and {Gensler}, Andr{\'e} and {Sick}, Bernhard}, journal = {arXiv e-prints}, keywords = {Applications,}, pages = {arXiv:2010.05898}, title = {{Quantile Surfaces – Generalizing Quantile Regression to Multivariate Targets}}, year = 2020 }
%0 Journal Article %1 bieshaar2020quantile %A Bieshaar, Maarten %A Schreiber, Jens %A Vogt, Stephan %A Gensler, André %A Sick, Bernhard %D 2020 %J arXiv e-prints %P arXiv:2010.05898 %T Quantile Surfaces -- Generalizing Quantile Regression to Multivariate Targets
Henze, J., Siefert, M., Bremicker-Trübelhorn, S., Asanalieva, N., Sick, B.: Probabilistic upscaling and aggregation of wind power forecasts Energy, Sustainability and Society. 10, 15 (2020). https://doi.org/10.1186/s13705-020-00247-4.
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Wind power forecasts of the expected wind feed-in for the next hours or days are necessary to integrate the generated volatile wind energy into power systems. Most forecasting models predict in some sense the best value, but they ignore the other possible outcomes which may arise because of forecasting uncertainties. Probabilistic forecasts, on the other hand, also predict a distribution of possible outcomes with their respective probabilities that specific power values will occur and therefore have higher information content. In this work, we address two problems that hinder the introduction of probabilistic forecasts in practice: (1) no measurement data are available for some wind farms and (2) the flexible aggregation of probabilistic forecasts for changing wind farm portfolios.
@article{henze2020probabilistic, abstract = {Wind power forecasts of the expected wind feed-in for the next hours or days are necessary to integrate the generated volatile wind energy into power systems. Most forecasting models predict in some sense the best value, but they ignore the other possible outcomes which may arise because of forecasting uncertainties. Probabilistic forecasts, on the other hand, also predict a distribution of possible outcomes with their respective probabilities that specific power values will occur and therefore have higher information content. In this work, we address two problems that hinder the introduction of probabilistic forecasts in practice: (1) no measurement data are available for some wind farms and (2) the flexible aggregation of probabilistic forecasts for changing wind farm portfolios.}, author = {Henze, Janosch and Siefert, Malte and Bremicker-Tr{\"u}belhorn, Sascha and Asanalieva, Nazgul and Sick, Bernhard}, journal = {Energy, Sustainability and Society}, keywords = {imported}, number = 1, pages = 15, publisher = {BMC}, title = {Probabilistic upscaling and aggregation of wind power forecasts}, volume = 10, year = 2020 }
%0 Journal Article %1 henze2020probabilistic %A Henze, Janosch %A Siefert, Malte %A Bremicker-Trübelhorn, Sascha %A Asanalieva, Nazgul %A Sick, Bernhard %D 2020 %I BMC %J Energy, Sustainability and Society %N 1 %P 15 %R 10.1186/s13705-020-00247-4 %T Probabilistic upscaling and aggregation of wind power forecasts %U https://doi.org/10.1186/s13705-020-00247-4 %V 10 %X Wind power forecasts of the expected wind feed-in for the next hours or days are necessary to integrate the generated volatile wind energy into power systems. Most forecasting models predict in some sense the best value, but they ignore the other possible outcomes which may arise because of forecasting uncertainties. Probabilistic forecasts, on the other hand, also predict a distribution of possible outcomes with their respective probabilities that specific power values will occur and therefore have higher information content. In this work, we address two problems that hinder the introduction of probabilistic forecasts in practice: (1) no measurement data are available for some wind farms and (2) the flexible aggregation of probabilistic forecasts for changing wind farm portfolios.
Kress, V., Schreck, S., Zernetsch, S., Doll, K., Sick, B.: Pose Based Action Recognition of Vulnerable Road Users Using Recurrent Neural Networks In: IEEE Symposium Series on Computational Intelligence (SSCI). bll 2723–2730. IEEE (2020). https://doi.org/10.1109/ssci47803.2020.9308462.
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@inproceedings{kress2020pose, author = {{Kress}, V. and {Schreck}, S. and {Zernetsch}, S. and {Doll}, K. and {Sick}, B.}, booktitle = {IEEE Symposium Series on Computational Intelligence (SSCI)}, keywords = {itegpub}, pages = {2723–2730}, publisher = {IEEE}, title = {Pose Based Action Recognition of Vulnerable Road Users Using Recurrent Neural Networks}, year = 2020 }
%0 Conference Paper %1 kress2020pose %A Kress, V. %A Schreck, S. %A Zernetsch, S. %A Doll, K. %A Sick, B. %B IEEE Symposium Series on Computational Intelligence (SSCI) %D 2020 %I IEEE %P 2723--2730 %R 10.1109/ssci47803.2020.9308462 %T Pose Based Action Recognition of Vulnerable Road Users Using Recurrent Neural Networks
Scheiner, N., Schumann, O., Kraus, F., Appenrodt, N., Dickmann, J., Sick, B.: Off-the-shelf sensor vs. experimental radar - How much resolution is necessary in automotive radar classification? In: IEEE International Conference on Information Fusion (FUSION). bll 1–8. IEEE (2020). https://doi.org/10.23919/fusion45008.2020.9190338.
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@inproceedings{scheiner2020shelf, author = {Scheiner, Nicolas and Schumann, Ole and Kraus, Florian and Appenrodt, Nils and Dickmann, J{\"{u}}rgen and Sick, Bernhard}, booktitle = {{IEEE} International Conference on Information Fusion (FUSION)}, keywords = {itegpub}, pages = {1–8}, publisher = {IEEE}, title = {Off-the-shelf sensor vs. experimental radar - How much resolution is necessary in automotive radar classification?}, year = 2020 }
%0 Conference Paper %1 scheiner2020shelf %A Scheiner, Nicolas %A Schumann, Ole %A Kraus, Florian %A Appenrodt, Nils %A Dickmann, Jürgen %A Sick, Bernhard %B IEEE International Conference on Information Fusion (FUSION) %D 2020 %I IEEE %P 1--8 %R 10.23919/fusion45008.2020.9190338 %T Off-the-shelf sensor vs. experimental radar - How much resolution is necessary in automotive radar classification?
Gruhl, C., Schmeißing, J., Tomforde, S., Sick, B.: Normal-Wishart clustering for novelty detection In: Workshop on Self-Improving System Integration (SISSY), ACSOS. bll 64–69. IEEE (2020). https://doi.org/10.1109/acsos-c51401.2020.00032.
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@inproceedings{gruhl2020normal, author = {Gruhl, Christian and Schmei{\ss}ing, J{\"o}rn and Tomforde, Sven and Sick, Bernhard}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS}, keywords = {itegpub}, pages = {64–69}, publisher = {IEEE}, title = {{Normal-Wishart} clustering for novelty detection}, year = 2020 }
%0 Conference Paper %1 gruhl2020normal %A Gruhl, Christian %A Schmeißing, Jörn %A Tomforde, Sven %A Sick, Bernhard %B Workshop on Self-Improving System Integration (SISSY), ACSOS %D 2020 %I IEEE %P 64--69 %R 10.1109/acsos-c51401.2020.00032 %T Normal-Wishart clustering for novelty detection
Scharei, K., Heidecker, F., Bieshaar, M.: Knowledge Representations in Technical Systems -- A Taxonomy arXiv e-prints. arXiv:2001.04835 (2020).
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@article{scharei2020knowledge, author = {Scharei, Kristina and Heidecker, Florian and Bieshaar, Maarten}, journal = {arXiv e-prints}, keywords = {Robotics}, pages = {arXiv:2001.04835}, title = {{Knowledge Representations in Technical Systems – A Taxonomy}}, year = 2020 }
%0 Journal Article %1 scharei2020knowledge %A Scharei, Kristina %A Heidecker, Florian %A Bieshaar, Maarten %D 2020 %J arXiv e-prints %P arXiv:2001.04835 %T Knowledge Representations in Technical Systems -- A Taxonomy
Haase-Schütz, C., Stal, R., Hertlein, H., Sick, B.: Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning arXiv e-prints. arXiv:2002.02705 (2020).
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@article{haaseschuetz2020iterative, author = {{Haase-Sch{\"u}tz}, Christian and {Stal}, Rainer and {Hertlein}, Heinz and {Sick}, Bernhard}, journal = {arXiv e-prints}, keywords = {Recognition,}, pages = {arXiv:2002.02705}, title = {{Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning}}, year = 2020 }
%0 Journal Article %1 haaseschuetz2020iterative %A Haase-Schütz, Christian %A Stal, Rainer %A Hertlein, Heinz %A Sick, Bernhard %D 2020 %J arXiv e-prints %P arXiv:2002.02705 %T Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning
Pham Minh, T., Kottke, D., Tsarenko, A., Gruhl, C., Sick, B.: Improving Self-Adaptation For Multi-Sensor Activity Recognition with Active Learning In: International Joint Conference on Neural Networks (IJCNN). IEEE (2020). https://doi.org/10.1109/ijcnn48605.2020.9206873.
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@inproceedings{phamminh2020improving, author = {{Pham Minh}, T. and {Kottke}, D. and {Tsarenko}, A. and {Gruhl}, C. and {Sick}, B.}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {itegpub}, publisher = {IEEE}, title = {Improving Self-Adaptation For Multi-Sensor Activity Recognition with Active Learning}, year = 2020 }
%0 Conference Paper %1 phamminh2020improving %A Pham Minh, T. %A Kottke, D. %A Tsarenko, A. %A Gruhl, C. %A Sick, B. %B International Joint Conference on Neural Networks (IJCNN) %D 2020 %I IEEE %R 10.1109/ijcnn48605.2020.9206873 %T Improving Self-Adaptation For Multi-Sensor Activity Recognition with Active Learning
He, Y., Henze, J., Sick, B.: Forecasting Power Grid States for Regional Energy Markets with Deep Neural Networks In: International Joint Conference on Neural Networks (IJCNN). IEEE (2020). https://doi.org/10.1109/ijcnn48605.2020.9207536.
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@inproceedings{he2020forecasting, author = {{He}, Y. and {Henze}, J. and {Sick}, B.}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {itegpub}, publisher = {IEEE}, title = {Forecasting Power Grid States for Regional Energy Markets with Deep Neural Networks}, year = 2020 }
%0 Conference Paper %1 he2020forecasting %A He, Y. %A Henze, J. %A Sick, B. %B International Joint Conference on Neural Networks (IJCNN) %D 2020 %I IEEE %R 10.1109/ijcnn48605.2020.9207536 %T Forecasting Power Grid States for Regional Energy Markets with Deep Neural Networks
Tomforde, S., Gruhl, C.: Fairness, performance, and robustness: is there a cap theorem for self-adaptive and self-organising systems? In: Workshop on Self-Improving System Integration (SISSY), ACSOS. bll 54–59. IEEE (2020). https://doi.org/10.1109/acsos-c51401.2020.00029.
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@inproceedings{tomforde2020fairness, author = {Tomforde, Sven and Gruhl, Christian}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS}, keywords = {itegpub}, pages = {54–59}, publisher = {IEEE}, title = {Fairness, performance, and robustness: is there a cap theorem for self-adaptive and self-organising systems?}, year = 2020 }
%0 Conference Paper %1 tomforde2020fairness %A Tomforde, Sven %A Gruhl, Christian %B Workshop on Self-Improving System Integration (SISSY), ACSOS %D 2020 %I IEEE %P 54--59 %R 10.1109/acsos-c51401.2020.00029 %T Fairness, performance, and robustness: is there a cap theorem for self-adaptive and self-organising systems?
Deist, S., Schreiber, J., Bieshaar, M., Sick, B.: Extended Coopetitive Soft Gating Ensemble arXiv e-prints. arXiv:2004.14026 (2020).
- [ BibTeX ]
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@article{deist2020extended, author = {Deist, Stephan and Schreiber, Jens and Bieshaar, Maarten and Sick, Bernhard}, journal = {arXiv e-prints}, keywords = {Machine}, pages = {arXiv:2004.14026}, title = {{Extended Coopetitive Soft Gating Ensemble}}, year = 2020 }
%0 Journal Article %1 deist2020extended %A Deist, Stephan %A Schreiber, Jens %A Bieshaar, Maarten %A Sick, Bernhard %D 2020 %J arXiv e-prints %P arXiv:2004.14026 %T Extended Coopetitive Soft Gating Ensemble
Englhardt, A., Trittenbach, H., Kottke, D., Sick, B., Böhm, K.: Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary arXiv e-prints. arXiv:2009.13853 (2020).
- [ BibTeX ]
- [ EndNote ]
@article{englhardt2020efficient, author = {{Englhardt}, Adrian and {Trittenbach}, Holger and {Kottke}, Daniel and {Sick}, Bernhard and {B{\"o}hm}, Klemens}, journal = {arXiv e-prints}, keywords = {Machine}, pages = {arXiv:2009.13853}, title = {{Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary}}, year = 2020 }
%0 Journal Article %1 englhardt2020efficient %A Englhardt, Adrian %A Trittenbach, Holger %A Kottke, Daniel %A Sick, Bernhard %A Böhm, Klemens %D 2020 %J arXiv e-prints %P arXiv:2009.13853 %T Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary
He, Y., Henze, J., Sick, B.: Continuous Learning of Deep Neural Networks to Improve Forecasts for Regional Energy Markets In: International Federation of Automatic Control (IFAC) World Congress. bll 12175–12182. Elsevier (2020). https://doi.org/10.1016/j.ifacol.2020.12.1017.
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@inproceedings{he2020continuous, author = {He, Yujiang and Henze, Janosch and Sick, Bernhard}, booktitle = {{International Federation of Automatic Control (IFAC) World Congress}}, keywords = {itegpub}, pages = {12175–12182}, publisher = {Elsevier}, title = {Continuous Learning of Deep Neural Networks to Improve Forecasts for Regional Energy Markets}, year = 2020 }
%0 Conference Paper %1 he2020continuous %A He, Yujiang %A Henze, Janosch %A Sick, Bernhard %B International Federation of Automatic Control (IFAC) World Congress %D 2020 %I Elsevier %P 12175--12182 %R 10.1016/j.ifacol.2020.12.1017 %T Continuous Learning of Deep Neural Networks to Improve Forecasts for Regional Energy Markets
Calma, A.: Active Learning with Uncertain Annotators, (2020). https://doi.org/10.17170/kobra-202009171814.
- [ BibTeX ]
- [ EndNote ]
@phdthesis{calma2020active, author = {Calma, Adrian}, keywords = {imported}, publisher = {kassel university press}, school = {University of Kassel}, title = {Active Learning with Uncertain Annotators}, year = 2020 }
%0 Thesis %1 calma2020active %A Calma, Adrian %D 2020 %I kassel university press %R 10.17170/kobra-202009171814 %T Active Learning with Uncertain Annotators
Tomforde, S., Gruhl, C., Sick, B.: A swarm-fleet infrastructure as a scenario for proactive, hybrid adaptation of system behaviour In: Workshop on Self -Aware Computing (SeAC), ACSOS. bll 166–169. IEEE (2020). https://doi.org/10.1109/acsos-c51401.2020.00050.
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@inproceedings{tomforde2020swarm, author = {Tomforde, Sven and Gruhl, Christian and Sick, Bernhard}, booktitle = {Workshop on Self -Aware Computing (SeAC), ACSOS}, keywords = {itegpub}, pages = {166–169}, publisher = {IEEE}, title = {A swarm-fleet infrastructure as a scenario for proactive, hybrid adaptation of system behaviour}, year = 2020 }
%0 Conference Paper %1 tomforde2020swarm %A Tomforde, Sven %A Gruhl, Christian %A Sick, Bernhard %B Workshop on Self -Aware Computing (SeAC), ACSOS %D 2020 %I IEEE %P 166--169 %R 10.1109/acsos-c51401.2020.00050 %T A swarm-fleet infrastructure as a scenario for proactive, hybrid adaptation of system behaviour

2019[ to top ]

Vogt, S., Braun, A., Dobschinski, J., Sick, B.: Wind Power Forecasting Based on Deep Neural Networks and Transfer Learning In: Wind Integration Workshop. , Dublin, Ireland (2019).
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- [ EndNote ]
@inproceedings{vogt2019wind, address = {Dublin, Ireland}, author = {Vogt, Stephan and Braun, Axel and Dobschinski, Jan and Sick, Bernhard}, booktitle = {Wind Integration Workshop}, keywords = {imported}, title = {Wind Power Forecasting Based on Deep Neural Networks and Transfer Learning}, year = 2019 }
%0 Conference Paper %1 vogt2019wind %A Vogt, Stephan %A Braun, Axel %A Dobschinski, Jan %A Sick, Bernhard %B Wind Integration Workshop %C Dublin, Ireland %D 2019 %T Wind Power Forecasting Based on Deep Neural Networks and Transfer Learning
Vogt, S., Braun, A., Dobschinski, J., Sick, B.: Wind Power Forecasting Based on Deep Neural Networks and Transfer Learning In: Wind Integration Workshop. , Dublin, Ireland (2019).
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@inproceedings{vogt2019wind, address = {Dublin, Ireland}, author = {Vogt, Stephan and Braun, Axel and Dobschinski, Jan and Sick, Bernhard}, booktitle = {Wind Integration Workshop}, keywords = {imported}, title = {Wind Power Forecasting Based on Deep Neural Networks and Transfer Learning}, year = 2019 }
%0 Conference Paper %1 vogt2019wind %A Vogt, Stephan %A Braun, Axel %A Dobschinski, Jan %A Sick, Bernhard %B Wind Integration Workshop %C Dublin, Ireland %D 2019 %T Wind Power Forecasting Based on Deep Neural Networks and Transfer Learning
Gensler, A.: Wind Power Ensemble Forecasting, (2019). https://doi.org/10.17170/kobra-202008181589.
- [ BibTeX ]
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@phdthesis{gensler2019wind, author = {Gensler, André}, keywords = {imported}, publisher = {kassel university press}, school = {University of Kassel}, title = {Wind Power Ensemble Forecasting}, year = 2019 }
%0 Thesis %1 gensler2019wind %A Gensler, André %D 2019 %I kassel university press %R 10.17170/kobra-202008181589 %T Wind Power Ensemble Forecasting
König, I., Heilmann, E., Henze, J., David, K., Wetzel, H., Sick, B.: Using grid supporting flexibility in electricity distribution networks In: INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft. bll 531–544. Gesellschaft für Informatik e.V., Bonn (2019). https://doi.org/10.18420/inf2019_70.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{koenig2019using, address = {Bonn}, author = {König, Immanuel and Heilmann, Erik and Henze, Janosch and David, Klaus and Wetzel, Heike and Sick, Bernhard}, booktitle = {INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft}, keywords = {imported}, pages = {531–544}, publisher = {Gesellschaft für Informatik e.V.}, title = {Using grid supporting flexibility in electricity distribution networks}, year = 2019 }
%0 Conference Paper %1 koenig2019using %A König, Immanuel %A Heilmann, Erik %A Henze, Janosch %A David, Klaus %A Wetzel, Heike %A Sick, Bernhard %B INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft %C Bonn %D 2019 %I Gesellschaft für Informatik e.V. %P 531--544 %R 10.18420/inf2019_70 %T Using grid supporting flexibility in electricity distribution networks
Knauer, U., Styp von Rekowski, C., Stecklina, M., Krokotsch, T., Pham Minh, T., Hauffe, V., Kilias, D., Ehrhardt, I., Sagischewski, H., Chmara, S., Seiffert, U.: Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers Remote Sensing. 11, 2788 (2019). https://doi.org/10.3390/rs11232788.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
In this paper, we evaluate different popular voting strategies for fusion of classifier results. A convolutional neural network (CNN) and different variants of random forest (RF) classifiers were trained to discriminate between 15 tree species based on airborne hyperspectral imaging data. The spectral data was preprocessed with a multi-class linear discriminant analysis (MCLDA) as a means to reduce dimensionality and to obtain spatial–spectral features. The best individual classifier was a CNN with a classification accuracy of 0.73 +/− 0.086. The classification performance increased to an accuracy of 0.78 +/− 0.053 by using precision weighted voting for a hybrid ensemble of the CNN and two RF classifiers. This voting strategy clearly outperformed majority voting (0.74), accuracy weighted voting (0.75), and presidential voting (0.75).
@article{knauer2019tree, abstract = {In this paper, we evaluate different popular voting strategies for fusion of classifier results. A convolutional neural network (CNN) and different variants of random forest (RF) classifiers were trained to discriminate between 15 tree species based on airborne hyperspectral imaging data. The spectral data was preprocessed with a multi-class linear discriminant analysis (MCLDA) as a means to reduce dimensionality and to obtain spatial–spectral features. The best individual classifier was a CNN with a classification accuracy of 0.73 +/− 0.086. The classification performance increased to an accuracy of 0.78 +/− 0.053 by using precision weighted voting for a hybrid ensemble of the CNN and two RF classifiers. This voting strategy clearly outperformed majority voting (0.74), accuracy weighted voting (0.75), and presidential voting (0.75).}, author = {Knauer, Uwe and Styp von Rekowski, Cornelius and Stecklina, Marianne and Krokotsch, Tilman and Pham Minh, Tuan and Hauffe, Viola and Kilias, David and Ehrhardt, Ina and Sagischewski, Herbert and Chmara, Sergej and Seiffert, Udo}, journal = {Remote Sensing}, keywords = {imported}, number = 23, pages = 2788, publisher = {MDPI}, title = {Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers}, volume = 11, year = 2019 }
%0 Journal Article %1 knauer2019tree %A Knauer, Uwe %A Styp von Rekowski, Cornelius %A Stecklina, Marianne %A Krokotsch, Tilman %A Pham Minh, Tuan %A Hauffe, Viola %A Kilias, David %A Ehrhardt, Ina %A Sagischewski, Herbert %A Chmara, Sergej %A Seiffert, Udo %D 2019 %I MDPI %J Remote Sensing %N 23 %P 2788 %R 10.3390/rs11232788 %T Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers %U https://www.mdpi.com/2072-4292/11/23/2788 %V 11 %X In this paper, we evaluate different popular voting strategies for fusion of classifier results. A convolutional neural network (CNN) and different variants of random forest (RF) classifiers were trained to discriminate between 15 tree species based on airborne hyperspectral imaging data. The spectral data was preprocessed with a multi-class linear discriminant analysis (MCLDA) as a means to reduce dimensionality and to obtain spatial–spectral features. The best individual classifier was a CNN with a classification accuracy of 0.73 +/− 0.086. The classification performance increased to an accuracy of 0.78 +/− 0.053 by using precision weighted voting for a hybrid ensemble of the CNN and two RF classifiers. This voting strategy clearly outperformed majority voting (0.74), accuracy weighted voting (0.75), and presidential voting (0.75).
Stein, A., Tomforde, S.: Transfer Learning is a Crucial Capability of Intelligent Systems Self-Integrating at Runtime In: Workshop on Self-Improving System Integration (SISSY), FAS*W. bll 32–35. IEEE (2019). https://doi.org/10.1109/FAS-W.2019.00021.
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@inproceedings{stein2019transfer, author = {{Stein}, A. and {Tomforde}, S.}, booktitle = {Workshop on Self-Improving System Integration (SISSY), FAS*W}, keywords = {imported}, pages = {32–35}, publisher = {IEEE}, title = {Transfer Learning is a Crucial Capability of Intelligent Systems Self-Integrating at Runtime}, year = 2019 }
%0 Conference Paper %1 stein2019transfer %A Stein, A. %A Tomforde, S. %B Workshop on Self-Improving System Integration (SISSY), FAS*W %D 2019 %I IEEE %P 32--35 %R 10.1109/FAS-W.2019.00021 %T Transfer Learning is a Crucial Capability of Intelligent Systems Self-Integrating at Runtime
Schreiber, J.: Transfer Learning in the Field of Renewable Energies -- A Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid In: Tomforde, S. en Sick, B. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2018. bll 75–87. kassel university press, Kassel, Germany (2019).
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@incollection{schreiber2019transfer, address = {Kassel, Germany}, author = {Schreiber, Jens}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2018}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {75–87}, publisher = {kassel university press}, title = {Transfer Learning in the Field of Renewable Energies – {A} Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid}, year = 2019 }
%0 Book Section %1 schreiber2019transfer %A Schreiber, Jens %B Organic Computing -- Doctoral Dissertation Colloquium 2018 %C Kassel, Germany %D 2019 %E Tomforde, S. %E Sick, B. %I kassel university press %P 75--87 %T Transfer Learning in the Field of Renewable Energies -- A Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid
Zernetsch, S., Reichert, H., Kress, V., Doll, K., Sick, B.: Trajectory Forecasts with Uncertainties of Vulnerable Road Users by Means of Neural Networks In: IEEE Intelligent Vehicles Symposium (IV). bll 810–815. IEEE (2019). https://doi.org/10.1109/IVS.2019.8814258.
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@inproceedings{zernetsch2019trajectory, author = {{Zernetsch}, S. and {Reichert}, H. and {Kress}, V. and {Doll}, K. and {Sick}, B.}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, keywords = {imported}, pages = {810–815}, publisher = {IEEE}, title = {Trajectory Forecasts with Uncertainties of Vulnerable Road Users by Means of Neural Networks}, year = 2019 }
%0 Conference Paper %1 zernetsch2019trajectory %A Zernetsch, S. %A Reichert, H. %A Kress, V. %A Doll, K. %A Sick, B. %B IEEE Intelligent Vehicles Symposium (IV) %D 2019 %I IEEE %P 810--815 %R 10.1109/IVS.2019.8814258 %T Trajectory Forecasts with Uncertainties of Vulnerable Road Users by Means of Neural Networks
Zernetsch, S., Reichert, H., Kress, V., Doll, K., Sick, B.: Trajectory Forecasts with Uncertainties of Vulnerable Road Users by Means of Neural Networks In: IEEE Intelligent Vehicles Symposium (IV). bll 810–815. IEEE (2019). https://doi.org/10.1109/IVS.2019.8814258.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{zernetsch2019trajectory, author = {{Zernetsch}, S. and {Reichert}, H. and {Kress}, V. and {Doll}, K. and {Sick}, B.}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, keywords = {imported}, pages = {810–815}, publisher = {IEEE}, title = {Trajectory Forecasts with Uncertainties of Vulnerable Road Users by Means of Neural Networks}, year = 2019 }
%0 Conference Paper %1 zernetsch2019trajectory %A Zernetsch, S. %A Reichert, H. %A Kress, V. %A Doll, K. %A Sick, B. %B IEEE Intelligent Vehicles Symposium (IV) %D 2019 %I IEEE %P 810--815 %R 10.1109/IVS.2019.8814258 %T Trajectory Forecasts with Uncertainties of Vulnerable Road Users by Means of Neural Networks
Heidecker, F., Bieshaar, M., Sick, B.: Towards Corner Case Identification in Cyclists’ Trajectories In: ACM Computer Science in Cars Symposium (CSCS). ACM (2019).
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- [ URL ]
@inproceedings{heidecker2019towards, author = {Heidecker, F. and Bieshaar, M. and Sick, B.}, booktitle = {ACM Computer Science in Cars Symposium (CSCS)}, keywords = {imported}, publisher = {ACM}, title = {Towards Corner Case Identification in Cyclists’ Trajectories}, year = 2019 }
%0 Conference Paper %1 heidecker2019towards %A Heidecker, F. %A Bieshaar, M. %A Sick, B. %B ACM Computer Science in Cars Symposium (CSCS) %D 2019 %I ACM %T Towards Corner Case Identification in Cyclists’ Trajectories %U https://cscs19.cispa.saarland/files/cscs19_camera_ready/28_fhmbbs_19_cscs_.pdf
Krupitzer, C., Tomforde, S.: The Organic Computing Doctoral Dissertation Colloquium: Status and Overview in 2019 In: INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beiträge). bll 545–554. Gesellschaft für Informatik e.V., Bonn (2019). https://doi.org/10.18420/inf2019_ws57.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{krupitzer2019organic, address = {Bonn}, author = {Krupitzer, Christian and Tomforde, Sven}, booktitle = {INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (Workshop-Beiträge)}, keywords = {imported}, pages = {545–554}, publisher = {Gesellschaft für Informatik e.V.}, title = {The Organic Computing Doctoral Dissertation Colloquium: Status and Overview in 2019}, year = 2019 }
%0 Conference Paper %1 krupitzer2019organic %A Krupitzer, Christian %A Tomforde, Sven %B INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beiträge) %C Bonn %D 2019 %I Gesellschaft für Informatik e.V. %P 545--554 %R 10.18420/inf2019_ws57 %T The Organic Computing Doctoral Dissertation Colloquium: Status and Overview in 2019
Schneegans, J., Bieshaar, M.: Smart Device Based Initial Movement Detection of Cyclists Using Convolutional Neural Networks In: Tomforde, S. en Sick, B. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2018. bll 45–60. kassel university press, Kassel, Germany (2019).
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@incollection{schneegans2019smart, address = {Kassel, Germany}, author = {Schneegans, Jan and Bieshaar, Maarten}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2018}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {45–60}, publisher = {kassel university press}, title = {Smart Device Based Initial Movement Detection of Cyclists Using Convolutional Neural Networks}, year = 2019 }
%0 Book Section %1 schneegans2019smart %A Schneegans, Jan %A Bieshaar, Maarten %B Organic Computing -- Doctoral Dissertation Colloquium 2018 %C Kassel, Germany %D 2019 %E Tomforde, S. %E Sick, B. %I kassel university press %P 45--60 %T Smart Device Based Initial Movement Detection of Cyclists Using Convolutional Neural Networks
Scheiner, N., Appenrodt, N., Dickmann, J., Sick, B.: Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles In: IEEE Intelligent Vehicles Symposium (IV). bll 642–649. IEEE, Paris, France (2019). https://doi.org/10.1109/ivs.2019.8813773.
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@inproceedings{scheiner2019radar, address = {Paris, France}, author = {Scheiner, Nicolas and Appenrodt, Nils and Dickmann, J{\"{u}}rgen and Sick, Bernhard}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, keywords = {imported}, pages = {642–649}, publisher = {IEEE}, title = {Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles}, year = 2019 }
%0 Conference Paper %1 scheiner2019radar %A Scheiner, Nicolas %A Appenrodt, Nils %A Dickmann, Jürgen %A Sick, Bernhard %B IEEE Intelligent Vehicles Symposium (IV) %C Paris, France %D 2019 %I IEEE %P 642--649 %R 10.1109/ivs.2019.8813773 %T Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles
Kress, V., Zernetsch, S., Doll, K., Sick, B.: Pose Based Trajectory Forecast of Vulnerable Road Users In: IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, Xiamen (2019). https://doi.org/10.1109/ssci44817.2019.9003023.
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@inproceedings{kress2019pose, address = {Xiamen}, author = {Kress, V. and Zernetsch, S. and Doll, K. and Sick, B.}, booktitle = {IEEE Symposium Series on Computational Intelligence (SSCI)}, keywords = {imported}, publisher = {IEEE}, title = {Pose Based Trajectory Forecast of Vulnerable Road Users}, year = 2019 }
%0 Conference Paper %1 kress2019pose %A Kress, V. %A Zernetsch, S. %A Doll, K. %A Sick, B. %B IEEE Symposium Series on Computational Intelligence (SSCI) %C Xiamen %D 2019 %I IEEE %R 10.1109/ssci44817.2019.9003023 %T Pose Based Trajectory Forecast of Vulnerable Road Users
Kress, V., Jung, J., Zernetsch, S., Doll, K., Sick, B.: Pose Based Start Intention Detection of Cyclists In: IEEE International Conference on Intelligent Transportation Systems (ITSC). bll 2381–2386. IEEE (2019). https://doi.org/10.1109/ITSC.2019.8917215.
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@inproceedings{kress2019posea, author = {{Kress}, V. and {Jung}, J. and {Zernetsch}, S. and {Doll}, K. and {Sick}, B.}, booktitle = {IEEE International Conference on Intelligent Transportation Systems (ITSC)}, keywords = {displays}, pages = {2381–2386}, publisher = {IEEE}, title = {Pose Based Start Intention Detection of Cyclists}, year = 2019 }
%0 Conference Paper %1 kress2019posea %A Kress, V. %A Jung, J. %A Zernetsch, S. %A Doll, K. %A Sick, B. %B IEEE International Conference on Intelligent Transportation Systems (ITSC) %D 2019 %I IEEE %P 2381--2386 %R 10.1109/ITSC.2019.8917215 %T Pose Based Start Intention Detection of Cyclists
Tomforde, S., Sick, B. reds: Organic Computing -- Doctoral Dissertation Colloquium 2018 kassel university press (2019). https://doi.org/10.19211/KUP9783737606974.
- [ BibTeX ]
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@book{tomforde2019organic, editor = {Tomforde, S. and Sick, B.}, keywords = {itegpub}, publisher = {kassel university press}, series = {Intelligent Embedded Systems}, title = {Organic Computing – Doctoral Dissertation Colloquium 2018}, volume = 13, year = 2019 }
%0 Book %1 tomforde2019organic %B Intelligent Embedded Systems %D 2019 %E Tomforde, S. %E Sick, B. %I kassel university press %R 10.19211/KUP9783737606974 %T Organic Computing -- Doctoral Dissertation Colloquium 2018 %V 13
Rudolph, S., Tomforde, S., Hähner, J.: On the Detection of Mutual Influences and Their Consideration in Reinforcement Learning Processes arXiv e-prints. arXiv:1905.04205 (2019).
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@article{rudolph2019detection, author = {{Rudolph}, Stefan and {Tomforde}, Sven and {H{\"a}hner}, J{\"o}rg}, journal = {arXiv e-prints}, keywords = {Science}, pages = {arXiv:1905.04205}, title = {{On the Detection of Mutual Influences and Their Consideration in Reinforcement Learning Processes}}, year = 2019 }
%0 Journal Article %1 rudolph2019detection %A Rudolph, Stefan %A Tomforde, Sven %A Hähner, Jörg %D 2019 %J arXiv e-prints %P arXiv:1905.04205 %T On the Detection of Mutual Influences and Their Consideration in Reinforcement Learning Processes
D’Angelo, M., Gerasimou, S., Ghahremani, S., Grohmann, J., Nunes, I., Pournaras, E., Tomforde, S.: On Learning in Collective Self-Adaptive Systems: State of Practice and a 3D Framework In: International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). bll 13–24. IEEE/ACM (2019). https://doi.org/10.1109/SEAMS.2019.00012.
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@inproceedings{dangelo2019learning, author = {{D'Angelo}, M. and {Gerasimou}, S. and {Ghahremani}, S. and {Grohmann}, J. and {Nunes}, I. and {Pournaras}, E. and {Tomforde}, S.}, booktitle = {International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)}, keywords = {imported}, pages = {13–24}, publisher = {IEEE/ACM}, title = {On Learning in Collective Self-Adaptive Systems: State of Practice and a 3D Framework}, year = 2019 }
%0 Conference Paper %1 dangelo2019learning %A D'Angelo, M. %A Gerasimou, S. %A Ghahremani, S. %A Grohmann, J. %A Nunes, I. %A Pournaras, E. %A Tomforde, S. %B International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) %D 2019 %I IEEE/ACM %P 13--24 %R 10.1109/SEAMS.2019.00012 %T On Learning in Collective Self-Adaptive Systems: State of Practice and a 3D Framework
Schlegel, B.: Off-Board Car Diagnostics Based on Heterogeneous, Highly Imbalanced and High-Dimensional Data Using Machine Learning Techniques, (2019). https://doi.org/10.17170/kobra-202008141582.
- [ BibTeX ]
- [ EndNote ]
@phdthesis{schlegel2019board, author = {Schlegel, Bernhard}, keywords = {imported}, publisher = {kassel university press}, school = {University of Kassel}, title = {Off-Board Car Diagnostics Based on Heterogeneous, Highly Imbalanced and High-Dimensional Data Using Machine Learning Techniques}, year = 2019 }
%0 Thesis %1 schlegel2019board %A Schlegel, Bernhard %D 2019 %I kassel university press %R 10.17170/kobra-202008141582 %T Off-Board Car Diagnostics Based on Heterogeneous, Highly Imbalanced and High-Dimensional Data Using Machine Learning Techniques
Rudolph, S., Tomforde, S., Hähner, J.: Mutual Influence-aware Runtime Learning of Self-adaptation Behavior ACM Transactions on Autonomous and Adaptive Systems. 14, 4 (2019). https://doi.org/10.1145/3345319.
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- [ EndNote ]
- [ URL ]
@article{rudolph2019mutual, address = {New York, NY, USA}, author = {Rudolph, Stefan and Tomforde, Sven and H\"{a}hner, J\"{o}rg}, journal = {ACM Transactions on Autonomous and Adaptive Systems}, keywords = {imported}, number = 1, pages = 4, publisher = {ACM}, title = {Mutual Influence-aware Runtime Learning of Self-adaptation Behavior}, volume = 14, year = 2019 }
%0 Journal Article %1 rudolph2019mutual %A Rudolph, Stefan %A Tomforde, Sven %A Hähner, Jörg %C New York, NY, USA %D 2019 %I ACM %J ACM Transactions on Autonomous and Adaptive Systems %N 1 %P 4 %R 10.1145/3345319 %T Mutual Influence-aware Runtime Learning of Self-adaptation Behavior %U http://doi.acm.org/10.1145/3345319 %V 14
Lesch, V., Krupitzer, C., Tomforde, S.: Multi-objective Optimisation in Hybrid Collaborating Adaptive Systems In: International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll 1–8. VDE (2019).
- [ BibTeX ]
- [ EndNote ]
@inproceedings{lesch2019multi, author = {{Lesch}, V. and {Krupitzer}, C. and {Tomforde}, S.}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, keywords = {imported}, pages = {1–8}, publisher = {VDE}, title = {Multi-objective Optimisation in Hybrid Collaborating Adaptive Systems}, year = 2019 }
%0 Conference Paper %1 lesch2019multi %A Lesch, V. %A Krupitzer, C. %A Tomforde, S. %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %D 2019 %I VDE %P 1--8 %T Multi-objective Optimisation in Hybrid Collaborating Adaptive Systems
Kottke, D., Schellinger, J., Huseljic, D., Sick, B.: Limitations of Assessing Active Learning Performance at Runtime arXiv e-prints. arXiv:1901.10338 (2019).
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- [ EndNote ]
@article{kottke2019limitations, author = {{Kottke}, Daniel and {Schellinger}, Jim and {Huseljic}, Denis and {Sick}, Bernhard}, journal = {arXiv e-prints}, keywords = {Machine}, pages = {arXiv:1901.10338}, title = {{Limitations of Assessing Active Learning Performance at Runtime}}, year = 2019 }
%0 Journal Article %1 kottke2019limitations %A Kottke, Daniel %A Schellinger, Jim %A Huseljic, Denis %A Sick, Bernhard %D 2019 %J arXiv e-prints %P arXiv:1901.10338 %T Limitations of Assessing Active Learning Performance at Runtime
Goldhammer, M., Köhler, S., Zernetsch, S., Doll, K., Sick, B., Dietmayer, K.: Intentions of Vulnerable Road Users -- Detection and Forecasting by Means of Machine Learning IEEE Transactions on Intelligent Transportation Systems. 21, 3035–3045 (2019). https://doi.org/10.1109/TITS.2019.2923319.
- [ BibTeX ]
- [ EndNote ]
@article{goldhammer2019intentions, author = {{Goldhammer}, M. and {Köhler}, S. and {Zernetsch}, S. and {Doll}, K. and {Sick}, B. and {Dietmayer}, K.}, journal = {IEEE Transactions on Intelligent Transportation Systems}, keywords = {imported}, number = 7, pages = {3035–3045}, publisher = {IEEE}, title = {Intentions of Vulnerable Road Users – {D}etection and Forecasting by Means of Machine Learning}, volume = 21, year = 2019 }
%0 Journal Article %1 goldhammer2019intentions %A Goldhammer, M. %A Köhler, S. %A Zernetsch, S. %A Doll, K. %A Sick, B. %A Dietmayer, K. %D 2019 %I IEEE %J IEEE Transactions on Intelligent Transportation Systems %N 7 %P 3035--3045 %R 10.1109/TITS.2019.2923319 %T Intentions of Vulnerable Road Users -- Detection and Forecasting by Means of Machine Learning %V 21
Draude, C., Lange, M., Sick, B. reds: INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beitr{{ä}}ge) Gesellschaft für Informatik e.V. (2019).
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- [ EndNote ]
- [ URL ]
@proceedings{draude2019informatik, editor = {Draude, Claude and Lange, Martin and Sick, Bernhard}, keywords = {imported}, publisher = {Gesellschaft für Informatik e.V.}, title = {INFORMATIK 2019: 50 Jahre Gesellschaft f{\"{u}}r Informatik – Informatik f{\"{u}}r Gesellschaft (Workshop-Beitr{\"{a}}ge)}, volume = {{P295}}, year = 2019 }
%0 Conference Proceedings %1 draude2019informatik %D 2019 %E Draude, Claude %E Lange, Martin %E Sick, Bernhard %I Gesellschaft für Informatik e.V. %T INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beitr{{ä}}ge) %U https://dl.gi.de/handle/20.500.12116/25040 %V P295 %@ 978-3-88579-689-3
Draude, C., Lange, M., Sick, B. reds: INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beitr{{ä}}ge) Gesellschaft für Informatik e.V. (2019).
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
@proceedings{draude2019informatik, editor = {Draude, Claude and Lange, Martin and Sick, Bernhard}, keywords = {imported}, publisher = {Gesellschaft für Informatik e.V.}, title = {INFORMATIK 2019: 50 Jahre Gesellschaft f{\"{u}}r Informatik – Informatik f{\"{u}}r Gesellschaft (Workshop-Beitr{\"{a}}ge)}, volume = {{P295}}, year = 2019 }
%0 Conference Proceedings %1 draude2019informatik %D 2019 %E Draude, Claude %E Lange, Martin %E Sick, Bernhard %I Gesellschaft für Informatik e.V. %T INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beitr{{ä}}ge) %U https://dl.gi.de/handle/20.500.12116/25040 %V P295 %@ 978-3-88579-689-3
Schreiber, J., Jessulat, M., Sick, B.: Generative Adversarial Networks for Operational Scenario Planning of Renewable Energy Farms: A Study on Wind and Photovoltaic In: International Conference on Artificial Neural Networks and Machine Learning (ICANN): Image Processing. bll 550–564. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30508-6_44.
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- [ EndNote ]
@inproceedings{schreiber2019generative, address = {Cham}, author = {Schreiber, Jens and Jessulat, Maik and Sick, Bernhard}, booktitle = {International Conference on Artificial Neural Networks and Machine Learning (ICANN): Image Processing}, keywords = {imported}, pages = {550–564}, publisher = {Springer}, title = {Generative Adversarial Networks for Operational Scenario Planning of Renewable Energy Farms: A Study on Wind and Photovoltaic}, year = 2019 }
%0 Conference Paper %1 schreiber2019generative %A Schreiber, Jens %A Jessulat, Maik %A Sick, Bernhard %B International Conference on Artificial Neural Networks and Machine Learning (ICANN): Image Processing %C Cham %D 2019 %I Springer %P 550--564 %R 10.1007/978-3-030-30508-6_44 %T Generative Adversarial Networks for Operational Scenario Planning of Renewable Energy Farms: A Study on Wind and Photovoltaic %@ 978-3-030-30508-6
Tomforde, S.: From "Normal" to "Abnormal": A Concept for Determining Expected Self-Adaptation Behaviour In: IEEE International Workshop on Evaluations and Measurements in Self-Aware Computing Systems (EMSAC-SeAC), FAS*W. bll 126–129. IEEE (2019). https://doi.org/10.1109/fas-w.2019.00041.
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@inproceedings{tomforde2019normal, author = {Tomforde, Sven}, booktitle = {IEEE International Workshop on Evaluations and Measurements in Self-Aware Computing Systems (EMSAC-SeAC), FAS*W}, keywords = {imported}, pages = {126–129}, publisher = {IEEE}, title = {From "Normal" to "Abnormal": A Concept for Determining Expected Self-Adaptation Behaviour}, year = 2019 }
%0 Conference Paper %1 tomforde2019normal %A Tomforde, Sven %B IEEE International Workshop on Evaluations and Measurements in Self-Aware Computing Systems (EMSAC-SeAC), FAS*W %D 2019 %I IEEE %P 126--129 %R 10.1109/fas-w.2019.00041 %T From "Normal" to "Abnormal": A Concept for Determining Expected Self-Adaptation Behaviour
Tomforde, S., Gelhausen, P., Gruhl, C., Haering, I., Sick, B.: Explicit Consideration of Resilience in Organic Computing Design Processes In: International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll 1–6. VDE (2019).
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@inproceedings{tomforde2019explicit, author = {{Tomforde}, S. and {Gelhausen}, P. and {Gruhl}, C. and {Haering}, I. and {Sick}, B.}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, keywords = {imported}, pages = {1–6}, publisher = {VDE}, title = {Explicit Consideration of Resilience in Organic Computing Design Processes}, year = 2019 }
%0 Conference Paper %1 tomforde2019explicit %A Tomforde, S. %A Gelhausen, P. %A Gruhl, C. %A Haering, I. %A Sick, B. %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %D 2019 %I VDE %P 1--6 %T Explicit Consideration of Resilience in Organic Computing Design Processes
Lesch, V., Krupitzer, C., Tomforde, S.: Emerging Self-Integration through Coordination of Autonomous Adaptive Systems In: Workshop on Self-Improving System Integration (SISSY), FAS*W. bll 6–9. IEEE (2019). https://doi.org/10.1109/FAS-W.2019.00016.
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@inproceedings{lesch2019emerging, author = {{Lesch}, V. and {Krupitzer}, C. and {Tomforde}, S.}, booktitle = {Workshop on Self-Improving System Integration (SISSY), FAS*W}, keywords = {imported}, pages = {6–9}, publisher = {IEEE}, title = {Emerging Self-Integration through Coordination of Autonomous Adaptive Systems}, year = 2019 }
%0 Conference Paper %1 lesch2019emerging %A Lesch, V. %A Krupitzer, C. %A Tomforde, S. %B Workshop on Self-Improving System Integration (SISSY), FAS*W %D 2019 %I IEEE %P 6--9 %R 10.1109/FAS-W.2019.00016 %T Emerging Self-Integration through Coordination of Autonomous Adaptive Systems
Lesch, V., Krupitzer, C., Tomforde, S.: Emerging Self-Integration through Coordination of Autonomous Adaptive Systems In: Workshop on Self-Improving System Integration (SISSY), FAS*W. bll 6–9. IEEE (2019). https://doi.org/10.1109/FAS-W.2019.00016.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{lesch2019emerging, author = {{Lesch}, V. and {Krupitzer}, C. and {Tomforde}, S.}, booktitle = {Workshop on Self-Improving System Integration (SISSY), FAS*W}, keywords = {imported}, pages = {6–9}, publisher = {IEEE}, title = {Emerging Self-Integration through Coordination of Autonomous Adaptive Systems}, year = 2019 }
%0 Conference Paper %1 lesch2019emerging %A Lesch, V. %A Krupitzer, C. %A Tomforde, S. %B Workshop on Self-Improving System Integration (SISSY), FAS*W %D 2019 %I IEEE %P 6--9 %R 10.1109/FAS-W.2019.00016 %T Emerging Self-Integration through Coordination of Autonomous Adaptive Systems
Botache, D., Dandan, L., Bieshaar, M., Sick, B.: Early Pedestrian Movement Detection Using Smart Devices Based on Human Activity Recognition In: INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beiträge). bll 229–238. Gesellschaft für Informatik e.V., Bonn (2019). https://doi.org/10.18420/inf2019_ws26.
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- [ EndNote ]
@inproceedings{botache2019early, address = {Bonn}, author = {Botache, Diego and Dandan, Liu and Bieshaar, Maarten and Sick, Bernhard}, booktitle = {INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (Workshop-Beiträge)}, keywords = {imported}, pages = {229–238}, publisher = {Gesellschaft für Informatik e.V.}, title = {Early Pedestrian Movement Detection Using Smart Devices Based on Human Activity Recognition}, year = 2019 }
%0 Conference Paper %1 botache2019early %A Botache, Diego %A Dandan, Liu %A Bieshaar, Maarten %A Sick, Bernhard %B INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beiträge) %C Bonn %D 2019 %I Gesellschaft für Informatik e.V. %P 229--238 %R 10.18420/inf2019_ws26 %T Early Pedestrian Movement Detection Using Smart Devices Based on Human Activity Recognition
Calma, A., Dellermann, D.: Decision Support with Hybrid Intelligence In: Tomforde, S. en Sick, B. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2018. bll 143–153. kassel university press, Kassel, Germany (2019).
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@incollection{calma2019decision, address = {Kassel, Germany}, author = {Calma, Adrian and Dellermann, Dominik}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2018}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {143–153}, publisher = {kassel university press}, title = {Decision Support with Hybrid Intelligence}, year = 2019 }
%0 Book Section %1 calma2019decision %A Calma, Adrian %A Dellermann, Dominik %B Organic Computing -- Doctoral Dissertation Colloquium 2018 %C Kassel, Germany %D 2019 %E Tomforde, S. %E Sick, B. %I kassel university press %P 143--153 %T Decision Support with Hybrid Intelligence
Sandrock, C., Herde, M., Calma, A., Kottke, D., Sick, B.: Combining Self-reported Confidences from Uncertain Annotators to Improve Label Quality In: International Joint Conference on Neural Networks (IJCNN). bll 1–8. IEEE (2019). https://doi.org/10.1109/IJCNN.2019.8852456.
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@inproceedings{sandrock2019combining, author = {{Sandrock}, C. and {Herde}, M. and {Calma}, A. and {Kottke}, D. and {Sick}, B.}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {annotators;confidence}, pages = {1–8}, publisher = {IEEE}, title = {Combining Self-reported Confidences from Uncertain Annotators to Improve Label Quality}, year = 2019 }
%0 Conference Paper %1 sandrock2019combining %A Sandrock, C. %A Herde, M. %A Calma, A. %A Kottke, D. %A Sick, B. %B International Joint Conference on Neural Networks (IJCNN) %D 2019 %I IEEE %P 1--8 %R 10.1109/IJCNN.2019.8852456 %T Combining Self-reported Confidences from Uncertain Annotators to Improve Label Quality
Hanika, T., Herde, M., Kuhn, J., Leimeister, J.M., Lukowicz, P., Oeste-Reiß, S., Schmidt, A., Sick, B., Stumme, G., Tomforde, S., Zweig, K.A.: Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields arXiv e-prints. arXiv:1905.07264 (2019).
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@article{hanika2019collaborative, author = {{Hanika}, Tom and {Herde}, Marek and {Kuhn}, Jochen and {Leimeister}, Jan Marco and {Lukowicz}, Paul and {Oeste-Rei{\ss}}, Sarah and {Schmidt}, Albrecht and {Sick}, Bernhard and {Stumme}, Gerd and {Tomforde}, Sven and {Zweig}, Katharina Anna}, journal = {arXiv e-prints}, keywords = {Machine}, pages = {arXiv:1905.07264}, title = {{Collaborative Interactive Learning – A clarification of terms and a differentiation from other research fields}}, year = 2019 }
%0 Journal Article %1 hanika2019collaborative %A Hanika, Tom %A Herde, Marek %A Kuhn, Jochen %A Leimeister, Jan Marco %A Lukowicz, Paul %A Oeste-Reiß, Sarah %A Schmidt, Albrecht %A Sick, Bernhard %A Stumme, Gerd %A Tomforde, Sven %A Zweig, Katharina Anna %D 2019 %J arXiv e-prints %P arXiv:1905.07264 %T Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields
Barnes, C.M., Bellman, K., Botev, J., Diaconescu, A., Esterle, L., Gruhl, C., Landauer, C., Lewis, P.R., Nelson, P.R., Stein, A., Stewart, C., Tomforde, S.: CHARIOT -- Towards a Continuous High-Level Adaptive Runtime Integration Testbed In: Workshop on Self-Improving System Integration (SISSY), FAS*W. bll 52–55. IEEE (2019). https://doi.org/10.1109/FAS-W.2019.00026.
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@inproceedings{barnes2019chariot, author = {{Barnes}, C. M. and {Bellman}, K. and {Botev}, J. and {Diaconescu}, A. and {Esterle}, L. and {Gruhl}, C. and {Landauer}, C. and {Lewis}, P. R. and {Nelson}, P. R. and {Stein}, A. and {Stewart}, C. and {Tomforde}, S.}, booktitle = {Workshop on Self-Improving System Integration (SISSY), FAS*W}, keywords = {imported}, pages = {52–55}, publisher = {IEEE}, title = {{CHARIOT} – {T}owards a Continuous High-Level Adaptive Runtime Integration Testbed}, year = 2019 }
%0 Conference Paper %1 barnes2019chariot %A Barnes, C. M. %A Bellman, K. %A Botev, J. %A Diaconescu, A. %A Esterle, L. %A Gruhl, C. %A Landauer, C. %A Lewis, P. R. %A Nelson, P. R. %A Stein, A. %A Stewart, C. %A Tomforde, S. %B Workshop on Self-Improving System Integration (SISSY), FAS*W %D 2019 %I IEEE %P 52--55 %R 10.1109/FAS-W.2019.00026 %T CHARIOT -- Towards a Continuous High-Level Adaptive Runtime Integration Testbed
Scheiner, N., Appenrodt, N., Dickmann, J., Sick, B.: Automated Ground Truth Estimation of Vulnerable Road Users in Automotive Radar Data Using GNSS In: IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM). bll 5–9. IEEE (2019). https://doi.org/10.1109/ICMIM.2019.8726801.
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@inproceedings{scheiner2019automated, author = {Scheiner, Nicolas and Appenrodt, Nils and Dickmann, J{\"{u}}rgen and Sick, Bernhard}, booktitle = {IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM)}, keywords = {imported}, pages = {5–9}, publisher = {IEEE}, title = {Automated Ground Truth Estimation of Vulnerable Road Users in Automotive Radar Data Using GNSS}, year = 2019 }
%0 Conference Paper %1 scheiner2019automated %A Scheiner, Nicolas %A Appenrodt, Nils %A Dickmann, Jürgen %A Sick, Bernhard %B IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM) %D 2019 %I IEEE %P 5--9 %R 10.1109/ICMIM.2019.8726801 %T Automated Ground Truth Estimation of Vulnerable Road Users in Automotive Radar Data Using GNSS
Scheiner, N., Haag, S., Appenrodt, N., Duraisamy, B., Dickmann, J., Fritzsche, M., Sick, B.: Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices In: International Radar Symposium (IRS). bll 1–10. , Ulm, Germany (2019). https://doi.org/10.23919/irs.2019.8768169.
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@inproceedings{scheiner2019automateda, address = {Ulm, Germany}, author = {Scheiner, Nicolas and Haag, Stefan and Appenrodt, Nils and Duraisamy, Bharanidhar and Dickmann, J{\"{u}}rgen and Fritzsche, Martin and Sick, Bernhard}, booktitle = {International Radar Symposium (IRS)}, keywords = {imported}, pages = {1–10}, title = {Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices}, year = 2019 }
%0 Conference Paper %1 scheiner2019automateda %A Scheiner, Nicolas %A Haag, Stefan %A Appenrodt, Nils %A Duraisamy, Bharanidhar %A Dickmann, Jürgen %A Fritzsche, Martin %A Sick, Bernhard %B International Radar Symposium (IRS) %C Ulm, Germany %D 2019 %P 1--10 %R 10.23919/irs.2019.8768169 %T Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices %@ 9783736998605
Scheiner, N., Appenrodt, N., Dickmann, J., Sick, B.: A Multi-Stage Clustering Framework for Automotive Radar Data In: IEEE International Conference on Intelligent Transportation Systems (ITSC). bll 2060–2067. IEEE (2019). https://doi.org/10.1109/itsc.2019.8916873.
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- [ EndNote ]
@inproceedings{scheiner2019multi, author = {Scheiner, Nicolas and Appenrodt, Nils and Dickmann, J{\"{u}}rgen and Sick, Bernhard}, booktitle = {IEEE International Conference on Intelligent Transportation Systems (ITSC)}, keywords = {imported}, pages = {2060–2067}, publisher = {IEEE}, title = {A Multi-Stage Clustering Framework for Automotive Radar Data}, year = 2019 }
%0 Conference Paper %1 scheiner2019multi %A Scheiner, Nicolas %A Appenrodt, Nils %A Dickmann, Jürgen %A Sick, Bernhard %B IEEE International Conference on Intelligent Transportation Systems (ITSC) %D 2019 %I IEEE %P 2060--2067 %R 10.1109/itsc.2019.8916873 %T A Multi-Stage Clustering Framework for Automotive Radar Data

2018[ to top ]

Krempl, G., Lemaire, V., Kottke, D., Calma, A., Holzinger, A., Polikar, R., Sick, B. reds: Workshop on Interactive Adaptive Learning (IAL), ECML PKDD (2018).
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
@proceedings{krempl2018workshop, editor = {Krempl, Georg and Lemaire, Vincent and Kottke, Daniel and Calma, Adrian and Holzinger, Andreas and Polikar, Robi and Sick, Bernhard}, keywords = {imported}, series = {{CEUR} Workshop Proceedings}, title = {Workshop on Interactive Adaptive Learning (IAL), ECML PKDD}, volume = 2192, year = 2018 }
%0 Conference Proceedings %1 krempl2018workshop %B CEUR Workshop Proceedings %D 2018 %E Krempl, Georg %E Lemaire, Vincent %E Kottke, Daniel %E Calma, Adrian %E Holzinger, Andreas %E Polikar, Robi %E Sick, Bernhard %T Workshop on Interactive Adaptive Learning (IAL), ECML PKDD %U http://ceur-ws.org/Vol-2192 %V 2192
Tomforde, S., Dehling, T., Haux, R., Huseljic, D., Kottke, D., Scheerbaum, J., Sick, B., Sunyaev, A., Wolf, K.-H.: Towards Proactive Health-enabling Living Environments: Simulation-based Study and Research Challenges In: International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll 1–8. VDE (2018).
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@inproceedings{tomforde2018towards, author = {Tomforde, Sven and Dehling, Tobias and Haux, Reinhold and Huseljic, Denis and Kottke, Daniel and Scheerbaum, Jonas and Sick, Bernhard and Sunyaev, Ali and Wolf, Klaus-Hendrik}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, keywords = {imported}, pages = {1–8}, publisher = {VDE}, title = {Towards Proactive Health-enabling Living Environments: Simulation-based Study and Research Challenges}, year = 2018 }
%0 Conference Paper %1 tomforde2018towards %A Tomforde, Sven %A Dehling, Tobias %A Haux, Reinhold %A Huseljic, Denis %A Kottke, Daniel %A Scheerbaum, Jonas %A Sick, Bernhard %A Sunyaev, Ali %A Wolf, Klaus-Hendrik %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %D 2018 %I VDE %P 1--8 %T Towards Proactive Health-enabling Living Environments: Simulation-based Study and Research Challenges
Tomforde, S., Dehling, T., Haux, R., Huseljic, D., Kottke, D., Scheerbaum, J., Sick, B., Sunyaev, A., Wolf, K.-H.: Towards Proactive Health-enabling Living Environments: Simulation-based Study and Research Challenges In: International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll 1–8. VDE (2018).
- [ BibTeX ]
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@inproceedings{tomforde2018towards, author = {Tomforde, Sven and Dehling, Tobias and Haux, Reinhold and Huseljic, Denis and Kottke, Daniel and Scheerbaum, Jonas and Sick, Bernhard and Sunyaev, Ali and Wolf, Klaus-Hendrik}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, keywords = {imported}, pages = {1–8}, publisher = {VDE}, title = {Towards Proactive Health-enabling Living Environments: Simulation-based Study and Research Challenges}, year = 2018 }
%0 Conference Paper %1 tomforde2018towards %A Tomforde, Sven %A Dehling, Tobias %A Haux, Reinhold %A Huseljic, Denis %A Kottke, Daniel %A Scheerbaum, Jonas %A Sick, Bernhard %A Sunyaev, Ali %A Wolf, Klaus-Hendrik %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %D 2018 %I VDE %P 1--8 %T Towards Proactive Health-enabling Living Environments: Simulation-based Study and Research Challenges
Jahn, A., Tomforde, S., Morold, M., David, K., Sick, B.: Towards Cooperative Self-adapting Activity Recognition In: International Joint Conference on Pervasive and Embedded Computing and Communication Systems (PECCS). bll 215–222 (2018). https://doi.org/10.5220/0006856102150222.
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@inproceedings{jahn2018towards, author = {Jahn, Andreas and Tomforde, Sven and Morold, Michel and David, Klaus and Sick, Bernhard}, booktitle = {International Joint Conference on Pervasive and Embedded Computing and Communication Systems (PECCS)}, keywords = {imported}, pages = {215–222}, title = {Towards Cooperative Self-adapting Activity Recognition}, year = 2018 }
%0 Conference Paper %1 jahn2018towards %A Jahn, Andreas %A Tomforde, Sven %A Morold, Michel %A David, Klaus %A Sick, Bernhard %B International Joint Conference on Pervasive and Embedded Computing and Communication Systems (PECCS) %D 2018 %P 215--222 %R 10.5220/0006856102150222 %T Towards Cooperative Self-adapting Activity Recognition
Kottke, D., Calma, A., Huseljic, D., Sandrock, C., Kachergis, G., Sick, B.: The Other Human in The Loop -- A Pilot Study to Find Selection Strategies for Active Learning In: International Joint Conference on Neural Networks (IJCNN). IEEE, Rio de Janiero, Brazil (2018). https://doi.org/10.1109/ijcnn.2018.8489637.
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@inproceedings{kottke2018other, address = {Rio de Janiero, Brazil}, author = {Kottke, Daniel and Calma, Adrian and Huseljic, Denis and Sandrock, Christoph and Kachergis, George and Sick, Bernhard}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {imported}, publisher = {IEEE}, title = {The Other Human in The Loop – A Pilot Study to Find Selection Strategies for Active Learning}, year = 2018 }
%0 Conference Paper %1 kottke2018other %A Kottke, Daniel %A Calma, Adrian %A Huseljic, Denis %A Sandrock, Christoph %A Kachergis, George %A Sick, Bernhard %B International Joint Conference on Neural Networks (IJCNN) %C Rio de Janiero, Brazil %D 2018 %I IEEE %R 10.1109/ijcnn.2018.8489637 %T The Other Human in The Loop -- A Pilot Study to Find Selection Strategies for Active Learning
Bieshaar, M., Depping, M., Schneegans, J., Sick, B.: Starting Movement Detection of Cyclists Using Smart Devices In: IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, Turin, Italy (2018). https://doi.org/10.1109/dsaa.2018.00042.
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@inproceedings{bieshaar2018starting, address = {Turin, Italy}, author = {Bieshaar, M. and Depping, M. and Schneegans, J. and Sick, B.}, booktitle = {IEEE International Conference on Data Science and Advanced Analytics (DSAA)}, keywords = {imported}, publisher = {IEEE}, title = {Starting Movement Detection of Cyclists Using Smart Devices}, year = 2018 }
%0 Conference Paper %1 bieshaar2018starting %A Bieshaar, M. %A Depping, M. %A Schneegans, J. %A Sick, B. %B IEEE International Conference on Data Science and Advanced Analytics (DSAA) %C Turin, Italy %D 2018 %I IEEE %R 10.1109/dsaa.2018.00042 %T Starting Movement Detection of Cyclists Using Smart Devices
Jänicke, M., Schmidt, V., Sick, B., Tomforde, S., Lukowicz, P., Schmeißing, J.: Smart Device Stealing and CANDIES In: International Conference on Agents and Artificial Intelligence (ICAART). bll 247–273. Springer (2018). https://doi.org/10.1007/978-3-030-05453-3_12.
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@inproceedings{jaenicke2018smart, author = {J{\"{a}}nicke, Martin and Schmidt, Viktor and Sick, Bernhard and Tomforde, Sven and Lukowicz, Paul and Schmei{\ss}ing, J{\"{o}}rn}, booktitle = {International Conference on Agents and Artificial Intelligence (ICAART)}, keywords = {imported}, pages = {247–273}, publisher = {Springer}, title = {Smart Device Stealing and {CANDIES}}, year = 2018 }
%0 Conference Paper %1 jaenicke2018smart %A Jänicke, Martin %A Schmidt, Viktor %A Sick, Bernhard %A Tomforde, Sven %A Lukowicz, Paul %A Schmeißing, Jörn %B International Conference on Agents and Artificial Intelligence (ICAART) %D 2018 %I Springer %P 247--273 %R 10.1007/978-3-030-05453-3_12 %T Smart Device Stealing and CANDIES
Calma, A., Reitmaier, T., Sick, B.: Semi-supervised active learning for support vector machines: A novel approach that exploits structure information in data Information Sciences. 456, 13–33 (2018). https://doi.org/10.1016/j.ins.2018.04.063.
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@article{calma2018semi, author = {Calma, A. and Reitmaier, T. and Sick, B.}, journal = {Information Sciences}, keywords = {imported}, pages = {13–33}, publisher = {Elsevier}, title = {Semi-supervised active learning for support vector machines: {A} novel approach that exploits structure information in data}, volume = 456, year = 2018 }
%0 Journal Article %1 calma2018semi %A Calma, A. %A Reitmaier, T. %A Sick, B. %D 2018 %I Elsevier %J Information Sciences %P 13--33 %R 10.1016/j.ins.2018.04.063 %T Semi-supervised active learning for support vector machines: A novel approach that exploits structure information in data %V 456
Jänicke, M., Sick, B., Tomforde, S.: Self-Adaptive Multi-Sensor Activity Recognition Systems Based on Gaussian Mixture Models Informatics. 5, 38 (2018). https://doi.org/10.3390/informatics5030038.
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@article{jaenicke2018self, author = {J{\"{a}}nicke, Martin and Sick, Bernhard and Tomforde, Sven}, journal = {Informatics}, keywords = {imported}, number = 3, pages = 38, publisher = {MDPI}, title = {Self-Adaptive Multi-Sensor Activity Recognition Systems Based on Gaussian Mixture Models}, volume = 5, year = 2018 }
%0 Journal Article %1 jaenicke2018self %A Jänicke, Martin %A Sick, Bernhard %A Tomforde, Sven %D 2018 %I MDPI %J Informatics %N 3 %P 38 %R 10.3390/informatics5030038 %T Self-Adaptive Multi-Sensor Activity Recognition Systems Based on Gaussian Mixture Models %V 5
Heck, H., Sick, B., Tomforde, S.: Security Issues in Self-Improving System Integration - Challenges and Solution Strategies In: Workshop on Self-Improving System Integration (SISSY), FAS*W. bll 176–181. IEEE (2018). https://doi.org/10.1109/fas-w.2018.00044.
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@inproceedings{heck2018security, author = {Heck, Henner and Sick, Bernhard and Tomforde, Sven}, booktitle = {Workshop on Self-Improving System Integration (SISSY), FAS*W}, keywords = {imported}, pages = {176–181}, publisher = {IEEE}, title = {Security Issues in Self-Improving System Integration - Challenges and Solution Strategies}, year = 2018 }
%0 Conference Paper %1 heck2018security %A Heck, Henner %A Sick, Bernhard %A Tomforde, Sven %B Workshop on Self-Improving System Integration (SISSY), FAS*W %D 2018 %I IEEE %P 176--181 %R 10.1109/fas-w.2018.00044 %T Security Issues in Self-Improving System Integration - Challenges and Solution Strategies
Henze, J., Kutzner, S., Sick, B.: Sampling Strategies for Representative Time Series in Load Flow Calculations In: Workshop on Data Analytics for Renewable Energy Integration (DARE), ECML PKDD. bll 27–48. Springer (2018). https://doi.org/10.1007/978-3-030-04303-2_3.
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@inproceedings{henze2018sampling, author = {Henze, Janosch and Kutzner, Stephan and Sick, Bernhard}, booktitle = {Workshop on Data Analytics for Renewable Energy Integration (DARE), ECML PKDD}, keywords = {imported}, pages = {27–48}, publisher = {Springer}, title = {Sampling Strategies for Representative Time Series in Load Flow Calculations}, year = 2018 }
%0 Conference Paper %1 henze2018sampling %A Henze, Janosch %A Kutzner, Stephan %A Sick, Bernhard %B Workshop on Data Analytics for Renewable Energy Integration (DARE), ECML PKDD %D 2018 %I Springer %P 27--48 %R 10.1007/978-3-030-04303-2_3 %T Sampling Strategies for Representative Time Series in Load Flow Calculations
Henze, J., Kutzner, S., Sick, B.: Sampling Strategies for Representative Time Series in Load Flow Calculations In: Workshop on Data Analytics for Renewable Energy Integration (DARE), ECML PKDD. bll 27–48. Springer (2018). https://doi.org/10.1007/978-3-030-04303-2_3.
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@inproceedings{henze2018sampling, author = {Henze, Janosch and Kutzner, Stephan and Sick, Bernhard}, booktitle = {Workshop on Data Analytics for Renewable Energy Integration (DARE), ECML PKDD}, keywords = {imported}, pages = {27–48}, publisher = {Springer}, title = {Sampling Strategies for Representative Time Series in Load Flow Calculations}, year = 2018 }
%0 Conference Paper %1 henze2018sampling %A Henze, Janosch %A Kutzner, Stephan %A Sick, Bernhard %B Workshop on Data Analytics for Renewable Energy Integration (DARE), ECML PKDD %D 2018 %I Springer %P 27--48 %R 10.1007/978-3-030-04303-2_3 %T Sampling Strategies for Representative Time Series in Load Flow Calculations
Scheiner, N., Appenrodt, N., Dickmann, J., Sick, B.: Radar-based Feature Design and Multiclass Classification for Road User Recognition In: IEEE Intelligent Vehicles Symposium (IV). bll 779–786. IEEE, Changshu, China (2018). https://doi.org/10.1109/ivs.2018.8500607.
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@inproceedings{scheiner2018radar, address = {Changshu, China}, author = {Scheiner, Nicolas and Appenrodt, Nils and Dickmann, J{\"{u}}rgen and Sick, Bernhard}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, keywords = {imported}, pages = {779–786}, publisher = {IEEE}, title = {Radar-based Feature Design and Multiclass Classification for Road User Recognition}, year = 2018 }
%0 Conference Paper %1 scheiner2018radar %A Scheiner, Nicolas %A Appenrodt, Nils %A Dickmann, Jürgen %A Sick, Bernhard %B IEEE Intelligent Vehicles Symposium (IV) %C Changshu, China %D 2018 %I IEEE %P 779--786 %R 10.1109/ivs.2018.8500607 %T Radar-based Feature Design and Multiclass Classification for Road User Recognition
Schreiber, J., Sick, B.: Quantifying the Influences on Probabilistic Wind Power Forecasts In: International Conference on Power and Renewable Energy (ICPRE). bll 1–6 (2018). https://doi.org/10.1051/e3sconf/20186406002.
- [ Abstract ]
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In recent years, probabilistic forecasts techniques were proposed in research as well as in applications to integrate volatile renewable energy resources into the electrical grid. These techniques allow decision makers to take the uncertainty of the prediction into account and, therefore, to devise optimal decisions, e.g., related to costs and risks in the electrical grid. However, it was yet not studied how the input, such as numerical weather predictions, affects the model output of forecasting models in detail. Therefore, we examine the potential influences with techniques from the field of sensitivity analysis on three different black-box models to obtain insights into differences and similarities of these probabilistic models. The analysis shows a considerable number of potential influences in those models depending on, e.g., the predicted probability and the type of model. These effects motivate the need to take various influences into account when models are tested, analyzed, or compared. Nevertheless, results of the sensitivity analysis will allow us to select a model with advantages in the practical application.
@inproceedings{schreiber2018quantifying, abstract = {In recent years, probabilistic forecasts techniques were proposed in research as well as in applications to integrate volatile renewable energy resources into the electrical grid. These techniques allow decision makers to take the uncertainty of the prediction into account and, therefore, to devise optimal decisions, e.g., related to costs and risks in the electrical grid. However, it was yet not studied how the input, such as numerical weather predictions, affects the model output of forecasting models in detail. Therefore, we examine the potential influences with techniques from the field of sensitivity analysis on three different black-box models to obtain insights into differences and similarities of these probabilistic models. The analysis shows a considerable number of potential influences in those models depending on, e.g., the predicted probability and the type of model. These effects motivate the need to take various influences into account when models are tested, analyzed, or compared. Nevertheless, results of the sensitivity analysis will allow us to select a model with advantages in the practical application.}, author = {Schreiber, Jens and Sick, Bernhard}, booktitle = {International Conference on Power and Renewable Energy (ICPRE)}, keywords = {imported}, pages = {1–6}, title = {Quantifying the Influences on Probabilistic Wind Power Forecasts}, year = 2018 }
%0 Conference Paper %1 schreiber2018quantifying %A Schreiber, Jens %A Sick, Bernhard %B International Conference on Power and Renewable Energy (ICPRE) %D 2018 %P 1--6 %R 10.1051/e3sconf/20186406002 %T Quantifying the Influences on Probabilistic Wind Power Forecasts %X In recent years, probabilistic forecasts techniques were proposed in research as well as in applications to integrate volatile renewable energy resources into the electrical grid. These techniques allow decision makers to take the uncertainty of the prediction into account and, therefore, to devise optimal decisions, e.g., related to costs and risks in the electrical grid. However, it was yet not studied how the input, such as numerical weather predictions, affects the model output of forecasting models in detail. Therefore, we examine the potential influences with techniques from the field of sensitivity analysis on three different black-box models to obtain insights into differences and similarities of these probabilistic models. The analysis shows a considerable number of potential influences in those models depending on, e.g., the predicted probability and the type of model. These effects motivate the need to take various influences into account when models are tested, analyzed, or compared. Nevertheless, results of the sensitivity analysis will allow us to select a model with advantages in the practical application.
Tomforde, S., Sick, B. reds: Organic Computing -- Doctoral Dissertation Colloquium 2017 kassel university press (2018). https://doi.org/10.19211/KUP9783737605151.
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@book{tomforde2018organic, editor = {Tomforde, S. and Sick, B.}, keywords = {itegpub}, publisher = {kassel university press}, series = {Intelligent Embedded Systems}, title = {Organic Computing – Doctoral Dissertation Colloquium 2017}, volume = 11, year = 2018 }
%0 Book %1 tomforde2018organic %B Intelligent Embedded Systems %D 2018 %E Tomforde, S. %E Sick, B. %I kassel university press %R 10.19211/KUP9783737605151 %T Organic Computing -- Doctoral Dissertation Colloquium 2017 %V 11
Gruhl, C., Sick, B.: Novelty detection with CANDIES: a holistic technique based on probabilistic models International Journal of Machine Learning and Cybernetics. 9, 927–945 (2018). https://doi.org/10.1007/s13042-016-0618-8.
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@article{gruhl2018novelty, author = {Gruhl, Christian and Sick, Bernhard}, journal = {International Journal of Machine Learning and Cybernetics}, keywords = {imported}, number = 6, pages = {927–945}, publisher = {Springer}, title = {Novelty detection with {CANDIES:} a holistic technique based on probabilistic models}, volume = 9, year = 2018 }
%0 Journal Article %1 gruhl2018novelty %A Gruhl, Christian %A Sick, Bernhard %D 2018 %I Springer %J International Journal of Machine Learning and Cybernetics %N 6 %P 927--945 %R 10.1007/s13042-016-0618-8 %T Novelty detection with CANDIES: a holistic technique based on probabilistic models %V 9
Calma, A., Oeste-Reiß, S., Sick, B., Leimeister, J.M.: Leveraging the Potentials of Dedicated Collaborative Interactive Learning: Conceptual Foundations to Overcome Uncertainty by Human-Machine Collaboration In: Hawaii International Conference on System Sciences (HICSS) (2018). https://doi.org/10.24251/hicss.2018.120.
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@inproceedings{calma2018leveraging, author = {Calma, Adrian and Oeste-Rei{\ss}, Sarah and Sick, Bernhard and Leimeister, Jan Marco}, booktitle = {Hawaii International Conference on System Sciences (HICSS)}, keywords = {imported}, title = {Leveraging the Potentials of Dedicated Collaborative Interactive Learning: Conceptual Foundations to Overcome Uncertainty by Human-Machine Collaboration}, year = 2018 }
%0 Conference Paper %1 calma2018leveraging %A Calma, Adrian %A Oeste-Reiß, Sarah %A Sick, Bernhard %A Leimeister, Jan Marco %B Hawaii International Conference on System Sciences (HICSS) %D 2018 %R 10.24251/hicss.2018.120 %T Leveraging the Potentials of Dedicated Collaborative Interactive Learning: Conceptual Foundations to Overcome Uncertainty by Human-Machine Collaboration
Kress, V., Jung, J., Zernetsch, S., Doll, K., Sick, B.: Human Pose Estimation in Real Traffic Scenes In: IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, Bangalore, India (2018). https://doi.org/10.1109/ssci.2018.8628660.
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@inproceedings{kress2018human, address = {Bangalore, India}, author = {Kress, V. and Jung, J. and Zernetsch, S. and Doll, K. and Sick, B.}, booktitle = {IEEE Symposium Series on Computational Intelligence (SSCI)}, keywords = {imported}, publisher = {IEEE}, title = {Human Pose Estimation in Real Traffic Scenes}, year = 2018 }
%0 Conference Paper %1 kress2018human %A Kress, V. %A Jung, J. %A Zernetsch, S. %A Doll, K. %A Sick, B. %B IEEE Symposium Series on Computational Intelligence (SSCI) %C Bangalore, India %D 2018 %I IEEE %R 10.1109/ssci.2018.8628660 %T Human Pose Estimation in Real Traffic Scenes
Breker, S., Rentmeister, J., Sick, B., Braun, M.: Hosting capacity of low-voltage grids for distributed generation: Classification by means of machine learning techniques Applied Soft Computing. 70, 195–207 (2018). https://doi.org/10.1016/j.asoc.2018.05.007.
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@article{breker2018hosting, author = {Breker, Sebastian and Rentmeister, Jan and Sick, Bernhard and Braun, Martin}, journal = {Applied Soft Computing}, keywords = {imported}, pages = {195–207}, publisher = {Elsevier}, title = {Hosting capacity of low-voltage grids for distributed generation: Classification by means of machine learning techniques}, volume = 70, year = 2018 }
%0 Journal Article %1 breker2018hosting %A Breker, Sebastian %A Rentmeister, Jan %A Sick, Bernhard %A Braun, Martin %D 2018 %I Elsevier %J Applied Soft Computing %P 195--207 %R 10.1016/j.asoc.2018.05.007 %T Hosting capacity of low-voltage grids for distributed generation: Classification by means of machine learning techniques %V 70
Jänicke, M., Schmidt, V., Sick, B., Tomforde, S., Lukowicz, P.: Hijacked Smart Devices -- Methodical Foundations for Autonomous Theft Awareness based on Activity Recognition and Novelty Detection In: International Conference on Agents and Artificial Intelligence (ICAART) (2018). https://doi.org/10.5220/0006594901310142.
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@inproceedings{jaenicke2018hijacked, author = {J{\"a}nicke, Martin and Schmidt, Viktor and Sick, Bernhard and Tomforde, Sven and Lukowicz, Paul}, booktitle = {International Conference on Agents and Artificial Intelligence (ICAART)}, keywords = {imported}, title = {Hijacked Smart Devices – Methodical Foundations for Autonomous Theft Awareness based on Activity Recognition and Novelty Detection}, year = 2018 }
%0 Conference Paper %1 jaenicke2018hijacked %A Jänicke, Martin %A Schmidt, Viktor %A Sick, Bernhard %A Tomforde, Sven %A Lukowicz, Paul %B International Conference on Agents and Artificial Intelligence (ICAART) %D 2018 %R 10.5220/0006594901310142 %T Hijacked Smart Devices -- Methodical Foundations for Autonomous Theft Awareness based on Activity Recognition and Novelty Detection
Kromat, T., Dehling, T., Haux, R., Peters, C., Sick, B., Tomforde, S., Wolf, K.-H., Sunyaev, A.: Gestaltungsraum für proactive Smart Homes zur Gesundheitsförderung In: Multikonferenz Wirtschaftsinformatik. , Lüneburg, Germany (2018).
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@inproceedings{kromat2018gestaltungsraum, address = {L{\"u}neburg, Germany}, author = {Kromat, Theresa and Dehling, Tobias and Haux, Reinhold and Peters, Christoph and Sick, Bernhard and Tomforde, Sven and Wolf, Klaus-Hendrik and Sunyaev, Ali}, booktitle = {Multikonferenz Wirtschaftsinformatik}, keywords = {imported}, title = {Gestaltungsraum für proactive Smart Homes zur Gesundheitsförderung}, year = 2018 }
%0 Conference Paper %1 kromat2018gestaltungsraum %A Kromat, Theresa %A Dehling, Tobias %A Haux, Reinhold %A Peters, Christoph %A Sick, Bernhard %A Tomforde, Sven %A Wolf, Klaus-Hendrik %A Sunyaev, Ali %B Multikonferenz Wirtschaftsinformatik %C Lüneburg, Germany %D 2018 %T Gestaltungsraum für proactive Smart Homes zur Gesundheitsförderung
Schlegel, B., Mrowca, A., Wolf, P., Sick, B., Steinhorst, S.: Generalizing Application Agnostic Remaining Useful Life Estimation Using Data-Driven Open Source Algorithms In: IEEE International Conference on Big Data Analysis (ICBDA). IEEE, Shanghai, China (2018). https://doi.org/10.1109/icbda.2018.8367659.
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@inproceedings{schlegel2018generalizing, address = {Shanghai, China}, author = {Schlegel, B. and Mrowca, A. and Wolf, P. and Sick, B. and Steinhorst, S.}, booktitle = {IEEE International Conference on Big Data Analysis (ICBDA)}, keywords = {imported}, publisher = {IEEE}, title = {Generalizing Application Agnostic Remaining Useful Life Estimation Using Data-Driven Open Source Algorithms}, year = 2018 }
%0 Conference Paper %1 schlegel2018generalizing %A Schlegel, B. %A Mrowca, A. %A Wolf, P. %A Sick, B. %A Steinhorst, S. %B IEEE International Conference on Big Data Analysis (ICBDA) %C Shanghai, China %D 2018 %I IEEE %R 10.1109/icbda.2018.8367659 %T Generalizing Application Agnostic Remaining Useful Life Estimation Using Data-Driven Open Source Algorithms
Zernetsch, S., Kress, V., Sick, B., Doll, K.: Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network In: IEEE Intelligent Vehicles Symposium (IV). bll 1–6. IEEE (2018). https://doi.org/10.1109/ivs.2018.8500428.
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@inproceedings{zernetsch2018early, author = {Zernetsch, Stefan and Kress, Viktor and Sick, Bernhard and Doll, Konrad}, booktitle = {{IEEE} Intelligent Vehicles Symposium (IV)}, keywords = {imported}, pages = {1–6}, publisher = {IEEE}, title = {Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network}, year = 2018 }
%0 Conference Paper %1 zernetsch2018early %A Zernetsch, Stefan %A Kress, Viktor %A Sick, Bernhard %A Doll, Konrad %B IEEE Intelligent Vehicles Symposium (IV) %D 2018 %I IEEE %P 1--6 %R 10.1109/ivs.2018.8500428 %T Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network
Schreiber, J., Bieshaar, M., Gensler, A., Sick, B., Deist, S.: Coopetitive Soft Gating Ensemble In: Workshop on Self-Improving System Integration (SISSY), FAS*W. IEEE, Trento, Italy (2018). https://doi.org/10.1109/fas-w.2018.00046.
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@inproceedings{schreiber2018coopetitive, address = {Trento, Italy}, author = {Schreiber, J. and Bieshaar, M. and Gensler, A. and Sick, B. and Deist, S.}, booktitle = {Workshop on Self-Improving System Integration (SISSY), FAS*W}, keywords = {imported}, publisher = {IEEE}, title = {Coopetitive Soft Gating Ensemble}, year = 2018 }
%0 Conference Paper %1 schreiber2018coopetitive %A Schreiber, J. %A Bieshaar, M. %A Gensler, A. %A Sick, B. %A Deist, S. %B Workshop on Self-Improving System Integration (SISSY), FAS*W %C Trento, Italy %D 2018 %I IEEE %R 10.1109/fas-w.2018.00046 %T Coopetitive Soft Gating Ensemble
Reitberger, G., Zernetsch, S., Bieshaar, M., Sick, B., Doll, K., Fuchs, E.: Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure In: IEEE International Conference on Intelligent Transportation Systems (ITSC). IEEE, Maui, HI (2018). https://doi.org/10.1109/itsc.2018.8569267.
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@inproceedings{reitberger2018cooperative, address = {Maui, HI}, author = {Reitberger, G. and Zernetsch, S. and Bieshaar, M. and Sick, B. and Doll, K. and Fuchs, E.}, booktitle = {IEEE International Conference on Intelligent Transportation Systems (ITSC)}, keywords = {imported}, publisher = {IEEE}, title = {Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure}, year = 2018 }
%0 Conference Paper %1 reitberger2018cooperative %A Reitberger, G. %A Zernetsch, S. %A Bieshaar, M. %A Sick, B. %A Doll, K. %A Fuchs, E. %B IEEE International Conference on Intelligent Transportation Systems (ITSC) %C Maui, HI %D 2018 %I IEEE %R 10.1109/itsc.2018.8569267 %T Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure
Bieshaar, M., Zernetsch, S., Hubert, A., Sick, B., Doll, K.: Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble IEEE Transactions on Intelligent Vehicles. 3, 534–544 (2018). https://doi.org/10.1109/tiv.2018.2873900.
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@article{bieshaar2018cooperative, author = {Bieshaar, M. and Zernetsch, S. and Hubert, A. and Sick, B. and Doll, K.}, journal = {IEEE Transactions on Intelligent Vehicles}, keywords = {imported}, number = 4, pages = {534–544}, publisher = {IEEE}, title = {Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble}, volume = 3, year = 2018 }
%0 Journal Article %1 bieshaar2018cooperative %A Bieshaar, M. %A Zernetsch, S. %A Hubert, A. %A Sick, B. %A Doll, K. %D 2018 %I IEEE %J IEEE Transactions on Intelligent Vehicles %N 4 %P 534--544 %R 10.1109/tiv.2018.2873900 %T Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble %V 3
Tomforde, S., Kantert, J., Müller-Schloer, C., Bödelt, S., Sick, B.: Comparing the Effects of Disturbances in Self-adaptive Systems - A Generalised Approach for the Quantification of Robustness Transactions on Computational Collective Intelligence. 28, 193–220 (2018). https://doi.org/10.1007/978-3-319-78301-7_9.
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Self-adaptation and self-organisation (SASO) are increasingly used in information and communication technology to master complexity and keep the administrative effort at an acceptable level. However, using SASO mechanisms is not an end in itself -- the primary goal is typically to allow for a higher autonomy of systems in order to react appropriately to disturbances and dynamics in the environmental conditions. We refer to this goal as achieving "robustness". During design-time, engineers have different possibilities to develop SASO mechanisms for an underlying control problem. When deciding which path to follow, an analysis of the inherent robustness of possible solutions is necessary. In this article, we present a novel quantification method for robustness that provides the basis to compare different control strategies in similar conditions.
@article{tomforde2018comparing, abstract = {Self-adaptation and self-organisation (SASO) are increasingly used in information and communication technology to master complexity and keep the administrative effort at an acceptable level. However, using SASO mechanisms is not an end in itself – the primary goal is typically to allow for a higher autonomy of systems in order to react appropriately to disturbances and dynamics in the environmental conditions. We refer to this goal as achieving "robustness". During design-time, engineers have different possibilities to develop SASO mechanisms for an underlying control problem. When deciding which path to follow, an analysis of the inherent robustness of possible solutions is necessary. In this article, we present a novel quantification method for robustness that provides the basis to compare different control strategies in similar conditions.}, author = {Tomforde, Sven and Kantert, Jan and M{\"{u}}ller{-}Schloer, Christian and B{\"{o}}delt, Sebastian and Sick, Bernhard}, journal = {Transactions on Computational Collective Intelligence}, keywords = {imported}, pages = {193–220}, publisher = {Springer}, title = {Comparing the Effects of Disturbances in Self-adaptive Systems - {A} Generalised Approach for the Quantification of Robustness}, volume = 28, year = 2018 }
%0 Journal Article %1 tomforde2018comparing %A Tomforde, Sven %A Kantert, Jan %A Müller-Schloer, Christian %A Bödelt, Sebastian %A Sick, Bernhard %D 2018 %I Springer %J Transactions on Computational Collective Intelligence %P 193--220 %R 10.1007/978-3-319-78301-7_9 %T Comparing the Effects of Disturbances in Self-adaptive Systems - A Generalised Approach for the Quantification of Robustness %V 28 %X Self-adaptation and self-organisation (SASO) are increasingly used in information and communication technology to master complexity and keep the administrative effort at an acceptable level. However, using SASO mechanisms is not an end in itself -- the primary goal is typically to allow for a higher autonomy of systems in order to react appropriately to disturbances and dynamics in the environmental conditions. We refer to this goal as achieving "robustness". During design-time, engineers have different possibilities to develop SASO mechanisms for an underlying control problem. When deciding which path to follow, an analysis of the inherent robustness of possible solutions is necessary. In this article, we present a novel quantification method for robustness that provides the basis to compare different control strategies in similar conditions.
Sick, B., Oeste-Reiß, S., Schmidt, A., Tomforde, S., Zweig, K.A.: Collaborative Interactive Learning Informatik Spektrum. 41, 52–55 (2018). https://doi.org/10.1007/s00287-017-1082-x.
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@article{sick2018collaborative, author = {Sick, Bernhard and Oeste{-}Rei{\ss}, Sarah and Schmidt, Albrecht and Tomforde, Sven and Zweig, Katharina Anna}, journal = {Informatik Spektrum}, keywords = {imported}, number = 1, pages = {52–55}, publisher = {Springer}, title = {Collaborative Interactive Learning}, volume = 41, year = 2018 }
%0 Journal Article %1 sick2018collaborative %A Sick, Bernhard %A Oeste-Reiß, Sarah %A Schmidt, Albrecht %A Tomforde, Sven %A Zweig, Katharina Anna %D 2018 %I Springer %J Informatik Spektrum %N 1 %P 52--55 %R 10.1007/s00287-017-1082-x %T Collaborative Interactive Learning %V 41
Scharei, K., Herde, M., Bieshaar, M., Calma, A., Kottke, D., Sick, B.: Automated Active Learning with a Robot Archives of Data Science, Series A (Online First). 5, 16 (2018). https://doi.org/10.5445/KSP/1000087327/16.
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@article{scharei2018automated, author = {Scharei, Kristina and Herde, Marek and Bieshaar, Maarten and Calma, Adrian and Kottke, Daniel and Sick, Bernhard}, journal = {Archives of Data Science, Series A (Online First)}, keywords = {imported}, number = 1, pages = 16, publisher = {KIT}, title = {Automated Active Learning with a Robot}, volume = 5, year = 2018 }
%0 Journal Article %1 scharei2018automated %A Scharei, Kristina %A Herde, Marek %A Bieshaar, Maarten %A Calma, Adrian %A Kottke, Daniel %A Sick, Bernhard %D 2018 %I KIT %J Archives of Data Science, Series A (Online First) %N 1 %P 16 %R 10.5445/KSP/1000087327/16 %T Automated Active Learning with a Robot %V 5
Gruhl, C., Tomforde, S., Sick, B.: Aspects of Measuring and Evaluating the Integration Status of a (Sub-)System at Runtime In: Workshop on Self-Improving System Integration (SISSY), FAS*W. bll 198–203. IEEE (2018). https://doi.org/10.1109/fas-w.2018.00047.
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@inproceedings{gruhl2018aspects, author = {Gruhl, Christian and Tomforde, Sven and Sick, Bernhard}, booktitle = {Workshop on Self-Improving System Integration (SISSY), FAS*W}, keywords = {imported}, pages = {198–203}, publisher = {IEEE}, title = {Aspects of Measuring and Evaluating the Integration Status of a (Sub-)System at Runtime}, year = 2018 }
%0 Conference Paper %1 gruhl2018aspects %A Gruhl, Christian %A Tomforde, Sven %A Sick, Bernhard %B Workshop on Self-Improving System Integration (SISSY), FAS*W %D 2018 %I IEEE %P 198--203 %R 10.1109/fas-w.2018.00047 %T Aspects of Measuring and Evaluating the Integration Status of a (Sub-)System at Runtime
Herde, M., Kottke, D., Calma, A., Bieshaar, M., Deist, S., Sick, B.: Active Sorting -- An Efficient Training of a Sorting Robot with Active Learning Techniques In: International Joint Conference on Neural Networks (IJCNN). IEEE, Rio de Janiero, Brazil (2018). https://doi.org/10.1109/ijcnn.2018.8489161.
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@inproceedings{herde2018active, address = {Rio de Janiero, Brazil}, author = {Herde, Marek and Kottke, Daniel and Calma, Adrian and Bieshaar, Maarten and Deist, Stephan and Sick, Bernhard}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {imported}, publisher = {IEEE}, title = {Active Sorting – An Efficient Training of a Sorting Robot with Active Learning Techniques}, year = 2018 }
%0 Conference Paper %1 herde2018active %A Herde, Marek %A Kottke, Daniel %A Calma, Adrian %A Bieshaar, Maarten %A Deist, Stephan %A Sick, Bernhard %B International Joint Conference on Neural Networks (IJCNN) %C Rio de Janiero, Brazil %D 2018 %I IEEE %R 10.1109/ijcnn.2018.8489161 %T Active Sorting -- An Efficient Training of a Sorting Robot with Active Learning Techniques
Calma, A., Stolz, M., Kottke, D., Tomforde, S., Sick, B.: Active Learning with Realistic Data -- A Case Study In: International Joint Conference on Neural Networks (IJCNN). IEEE, Rio de Janiero, Brazil (2018). https://doi.org/10.1109/ijcnn.2018.8489394.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{calma2018active, address = {Rio de Janiero, Brazil}, author = {Calma, Adrian and Stolz, Moritz and Kottke, Daniel and Tomforde, Sven and Sick, Bernhard}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {imported}, publisher = {IEEE}, title = {Active Learning with Realistic Data – A Case Study}, year = 2018 }
%0 Conference Paper %1 calma2018active %A Calma, Adrian %A Stolz, Moritz %A Kottke, Daniel %A Tomforde, Sven %A Sick, Bernhard %B International Joint Conference on Neural Networks (IJCNN) %C Rio de Janiero, Brazil %D 2018 %I IEEE %R 10.1109/ijcnn.2018.8489394 %T Active Learning with Realistic Data -- A Case Study
Gensler, A., Sick, B., Vogt, S.: A review of uncertainty representations and metaverification of uncertainty assessment techniques for renewable energies Renewable and Sustainable Energy Reviews. 96, 352–379 (2018). https://doi.org/10.1016/j.rser.2018.07.042.
- [ BibTeX ]
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@article{gensler2018review, author = {Gensler, Andr{\'e} and Sick, Bernhard and Vogt, Stephan}, journal = {Renewable and Sustainable Energy Reviews}, keywords = {imported}, pages = {352–379}, publisher = {Elsevier}, title = {A review of uncertainty representations and metaverification of uncertainty assessment techniques for renewable energies}, volume = 96, year = 2018 }
%0 Journal Article %1 gensler2018review %A Gensler, André %A Sick, Bernhard %A Vogt, Stephan %D 2018 %I Elsevier %J Renewable and Sustainable Energy Reviews %P 352--379 %R 10.1016/j.rser.2018.07.042 %T A review of uncertainty representations and metaverification of uncertainty assessment techniques for renewable energies %V 96
Gensler, A., Sick, B.: A Multi-Scheme Ensemble Using Coopetitive Soft-Gating With Application to Power Forecasting for Renewable Energy Generation arXiv e-prints. arXiv:1803.06344 (2018).
- [ BibTeX ]
- [ EndNote ]
@article{gensler2018multi, author = {Gensler, Andr{\'e} and Sick, Bernhard}, journal = {arXiv e-prints}, keywords = {Machine}, pages = {arXiv:1803.06344}, title = {{A Multi-Scheme Ensemble Using Coopetitive Soft-Gating With Application to Power Forecasting for Renewable Energy Generation}}, year = 2018 }
%0 Journal Article %1 gensler2018multi %A Gensler, André %A Sick, Bernhard %D 2018 %J arXiv e-prints %P arXiv:1803.06344 %T A Multi-Scheme Ensemble Using Coopetitive Soft-Gating With Application to Power Forecasting for Renewable Energy Generation
Calma, A., Kuhn, J., Leimeister, J.M., Lukowicz, P., Oeste-Reiss, S., Schmidt, A., Sick, B., Stumme, G., Tomforde, S., Zweig, A.K.: A Concept for Productivity Tracking based on Collaborative Interactive Learning Techniques In: Workshop on Self-Optimisation in Autonomic and Organic Computing Systems (SAOS), ARCS. bll 150–159. VDE, London, UK (2018).
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
@inproceedings{calma2018concept, address = {London, UK}, author = {Calma, Adrian and Kuhn, Jochen and Leimeister, Jan Marco and Lukowicz, Paul and Oeste-Rei\ss, Sarah and Schmidt, Albrecht and Sick, Bernhard and Stumme, Gerd and Tomforde, Sven and Zweig, Anna Katharina}, booktitle = {Workshop on Self-Optimisation in Autonomic and Organic Computing Systems (SAOS), ARCS}, keywords = {imported}, pages = {150–159}, publisher = {VDE}, title = {A Concept for Productivity Tracking based on Collaborative Interactive Learning Techniques}, year = 2018 }
%0 Conference Paper %1 calma2018concept %A Calma, Adrian %A Kuhn, Jochen %A Leimeister, Jan Marco %A Lukowicz, Paul %A Oeste-Reiss, Sarah %A Schmidt, Albrecht %A Sick, Bernhard %A Stumme, Gerd %A Tomforde, Sven %A Zweig, Anna Katharina %B Workshop on Self-Optimisation in Autonomic and Organic Computing Systems (SAOS), ARCS %C London, UK %D 2018 %I VDE %P 150--159 %T A Concept for Productivity Tracking based on Collaborative Interactive Learning Techniques %U https://ieeexplore.ieee.org/document/8385431

2017[ to top ]

Bieshaar, M.: Where is my Device? Detecting the Smart Device’s Wearing Position in the Context of Active Safety for Vulnerable Road Users In: Tomforde, S. en Sick, B. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2017. bll 27–37. kassel university press, Kassel, Germany (2017).
- [ BibTeX ]
- [ EndNote ]
@incollection{bieshaar2017where, address = {Kassel, Germany}, author = {Bieshaar, M.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2017}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {27–37}, publisher = {kassel university press}, title = {Where is my Device? Detecting the Smart Device's Wearing Position in the Context of Active Safety for Vulnerable Road Users}, year = 2017 }
%0 Book Section %1 bieshaar2017where %A Bieshaar, M. %B Organic Computing -- Doctoral Dissertation Colloquium 2017 %C Kassel, Germany %D 2017 %E Tomforde, S. %E Sick, B. %I kassel university press %P 27--37 %T Where is my Device? Detecting the Smart Device's Wearing Position in the Context of Active Safety for Vulnerable Road Users
Bellman, K., Botev, J., Hildmann, H., Lewis, P.R., Marsh, S., Pitt, J., Scholtes, I., Tomforde, S.: Socially-Sensitive Systems Design: Exploring Social Potential IEEE Technology and Society Magazine. 36, 72–80 (2017). https://doi.org/10.1109/mts.2017.2728727.
- [ BibTeX ]
- [ EndNote ]
@article{bellman2017socially, author = {Bellman, K. and Botev, J. and Hildmann, H. and Lewis, P. R. and Marsh, S. and Pitt, J. and Scholtes, I. and Tomforde, S.}, journal = {IEEE Technology and Society Magazine}, keywords = {imported}, number = 3, pages = {72–80}, publisher = {IEEE}, title = {Socially-Sensitive Systems Design: Exploring Social Potential}, volume = 36, year = 2017 }
%0 Journal Article %1 bellman2017socially %A Bellman, K. %A Botev, J. %A Hildmann, H. %A Lewis, P. R. %A Marsh, S. %A Pitt, J. %A Scholtes, I. %A Tomforde, S. %D 2017 %I IEEE %J IEEE Technology and Society Magazine %N 3 %P 72--80 %R 10.1109/mts.2017.2728727 %T Socially-Sensitive Systems Design: Exploring Social Potential %V 36
Calma, A., Sick, B.: Simulation of Annotators for Active Learning: Uncertain Oracles In: Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. bll 49–58 (2017).
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- [ URL ]
@inproceedings{calma2017simulation, author = {Calma, Adrian and Sick, Bernhard}, booktitle = {Workshop on Interactive Adaptive Learning (IAL), ECML PKDD}, keywords = {imported}, pages = {49–58}, series = {CEUR Workshop Proceedings}, title = {Simulation of Annotators for Active Learning: Uncertain Oracles}, year = 2017 }
%0 Conference Paper %1 calma2017simulation %A Calma, Adrian %A Sick, Bernhard %B Workshop on Interactive Adaptive Learning (IAL), ECML PKDD %D 2017 %P 49--58 %T Simulation of Annotators for Active Learning: Uncertain Oracles %U http://ceur-ws.org/Vol-1924/ialatecml_paper4.pdf
Stein, A., Rudolph, S., Tomforde, S., Hähner, J.: Self-Learning Smart Cameras -- Harnessing the Generalisation Capability of XCS In: International Joint Conference on Computational Intelligence (IJCCI). , Funchal, Portugal (2017). https://doi.org/10.5220/0006512101290140.
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@inproceedings{stein2017self, address = {Funchal, Portugal}, author = {Stein, A. and Rudolph, S. and Tomforde, S. and Hähner, J}, booktitle = {International Joint Conference on Computational Intelligence (IJCCI)}, keywords = {imported}, title = {Self-Learning Smart Cameras – Harnessing the Generalisation Capability of {XCS}}, year = 2017 }
%0 Conference Paper %1 stein2017self %A Stein, A. %A Rudolph, S. %A Tomforde, S. %A Hähner, J %B International Joint Conference on Computational Intelligence (IJCCI) %C Funchal, Portugal %D 2017 %R 10.5220/0006512101290140 %T Self-Learning Smart Cameras -- Harnessing the Generalisation Capability of XCS
Bannach, D., Jänicke, M., Rey, V.F., Tomforde, S., Sick, B., Lukowicz, P.: Self-Adaptation of Activity Recognition Systems to New Sensors arXiv e-prints. arXiv:1701.08528 (2017).
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@article{bannach2017self, author = {{Bannach}, David and {J{\"a}nicke}, Martin and {Rey}, Vitor F. and {Tomforde}, Sven and {Sick}, Bernhard and {Lukowicz}, Paul}, journal = {arXiv e-prints}, keywords = {Recognition,}, pages = {arXiv:1701.08528}, title = {{Self-Adaptation of Activity Recognition Systems to New Sensors}}, year = 2017 }
%0 Journal Article %1 bannach2017self %A Bannach, David %A Jänicke, Martin %A Rey, Vitor F. %A Tomforde, Sven %A Sick, Bernhard %A Lukowicz, Paul %D 2017 %J arXiv e-prints %P arXiv:1701.08528 %T Self-Adaptation of Activity Recognition Systems to New Sensors
Kantert, J., Tomforde, S., Müller-Schloer, C., Edenhofer, S., Sick, B.: Quantitative Robustness -- A Generalised Approach to Compare the Impact of Disturbances in Self-organising Systems In: International Conference on Agents and Artificial Intelligence (ICAART). bll 39–50. , Porto, Portugal (2017). https://doi.org/10.5220/0006137300390050.
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- [ EndNote ]
@inproceedings{kantert2017quantitative, address = {Porto, Portugal}, author = {Kantert, J. and Tomforde, S. and M{\"u}ller-Schloer, C. and Edenhofer, S. and Sick, B.}, booktitle = {International Conference on Agents and Artificial Intelligence (ICAART)}, keywords = {imported}, pages = {39–50}, title = {Quantitative Robustness – A Generalised Approach to Compare the Impact of Disturbances in Self-organising Systems}, year = 2017 }
%0 Conference Paper %1 kantert2017quantitative %A Kantert, J. %A Tomforde, S. %A Müller-Schloer, C. %A Edenhofer, S. %A Sick, B. %B International Conference on Agents and Artificial Intelligence (ICAART) %C Porto, Portugal %D 2017 %P 39--50 %R 10.5220/0006137300390050 %T Quantitative Robustness -- A Generalised Approach to Compare the Impact of Disturbances in Self-organising Systems
Gensler, A., Sick, B.: Probabilistic wind power forecasting: A multi-scheme ensemble technique with gradual coopetitive soft gating In: IEEE Symposium Series on Computational Intelligence (SSCI). bll 1–10. IEEE (2017). https://doi.org/10.1109/SSCI.2017.8285344.
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@inproceedings{gensler2017probabilistic, author = {{Gensler}, A. and {Sick}, B.}, booktitle = {IEEE Symposium Series on Computational Intelligence (SSCI)}, keywords = {itegpub}, pages = {1–10}, publisher = {IEEE}, title = {Probabilistic wind power forecasting: A multi-scheme ensemble technique with gradual coopetitive soft gating}, year = 2017 }
%0 Conference Paper %1 gensler2017probabilistic %A Gensler, A. %A Sick, B. %B IEEE Symposium Series on Computational Intelligence (SSCI) %D 2017 %I IEEE %P 1--10 %R 10.1109/SSCI.2017.8285344 %T Probabilistic wind power forecasting: A multi-scheme ensemble technique with gradual coopetitive soft gating
Gensler, A., Sick, B.: Performing event detection in time series with SwiftEvent: an algorithm with supervised learning of detection criteria Pattern Analysis and Applications. 21, 543–562 (2017). https://doi.org/10.1007/s10044-017-0657-0.
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@article{gensler2017performing, author = {Gensler, A. and Sick, B.}, journal = {Pattern Analysis and Applications}, keywords = {imported}, number = 2, pages = {543–562}, publisher = {Springer}, title = {Performing event detection in time series with {SwiftEvent}: an algorithm with supervised learning of detection criteria}, volume = 21, year = 2017 }
%0 Journal Article %1 gensler2017performing %A Gensler, A. %A Sick, B. %D 2017 %I Springer %J Pattern Analysis and Applications %N 2 %P 543--562 %R 10.1007/s10044-017-0657-0 %T Performing event detection in time series with SwiftEvent: an algorithm with supervised learning of detection criteria %V 21
Tomforde, S., Sick, B., Müller-Schloer, C.: Organic Computing in the Spotlight arXiv e-prints. arXiv:1701.08125 (2017).
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@article{tomforde2017organic, author = {{Tomforde}, Sven and {Sick}, Bernhard and {M{\"u}ller-Schloer}, Christian}, journal = {arXiv e-prints}, keywords = {Science}, pages = {arXiv:1701.08125}, title = {{Organic Computing in the Spotlight}}, year = 2017 }
%0 Journal Article %1 tomforde2017organic %A Tomforde, Sven %A Sick, Bernhard %A Müller-Schloer, Christian %D 2017 %J arXiv e-prints %P arXiv:1701.08125 %T Organic Computing in the Spotlight
Müller-Schloer, C., Tomforde, S.: Organic Computing -- Techncial Systems for Survival in the Real World Birkhäuser Verlag (2017). https://doi.org/10.1007/978-3-319-68477-2.
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@book{muellerschloer2017organic, author = {M\"uller-Schloer, Christian and Tomforde, Sven}, keywords = {imported}, publisher = {Birkh\"auser Verlag}, series = {Autonomic Systems}, title = {Organic Computing – Techncial Systems for Survival in the Real World}, year = 2017 }
%0 Book %1 muellerschloer2017organic %A Müller-Schloer, Christian %A Tomforde, Sven %B Autonomic Systems %D 2017 %I Birkhäuser Verlag %R 10.1007/978-3-319-68477-2 %T Organic Computing -- Techncial Systems for Survival in the Real World
Tomforde, S., Sick, B. reds: Organic Computing -- Doctoral Dissertation Colloquium 2016 kassel university press (2017). https://doi.org/10.19211/KUP9783737603010.
- [ BibTeX ]
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@book{tomforde2017organica, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, publisher = {kassel university press}, series = {Intelligent Embedded Systems}, title = {Organic Computing – Doctoral Dissertation Colloquium 2016}, volume = 10, year = 2017 }
%0 Book %1 tomforde2017organica %B Intelligent Embedded Systems %D 2017 %E Tomforde, S. %E Sick, B. %I kassel university press %R 10.19211/KUP9783737603010 %T Organic Computing -- Doctoral Dissertation Colloquium 2016 %V 10
Wolf, J.-H., Dehling, T., Haux, R., Sick, B., Sunyaev, A., Tomforde, S.: On Methodological and Technological Challenges for Proactive Health Management in Smart Homes In: International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH). bll 209–212. , Athens, Greece (2017).
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@inproceedings{wolf2017methodological, address = {Athens, Greece}, author = {Wolf, J.-H. and Dehling, T. and Haux, R. and Sick, B. and Sunyaev, A. and Tomforde, S.}, booktitle = {International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH)}, keywords = {imported}, pages = {209–212}, title = {On Methodological and Technological Challenges for Proactive Health Management in Smart Homes}, year = 2017 }
%0 Conference Paper %1 wolf2017methodological %A Wolf, J.-H. %A Dehling, T. %A Haux, R. %A Sick, B. %A Sunyaev, A. %A Tomforde, S. %B International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH) %C Athens, Greece %D 2017 %P 209--212 %T On Methodological and Technological Challenges for Proactive Health Management in Smart Homes
Tomforde, S., Kantert, J., Sick, B.: Measuring Self Organisation at Runtime -- A Quantification Method based on Divergence Measures In: International Conference on Agents and Artificial Intelligence (ICAART). bll 96–106. , Porto, Portugal (2017). https://doi.org/10.5220/0006240400960106.
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@inproceedings{tomforde2017measuring, address = {Porto, Portugal}, author = {Tomforde, S. and Kantert, J. and Sick, B.}, booktitle = {International Conference on Agents and Artificial Intelligence (ICAART)}, keywords = {imported}, pages = {96–106}, title = {Measuring Self Organisation at Runtime – A Quantification Method based on Divergence Measures}, year = 2017 }
%0 Conference Paper %1 tomforde2017measuring %A Tomforde, S. %A Kantert, J. %A Sick, B. %B International Conference on Agents and Artificial Intelligence (ICAART) %C Porto, Portugal %D 2017 %P 96--106 %R 10.5220/0006240400960106 %T Measuring Self Organisation at Runtime -- A Quantification Method based on Divergence Measures
Beyer, C., Bieshaar, M., Calma, A., Heck, H., Kottke, D., Würtz, R.: Learning Without Ground Truth In: Tomforde, S. en Sick, B. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2017. kassel university press, Bochum, Germany (2017).
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@incollection{beyer2017learning, address = {Bochum, Germany}, author = {Beyer, C. and Bieshaar, M. and Calma, A. and Heck, H. and Kottke, D. and W{\"u}rtz, R.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2017}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, publisher = {kassel university press}, title = {Learning Without Ground Truth}, year = 2017 }
%0 Book Section %1 beyer2017learning %A Beyer, C. %A Bieshaar, M. %A Calma, A. %A Heck, H. %A Kottke, D. %A Würtz, R. %B Organic Computing -- Doctoral Dissertation Colloquium 2017 %C Bochum, Germany %D 2017 %E Tomforde, S. %E Sick, B. %I kassel university press %T Learning Without Ground Truth
Calma, A., Kottke, D., Sick, B., Tomforde, S.: Learning to Learn: Dynamic Runtime Exploitation of Various Knowledge Sources and Machine Learning Paradigms In: Workshop on Self-Improving System Integration (SISSY), FAS*W. bll 109–116. IEEE, Tucson, AZ (2017). https://doi.org/10.1109/fas-w.2017.129.
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@inproceedings{calma2017learning, address = {Tucson, AZ}, author = {Calma, A. and Kottke, D. and Sick, B. and Tomforde, S.}, booktitle = {Workshop on Self-Improving System Integration (SISSY), FAS*W}, keywords = {imported}, pages = {109–116}, publisher = {IEEE}, title = {Learning to Learn: Dynamic Runtime Exploitation of Various Knowledge Sources and Machine Learning Paradigms}, year = 2017 }
%0 Conference Paper %1 calma2017learning %A Calma, A. %A Kottke, D. %A Sick, B. %A Tomforde, S. %B Workshop on Self-Improving System Integration (SISSY), FAS*W %C Tucson, AZ %D 2017 %I IEEE %P 109--116 %R 10.1109/fas-w.2017.129 %T Learning to Learn: Dynamic Runtime Exploitation of Various Knowledge Sources and Machine Learning Paradigms
Stein, A., Rauh, D., Tomforde, S., Hähner, J.: Interpolation in the eXtended Classifier System: An Architectural Perspective Journal of Systems Architecture. 75, 79–94 (2017). https://doi.org/10.1016/j.sysarc.2017.01.010.
- [ BibTeX ]
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@article{stein2017interpolation, author = {Stein, A. and Rauh, D. and Tomforde, S. and H{\"a}hner, J.}, journal = {Journal of Systems Architecture}, keywords = {imported}, pages = {79–94}, publisher = {Elsevier}, title = {{Interpolation in the e{X}tended {C}lassifier {S}ystem: An Architectural Perspective}}, volume = 75, year = 2017 }
%0 Journal Article %1 stein2017interpolation %A Stein, A. %A Rauh, D. %A Tomforde, S. %A Hähner, J. %D 2017 %I Elsevier %J Journal of Systems Architecture %P 79--94 %R 10.1016/j.sysarc.2017.01.010 %T Interpolation in the eXtended Classifier System: An Architectural Perspective %V 75
Würtz, R.P., Tomforde, S., Calma, A., Kottke, D., Sick, B.: Interactive Learning Without Ground Truth In: Tomforde, S. en Sick, B. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2017. bll 1–4. kassel university press, Kassel, Germany (2017).
- [ BibTeX ]
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@incollection{wuertz2017interactive, address = {Kassel, Germany}, author = {Würtz, Rolf P. and Tomforde, Sven and Calma, Adrian and Kottke, Daniel and Sick, Bernhard}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2017}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {1–4}, publisher = {kassel university press}, title = {Interactive Learning Without Ground Truth}, year = 2017 }
%0 Book Section %1 wuertz2017interactive %A Würtz, Rolf P. %A Tomforde, Sven %A Calma, Adrian %A Kottke, Daniel %A Sick, Bernhard %B Organic Computing -- Doctoral Dissertation Colloquium 2017 %C Kassel, Germany %D 2017 %E Tomforde, S. %E Sick, B. %I kassel university press %P 1--4 %T Interactive Learning Without Ground Truth
Henze, J., Kneiske, T., Braun, M., Sick, B.: Identifying Representative Load Time Series for Load Flow Calculations In: Workshop on Data Analytics for Renewable Energy Integration (DARE), ECML PKDD. bll 83–93. Springer, Cham, Switzerland (2017). https://doi.org/10.1007/978-3-319-71643-5_8.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Power system analyses increasingly use annual time series for temporal and spatial assessment of operational and also planning aspects. These analyses are often limited due to the computational time of the large amount of load flow calculation. By introducing algorithms which are capable of generating shorter and representative time series of measured load or power generation time series, the calculation time for load flow calculations can be reduced. We present a method which is capable of extracting features from the time series and use those features to create a representative time series. Furthermore, we show that our method is capable of maintaining the most important statistical features of the original time series by applying a Fisher-Pitman Permutation test.
@inproceedings{henze2017identifying, abstract = {Power system analyses increasingly use annual time series for temporal and spatial assessment of operational and also planning aspects. These analyses are often limited due to the computational time of the large amount of load flow calculation. By introducing algorithms which are capable of generating shorter and representative time series of measured load or power generation time series, the calculation time for load flow calculations can be reduced. We present a method which is capable of extracting features from the time series and use those features to create a representative time series. Furthermore, we show that our method is capable of maintaining the most important statistical features of the original time series by applying a Fisher-Pitman Permutation test.}, address = {Cham, Switzerland}, author = {Henze, Janosch and Kneiske, Tanja and Braun, Martin and Sick, Bernhard}, booktitle = {Workshop on Data Analytics for Renewable Energy Integration (DARE), ECML PKDD}, keywords = {imported}, pages = {83–93}, publisher = {Springer}, title = {Identifying Representative Load Time Series for Load Flow Calculations}, year = 2017 }
%0 Conference Paper %1 henze2017identifying %A Henze, Janosch %A Kneiske, Tanja %A Braun, Martin %A Sick, Bernhard %B Workshop on Data Analytics for Renewable Energy Integration (DARE), ECML PKDD %C Cham, Switzerland %D 2017 %I Springer %P 83--93 %R 10.1007/978-3-319-71643-5_8 %T Identifying Representative Load Time Series for Load Flow Calculations %X Power system analyses increasingly use annual time series for temporal and spatial assessment of operational and also planning aspects. These analyses are often limited due to the computational time of the large amount of load flow calculation. By introducing algorithms which are capable of generating shorter and representative time series of measured load or power generation time series, the calculation time for load flow calculations can be reduced. We present a method which is capable of extracting features from the time series and use those features to create a representative time series. Furthermore, we show that our method is capable of maintaining the most important statistical features of the original time series by applying a Fisher-Pitman Permutation test.
Kantert, J., Tomforde, S., Scharrer, R., Weber, S., Müller-Schloer, C., Edenhofer, S.: Identification and Classification of Agent Behaviour at Runtime in Open, Trust-based Organic Computing Systems Journal of Systems Architecture. 75, 68–78 (2017). https://doi.org/10.1016/j.sysarc.2017.02.003.
- [ BibTeX ]
- [ EndNote ]
@article{kantert2017identification, author = {Kantert, J. and Tomforde, S. and Scharrer, R. and Weber, S. and M{\"u}ller-Schloer, C. and Edenhofer, S.}, journal = {Journal of Systems Architecture}, keywords = {imported}, pages = {68–78}, publisher = {Elsevier}, title = {Identification and Classification of Agent Behaviour at Runtime in Open, Trust-based Organic Computing Systems}, volume = 75, year = 2017 }
%0 Journal Article %1 kantert2017identification %A Kantert, J. %A Tomforde, S. %A Scharrer, R. %A Weber, S. %A Müller-Schloer, C. %A Edenhofer, S. %D 2017 %I Elsevier %J Journal of Systems Architecture %P 68--78 %R 10.1016/j.sysarc.2017.02.003 %T Identification and Classification of Agent Behaviour at Runtime in Open, Trust-based Organic Computing Systems %V 75
Gruhl, C.: Highly Autonomous Learning in Collaborative, Technical Systems In: Tomforde, S. en Sick, B. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2017. kassel university press, Kassel, Germany (2017).
- [ BibTeX ]
- [ EndNote ]
@incollection{gruhl2017highly, address = {Kassel, Germany}, author = {Gruhl, C.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2017}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, publisher = {kassel university press}, series = {Intelligent Embedded Systems}, title = {Highly Autonomous Learning in Collaborative, Technical Systems}, year = 2017 }
%0 Book Section %1 gruhl2017highly %A Gruhl, C. %B Organic Computing -- Doctoral Dissertation Colloquium 2017 %C Kassel, Germany %D 2017 %E Tomforde, S. %E Sick, B. %I kassel university press %T Highly Autonomous Learning in Collaborative, Technical Systems
Bieshaar, M., Reitberger, G., Kress, V., Zernetsch, S., Doll, K., Fuchs, E., Sick, B.: Highly Automated Learning for Improved Active Safety of Vulnerable Road Users In: ACM Computer Science in Cars Symposium (CSCS). ACM, Munich, Germany (2017).
- [ BibTeX ]
- [ EndNote ]
@inproceedings{bieshaar2017highly, address = {Munich, Germany}, author = {Bieshaar, M. and Reitberger, G. and Kre\ss, V. and Zernetsch, S. and Doll, K. and Fuchs, E. and Sick, B.}, booktitle = {ACM Computer Science in Cars Symposium (CSCS)}, keywords = {imported}, publisher = {ACM}, title = {Highly Automated Learning for Improved Active Safety of Vulnerable Road Users}, year = 2017 }
%0 Conference Paper %1 bieshaar2017highly %A Bieshaar, M. %A Reitberger, G. %A Kress, V. %A Zernetsch, S. %A Doll, K. %A Fuchs, E. %A Sick, B. %B ACM Computer Science in Cars Symposium (CSCS) %C Munich, Germany %D 2017 %I ACM %T Highly Automated Learning for Improved Active Safety of Vulnerable Road Users
Kottke, D.: Enhanced Probabilistic Active Learning: Cost-sensitive, Unbalanced, Time-variant, Self-optimising, and Parameter-free In: Tomforde, S. en Sick, B. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2017. bll 67–78. kassel university press, Kassel, Germany (2017).
- [ BibTeX ]
- [ EndNote ]
@incollection{kottke2017enhanced, address = {Kassel, Germany}, author = {Kottke, Daniel}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2017}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {67–78}, publisher = {kassel university press}, title = {Enhanced Probabilistic Active Learning: Cost-sensitive, Unbalanced, Time-variant, Self-optimising, and Parameter-free}, year = 2017 }
%0 Book Section %1 kottke2017enhanced %A Kottke, Daniel %B Organic Computing -- Doctoral Dissertation Colloquium 2017 %C Kassel, Germany %D 2017 %E Tomforde, S. %E Sick, B. %I kassel university press %P 67--78 %T Enhanced Probabilistic Active Learning: Cost-sensitive, Unbalanced, Time-variant, Self-optimising, and Parameter-free
Kurt, M.: Development of an Offshore Specific Wind Power Forecasting System, (2017).
- [ BibTeX ]
- [ EndNote ]
@phdthesis{kurt2017development, address = {Kassel, Germany}, author = {Kurt, Melih}, keywords = {imported}, publisher = {kassel university press}, school = {University of Kassel}, title = {Development of an Offshore Specific Wind Power Forecasting System}, year = 2017 }
%0 Thesis %1 kurt2017development %A Kurt, Melih %C Kassel, Germany %D 2017 %I kassel university press %T Development of an Offshore Specific Wind Power Forecasting System
Bieshaar, M., Reitberger, G., Zernetsch, S., Sick, B., Fuchs, E., Doll, K.: Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence In: Automatisiertes und vernetztes Fahren Symposium (AAET). bll 67–87. , Braunschweig, Germany (2017).
- [ BibTeX ]
- [ EndNote ]
@inproceedings{bieshaar2017detecting, address = {Braunschweig, Germany}, author = {Bieshaar, M. and Reitberger, G. and Zernetsch, S. and Sick, B. and Fuchs, E. and Doll, K.}, booktitle = {Automatisiertes und vernetztes Fahren Symposium (AAET)}, keywords = {imported}, pages = {67–87}, title = {Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence}, year = 2017 }
%0 Conference Paper %1 bieshaar2017detecting %A Bieshaar, M. %A Reitberger, G. %A Zernetsch, S. %A Sick, B. %A Fuchs, E. %A Doll, K. %B Automatisiertes und vernetztes Fahren Symposium (AAET) %C Braunschweig, Germany %D 2017 %P 67--87 %T Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence
Schlegel, B., Sick, B.: Dealing with class imbalance the scalable way: Evaluation of various techniques based on classification grade and computational complexity In: Workshop on Data Science and Big Data Analytics (DSBDA), ICDM. bll 69–78. IEEE (2017). https://doi.org/10.1109/icdmw.2017.16.
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- [ EndNote ]
@inproceedings{schlegel2017dealing, author = {Schlegel, Bernhard and Sick, Bernhard}, booktitle = {Workshop on Data Science and Big Data Analytics (DSBDA), ICDM}, keywords = {imported}, pages = {69–78}, publisher = {IEEE}, title = {Dealing with class imbalance the scalable way: Evaluation of various techniques based on classification grade and computational complexity}, year = 2017 }
%0 Conference Paper %1 schlegel2017dealing %A Schlegel, Bernhard %A Sick, Bernhard %B Workshop on Data Science and Big Data Analytics (DSBDA), ICDM %D 2017 %I IEEE %P 69--78 %R 10.1109/icdmw.2017.16 %T Dealing with class imbalance the scalable way: Evaluation of various techniques based on classification grade and computational complexity
Bieshaar, M., Zernetsch, S., Depping, M., Sick, B., Doll, K.: Cooperative Starting Intention Detection of Cyclists Based on Smart Devices and Infrastructure In: IEEE International Conference on Intelligent Transportation Systems (ITSC). IEEE, Yokohama, Japan (2017). https://doi.org/10.1109/itsc.2017.8317691.
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@inproceedings{bieshaar2017cooperative, address = {Yokohama, Japan}, author = {Bieshaar, M. and Zernetsch, S. and Depping, M. and Sick, B. and Doll, K.}, booktitle = {IEEE International Conference on Intelligent Transportation Systems (ITSC)}, keywords = {imported}, publisher = {IEEE}, title = {Cooperative Starting Intention Detection of Cyclists Based on Smart Devices and Infrastructure}, year = 2017 }
%0 Conference Paper %1 bieshaar2017cooperative %A Bieshaar, M. %A Zernetsch, S. %A Depping, M. %A Sick, B. %A Doll, K. %B IEEE International Conference on Intelligent Transportation Systems (ITSC) %C Yokohama, Japan %D 2017 %I IEEE %R 10.1109/itsc.2017.8317691 %T Cooperative Starting Intention Detection of Cyclists Based on Smart Devices and Infrastructure
Kottke, D., Calma, A., Huseljic, D., Krempl, G., Sick, B.: Challenges of Reliable, Realistic and Comparable Active Learning Evaluation In: Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. bll 2–14 (2017).
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
@inproceedings{kottke2017challenges, author = {Kottke, Daniel and Calma, Adrian and Huseljic, Denis and Krempl, Georg and Sick, Bernhard}, booktitle = {Workshop on Interactive Adaptive Learning (IAL), ECML PKDD}, keywords = {imported}, pages = {2–14}, series = {CEUR Workshop Proceedings}, title = {Challenges of Reliable, Realistic and Comparable Active Learning Evaluation}, year = 2017 }
%0 Conference Paper %1 kottke2017challenges %A Kottke, Daniel %A Calma, Adrian %A Huseljic, Denis %A Krempl, Georg %A Sick, Bernhard %B Workshop on Interactive Adaptive Learning (IAL), ECML PKDD %D 2017 %P 2--14 %T Challenges of Reliable, Realistic and Comparable Active Learning Evaluation %U http://ceur-ws.org/Vol-1924/ialatecml_paper0.pdf
Calma, A.: Case Study on Pool-based Active Learning with Human Oracles In: Tomforde, S. en Sick, B. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2017. bll 39–49. kassel university press, Kassel, Germany (2017).
- [ BibTeX ]
- [ EndNote ]
@incollection{calma2017case, address = {Kassel, Germany}, author = {Calma, A.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2017}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {39–49}, publisher = {kassel university press}, title = {Case Study on Pool-based Active Learning with Human Oracles}, year = 2017 }
%0 Book Section %1 calma2017case %A Calma, A. %B Organic Computing -- Doctoral Dissertation Colloquium 2017 %C Kassel, Germany %D 2017 %E Tomforde, S. %E Sick, B. %I kassel university press %P 39--49 %T Case Study on Pool-based Active Learning with Human Oracles
Gruhl, C., Beer, F., Heck, H., Sick, B., Bühler, U., Wacker, A., Tomforde, S.: A Concept for Intelligent Collaborative Network Intrusion Detection In: International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. VDE (2017).
- [ BibTeX ]
- [ EndNote ]
@inproceedings{gruhl2017concept, author = {Gruhl, C. and Beer, F. and Heck, H. and Sick, B. and B{\"u}hler, U. and Wacker, A. and Tomforde, S.}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, keywords = {imported}, publisher = {VDE}, title = {A Concept for Intelligent Collaborative Network Intrusion Detection}, year = 2017 }
%0 Conference Paper %1 gruhl2017concept %A Gruhl, C. %A Beer, F. %A Heck, H. %A Sick, B. %A Bühler, U. %A Wacker, A. %A Tomforde, S. %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %D 2017 %I VDE %T A Concept for Intelligent Collaborative Network Intrusion Detection

2016[ to top ]

Zernetsch, S., Kohnen, S., Goldhammer, M., Doll, K., Sick, B.: Trajectory Prediction of Cyclists Using a Physical Model and an Artificial Neural Network In: IEEE Intelligent Vehicles Symposium (IV). bll 833–838. IEEE, Gothenburg, Sweden (2016). https://doi.org/10.1109/ivs.2016.7535484.
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@inproceedings{zernetsch2016trajectory, address = {Gothenburg, Sweden}, author = {Zernetsch, S. and Kohnen, S. and Goldhammer, M. and Doll, K. and Sick, B.}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, keywords = {imported}, pages = {833–838}, publisher = {IEEE}, title = {Trajectory Prediction of Cyclists Using a Physical Model and an {Artificial Neural Network}}, year = 2016 }
%0 Conference Paper %1 zernetsch2016trajectory %A Zernetsch, S. %A Kohnen, S. %A Goldhammer, M. %A Doll, K. %A Sick, B. %B IEEE Intelligent Vehicles Symposium (IV) %C Gothenburg, Sweden %D 2016 %I IEEE %P 833--838 %R 10.1109/ivs.2016.7535484 %T Trajectory Prediction of Cyclists Using a Physical Model and an Artificial Neural Network
Jänicke, M., Tomforde, S., Sick, B.: Towards Self-Improving Activity Recognition Systems based on Probabilistic, Generative Models In: Workshop on Self-Improving System Integration (SISSY), ICAC. bll 285–291. IEEE, Würzburg, Germany (2016). https://doi.org/10.1109/icac.2016.22.
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- [ EndNote ]
@inproceedings{jaenicke2016towards, address = {W{\"u}rzburg, Germany}, author = {J{\"a}nicke, M. and Tomforde, S. and Sick, B.}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ICAC}, keywords = {imported}, pages = {285–291}, publisher = {IEEE}, title = {Towards Self-Improving Activity Recognition Systems based on Probabilistic, Generative Models}, year = 2016 }
%0 Conference Paper %1 jaenicke2016towards %A Jänicke, M. %A Tomforde, S. %A Sick, B. %B Workshop on Self-Improving System Integration (SISSY), ICAC %C Würzburg, Germany %D 2016 %I IEEE %P 285--291 %R 10.1109/icac.2016.22 %T Towards Self-Improving Activity Recognition Systems based on Probabilistic, Generative Models
Heck, H., Wacker, A., Rudolph, S., Gruhl, C., Sick, B., Tomforde, S.: Towards Autonomous Self-tests at Runtime In: IEEE International Workshop on Quality Assurance for Self-Adaptive, Self-Organising Systems (QA4SASO), FAS*W. bll 98–99. IEEE (2016). https://doi.org/10.1109/fas-w.2016.32.
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- [ EndNote ]
@inproceedings{heck2016towards, author = {Heck, H. and Wacker, A. and Rudolph, S. and Gruhl, C. and Sick, B. and Tomforde, S.}, booktitle = {IEEE International Workshop on Quality Assurance for Self-Adaptive, Self-Organising Systems (QA4SASO), FAS*W}, keywords = {imported}, pages = {98–99}, publisher = {IEEE}, title = {Towards Autonomous Self-tests at Runtime}, year = 2016 }
%0 Conference Paper %1 heck2016towards %A Heck, H. %A Wacker, A. %A Rudolph, S. %A Gruhl, C. %A Sick, B. %A Tomforde, S. %B IEEE International Workshop on Quality Assurance for Self-Adaptive, Self-Organising Systems (QA4SASO), FAS*W %D 2016 %I IEEE %P 98--99 %R 10.1109/fas-w.2016.32 %T Towards Autonomous Self-tests at Runtime
Fisch, D., Gruhl, C., Kalkowski, E., Sick, B., Ovaska, S.J.: Towards Automation of Knowledge Understanding: An Approach for Probabilistic Generative Classifiers Information Sciences. 370--371, 476–496 (2016). https://doi.org/10.1016/j.ins.2016.08.016.
- [ BibTeX ]
- [ EndNote ]
@article{fisch2016towards, author = {Fisch, D. and Gruhl, C. and Kalkowski, E. and Sick, B. and Ovaska, S. J.}, journal = {Information Sciences}, keywords = {imported}, pages = {476–496}, publisher = {Elsevier}, title = {Towards Automation of Knowledge Understanding: An Approach for Probabilistic Generative Classifiers}, volume = {370–371}, year = 2016 }
%0 Journal Article %1 fisch2016towards %A Fisch, D. %A Gruhl, C. %A Kalkowski, E. %A Sick, B. %A Ovaska, S. J. %D 2016 %I Elsevier %J Information Sciences %P 476--496 %R 10.1016/j.ins.2016.08.016 %T Towards Automation of Knowledge Understanding: An Approach for Probabilistic Generative Classifiers %V 370--371
Lewis, P., Bellman, K., Botev, J., Hildmann, H., Marsh, S., Pitt, J., Scholtes, I., Tomforde, S.: Socially-Sensitive Systems Design , Dagstuhl, Germany (2016).
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@techreport{lewis2016socially, address = {Dagstuhl, Germany}, author = {Lewis, Peter and Bellman, Kirstie and Botev, Jean and Hildmann, Hanno and Marsh, Stephen and Pitt, Jeremy and Scholtes, Ingo and Tomforde, Sven}, keywords = {imported}, pages = {142–147}, title = {Socially-Sensitive Systems Design}, year = 2016 }
%0 Report %1 lewis2016socially %A Lewis, Peter %A Bellman, Kirstie %A Botev, Jean %A Hildmann, Hanno %A Marsh, Stephen %A Pitt, Jeremy %A Scholtes, Ingo %A Tomforde, Sven %C Dagstuhl, Germany %D 2016 %P 142--147 %T Socially-Sensitive Systems Design
Lewis, P., Bellman, K., Botev, J., Hildmann, H., Marsh, S., Pitt, J., Scholtes, I., Tomforde, S.: Socially-Sensitive Systems Design , Dagstuhl, Germany (2016).
- [ BibTeX ]
- [ EndNote ]
@techreport{lewis2016socially, address = {Dagstuhl, Germany}, author = {Lewis, Peter and Bellman, Kirstie and Botev, Jean and Hildmann, Hanno and Marsh, Stephen and Pitt, Jeremy and Scholtes, Ingo and Tomforde, Sven}, keywords = {imported}, pages = {142–147}, title = {Socially-Sensitive Systems Design}, year = 2016 }
%0 Report %1 lewis2016socially %A Lewis, Peter %A Bellman, Kirstie %A Botev, Jean %A Hildmann, Hanno %A Marsh, Stephen %A Pitt, Jeremy %A Scholtes, Ingo %A Tomforde, Sven %C Dagstuhl, Germany %D 2016 %P 142--147 %T Socially-Sensitive Systems Design
Reitmaier, T., Calma, A., Sick, B.: Semi-Supervised Active Learning for Support Vector Machines: A Novel Approach that Exploits Structure Information in Data arXiv e-prints. arXiv:1610.03995 (2016).
- [ BibTeX ]
- [ EndNote ]
@article{reitmaier2016semi, author = {{Reitmaier}, Tobias and {Calma}, Adrian and {Sick}, Bernhard}, journal = {arXiv e-prints}, keywords = {Machine}, pages = {arXiv:1610.03995}, title = {{Semi-Supervised Active Learning for Support Vector Machines: A Novel Approach that Exploits Structure Information in Data}}, year = 2016 }
%0 Journal Article %1 reitmaier2016semi %A Reitmaier, Tobias %A Calma, Adrian %A Sick, Bernhard %D 2016 %J arXiv e-prints %P arXiv:1610.03995 %T Semi-Supervised Active Learning for Support Vector Machines: A Novel Approach that Exploits Structure Information in Data
Calma, A., Reitmaier, T., Sick, B.: Resp-kNN: A probabilistic k-nearest neighbor classifier for sparsely labeled data In: International Joint Conference on Neural Networks (IJCNN). bll 4040–4047. IEEE, Vancouver, BC (2016). https://doi.org/10.1109/ijcnn.2016.7727725.
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- [ EndNote ]
@inproceedings{calma2016resp, address = {Vancouver, BC}, author = {Calma, A. and Reitmaier, T. and Sick, B.}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {imported}, pages = {4040–4047}, publisher = {IEEE}, title = {{Resp-kNN}: A probabilistic k-nearest neighbor classifier for sparsely labeled data}, year = 2016 }
%0 Conference Paper %1 calma2016resp %A Calma, A. %A Reitmaier, T. %A Sick, B. %B International Joint Conference on Neural Networks (IJCNN) %C Vancouver, BC %D 2016 %I IEEE %P 4040--4047 %R 10.1109/ijcnn.2016.7727725 %T Resp-kNN: A probabilistic k-nearest neighbor classifier for sparsely labeled data
Gruhl, C.: Probabilistic Obsoleteness Detection for Gaussian Mixture Models In: Tomforde, S. en Sick, B. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2016. bll 45–56. kassel university press, Kassel, Germany (2016).
- [ BibTeX ]
- [ EndNote ]
@incollection{gruhl2016probabilistic, address = {Kassel, Germany}, author = {Gruhl, C.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2016}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {45–56}, publisher = {kassel university press}, title = {Probabilistic Obsoleteness Detection for Gaussian Mixture Models}, year = 2016 }
%0 Book Section %1 gruhl2016probabilistic %A Gruhl, C. %B Organic Computing -- Doctoral Dissertation Colloquium 2016 %C Kassel, Germany %D 2016 %E Tomforde, S. %E Sick, B. %I kassel university press %P 45--56 %T Probabilistic Obsoleteness Detection for Gaussian Mixture Models
Kottke, D., Krempl, G., Stecklina, M., Styp von Rekowski, C., Sabsch, T., Pham Minh, T., Deliano, M., Spiliopoulou, M., Sick, B.: Probabilistic Active Learning for Active Class Selection In: Workshop on the Future of Interactive Learning Machines, NIPS. bll 1–9. , Barcelona, Spain (2016).
- [ BibTeX ]
- [ EndNote ]
@inproceedings{kottke2016probabilistic, address = {Barcelona, Spain}, author = {Kottke, D. and Krempl, G. and Stecklina, M. and {Styp von Rekowski}, C. and Sabsch, T. and Pham Minh, T. and Deliano, M. and Spiliopoulou, M. and Sick, B.}, booktitle = {Workshop on the Future of Interactive Learning Machines, NIPS}, keywords = {imported}, pages = {1–9}, title = {Probabilistic Active Learning for Active Class Selection}, year = 2016 }
%0 Conference Paper %1 kottke2016probabilistic %A Kottke, D. %A Krempl, G. %A Stecklina, M. %A Styp von Rekowski, C. %A Sabsch, T. %A Pham Minh, T. %A Deliano, M. %A Spiliopoulou, M. %A Sick, B. %B Workshop on the Future of Interactive Learning Machines, NIPS %C Barcelona, Spain %D 2016 %P 1--9 %T Probabilistic Active Learning for Active Class Selection
Calma, A.: Pals: Interactive Pool-based Active Learning System with Uncertain Oracles In: Sick, B. en Tomforde, S. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2016. bll 35–44. kassel university press, Kassel, Germany (2016).
- [ BibTeX ]
- [ EndNote ]
@incollection{calma2016pals, address = {Kassel, Germany}, author = {Calma, A.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2016}, editor = {Sick, B. and Tomforde, S.}, keywords = {imported}, pages = {35–44}, publisher = {kassel university press}, title = {Pals: Interactive Pool-based Active Learning System with Uncertain Oracles}, year = 2016 }
%0 Book Section %1 calma2016pals %A Calma, A. %B Organic Computing -- Doctoral Dissertation Colloquium 2016 %C Kassel, Germany %D 2016 %E Sick, B. %E Tomforde, S. %I kassel university press %P 35--44 %T Pals: Interactive Pool-based Active Learning System with Uncertain Oracles
Heck, H., Gruhl, C., Rudolph, S., Wacker, A., Sick, B., Hähner, J.: Multi-k-Resilience in Distributed Adaptive Cyber-Physical Systems In: International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll 1–8. VDE, Nuremberg, Germany (2016).
- [ BibTeX ]
- [ EndNote ]
@inproceedings{heck2016multi, address = {Nuremberg, Germany}, author = {Heck, H. and Gruhl, C. and Rudolph, S. and Wacker, A. and Sick, B. and H{\"a}hner, J.}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, keywords = {imported}, pages = {1–8}, publisher = {VDE}, title = {Multi-k-Resilience in Distributed Adaptive Cyber-Physical Systems}, year = 2016 }
%0 Conference Paper %1 heck2016multi %A Heck, H. %A Gruhl, C. %A Rudolph, S. %A Wacker, A. %A Sick, B. %A Hähner, J. %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %C Nuremberg, Germany %D 2016 %I VDE %P 1--8 %T Multi-k-Resilience in Distributed Adaptive Cyber-Physical Systems
Heck, H., Gruhl, C., Rudolph, S., Wacker, A., Sick, B., Hähner, J.: Multi-k-Resilience in Distributed Adaptive Cyber-Physical Systems In: International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll 1–8. VDE, Nuremberg, Germany (2016).
- [ BibTeX ]
- [ EndNote ]
@inproceedings{heck2016multi, address = {Nuremberg, Germany}, author = {Heck, H. and Gruhl, C. and Rudolph, S. and Wacker, A. and Sick, B. and H{\"a}hner, J.}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, keywords = {imported}, pages = {1–8}, publisher = {VDE}, title = {Multi-k-Resilience in Distributed Adaptive Cyber-Physical Systems}, year = 2016 }
%0 Conference Paper %1 heck2016multi %A Heck, H. %A Gruhl, C. %A Rudolph, S. %A Wacker, A. %A Sick, B. %A Hähner, J. %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %C Nuremberg, Germany %D 2016 %I VDE %P 1--8 %T Multi-k-Resilience in Distributed Adaptive Cyber-Physical Systems
Rudolph, S., Kantert, J., Jänen, U., Tomforde, S., Hähner, J., Müller-Schloer, C.: Measuring Self-Organisation Processes in Smart Camera Networks In: International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll 1–6. VDE, Nuremberg, Germany (2016).
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- [ EndNote ]
@inproceedings{rudolph2016measuring, address = {Nuremberg, Germany}, author = {Rudolph, S. and Kantert, J. and J{\"a}nen, U. and Tomforde, S. and H{\"a}hner, J. and M{\"u}ller-Schloer, C.}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, chapter = 14, keywords = {imported}, pages = {1–6}, publisher = {VDE}, title = {Measuring Self-Organisation Processes in Smart Camera Networks}, year = 2016 }
%0 Conference Paper %1 rudolph2016measuring %A Rudolph, S. %A Kantert, J. %A Jänen, U. %A Tomforde, S. %A Hähner, J. %A Müller-Schloer, C. %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %C Nuremberg, Germany %D 2016 %I VDE %P 1--6 %T Measuring Self-Organisation Processes in Smart Camera Networks %& 14
Bahle, G., Calma, A., Leimeister, J.M., Lukowicz, P., Oeste-Reiss, S., Reitmaier, T., Schmidt, A., Sick, B., Stumme, G., Zweig, K.A.: Lifelong Learning and Collaboration of Smart Technical Systems in Open-Ended Environments -- Opportunistic Collaborative Interactive Learning In: Workshop on Self-Improving System Integration (SISSY), ICAC. bll 1–10. IEEE, Würzburg, Germany (2016). https://doi.org/10.1109/icac.2016.36.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{bahle2016lifelong, address = {W{\"u}rzburg, Germany}, author = {Bahle, G. and Calma, A. and Leimeister, J. M. and Lukowicz, P. and Oeste-Rei\ss, S. and Reitmaier, T. and Schmidt, A. and Sick, B. and Stumme, G. and Zweig, K. A.}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ICAC}, keywords = {imported}, pages = {1–10}, publisher = {IEEE}, title = {Lifelong Learning and Collaboration of Smart Technical Systems in Open-Ended Environments – Opportunistic Collaborative Interactive Learning}, year = 2016 }
%0 Conference Paper %1 bahle2016lifelong %A Bahle, G. %A Calma, A. %A Leimeister, J. M. %A Lukowicz, P. %A Oeste-Reiss, S. %A Reitmaier, T. %A Schmidt, A. %A Sick, B. %A Stumme, G. %A Zweig, K. A. %B Workshop on Self-Improving System Integration (SISSY), ICAC %C Würzburg, Germany %D 2016 %I IEEE %P 1--10 %R 10.1109/icac.2016.36 %T Lifelong Learning and Collaboration of Smart Technical Systems in Open-Ended Environments -- Opportunistic Collaborative Interactive Learning
Lang, D., Kottke, D., Krempl, G., Spiliopoulou, M.: Investigating Exploratory Capabilities of Uncertainty Sampling using SVMs in Active Learning In: Workshop on Active Learning: Applications, Foundations and Emerging Trends, iKnow. bll 25–34. , Graz, Austria (2016).
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@inproceedings{lang2016investigating, address = {Graz, Austria}, author = {Lang, D. and Kottke, D. and Krempl, G. and Spiliopoulou, M.}, booktitle = {Workshop on Active Learning: Applications, Foundations and Emerging Trends, iKnow}, keywords = {imported}, pages = {25–34}, series = {{CEUR} Workshop Proceedings}, title = {Investigating Exploratory Capabilities of Uncertainty Sampling using {SVMs} in Active Learning}, year = 2016 }
%0 Conference Paper %1 lang2016investigating %A Lang, D. %A Kottke, D. %A Krempl, G. %A Spiliopoulou, M. %B Workshop on Active Learning: Applications, Foundations and Emerging Trends, iKnow %C Graz, Austria %D 2016 %P 25--34 %T Investigating Exploratory Capabilities of Uncertainty Sampling using SVMs in Active Learning %U http://ceur-ws.org/Vol-1707/alatiknow_paper4.pdf
Stein, A., Eymüller, C., Rauh, D., Tomforde, S., Hähner, J.: Interpolation-based Classifier Generation in XCSF In: IEEE Congress on Evolutionary Computation (CEC). bll 3990–3998. IEEE, Vancouver, BC (2016). https://doi.org/10.1109/cec.2016.7744296.
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@inproceedings{stein2016interpolation, address = {Vancouver, BC}, author = {Stein, A. and Eym{\"u}ller, C. and Rauh, D. and Tomforde, S. and H{\"a}hner, J.}, booktitle = {IEEE Congress on Evolutionary Computation (CEC)}, keywords = {imported}, pages = {3990–3998}, publisher = {IEEE}, title = {Interpolation-based Classifier Generation in {XCSF}}, year = 2016 }
%0 Conference Paper %1 stein2016interpolation %A Stein, A. %A Eymüller, C. %A Rauh, D. %A Tomforde, S. %A Hähner, J. %B IEEE Congress on Evolutionary Computation (CEC) %C Vancouver, BC %D 2016 %I IEEE %P 3990--3998 %R 10.1109/cec.2016.7744296 %T Interpolation-based Classifier Generation in XCSF
Diaconescu, A., Frey, S., Müller-Schloer, C., Pitt, J., Tomforde, S.: Goal-oriented Holonics for Complex System (Self-)Integration: Concepts and Case Studies In: IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO). bll 100–109. IEEE, Augsburg, Germany (2016). https://doi.org/10.1109/saso.2016.16.
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@inproceedings{diaconescu2016goal, address = {Augsburg, Germany}, author = {Diaconescu, A. and Frey, S. and M{\"u}ller-Schloer, C. and Pitt, J. and Tomforde, S.}, booktitle = {IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO)}, keywords = {imported}, pages = {100–109}, publisher = {IEEE}, title = {Goal-oriented Holonics for Complex System (Self-)Integration: Concepts and Case Studies}, year = 2016 }
%0 Conference Paper %1 diaconescu2016goal %A Diaconescu, A. %A Frey, S. %A Müller-Schloer, C. %A Pitt, J. %A Tomforde, S. %B IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO) %C Augsburg, Germany %D 2016 %I IEEE %P 100--109 %R 10.1109/saso.2016.16 %T Goal-oriented Holonics for Complex System (Self-)Integration: Concepts and Case Studies
Kalkowski, E., Sick, B.: Generative Exponential Smoothing and Generative ARMA Models to Forecast Time-Variant Rates or Probabilities In: International Work-Conference on Time Series (ITISE): Selected Contributions. bll 75–88. Springer, Cham, Switzerland (2016). https://doi.org/10.1007/978-3-319-28725-6_6.
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@inproceedings{kalkowski2016generative, address = {Cham, Switzerland}, author = {Kalkowski, E. and Sick, B.}, booktitle = {International Work-Conference on Time Series (ITISE): Selected Contributions}, keywords = {imported}, pages = {75–88}, publisher = {Springer}, title = {Generative Exponential Smoothing and Generative {ARMA} Models to Forecast Time-Variant Rates or Probabilities}, year = 2016 }
%0 Conference Paper %1 kalkowski2016generative %A Kalkowski, E. %A Sick, B. %B International Work-Conference on Time Series (ITISE): Selected Contributions %C Cham, Switzerland %D 2016 %I Springer %P 75--88 %R 10.1007/978-3-319-28725-6_6 %T Generative Exponential Smoothing and Generative ARMA Models to Forecast Time-Variant Rates or Probabilities
Calma, A., Leimeister, J.M., Lukowicz, P., Oeste-Reiß, S., Reitmaier, T., Schmidt, A., Sick, B., Stumme, G., Zweig, K.A.: From Active Learning to Dedicated Collaborative Interactive Learning In: International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll 1–8. VDE, Nuremberg, Germany (2016).
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@inproceedings{calma2016active, address = {Nuremberg, Germany}, author = {Calma, A. and Leimeister, J. M. and Lukowicz, P. and Oeste-Rei{\ss}, S. and Reitmaier, T. and Schmidt, A. and Sick, B. and Stumme, G. and Zweig, K. A.}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, keywords = {imported}, pages = {1–8}, publisher = {VDE}, title = {From Active Learning to Dedicated Collaborative Interactive Learning}, year = 2016 }
%0 Conference Paper %1 calma2016active %A Calma, A. %A Leimeister, J. M. %A Lukowicz, P. %A Oeste-Reiß, S. %A Reitmaier, T. %A Schmidt, A. %A Sick, B. %A Stumme, G. %A Zweig, K. A. %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %C Nuremberg, Germany %D 2016 %I VDE %P 1--8 %T From Active Learning to Dedicated Collaborative Interactive Learning
Gensler, A., Sick, B.: Forecasting Wind Power -- An Ensemble Technique With Gradual Coopetitive Weighting Based on Weather Situation In: International Joint Conference on Neural Networks (IJCNN). bll 4976–4984. IEEE, Vancouver, BC (2016). https://doi.org/10.1109/ijcnn.2016.7727855.
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@inproceedings{gensler2016forecasting, address = {Vancouver, BC}, author = {Gensler, A. and Sick, B.}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {imported}, pages = {4976–4984}, publisher = {IEEE}, title = {Forecasting Wind Power – An Ensemble Technique With Gradual Coopetitive Weighting Based on Weather Situation}, year = 2016 }
%0 Conference Paper %1 gensler2016forecasting %A Gensler, A. %A Sick, B. %B International Joint Conference on Neural Networks (IJCNN) %C Vancouver, BC %D 2016 %I IEEE %P 4976--4984 %R 10.1109/ijcnn.2016.7727855 %T Forecasting Wind Power -- An Ensemble Technique With Gradual Coopetitive Weighting Based on Weather Situation
Calma, A.: Exploit the Potential of the Group: Putting Humans in the Dedicated Collaborative Interactive Learning Loop In: Sick, B. en Tomforde, S. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2016. kassel university press, Kassel, Germany (2016).
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@incollection{calma2016exploit, address = {Kassel, Germany}, author = {Calma, A.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2016}, editor = {Sick, B. and Tomforde, S.}, keywords = {imported}, publisher = {kassel university press}, title = {Exploit the Potential of the Group: Putting Humans in the Dedicated Collaborative Interactive Learning Loop}, year = 2016 }
%0 Book Section %1 calma2016exploit %A Calma, A. %B Organic Computing -- Doctoral Dissertation Colloquium 2016 %C Kassel, Germany %D 2016 %E Sick, B. %E Tomforde, S. %I kassel university press %T Exploit the Potential of the Group: Putting Humans in the Dedicated Collaborative Interactive Learning Loop
Tomforde, S., Meier, D., Stein, A., von Mammen, S.: Distributed Resource Allocation as Co-Evolution Problem In: IEEE Congress on Evolutionary Computation (CEC). bll 1815–1822. IEEE, Vancouver, BC, Canada (2016). https://doi.org/10.1109/cec.2016.7744009.
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@inproceedings{tomforde2016distributed, address = {Vancouver, BC, Canada}, author = {Tomforde, S. and Meier, D. and Stein, A. and von Mammen, S.}, booktitle = {IEEE Congress on Evolutionary Computation (CEC)}, keywords = {imported}, pages = {1815–1822}, publisher = {IEEE}, title = {Distributed Resource Allocation as Co-Evolution Problem}, year = 2016 }
%0 Conference Paper %1 tomforde2016distributed %A Tomforde, S. %A Meier, D. %A Stein, A. %A von Mammen, S. %B IEEE Congress on Evolutionary Computation (CEC) %C Vancouver, BC, Canada %D 2016 %I IEEE %P 1815--1822 %R 10.1109/cec.2016.7744009 %T Distributed Resource Allocation as Co-Evolution Problem
Gensler, A., Henze, J., Sick, B., Raabe, N.: Deep Learning for Solar Power Forecasting -- An Approach using Autoencoder and LSTM Neural Networks In: IEEE International Conference on Systems, Man and Cybernetics (SMC). bll 2858–2865. IEEE, Budapest, Hungary (2016). https://doi.org/10.1109/smc.2016.7844673.
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@inproceedings{gensler2016deep, address = {Budapest, Hungary}, author = {Gensler, A. and Henze, J. and Sick, B. and Raabe, N.}, booktitle = {IEEE International Conference on Systems, Man and Cybernetics (SMC)}, keywords = {imported}, pages = {2858–2865}, publisher = {IEEE}, title = {Deep Learning for Solar Power Forecasting – An Approach using Autoencoder and {LSTM} Neural Networks}, year = 2016 }
%0 Conference Paper %1 gensler2016deep %A Gensler, A. %A Henze, J. %A Sick, B. %A Raabe, N. %B IEEE International Conference on Systems, Man and Cybernetics (SMC) %C Budapest, Hungary %D 2016 %I IEEE %P 2858--2865 %R 10.1109/smc.2016.7844673 %T Deep Learning for Solar Power Forecasting -- An Approach using Autoencoder and LSTM Neural Networks
Edenhofer, S., Tomforde, S., Fischer, D., Hähner, J., Menzel, F., von Mammen, S.: Decentralised Trust-Management Inspired by Ant Pheromones International Journal of Mobile Network Design and Innovation. 7, 46–55 (2016). https://doi.org/10.1504/ijmndi.2017.082804.
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@article{edenhofer2016decentralised, author = {Edenhofer, S. and Tomforde, S. and Fischer, D. and H{\"a}hner, J. and Menzel, F. and von Mammen, S.}, journal = {International Journal of Mobile Network Design and Innovation}, keywords = {imported}, number = 1, pages = {46–55}, publisher = {Inderscience}, title = {Decentralised Trust-Management Inspired by Ant Pheromones}, volume = 7, year = 2016 }
%0 Journal Article %1 edenhofer2016decentralised %A Edenhofer, S. %A Tomforde, S. %A Fischer, D. %A Hähner, J. %A Menzel, F. %A von Mammen, S. %D 2016 %I Inderscience %J International Journal of Mobile Network Design and Innovation %N 1 %P 46--55 %R 10.1504/ijmndi.2017.082804 %T Decentralised Trust-Management Inspired by Ant Pheromones %V 7
Stein, A., Tomforde, S., Rauh, D., Hähner, J.: Dealing with Unforeseen Situations in the Context of Self-Adaptive Urban Traffic Control: How to Bridge the Gap In: IEEE International Conference on Autonomic Computing (ICAC). bll 167–172. IEEE, Würzburg, Germany (2016). https://doi.org/10.1109/icac.2016.20.
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@inproceedings{stein2016dealing, address = {W{\"u}rzburg, Germany}, author = {Stein, A. and Tomforde, S. and Rauh, D. and H{\"a}hner, J.}, booktitle = {IEEE International Conference on Autonomic Computing (ICAC)}, keywords = {imported}, pages = {167–172}, publisher = {IEEE}, title = {Dealing with Unforeseen Situations in the Context of Self-Adaptive Urban Traffic Control: How to Bridge the Gap}, year = 2016 }
%0 Conference Paper %1 stein2016dealing %A Stein, A. %A Tomforde, S. %A Rauh, D. %A Hähner, J. %B IEEE International Conference on Autonomic Computing (ICAC) %C Würzburg, Germany %D 2016 %I IEEE %P 167--172 %R 10.1109/icac.2016.20 %T Dealing with Unforeseen Situations in the Context of Self-Adaptive Urban Traffic Control: How to Bridge the Gap
Kantert, J., Tomforde, S., Weber, S., Müller-Schloer, C.: Coverage-guided Intelligent Test Loop -- A Concept for Applying Instrumented Testing to Self-organising Systems In: International Conference on Informatics in Control, Automation and Robotics (ICINCO). bll 221–226. , Lisbon, Portugal (2016). https://doi.org/10.5220/0005992702210226.
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@inproceedings{kantert2016coverage, address = {Lisbon, Portugal}, author = {Kantert, J. and Tomforde, S. and Weber, S. and M{\"u}ller-Schloer, C.}, booktitle = {International Conference on Informatics in Control, Automation and Robotics (ICINCO)}, keywords = {imported}, pages = {221–226}, title = {Coverage-guided Intelligent Test Loop – A Concept for Applying Instrumented Testing to Self-organising Systems}, year = 2016 }
%0 Conference Paper %1 kantert2016coverage %A Kantert, J. %A Tomforde, S. %A Weber, S. %A Müller-Schloer, C. %B International Conference on Informatics in Control, Automation and Robotics (ICINCO) %C Lisbon, Portugal %D 2016 %P 221--226 %R 10.5220/0005992702210226 %T Coverage-guided Intelligent Test Loop -- A Concept for Applying Instrumented Testing to Self-organising Systems
Kalkowski, E., Sick, B.: Correlation of Ontology-Based Semantic Similarity and Human Judgement for a Domain Specific Fashion Ontology In: International Conference on Web Engineering (ICWE). bll 207–224. Springer (2016). https://doi.org/10.1007/978-3-319-38791-8_12.
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@inproceedings{kalkowski2016correlation, author = {Kalkowski, E. and Sick, B.}, booktitle = {International Conference on Web Engineering (ICWE)}, keywords = {imported}, pages = {207–224}, publisher = {Springer}, title = {Correlation of Ontology-Based Semantic Similarity and Human Judgement for a Domain Specific Fashion Ontology}, year = 2016 }
%0 Conference Paper %1 kalkowski2016correlation %A Kalkowski, E. %A Sick, B. %B International Conference on Web Engineering (ICWE) %D 2016 %I Springer %P 207--224 %R 10.1007/978-3-319-38791-8_12 %T Correlation of Ontology-Based Semantic Similarity and Human Judgement for a Domain Specific Fashion Ontology
Kreil, M., Sick, B., Lukowicz, P.: Coping with variability in motion based activity recognition In: International Workshop on Sensor-based Activity Recognition and Interaction (iWOAR). bll 1–8. , Rostock, Germany (2016). https://doi.org/10.1145/2948963.2948967.
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@inproceedings{kreil2016coping, address = {Rostock, Germany}, author = {Kreil, M. and Sick, B. and Lukowicz, P.}, booktitle = {International Workshop on Sensor-based Activity Recognition and Interaction (iWOAR)}, keywords = {imported}, pages = {1–8}, title = {Coping with variability in motion based activity recognition}, year = 2016 }
%0 Conference Paper %1 kreil2016coping %A Kreil, M. %A Sick, B. %A Lukowicz, P. %B International Workshop on Sensor-based Activity Recognition and Interaction (iWOAR) %C Rostock, Germany %D 2016 %P 1--8 %R 10.1145/2948963.2948967 %T Coping with variability in motion based activity recognition
Kantert, J., Tomforde, S., Kauder, M., Scharrer, R., Edenhofer, S., Hähner, J., Müller-Schloer, C.: Controlling Negative Emergent Behavior by Graph Analysis at Runtime ACM Transactions on Autonomous and Adaptive Systems. 11, 7 (2016). https://doi.org/10.1145/2890507.
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@article{kantert2016controlling, author = {Kantert, J. and Tomforde, S. and Kauder, M. and Scharrer, R. and Edenhofer, S. and H{\"a}hner, J. and M{\"u}ller-Schloer, C.}, journal = {ACM Transactions on Autonomous and Adaptive Systems}, keywords = {imported}, number = 2, pages = 7, publisher = {ACM}, title = {Controlling Negative Emergent Behavior by Graph Analysis at Runtime}, volume = 11, year = 2016 }
%0 Journal Article %1 kantert2016controlling %A Kantert, J. %A Tomforde, S. %A Kauder, M. %A Scharrer, R. %A Edenhofer, S. %A Hähner, J. %A Müller-Schloer, C. %D 2016 %I ACM %J ACM Transactions on Autonomous and Adaptive Systems %N 2 %P 7 %R 10.1145/2890507 %T Controlling Negative Emergent Behavior by Graph Analysis at Runtime %V 11
Kantert, J., Reinhard, F., von Zengen, G., Tomforde, S., Weber, S., Wolf, L., Müller-Schloer, C.: Combining Trust and ETX to Provide Robust Wireless Sensor Networks In: International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll 1–7. VDE, Nuremberg, Germany (2016).
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@inproceedings{kantert2016combining, address = {Nuremberg, Germany}, author = {Kantert, J. and Reinhard, F. and von Zengen, G. and Tomforde, S. and Weber, S. and Wolf, L. and M{\"u}ller-Schloer, C.}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, chapter = 16, keywords = {imported}, pages = {1–7}, publisher = {VDE}, title = {Combining Trust and {ETX} to Provide Robust Wireless Sensor Networks}, year = 2016 }
%0 Conference Paper %1 kantert2016combining %A Kantert, J. %A Reinhard, F. %A von Zengen, G. %A Tomforde, S. %A Weber, S. %A Wolf, L. %A Müller-Schloer, C. %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %C Nuremberg, Germany %D 2016 %I VDE %P 1--7 %T Combining Trust and ETX to Provide Robust Wireless Sensor Networks %& 16
Breker, S., Sick, B.: Combinations of uncertain ordinal expert statements: The combination rule EIDMR and its application to low-voltage grid classification with SVM In: International Joint Conference on Neural Networks (IJCNN). bll 2164–2173. IEEE, Vancouver, BC (2016). https://doi.org/10.1109/ijcnn.2016.7727467.
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@inproceedings{breker2016combinations, address = {Vancouver, BC}, author = {Breker, S. and Sick, B.}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {imported}, pages = {2164–2173}, publisher = {IEEE}, title = {Combinations of uncertain ordinal expert statements: The combination rule {EIDMR} and its application to low-voltage grid classification with {SVM}}, year = 2016 }
%0 Conference Paper %1 breker2016combinations %A Breker, S. %A Sick, B. %B International Joint Conference on Neural Networks (IJCNN) %C Vancouver, BC %D 2016 %I IEEE %P 2164--2173 %R 10.1109/ijcnn.2016.7727467 %T Combinations of uncertain ordinal expert statements: The combination rule EIDMR and its application to low-voltage grid classification with SVM
Hähner, J., Tomforde, S.: Cellular Traffic Offloading through Network-Assisted Ad-Hoc Routing in Cellular Networks In: IEEE Symposium on Computers and Communications (ISCC). bll 469–476. IEEE, Messina, Italy (2016). https://doi.org/10.1109/iscc.2016.7543783.
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@inproceedings{haehner2016cellular, address = {Messina, Italy}, author = {H{\"a}hner, J. and Tomforde, S.}, booktitle = {IEEE Symposium on Computers and Communications (ISCC)}, keywords = {imported}, pages = {469–476}, publisher = {IEEE}, title = {Cellular Traffic Offloading through Network-Assisted Ad-Hoc Routing in Cellular Networks}, year = 2016 }
%0 Conference Paper %1 haehner2016cellular %A Hähner, J. %A Tomforde, S. %B IEEE Symposium on Computers and Communications (ISCC) %C Messina, Italy %D 2016 %I IEEE %P 469--476 %R 10.1109/iscc.2016.7543783 %T Cellular Traffic Offloading through Network-Assisted Ad-Hoc Routing in Cellular Networks
Stein, A., Rauh, D., Tomforde, S., Hähner, J.: Augmenting the Algorithmic Structure of XCS by Means of Interpolation In: International Conference on Architecture of Computing Systems (ARCS). bll 348–360. Springer, Nuremberg, Germany (2016). https://doi.org/10.1007/978-3-319-30695-7_26.
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@inproceedings{stein2016augmenting, address = {Nuremberg, Germany}, author = {Stein, A. and Rauh, D. and Tomforde, S. and H{\"a}hner, J.}, booktitle = {International Conference on Architecture of Computing Systems (ARCS)}, keywords = {imported}, pages = {348–360}, publisher = {Springer}, title = {Augmenting the Algorithmic Structure of {XCS} by Means of Interpolation}, year = 2016 }
%0 Conference Paper %1 stein2016augmenting %A Stein, A. %A Rauh, D. %A Tomforde, S. %A Hähner, J. %B International Conference on Architecture of Computing Systems (ARCS) %C Nuremberg, Germany %D 2016 %I Springer %P 348--360 %R 10.1007/978-3-319-30695-7_26 %T Augmenting the Algorithmic Structure of XCS by Means of Interpolation
Pirkl, G., Hevesi, P., Lukowicz, P., Klein, P., Heisel, C., Gröber, S., Kuhn, J., Sick, B.: Any Problems? A wearable sensor-based platform for representational learning-analytics In: ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp). bll 353–356. ACM, Heidelberg, Germany (2016). https://doi.org/10.1145/2968219.2971383.
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@inproceedings{pirkl2016any, address = {Heidelberg, Germany}, author = {Pirkl, G. and Hevesi, P. and Lukowicz, P. and Klein, P. and Heisel, C. and Gr{\"o}ber, S. and Kuhn, J. and Sick, B.}, booktitle = {ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp)}, keywords = {imported}, pages = {353–356}, publisher = {ACM}, title = {Any Problems? {A} wearable sensor-based platform for representational learning-analytics}, year = 2016 }
%0 Conference Paper %1 pirkl2016any %A Pirkl, G. %A Hevesi, P. %A Lukowicz, P. %A Klein, P. %A Heisel, C. %A Gröber, S. %A Kuhn, J. %A Sick, B. %B ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) %C Heidelberg, Germany %D 2016 %I ACM %P 353--356 %R 10.1145/2968219.2971383 %T Any Problems? A wearable sensor-based platform for representational learning-analytics
Tomforde, S., Rudolph, S., Bellman, K., Würtz, R.P.: An Organic Computing Perspective on Self-Improving System Interwearing at Runtime In: Workshop on Self-Improving System Integration (SISSY), ICAC. bll 276–284. IEEE, Würzburg, Germany (2016). https://doi.org/10.1109/icac.2016.15.
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@inproceedings{tomforde2016organic, address = {W{\"u}rzburg, Germany}, author = {Tomforde, S. and Rudolph, S. and Bellman, K. and W{\"u}rtz, R. P.}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ICAC}, keywords = {imported}, pages = {276–284}, publisher = {IEEE}, title = {An {Organic Computing} Perspective on Self-Improving System Interwearing at Runtime}, year = 2016 }
%0 Conference Paper %1 tomforde2016organic %A Tomforde, S. %A Rudolph, S. %A Bellman, K. %A Würtz, R. P. %B Workshop on Self-Improving System Integration (SISSY), ICAC %C Würzburg, Germany %D 2016 %I IEEE %P 276--284 %R 10.1109/icac.2016.15 %T An Organic Computing Perspective on Self-Improving System Interwearing at Runtime
von Mammen, S., Tomforde, S., Hähner, J.: An Organic Computing Approach to Self-organising Robot Ensembles Frontiers in Robotics and AI. 3, 67 (2016). https://doi.org/10.3389/frobt.2016.00067.
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@article{mammen2016organic, author = {von Mammen, S. and Tomforde, S. and H{\"a}hner, J.}, journal = {Frontiers in Robotics and AI}, keywords = {imported}, pages = 67, publisher = {Frontiers}, title = {An Organic Computing Approach to Self-organising Robot Ensembles}, volume = 3, year = 2016 }
%0 Journal Article %1 mammen2016organic %A von Mammen, S. %A Tomforde, S. %A Hähner, J. %D 2016 %I Frontiers %J Frontiers in Robotics and AI %P 67 %R 10.3389/frobt.2016.00067 %T An Organic Computing Approach to Self-organising Robot Ensembles %V 3
Kantert, J., Reinbold, C., Tomforde, S., Müller-Schloer, C.: An Evaluation of Two Trust-based Autonomic/Organic Grid Computing Systems for Volunteer-Based Distributed Rendering In: IEEE International Conference on Autonomic Computing (ICAC). bll 137–146. IEEE, Würzburg, Germany (2016). https://doi.org/10.1109/icac.2016.55.
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@inproceedings{kantert2016evaluation, address = {W{\"u}rzburg, Germany}, author = {Kantert, J. and Reinbold, C. and Tomforde, S. and M{\"u}ller-Schloer, C.}, booktitle = {IEEE International Conference on Autonomic Computing (ICAC)}, keywords = {imported}, pages = {137–146}, publisher = {IEEE}, title = {An Evaluation of Two Trust-based {Autonomic/Organic Grid Computing Systems} for Volunteer-Based Distributed Rendering}, year = 2016 }
%0 Conference Paper %1 kantert2016evaluation %A Kantert, J. %A Reinbold, C. %A Tomforde, S. %A Müller-Schloer, C. %B IEEE International Conference on Autonomic Computing (ICAC) %C Würzburg, Germany %D 2016 %I IEEE %P 137--146 %R 10.1109/icac.2016.55 %T An Evaluation of Two Trust-based Autonomic/Organic Grid Computing Systems for Volunteer-Based Distributed Rendering
Gensler, A., Sick, B., Pankraz, V.: An Analog Ensemble-Based Similarity Search Technique for Solar Power Forecasting In: IEEE International Conference on Systems, Man and Cybernetics (SMC). bll 2850–2857. IEEE (2016). https://doi.org/10.1109/SMC.2016.7844672.
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@inproceedings{gensler2016analog, author = {Gensler, A. and Sick, B. and Pankraz, V.}, booktitle = {IEEE International Conference on Systems, Man and Cybernetics (SMC)}, keywords = {imported}, pages = {2850–2857}, publisher = {IEEE}, title = {An Analog Ensemble-Based Similarity Search Technique for Solar Power Forecasting}, year = 2016 }
%0 Conference Paper %1 gensler2016analog %A Gensler, A. %A Sick, B. %A Pankraz, V. %B IEEE International Conference on Systems, Man and Cybernetics (SMC) %D 2016 %I IEEE %P 2850--2857 %R 10.1109/SMC.2016.7844672 %T An Analog Ensemble-Based Similarity Search Technique for Solar Power Forecasting
Krempl, G., Lemaire, V., Lughofer, E., Kottke, D. reds: Active Learning: Applications, Foundations and Emerging Trends, iKNOW , Graz, Austria (2016).
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
@proceedings{krempl2016active, address = {Graz, Austria}, editor = {Krempl, G. and Lemaire, V. and Lughofer, E. and Kottke, D.}, keywords = {imported}, series = {{CEUR} Workshop Proceedings}, title = {Active Learning: Applications, Foundations and Emerging Trends, {iKNOW}}, volume = 1707, year = 2016 }
%0 Conference Proceedings %1 krempl2016active %B CEUR Workshop Proceedings %C Graz, Austria %D 2016 %E Krempl, G. %E Lemaire, V. %E Lughofer, E. %E Kottke, D. %T Active Learning: Applications, Foundations and Emerging Trends, iKNOW %U http://ceur-ws.org/Vol-1707/ %V 1707
Gensler, A., Sick, B., Vogt, S.: A Review of Deterministic Error Scores and Normalization Techniques for Power Forecasting Algorithms In: IEEE Symposium Series on Computational Intelligence (SSCI). bll 1–9. IEEE, Athens, Greece (2016). https://doi.org/10.1109/ssci.2016.7849848.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{gensler2016review, address = {Athens, Greece}, author = {Gensler, A. and Sick, B. and Vogt, S.}, booktitle = {IEEE Symposium Series on Computational Intelligence (SSCI)}, keywords = {imported}, pages = {1–9}, publisher = {IEEE}, title = {A Review of Deterministic Error Scores and Normalization Techniques for Power Forecasting Algorithms}, year = 2016 }
%0 Conference Paper %1 gensler2016review %A Gensler, A. %A Sick, B. %A Vogt, S. %B IEEE Symposium Series on Computational Intelligence (SSCI) %C Athens, Greece %D 2016 %I IEEE %P 1--9 %R 10.1109/ssci.2016.7849848 %T A Review of Deterministic Error Scores and Normalization Techniques for Power Forecasting Algorithms
Hähner, J., von Mammen, S., Timpf, S., Tomforde, S., Sick, B., Geihs, K., Goeble, T., Hornung, G., Stumme, G.: "Know thyselves" -- Computational Self-Reflection in Collective Technical Systems In: International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll 1–8. VDE, Nuremberg, Germany (2016).
- [ BibTeX ]
- [ EndNote ]
@inproceedings{haehner2016know, address = {Nuremberg, Germany}, author = {H{\"a}hner, J. and von Mammen, S. and Timpf, S. and Tomforde, S. and Sick, B. and Geihs, K. and Goeble, T. and Hornung, G. and Stumme, G.}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, keywords = {imported}, pages = {1–8}, publisher = {VDE}, title = {"{K}now thyselves" – Computational Self-Reflection in Collective Technical Systems}, year = 2016 }
%0 Conference Paper %1 haehner2016know %A Hähner, J. %A von Mammen, S. %A Timpf, S. %A Tomforde, S. %A Sick, B. %A Geihs, K. %A Goeble, T. %A Hornung, G. %A Stumme, G. %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %C Nuremberg, Germany %D 2016 %I VDE %P 1--8 %T "Know thyselves" -- Computational Self-Reflection in Collective Technical Systems

2015[ to top ]

Reitmaier, T., Calma, A., Sick, B.: Transductive active learning -- A new semi-supervised learning approach based on iteratively refined generative models to capture structure in data Information Sciences. 293, 275–298 (2015). https://doi.org/10.1016/j.ins.2014.09.009.
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- [ EndNote ]
@article{reitmaier2015transductive, author = {Reitmaier, T. and Calma, A. and Sick, B.}, journal = {Information Sciences}, keywords = {imported}, pages = {275–298}, publisher = {Elsevier}, title = {Transductive active learning – A new semi-supervised learning approach based on iteratively refined generative models to capture structure in data}, volume = 293, year = 2015 }
%0 Journal Article %1 reitmaier2015transductive %A Reitmaier, T. %A Calma, A. %A Sick, B. %D 2015 %I Elsevier %J Information Sciences %P 275--298 %R 10.1016/j.ins.2014.09.009 %T Transductive active learning -- A new semi-supervised learning approach based on iteratively refined generative models to capture structure in data %V 293
Goldhammer, M., Köhler, S., Doll, K., Sick, B.: Track-Based Forecasting of Pedestrian Behavior by Polynomial Approximation and Multilayer Perceptions In: SAI Intelligent Systems Conference (IntelliSys). bll 259–279. Springer, Cham, Switzerland (2015). https://doi.org/10.1007/978-3-319-33386-1_13.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{goldhammer2015track, address = {Cham, Switzerland}, author = {Goldhammer, M. and K{\"o}hler, S. and Doll, K. and Sick, B.}, booktitle = {SAI Intelligent Systems Conference (IntelliSys)}, keywords = {imported}, pages = {259–279}, publisher = {Springer}, series = {Studies in Computational Intelligence}, title = {Track-Based Forecasting of Pedestrian Behavior by Polynomial Approximation and Multilayer Perceptions}, year = 2015 }
%0 Conference Paper %1 goldhammer2015track %A Goldhammer, M. %A Köhler, S. %A Doll, K. %A Sick, B. %B SAI Intelligent Systems Conference (IntelliSys) %C Cham, Switzerland %D 2015 %I Springer %P 259--279 %R 10.1007/978-3-319-33386-1_13 %T Track-Based Forecasting of Pedestrian Behavior by Polynomial Approximation and Multilayer Perceptions
Reitmaier, T., Sick, B.: The Responsibility Weighted Mahalanobis Kernel for Semi-Supervised Training of Support Vector Machines for Classification Information Sciences. 323, 179–198 (2015). https://doi.org/10.1016/j.ins.2015.06.027.
- [ BibTeX ]
- [ EndNote ]
@article{reitmaier2015responsibility, author = {Reitmaier, T. and Sick, B.}, journal = {Information Sciences}, keywords = {imported}, pages = {179–198}, publisher = {Elsevier}, title = {The Responsibility Weighted {M}ahalanobis Kernel for Semi-Supervised Training of Support Vector Machines for Classification}, volume = 323, year = 2015 }
%0 Journal Article %1 reitmaier2015responsibility %A Reitmaier, T. %A Sick, B. %D 2015 %I Elsevier %J Information Sciences %P 179--198 %R 10.1016/j.ins.2015.06.027 %T The Responsibility Weighted Mahalanobis Kernel for Semi-Supervised Training of Support Vector Machines for Classification %V 323
Jänicke, M.: Self-adapting Multi-Sensor System Using Classifiers Based on Gaussian Mixture Models In: Tomforde, S. en Sick, B. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2015. bll 109–120. kassel university press, Kassel, Germany (2015).
- [ BibTeX ]
- [ EndNote ]
@incollection{jaenicke2015self, address = {Kassel, Germany}, author = {J{\"a}nicke, M.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2015}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {109–120}, publisher = {kassel university press}, title = {Self-adapting Multi-Sensor System Using Classifiers Based on Gaussian Mixture Models}, year = 2015 }
%0 Book Section %1 jaenicke2015self %A Jänicke, M. %B Organic Computing -- Doctoral Dissertation Colloquium 2015 %C Kassel, Germany %D 2015 %E Tomforde, S. %E Sick, B. %I kassel university press %P 109--120 %T Self-adapting Multi-Sensor System Using Classifiers Based on Gaussian Mixture Models
Hähner, J., Brinkschulte, U., Lukowicz, P., Mostaghim, S., Sick, B., Tomforde, S.: Runtime Self-Integration as Key Challenge for Mastering Interwoven Systems In: International Conference on Architecture of Computing Systems (ARCS). bll 1–8. VDE, Porto, Portugal (2015).
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
@inproceedings{haehner2015runtime, address = {Porto, Portugal}, author = {H{\"a}hner, J. and Brinkschulte, U. and Lukowicz, P. and Mostaghim, S. and Sick, B. and Tomforde, S.}, booktitle = {International Conference on Architecture of Computing Systems (ARCS)}, keywords = {imported}, pages = {1–8}, publisher = {VDE}, title = {Runtime Self-Integration as Key Challenge for Mastering Interwoven Systems}, year = 2015 }
%0 Conference Paper %1 haehner2015runtime %A Hähner, J. %A Brinkschulte, U. %A Lukowicz, P. %A Mostaghim, S. %A Sick, B. %A Tomforde, S. %B International Conference on Architecture of Computing Systems (ARCS) %C Porto, Portugal %D 2015 %I VDE %P 1--8 %T Runtime Self-Integration as Key Challenge for Mastering Interwoven Systems %U https://ieeexplore.ieee.org/document/7107107
Tomforde, S., Sick, B. reds: Organic Computing -- Doctoral Dissertation Colloquium 2015 kassel university press (2015). https://doi.org/10.19211/KUP9783737600293.
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- [ EndNote ]
@book{tomforde2015organic, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, publisher = {kassel university press}, series = {Intelligent Embedded Systems}, title = {Organic Computing – Doctoral Dissertation Colloquium 2015}, volume = 7, year = 2015 }
%0 Book %1 tomforde2015organic %B Intelligent Embedded Systems %D 2015 %E Tomforde, S. %E Sick, B. %I kassel university press %R 10.19211/KUP9783737600293 %T Organic Computing -- Doctoral Dissertation Colloquium 2015 %V 7
Heck, H., Edenhofer, S., Gruhl, C., Lund, A., Shuka, R., Hähner, J.: On the Application Possibilities of Organic Computing Principles in Socio-technical Systems In: Tomforde, S. en Sick, B. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2015. bll 165–170. kassel university press, Kassel, Germany (2015).
- [ BibTeX ]
- [ EndNote ]
@incollection{heck2015application, address = {Kassel, Germany}, author = {Heck, H. and Edenhofer, S. and Gruhl, C. and Lund, A. and Shuka, R. and H{\"a}hner, J.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2015}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {165–170}, publisher = {kassel university press}, title = {On the Application Possibilities of Organic Computing Principles in Socio-technical Systems}, year = 2015 }
%0 Book Section %1 heck2015application %A Heck, H. %A Edenhofer, S. %A Gruhl, C. %A Lund, A. %A Shuka, R. %A Hähner, J. %B Organic Computing -- Doctoral Dissertation Colloquium 2015 %C Kassel, Germany %D 2015 %E Tomforde, S. %E Sick, B. %I kassel university press %P 165--170 %T On the Application Possibilities of Organic Computing Principles in Socio-technical Systems
Breker, S.: Klassifikation von Niederspannungsnetzen mit Support Vector Machines: Bewertung des Aufnahmevermögens für Dezentrale Erzeugungsanlagen, (2015).
- [ BibTeX ]
- [ EndNote ]
@phdthesis{breker2015klassifikation, address = {Kassel, Germany}, author = {Breker, S.}, keywords = {imported}, number = 6, pages = {1–170}, publisher = {kassel university press}, school = {University of Kassel}, series = {Intelligent Embedded Systems}, title = {Klassifikation von Niederspannungsnetzen mit Support Vector Machines: Bewertung des Aufnahmeverm{\"o}gens f{\"u}r Dezentrale Erzeugungsanlagen}, year = 2015 }
%0 Thesis %1 breker2015klassifikation %A Breker, S. %B Intelligent Embedded Systems %C Kassel, Germany %D 2015 %I kassel university press %N 6 %P 1--170 %T Klassifikation von Niederspannungsnetzen mit Support Vector Machines: Bewertung des Aufnahmevermögens für Dezentrale Erzeugungsanlagen
Calma, A., Jänicke, M., Kantert, J., Kopal, N., Siefert, F., Tomforde, S.: Horizontal Integration of Organic Computing and Control Theory Concepts In: Tomforde, S. en Sick, B. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2015. bll 157–164. kassel university press, Kassel, Germany (2015).
- [ BibTeX ]
- [ EndNote ]
@incollection{calma2015horizontal, address = {Kassel, Germany}, author = {Calma, A. and J{\"a}nicke, M. and Kantert, J. and Kopal, N. and Siefert, F. and Tomforde, S.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2015}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {157–164}, publisher = {kassel university press}, title = {Horizontal Integration of Organic Computing and Control Theory Concepts}, year = 2015 }
%0 Book Section %1 calma2015horizontal %A Calma, A. %A Jänicke, M. %A Kantert, J. %A Kopal, N. %A Siefert, F. %A Tomforde, S. %B Organic Computing -- Doctoral Dissertation Colloquium 2015 %C Kassel, Germany %D 2015 %E Tomforde, S. %E Sick, B. %I kassel university press %P 157--164 %T Horizontal Integration of Organic Computing and Control Theory Concepts
Kalkowski, E., Sick, B.: Generative Exponential Smoothing Models for Rate Forecasting with Uncertainty Estimation In: International Work-Conference on Time Series (ITISE). bll 806–817. , Granada, Spain (2015).
- [ BibTeX ]
- [ EndNote ]
@inproceedings{kalkowski2015generative, address = {Granada, Spain}, author = {Kalkowski, E. and Sick, B.}, booktitle = {International Work-Conference on Time Series (ITISE)}, keywords = {imported}, pages = {806–817}, title = {Generative Exponential Smoothing Models for Rate Forecasting with Uncertainty Estimation}, year = 2015 }
%0 Conference Paper %1 kalkowski2015generative %A Kalkowski, E. %A Sick, B. %B International Work-Conference on Time Series (ITISE) %C Granada, Spain %D 2015 %P 806--817 %T Generative Exponential Smoothing Models for Rate Forecasting with Uncertainty Estimation
Gensler, A., Gruber, T., Sick, B.: Fast Feature Extraction for Time Series Analysis Using Least-squares Approximations with Orthogonal Basis Functions In: International Symposium on Temporal Representation and Reasoning (TIME). bll 29–37. IEEE, Kassel, Germany (2015). https://doi.org/10.1109/time.2015.21.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{gensler2015fast, address = {Kassel, Germany}, author = {Gensler, A. and Gruber, T. and Sick, B.}, booktitle = {International Symposium on Temporal Representation and Reasoning (TIME)}, keywords = {imported}, pages = {29–37}, publisher = {IEEE}, title = {Fast Feature Extraction for Time Series Analysis Using Least-squares Approximations with Orthogonal Basis Functions}, year = 2015 }
%0 Conference Paper %1 gensler2015fast %A Gensler, A. %A Gruber, T. %A Sick, B. %B International Symposium on Temporal Representation and Reasoning (TIME) %C Kassel, Germany %D 2015 %I IEEE %P 29--37 %R 10.1109/time.2015.21 %T Fast Feature Extraction for Time Series Analysis Using Least-squares Approximations with Orthogonal Basis Functions
Breker, S., Sick, B.: Effiziente Bewertung des Anschlu\ss{}potentials von Niederspannungsnetzen für dezentrale Erzeugungsanlagen: Klassifikation mit Methoden der Computational Intelligence In: Tagung Nachhaltige Energieversorgung und Integration von Speichern (NEIS). bll 51–56. , Hamburg, Germany (2015). https://doi.org/10.1007/978-3-658-10958-5_8.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{breker2015effiziente, address = {Hamburg, Germany}, author = {Breker, S. and Sick, B.}, booktitle = {Tagung Nachhaltige Energieversorgung und Integration von Speichern (NEIS)}, keywords = {imported}, pages = {51–56}, title = {{Effiziente Bewertung des Anschlu\ss{}potentials von Niederspannungsnetzen f{\"u}r dezentrale Erzeugungsanlagen: Klassifikation mit Methoden der Computational Intelligence}}, year = 2015 }
%0 Conference Paper %1 breker2015effiziente %A Breker, S. %A Sick, B. %B Tagung Nachhaltige Energieversorgung und Integration von Speichern (NEIS) %C Hamburg, Germany %D 2015 %P 51--56 %R 10.1007/978-3-658-10958-5_8 %T Effiziente Bewertung des Anschlu\ss{}potentials von Niederspannungsnetzen für dezentrale Erzeugungsanlagen: Klassifikation mit Methoden der Computational Intelligence
Stone, T.C., Haas, S., Breitenstein, S., Wiesner, K., Sick, B.: Car Drive Classification and Context Recognition for Personalized Entertainment Preference Learning International Journal on Advances in Software. 8, 53–64 (2015).
- [ BibTeX ]
- [ EndNote ]
@article{stone2015car, author = {Stone, T. C. and Haas, S. and Breitenstein, S. and Wiesner, K. and Sick, B.}, journal = {International Journal on Advances in Software}, keywords = {imported}, number = {1–2}, pages = {53–64}, publisher = {IARIA}, title = {Car Drive Classification and Context Recognition for Personalized Entertainment Preference Learning}, volume = 8, year = 2015 }
%0 Journal Article %1 stone2015car %A Stone, T. C. %A Haas, S. %A Breitenstein, S. %A Wiesner, K. %A Sick, B. %D 2015 %I IARIA %J International Journal on Advances in Software %N 1--2 %P 53--64 %T Car Drive Classification and Context Recognition for Personalized Entertainment Preference Learning %V 8
Breker, S., Claudi, A., Sick, B.: Capacity of Low-Voltage Grids for Distributed Generation: Classification by Means of Stochastic Simulations IEEE Transactions on Power Systems. 30, 689–700 (2015). https://doi.org/10.1109/tpwrs.2014.2332361.
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- [ EndNote ]
@article{breker2015capacity, author = {Breker, S. and Claudi, A. and Sick, B.}, journal = {IEEE Transactions on Power Systems}, keywords = {imported}, number = 2, pages = {689–700}, publisher = {IEEE}, title = {Capacity of Low-Voltage Grids for Distributed Generation: Classification by Means of Stochastic Simulations}, volume = 30, year = 2015 }
%0 Journal Article %1 breker2015capacity %A Breker, S. %A Claudi, A. %A Sick, B. %D 2015 %I IEEE %J IEEE Transactions on Power Systems %N 2 %P 689--700 %R 10.1109/tpwrs.2014.2332361 %T Capacity of Low-Voltage Grids for Distributed Generation: Classification by Means of Stochastic Simulations %V 30
Goldhammer, M., Köhler, S., Doll, K., Sick, B.: Camera Based Pedestrian Path Prediction by Means of Polynominal Least-squares Approximation and Multilayer Perceptron Neural Networks In: SAI Intelligent Systems Conference (IntelliSys). bll 390–399. Springer, London, UK (2015). https://doi.org/10.1109/IntelliSys.2015.7361171.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{goldhammer2015camera, address = {London, UK}, author = {Goldhammer, M. and K\"ohler, S. and Doll, K. and Sick, B.}, booktitle = {SAI Intelligent Systems Conference (IntelliSys)}, keywords = {imported}, pages = {390–399}, publisher = {Springer}, title = {Camera Based Pedestrian Path Prediction by Means of Polynominal Least-squares Approximation and Multilayer Perceptron Neural Networks}, year = 2015 }
%0 Conference Paper %1 goldhammer2015camera %A Goldhammer, M. %A Köhler, S. %A Doll, K. %A Sick, B. %B SAI Intelligent Systems Conference (IntelliSys) %C London, UK %D 2015 %I Springer %P 390--399 %R 10.1109/IntelliSys.2015.7361171 %T Camera Based Pedestrian Path Prediction by Means of Polynominal Least-squares Approximation and Multilayer Perceptron Neural Networks
Rudolph, J., Breker, S., Sick, B.: Bewertung verschiedener Spannungsregelungskonzepte in einem einspeisegeprägten Mittelspannungsnetz und Ausblick auf neue Konzepte basierend auf Methoden der Computational Intelligence In: Tagung Nachhaltige Energieversorgung und Integration von Speichern (NEIS). bll 57–63. , Hamburg, Germany (2015). https://doi.org/10.1007/978-3-658-10958-5_9.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{rudolph2015bewertung, address = {Hamburg, Germany}, author = {Rudolph, J. and Breker, S. and Sick, B.}, booktitle = {Tagung Nachhaltige Energieversorgung und Integration von Speichern (NEIS)}, keywords = {imported}, pages = {57–63}, title = {Bewertung verschiedener {Spannungsregelungskonzepte} in einem einspeisegepr{\"a}gten {Mittelspannungsnetz und Ausblick auf neue Konzepte basierend auf Methoden der Computational Intelligence}}, year = 2015 }
%0 Conference Paper %1 rudolph2015bewertung %A Rudolph, J. %A Breker, S. %A Sick, B. %B Tagung Nachhaltige Energieversorgung und Integration von Speichern (NEIS) %C Hamburg, Germany %D 2015 %P 57--63 %R 10.1007/978-3-658-10958-5_9 %T Bewertung verschiedener Spannungsregelungskonzepte in einem einspeisegeprägten Mittelspannungsnetz und Ausblick auf neue Konzepte basierend auf Methoden der Computational Intelligence
Gruhl, C.: Anomalies in Generative Trajectory Models -- Discovering Suspicious Traces with Novelty Detection Methods In: Tomforde, S. en Sick, B. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2015. bll 95–107. kassel university press, Kassel, Germany (2015).
- [ BibTeX ]
- [ EndNote ]
@incollection{gruhl2015anomalies, address = {Kassel, Germany}, author = {Gruhl, C.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2015}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {95–107}, publisher = {kassel university press}, title = {Anomalies in Generative Trajectory Models – Discovering Suspicious Traces with Novelty Detection Methods}, year = 2015 }
%0 Book Section %1 gruhl2015anomalies %A Gruhl, C. %B Organic Computing -- Doctoral Dissertation Colloquium 2015 %C Kassel, Germany %D 2015 %E Tomforde, S. %E Sick, B. %I kassel university press %P 95--107 %T Anomalies in Generative Trajectory Models -- Discovering Suspicious Traces with Novelty Detection Methods
Stone, T.C., Huber, A., Siwy, R., Sick, B.: Analyse des Fahrerverhaltens zur Entwicklung von intelligenten Komfortfunktionen Elektronik automotive. 2, 32–36 (2015).
- [ BibTeX ]
- [ EndNote ]
@article{stone2015analyse, address = {Landshut, Germany}, author = {Stone, T. C. and Huber, A. and Siwy, R. and Sick, B.}, journal = {Elektronik automotive}, keywords = {imported}, number = 2, pages = {32–36}, publisher = {WEKA Fachmedien}, title = {{Analyse des Fahrerverhaltens zur Entwicklung von intelligenten Komfortfunktionen}}, volume = 2, year = 2015 }
%0 Journal Article %1 stone2015analyse %A Stone, T. C. %A Huber, A. %A Siwy, R. %A Sick, B. %C Landshut, Germany %D 2015 %I WEKA Fachmedien %J Elektronik automotive %N 2 %P 32--36 %T Analyse des Fahrerverhaltens zur Entwicklung von intelligenten Komfortfunktionen %V 2
Rudolph, S., Tomforde, S., Sick, B., Heck, H., Wacker, A., Hähner, J.: An Online Influence Detection Algorithm for Organic Computing Systems In: International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll 1–8. VDE, Porto, Portugal (2015).
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
@inproceedings{rudolph2015online, address = {Porto, Portugal}, author = {Rudolph, S. and Tomforde, S. and Sick, B. and Heck, H. and Wacker, A. and H{\"a}hner, J.}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, keywords = {imported}, pages = {1–8}, publisher = {VDE}, title = {An Online Influence Detection Algorithm for Organic Computing Systems}, year = 2015 }
%0 Conference Paper %1 rudolph2015online %A Rudolph, S. %A Tomforde, S. %A Sick, B. %A Heck, H. %A Wacker, A. %A Hähner, J. %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %C Porto, Portugal %D 2015 %I VDE %P 1--8 %T An Online Influence Detection Algorithm for Organic Computing Systems %U https://www.vde-verlag.de/proceedings-de/563657011.html
Reitmaier, T.: Aktives Lernen für Klassifikationsprobleme unter der Nutzung von Strukturinformationen, (2015).
- [ BibTeX ]
- [ EndNote ]
@phdthesis{reitmaier2015aktivesa, address = {Kassel, Germany}, author = {Reitmaier, T.}, keywords = {imported}, publisher = {kassel university press}, school = {University of Kassel}, series = {Intelligent Embedded Systems}, title = {{Aktives Lernen f{\"u}r Klassifikationsprobleme unter der Nutzung von Strukturinformationen}}, year = 2015 }
%0 Thesis %1 reitmaier2015aktivesa %A Reitmaier, T. %B Intelligent Embedded Systems %C Kassel, Germany %D 2015 %I kassel university press %T Aktives Lernen für Klassifikationsprobleme unter der Nutzung von Strukturinformationen
Reitmaier, T.: Aktives Lernen für Klassifikationsprobleme unter der Nutzung von Strukturinformation In: Lecture Notes in Informatics: Ausgezeichnete Informatikdissertationen. bll 239–248 (2015).
- [ BibTeX ]
- [ EndNote ]
@inproceedings{reitmaier2015aktives, author = {Reitmaier, T.}, booktitle = {Lecture Notes in Informatics: Ausgezeichnete Informatikdissertationen}, keywords = {imported}, pages = {239–248}, title = {{Aktives Lernen für Klassifikationsprobleme unter der Nutzung von Strukturinformation}}, year = 2015 }
%0 Conference Paper %1 reitmaier2015aktives %A Reitmaier, T. %B Lecture Notes in Informatics: Ausgezeichnete Informatikdissertationen %D 2015 %P 239--248 %T Aktives Lernen für Klassifikationsprobleme unter der Nutzung von Strukturinformation
Jahn, A., Lau, S.L., David, K., Sick, B.: A Tool Chain for Context Detection Automating the Investigation of a Multitude of Parameter Sets In: International Workshop on Mobile and Context Aware Services (MOCS), VTC. bll 1–5. , Boston, MA (2015).
- [ BibTeX ]
- [ EndNote ]
@inproceedings{jahn2015tool, address = {Boston, MA}, author = {Jahn, A. and Lau, S. L. and David, K. and Sick, B.}, booktitle = {International Workshop on Mobile and Context Aware Services (MOCS), VTC}, keywords = {imported}, pages = {1–5}, title = {A Tool Chain for Context Detection Automating the Investigation of a Multitude of Parameter Sets}, year = 2015 }
%0 Conference Paper %1 jahn2015tool %A Jahn, A. %A Lau, S. L. %A David, K. %A Sick, B. %B International Workshop on Mobile and Context Aware Services (MOCS), VTC %C Boston, MA %D 2015 %P 1--5 %T A Tool Chain for Context Detection Automating the Investigation of a Multitude of Parameter Sets
Jahn, A., Lau, S.L., David, K., Sick, B.: A Tool Chain for Context Detection Automating the Investigation of a Multitude of Parameter Sets In: International Workshop on Mobile and Context Aware Services (MOCS), VTC. bll 1–5. , Boston, MA (2015).
- [ BibTeX ]
- [ EndNote ]
@inproceedings{jahn2015tool, address = {Boston, MA}, author = {Jahn, A. and Lau, S. L. and David, K. and Sick, B.}, booktitle = {International Workshop on Mobile and Context Aware Services (MOCS), VTC}, keywords = {imported}, pages = {1–5}, title = {A Tool Chain for Context Detection Automating the Investigation of a Multitude of Parameter Sets}, year = 2015 }
%0 Conference Paper %1 jahn2015tool %A Jahn, A. %A Lau, S. L. %A David, K. %A Sick, B. %B International Workshop on Mobile and Context Aware Services (MOCS), VTC %C Boston, MA %D 2015 %P 1--5 %T A Tool Chain for Context Detection Automating the Investigation of a Multitude of Parameter Sets
Calma, A., Reitmaier, T., Sick, B., Lukowicz, P., Embrechts, M.: A New Vision of Collaborative Active Learning arXiv e-prints. arXiv:1504.00284 (2015).
- [ BibTeX ]
- [ EndNote ]
@article{calma2015new, author = {Calma, Adrian and Reitmaier, Tobias and Sick, Bernhard and Lukowicz, Paul and Embrechts, Mark}, journal = {arXiv e-prints}, keywords = {Machine}, pages = {arXiv:1504.00284}, title = {{A New Vision of Collaborative Active Learning}}, year = 2015 }
%0 Journal Article %1 calma2015new %A Calma, Adrian %A Reitmaier, Tobias %A Sick, Bernhard %A Lukowicz, Paul %A Embrechts, Mark %D 2015 %J arXiv e-prints %P arXiv:1504.00284 %T A New Vision of Collaborative Active Learning
Rudolph, S., Tomforde, S., Sick, B., Hähner, J.: A Mutual Influence Detection Algorithm for Systems with Local Performance Measurement In: IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO). bll 144–149. IEEE, Cambridge, MA (2015). https://doi.org/10.1109/saso.2015.23.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{rudolph2015mutual, address = {Cambridge, MA}, author = {Rudolph, S. and Tomforde, S. and Sick, B. and H{\"a}hner, J.}, booktitle = {IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO)}, keywords = {imported}, pages = {144–149}, publisher = {IEEE}, title = {A Mutual Influence Detection Algorithm for Systems with Local Performance Measurement}, year = 2015 }
%0 Conference Paper %1 rudolph2015mutual %A Rudolph, S. %A Tomforde, S. %A Sick, B. %A Hähner, J. %B IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO) %C Cambridge, MA %D 2015 %I IEEE %P 144--149 %R 10.1109/saso.2015.23 %T A Mutual Influence Detection Algorithm for Systems with Local Performance Measurement
Embrechts, M., Sick, B.: A Generalized Hebb (GH) rule based on a cross-entropy error function for deep belief recursive learning In: International Conference on Neural Networks - Fuzzy Systems (NN-FS). bll 21–24. , Vienna, Austria (2015).
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
@inproceedings{embrechts2015generalized, address = {Vienna, Austria}, author = {Embrechts, M. and Sick, B.}, booktitle = {International Conference on Neural Networks - Fuzzy Systems (NN-FS)}, keywords = {imported}, pages = {21–24}, title = {A {Generalized Hebb (GH)} rule based on a cross-entropy error function for deep belief recursive learning}, year = 2015 }
%0 Conference Paper %1 embrechts2015generalized %A Embrechts, M. %A Sick, B. %B International Conference on Neural Networks - Fuzzy Systems (NN-FS) %C Vienna, Austria %D 2015 %P 21--24 %T A Generalized Hebb (GH) rule based on a cross-entropy error function for deep belief recursive learning %U http://www.inase.org/library/2015/vienna/bypaper/APNE/APNE-02.pdf
Gruhl, C., Sick, B., Wacker, A., Tomforde, S., Hähner, J.: A building block for awareness in technical systems: Online novelty detection and reaction with an application in intrusion detection In: IEEE International Conference on Awareness Science and Technology (iCAST). bll 194–200. IEEE, Qinhuangdao, China (2015). https://doi.org/10.1109/icawst.2015.7314046.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{gruhl2015building, address = {Qinhuangdao, China}, author = {Gruhl, C. and Sick, B. and Wacker, A. and Tomforde, S. and H{\"a}hner, J.}, booktitle = {IEEE International Conference on Awareness Science and Technology (iCAST)}, keywords = {imported}, pages = {194–200}, publisher = {IEEE}, title = {A building block for awareness in technical systems: Online novelty detection and reaction with an application in intrusion detection}, year = 2015 }
%0 Conference Paper %1 gruhl2015building %A Gruhl, C. %A Sick, B. %A Wacker, A. %A Tomforde, S. %A Hähner, J. %B IEEE International Conference on Awareness Science and Technology (iCAST) %C Qinhuangdao, China %D 2015 %I IEEE %P 194--200 %R 10.1109/icawst.2015.7314046 %T A building block for awareness in technical systems: Online novelty detection and reaction with an application in intrusion detection
Calma, A.: 4DSPro: A New Selection Strategy for Pool-based Active Learning In: Tomforde, S. en Sick, B. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2015. bll 121–133. kassel university press, Kassel, Germany (2015).
- [ BibTeX ]
- [ EndNote ]
@incollection{calma20154dspro, address = {Kassel, Germany}, author = {Calma, A.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2015}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {121–133}, publisher = {kassel university press}, title = {{4DSPro}: A New Selection Strategy for Pool-based Active Learning}, year = 2015 }
%0 Book Section %1 calma20154dspro %A Calma, A. %B Organic Computing -- Doctoral Dissertation Colloquium 2015 %C Kassel, Germany %D 2015 %E Tomforde, S. %E Sick, B. %I kassel university press %P 121--133 %T 4DSPro: A New Selection Strategy for Pool-based Active Learning

2014[ to top ]

Gensler, A., Sick, B., Willkomm, J.: Temporal data analytics based on eigenmotif and shape space representations of time series In: IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP). bll 753–757. IEEE, Xian, China (2014). https://doi.org/10.1109/chinasip.2014.6889345.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{gensler2014temporal, address = {Xian, China}, author = {Gensler, A. and Sick, B. and Willkomm, J.}, booktitle = {IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP)}, keywords = {imported}, pages = {753–757}, publisher = {IEEE}, title = {Temporal data analytics based on eigenmotif and shape space representations of time series}, year = 2014 }
%0 Conference Paper %1 gensler2014temporal %A Gensler, A. %A Sick, B. %A Willkomm, J. %B IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP) %C Xian, China %D 2014 %I IEEE %P 753--757 %R 10.1109/chinasip.2014.6889345 %T Temporal data analytics based on eigenmotif and shape space representations of time series
Al-Falouji, G., Prestel, D., Scharfenberg, G., Mandl, R., Deinzer, A., Halang, W., Margraf-Stiksrud, J., Sick, B., Deinzer, R.: SMART-iBrush -- Individuelle Unterstützung der Zahnreinigung durch Messung von Bewegung und Druck mit einer intelligenten Zahnbürste In: Transdisziplinäre Konferenz zum Thema "Technische Unterstützungssysteme, die die Menschen wirklich wollen". bll 315–327 (2014).
- [ BibTeX ]
- [ EndNote ]
@inproceedings{alfalouji2014smart, author = {Al-Falouji, G. and Prestel, D. and Scharfenberg, G. and Mandl, R. and Deinzer, A. and Halang, W. and Margraf-Stiksrud, J. and Sick, B. and Deinzer, R.}, booktitle = {Transdisziplin{\"a}re Konferenz zum Thema "Technische Unterst{\"u}tzungssysteme, die die Menschen wirklich wollen"}, keywords = {imported}, pages = {315–327}, title = {{SMART-iBrush – Individuelle Unterst{\"u}tzung der Zahnreinigung durch Messung von Bewegung und Druck mit einer intelligenten Zahnb{\"u}rste}}, year = 2014 }
%0 Conference Paper %1 alfalouji2014smart %A Al-Falouji, G. %A Prestel, D. %A Scharfenberg, G. %A Mandl, R. %A Deinzer, A. %A Halang, W. %A Margraf-Stiksrud, J. %A Sick, B. %A Deinzer, R. %B Transdisziplinäre Konferenz zum Thema "Technische Unterstützungssysteme, die die Menschen wirklich wollen" %D 2014 %P 315--327 %T SMART-iBrush -- Individuelle Unterstützung der Zahnreinigung durch Messung von Bewegung und Druck mit einer intelligenten Zahnbürste
Al-Falouji, G., Prestel, D., Scharfenberg, G., Mandl, R., Deinzer, A., Halang, W., Margraf-Stiksrud, J., Sick, B., Deinzer, R.: SMART-iBrush -- Individuelle Unterstützung der Zahnreinigung durch Messung von Bewegung und Druck mit einer intelligenten Zahnbürste In: Transdisziplinäre Konferenz zum Thema "Technische Unterstützungssysteme, die die Menschen wirklich wollen". bll 315–327 (2014).
- [ BibTeX ]
- [ EndNote ]
@inproceedings{alfalouji2014smart, author = {Al-Falouji, G. and Prestel, D. and Scharfenberg, G. and Mandl, R. and Deinzer, A. and Halang, W. and Margraf-Stiksrud, J. and Sick, B. and Deinzer, R.}, booktitle = {Transdisziplin{\"a}re Konferenz zum Thema "Technische Unterst{\"u}tzungssysteme, die die Menschen wirklich wollen"}, keywords = {imported}, pages = {315–327}, title = {{SMART-iBrush – Individuelle Unterst{\"u}tzung der Zahnreinigung durch Messung von Bewegung und Druck mit einer intelligenten Zahnb{\"u}rste}}, year = 2014 }
%0 Conference Paper %1 alfalouji2014smart %A Al-Falouji, G. %A Prestel, D. %A Scharfenberg, G. %A Mandl, R. %A Deinzer, A. %A Halang, W. %A Margraf-Stiksrud, J. %A Sick, B. %A Deinzer, R. %B Transdisziplinäre Konferenz zum Thema "Technische Unterstützungssysteme, die die Menschen wirklich wollen" %D 2014 %P 315--327 %T SMART-iBrush -- Individuelle Unterstützung der Zahnreinigung durch Messung von Bewegung und Druck mit einer intelligenten Zahnbürste
Früchtl, M.: Sicherheit eingebetteter Systeme auf Basis arithmetischer Codierungen, (2014).
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- [ EndNote ]
@phdthesis{fruechtl2014sicherheit, address = {Kassel, Germany}, author = {Fr{\"u}chtl, M.}, keywords = {imported}, publisher = {kassel university press}, school = {University of Kassel}, series = {Intelligent Embedded Systems}, title = {Sicherheit eingebetteter Systeme auf Basis arithmetischer Codierungen}, year = 2014 }
%0 Thesis %1 fruechtl2014sicherheit %A Früchtl, M. %B Intelligent Embedded Systems %C Kassel, Germany %D 2014 %I kassel university press %T Sicherheit eingebetteter Systeme auf Basis arithmetischer Codierungen
Kalkowski, E.: Self-Extending Training Sets: Using Ontologies to Improve Machine Learning Performance In: Tomforde, S. en Sick, B. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2014. bll 111–125. kassel university press, Kassel, Germany (2014).
- [ BibTeX ]
- [ EndNote ]
@incollection{kalkowski2014self, address = {Kassel, Germany}, author = {Kalkowski, E.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2014}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {111–125}, publisher = {kassel university press}, title = {Self-Extending Training Sets: Using Ontologies to Improve Machine Learning Performance}, year = 2014 }
%0 Book Section %1 kalkowski2014self %A Kalkowski, E. %B Organic Computing -- Doctoral Dissertation Colloquium 2014 %C Kassel, Germany %D 2014 %E Tomforde, S. %E Sick, B. %I kassel university press %P 111--125 %T Self-Extending Training Sets: Using Ontologies to Improve Machine Learning Performance
Jänicke, M., Sick, B., Lukowicz, P., Bannach, D.: Self-Adapting Multi-sensor Systems: A Concept for Self-Improvement and Self-Healing Techniques In: Workshop on Self-Improving System Integration (SISSY), SASO. bll 128–136. IEEE, London, UK (2014). https://doi.org/10.1109/sasow.2014.22.
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- [ EndNote ]
@inproceedings{jaenicke2014self, address = {London, UK}, author = {J{\"a}nicke, M. and Sick, B. and Lukowicz, P. and Bannach, D.}, booktitle = {Workshop on Self-Improving System Integration (SISSY), SASO}, keywords = {imported}, pages = {128–136}, publisher = {IEEE}, title = {Self-Adapting Multi-sensor Systems: A Concept for Self-Improvement and Self-Healing Techniques}, year = 2014 }
%0 Conference Paper %1 jaenicke2014self %A Jänicke, M. %A Sick, B. %A Lukowicz, P. %A Bannach, D. %B Workshop on Self-Improving System Integration (SISSY), SASO %C London, UK %D 2014 %I IEEE %P 128--136 %R 10.1109/sasow.2014.22 %T Self-Adapting Multi-sensor Systems: A Concept for Self-Improvement and Self-Healing Techniques
Gruhl, C.: Self-Adapting Generative Modeling Techniques -- A Basic Building Block for Many Organic Computing Techniques In: Tomforde, S. en Sick, B. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2014. bll 99–109. kassel university press, Kassel, Germany (2014).
- [ BibTeX ]
- [ EndNote ]
@incollection{gruhl2014self, address = {Kassel, Germany}, author = {Gruhl, C.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2014}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {99–109}, publisher = {kassel university press}, title = {Self-Adapting Generative Modeling Techniques – A Basic Building Block for Many Organic Computing Techniques}, year = 2014 }
%0 Book Section %1 gruhl2014self %A Gruhl, C. %B Organic Computing -- Doctoral Dissertation Colloquium 2014 %C Kassel, Germany %D 2014 %E Tomforde, S. %E Sick, B. %I kassel university press %P 99--109 %T Self-Adapting Generative Modeling Techniques -- A Basic Building Block for Many Organic Computing Techniques
Reitmaier, T., Calma, A.: Resp-kNN: A Semi-Supervised kNN-Classifier for Sparsely Labeled Data in the Field of Organic Computing In: Tomforde, S. en Sick, B. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2014. bll 85–97. kassel university press, Kassel, Germany (2014).
- [ BibTeX ]
- [ EndNote ]
@incollection{reitmaier2014resp, address = {Kassel, Germany}, author = {Reitmaier, T. and Calma, A.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2014}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {85–97}, publisher = {kassel university press}, title = {Resp-{kNN}: A Semi-Supervised {kNN}-Classifier for Sparsely Labeled Data in the Field of Organic Computing}, year = 2014 }
%0 Book Section %1 reitmaier2014resp %A Reitmaier, T. %A Calma, A. %B Organic Computing -- Doctoral Dissertation Colloquium 2014 %C Kassel, Germany %D 2014 %E Tomforde, S. %E Sick, B. %I kassel university press %P 85--97 %T Resp-kNN: A Semi-Supervised kNN-Classifier for Sparsely Labeled Data in the Field of Organic Computing
Herwig, B., Frommann, U., Gruber, T., Sick, B.: Programmierkompetenz prüfen … am Beispiel der Vorlesung "Einführung in C" an der Universität Kassel In: Neues Handbuch Hochschullehre. bll 71–94. Raabe (2014).
- [ BibTeX ]
- [ EndNote ]
@incollection{herwig2014programmierkompetenz, author = {Herwig, B. and Frommann, U. and Gruber, T. and Sick, B.}, booktitle = {Neues Handbuch Hochschullehre}, keywords = {imported}, number = 64, pages = {71–94}, publisher = {Raabe}, title = {Programmierkompetenz pr{\"u}fen … am Beispiel der Vorlesung "Einf{\"u}hrung in C" an der Universit{\"a}t Kassel}, year = 2014 }
%0 Book Section %1 herwig2014programmierkompetenz %A Herwig, B. %A Frommann, U. %A Gruber, T. %A Sick, B. %B Neues Handbuch Hochschullehre %D 2014 %I Raabe %N 64 %P 71--94 %T Programmierkompetenz prüfen … am Beispiel der Vorlesung "Einführung in C" an der Universität Kassel
Goldhammer, M., Doll, K., Brunsmann, U., Gensler, A., Sick, B.: Pedestrian’s Trajectory Forecast in Public Traffic with Artificial Neural Networks In: International Conference on Pattern Recognition (ICPR). bll 4110–4115. IEEE, Stockholm, Sweden (2014). https://doi.org/10.1109/icpr.2014.704.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{goldhammer2014pedestrians, address = {Stockholm, Sweden}, author = {Goldhammer, M. and Doll, K. and Brunsmann, U. and Gensler, A. and Sick, B.}, booktitle = {International Conference on Pattern Recognition (ICPR)}, keywords = {imported}, pages = {4110–4115}, publisher = {IEEE}, title = {Pedestrian's Trajectory Forecast in Public Traffic with Artificial Neural Networks}, year = 2014 }
%0 Conference Paper %1 goldhammer2014pedestrians %A Goldhammer, M. %A Doll, K. %A Brunsmann, U. %A Gensler, A. %A Sick, B. %B International Conference on Pattern Recognition (ICPR) %C Stockholm, Sweden %D 2014 %I IEEE %P 4110--4115 %R 10.1109/icpr.2014.704 %T Pedestrian's Trajectory Forecast in Public Traffic with Artificial Neural Networks
Tomforde, S., Sick, B. reds: Organic Computing -- Doctoral Dissertation Colloquium 2014 kassel university press (2014).
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
@book{tomforde2014organic, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, publisher = {kassel university press}, series = {Intelligent Embedded Systems}, title = {Organic Computing – Doctoral Dissertation Colloquium 2014}, volume = 4, year = 2014 }
%0 Book %1 tomforde2014organic %B Intelligent Embedded Systems %D 2014 %E Tomforde, S. %E Sick, B. %I kassel university press %T Organic Computing -- Doctoral Dissertation Colloquium 2014 %U https://www.uni-kassel.de/ub/publizieren/kassel-university-press/verlagsprogramm?h=9783862198320 %V 4
Pree, H., Herwig, B., Gruber, T., Sick, B., David, K., Lukowicz, P.: On General Purpose Time Series Similarity Measures and Their Use as Kernel Functions in Support Vector Machines Information Sciences. 281, 478–495 (2014). https://doi.org/10.1016/j.ins.2014.05.025.
- [ BibTeX ]
- [ EndNote ]
@article{pree2014general, author = {Pree, H. and Herwig, B. and Gruber, T. and Sick, B. and David, K. and Lukowicz, P.}, journal = {Information Sciences}, keywords = {imported}, pages = {478–495}, publisher = {Elsevier}, title = {On General Purpose Time Series Similarity Measures and Their Use as Kernel Functions in Support Vector Machines}, volume = 281, year = 2014 }
%0 Journal Article %1 pree2014general %A Pree, H. %A Herwig, B. %A Gruber, T. %A Sick, B. %A David, K. %A Lukowicz, P. %D 2014 %I Elsevier %J Information Sciences %P 478--495 %R 10.1016/j.ins.2014.05.025 %T On General Purpose Time Series Similarity Measures and Their Use as Kernel Functions in Support Vector Machines %V 281
Gensler, A., Sick, B., Pankraz, V.: Novel Criteria to Measure Performance of Time Series Segmentation Techniques In: Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML), LWA. bll 192–204. , Aachen, Germany (2014).
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
@inproceedings{gensler2014novel, address = {Aachen, Germany}, author = {Gensler, A. and Sick, B. and Pankraz, V.}, booktitle = {Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML), LWA}, keywords = {imported}, pages = {192–204}, title = {Novel Criteria to Measure Performance of Time Series Segmentation Techniques}, year = 2014 }
%0 Conference Paper %1 gensler2014novel %A Gensler, A. %A Sick, B. %A Pankraz, V. %B Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML), LWA %C Aachen, Germany %D 2014 %P 192--204 %T Novel Criteria to Measure Performance of Time Series Segmentation Techniques %U http://ceur-ws.org/Vol-1226/paper31.pdf
Stone, T., Birth, O., Gensler, A., Huber, A., Jänicke, M., Sick, B.: Location based learning of user behavior for proactive recommender systems in car comfort functions In: INFORMATIK 2014. bll 2121–2132. Gesellschaft für Informatik e.V (2014).
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
@inproceedings{stone2014location, author = {Stone, T. and Birth, O. and Gensler, A. and Huber, A. and J{\"a}nicke, M. and Sick, B.}, booktitle = {INFORMATIK 2014}, keywords = {imported}, pages = {2121–2132}, publisher = {Gesellschaft für Informatik e.V.}, title = {Location based learning of user behavior for proactive recommender systems in car comfort functions}, year = 2014 }
%0 Conference Paper %1 stone2014location %A Stone, T. %A Birth, O. %A Gensler, A. %A Huber, A. %A Jänicke, M. %A Sick, B. %B INFORMATIK 2014 %D 2014 %I Gesellschaft für Informatik e.V. %P 2121--2132 %T Location based learning of user behavior for proactive recommender systems in car comfort functions %U https://dl.gi.de/handle/20.500.12116/2842
Fisch, D., Kalkowski, E., Sick, B.: Knowledge Fusion for Probabilistic Generative Classifiers with Data Mining Applications IEEE Transactions on Knowledge and Data Engineering. 26, 652–666 (2014). https://doi.org/10.1109/tkde.2013.20.
- [ BibTeX ]
- [ EndNote ]
@article{fisch2014knowledge, author = {Fisch, D. and Kalkowski, E. and Sick, B.}, journal = {IEEE Transactions on Knowledge and Data Engineering}, keywords = {imported}, number = 3, pages = {652–666}, publisher = {IEEE}, title = {Knowledge Fusion for Probabilistic Generative Classifiers with Data Mining Applications}, volume = 26, year = 2014 }
%0 Journal Article %1 fisch2014knowledge %A Fisch, D. %A Kalkowski, E. %A Sick, B. %D 2014 %I IEEE %J IEEE Transactions on Knowledge and Data Engineering %N 3 %P 652--666 %R 10.1109/tkde.2013.20 %T Knowledge Fusion for Probabilistic Generative Classifiers with Data Mining Applications %V 26
Tomforde, S., Hähner, J., Sick, B.: Interwoven Systems Informatik Spektrum. 37, 483–487 (2014). https://doi.org/10.1007/s00287-014-0827-z.
- [ BibTeX ]
- [ EndNote ]
@article{tomforde2014interwoven, author = {Tomforde, S. and H{\"a}hner, J. and Sick, B.}, journal = {Informatik Spektrum}, keywords = {imported}, number = 5, pages = {483–487}, publisher = {Springer}, title = {Interwoven Systems}, volume = 37, year = 2014 }
%0 Journal Article %1 tomforde2014interwoven %A Tomforde, S. %A Hähner, J. %A Sick, B. %D 2014 %I Springer %J Informatik Spektrum %N 5 %P 483--487 %R 10.1007/s00287-014-0827-z %T Interwoven Systems %V 37
Tomforde, S., Hähner, J., Seebach, H., Reif, W., Sick, B., Wacker, A., Scholtes, I.: Engineering and Mastering Interwoven Systems In: International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll 1–8. VDE, L"ubeck, Germany (2014).
- [ BibTeX ]
- [ EndNote ]
@inproceedings{tomforde2014engineering, address = {L{"u}beck, Germany}, author = {Tomforde, S. and H{\"a}hner, J. and Seebach, H. and Reif, W. and Sick, B. and Wacker, A. and Scholtes, I.}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, keywords = {imported}, pages = {1–8}, publisher = {VDE}, title = {Engineering and Mastering Interwoven Systems}, year = 2014 }
%0 Conference Paper %1 tomforde2014engineering %A Tomforde, S. %A Hähner, J. %A Seebach, H. %A Reif, W. %A Sick, B. %A Wacker, A. %A Scholtes, I. %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %C L"ubeck, Germany %D 2014 %I VDE %P 1--8 %T Engineering and Mastering Interwoven Systems
Kreil, M., Sick, B., Lukowicz, P.: Dealing with human variability in motion based, wearable activity recognition In: Symposium on Activity and Context Modeling and Recognition (ACOMORE), PerCom. bll 36–40. IEEE, Budapest, Hungary (2014). https://doi.org/10.1109/percomw.2014.6815161.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{kreil2014dealing, address = {Budapest, Hungary}, author = {Kreil, M. and Sick, B. and Lukowicz, P.}, booktitle = {Symposium on Activity and Context Modeling and Recognition (ACOMORE), PerCom}, keywords = {imported}, pages = {36–40}, publisher = {IEEE}, title = {Dealing with human variability in motion based, wearable activity recognition}, year = 2014 }
%0 Conference Paper %1 kreil2014dealing %A Kreil, M. %A Sick, B. %A Lukowicz, P. %B Symposium on Activity and Context Modeling and Recognition (ACOMORE), PerCom %C Budapest, Hungary %D 2014 %I IEEE %P 36--40 %R 10.1109/percomw.2014.6815161 %T Dealing with human variability in motion based, wearable activity recognition
Haas, S., Wiesner, K., Stone, T.C.: Car Ride Classification for Drive Context Recognition In: International Conference on Mobile Services, Resources, and Users (MOBILITY) (2014).
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
@inproceedings{haas2014car, author = {Haas, Stefan and Wiesner, Kevin and Stone, Thomas Christian}, booktitle = {International Conference on Mobile Services, Resources, and Users (MOBILITY)}, keywords = {imported}, title = {Car Ride Classification for Drive Context Recognition}, year = 2014 }
%0 Conference Paper %1 haas2014car %A Haas, Stefan %A Wiesner, Kevin %A Stone, Thomas Christian %B International Conference on Mobile Services, Resources, and Users (MOBILITY) %D 2014 %T Car Ride Classification for Drive Context Recognition %U http://www.thinkmind.org/articles/mobility_2014_3_30_70023.pdf
Goldhammer, M., Hubert, A., Köhler, S., Zindler, K., Brunsmann, U., Doll, K., Sick, B.: Analysis on termination of pedestrians’ gait at urban intersections In: IEEE International Conference on Intelligent Transportation Systems (ITSC). bll 1758–1763. IEEE, Qingdao, China (2014). https://doi.org/10.1109/itsc.2014.6957947.
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- [ EndNote ]
@inproceedings{goldhammer2014analysis, address = {Qingdao, China}, author = {Goldhammer, M. and Hubert, A. and K{\"o}hler, S. and Zindler, K. and Brunsmann, U. and Doll, K. and Sick, B.}, booktitle = {IEEE International Conference on Intelligent Transportation Systems (ITSC)}, keywords = {imported}, pages = {1758–1763}, publisher = {IEEE}, title = {Analysis on termination of pedestrians' gait at urban intersections}, year = 2014 }
%0 Conference Paper %1 goldhammer2014analysis %A Goldhammer, M. %A Hubert, A. %A Köhler, S. %A Zindler, K. %A Brunsmann, U. %A Doll, K. %A Sick, B. %B IEEE International Conference on Intelligent Transportation Systems (ITSC) %C Qingdao, China %D 2014 %I IEEE %P 1758--1763 %R 10.1109/itsc.2014.6957947 %T Analysis on termination of pedestrians' gait at urban intersections
Tomforde, S., Hähner, J., von Mammen, S., Gruhl, C., Sick, B., Geihs, K.: "Know thyself" -- Computational Self-Reflection in Intelligent Technical Systems In: Workshop on Self-Improving System Integration (SISSY), SASO. IEEE, Braunschweig, Germany (2014). https://doi.org/10.1109/SASOW.2014.25.
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- [ EndNote ]
@inproceedings{tomforde2014know, address = {Braunschweig, Germany}, author = {Tomforde, S. and H{\"a}hner, J. and von Mammen, S. and Gruhl, C. and Sick, B. and Geihs, K.}, booktitle = {Workshop on Self-Improving System Integration (SISSY), SASO}, keywords = {imported}, publisher = {IEEE}, title = {"{K}now thyself" – Computational Self-Reflection in Intelligent Technical Systems}, year = 2014 }
%0 Conference Paper %1 tomforde2014know %A Tomforde, S. %A Hähner, J. %A von Mammen, S. %A Gruhl, C. %A Sick, B. %A Geihs, K. %B Workshop on Self-Improving System Integration (SISSY), SASO %C Braunschweig, Germany %D 2014 %I IEEE %R 10.1109/SASOW.2014.25 %T "Know thyself" -- Computational Self-Reflection in Intelligent Technical Systems

2013[ to top ]

Jänicke, M.: Self-Adaption of Multi-Sensor-Systems with Organic Computing Techniques In: Tomforde, S. (red) Organic Computing -- Doctoral Dissertation Colloquium 2013. bll 20–23. kassel university press, Augsburg, Germany (2013).
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@incollection{jaenicke2013self, address = {Augsburg, Germany}, author = {J{\"a}nicke, M.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2013}, editor = {Tomforde, S.}, keywords = {imported}, pages = {20–23}, publisher = {kassel university press}, title = {Self-Adaption of Multi-Sensor-Systems with Organic Computing Techniques}, year = 2013 }
%0 Book Section %1 jaenicke2013self %A Jänicke, M. %B Organic Computing -- Doctoral Dissertation Colloquium 2013 %C Augsburg, Germany %D 2013 %E Tomforde, S. %I kassel university press %P 20--23 %T Self-Adaption of Multi-Sensor-Systems with Organic Computing Techniques
Reitmaier, T., Sick, B.: Let us know your decision: Pool-based active training of a generative classifier with the selection strategy 4DS Information Sciences. 230, 106–131 (2013). https://doi.org/10.1016/j.ins.2012.11.015.
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@article{reitmaier2013let, author = {Reitmaier, T. and Sick, B.}, journal = {Information Sciences}, keywords = {imported}, pages = {106–131}, publisher = {Elsevier}, title = {Let us know your decision: Pool-based active training of a generative classifier with the selection strategy {4DS}}, volume = 230, year = 2013 }
%0 Journal Article %1 reitmaier2013let %A Reitmaier, T. %A Sick, B. %D 2013 %I Elsevier %J Information Sciences %P 106--131 %R 10.1016/j.ins.2012.11.015 %T Let us know your decision: Pool-based active training of a generative classifier with the selection strategy 4DS %V 230
Kaufmann, P., Glette, K., Gruber, T., Platzner, M., Torresen, J., Sick, B.: Classification of Electromyographic Signals: Comparing Evolvable Hardware to Conventional Classifiers IEEE Transactions on Evolutionary Computation. 17, 46–63 (2013). https://doi.org/10.1109/tevc.2012.2185845.
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@article{kaufmann2013classification, author = {Kaufmann, P. and Glette, K. and Gruber, T. and Platzner, M. and Torresen, J. and Sick, B.}, journal = {IEEE Transactions on Evolutionary Computation}, keywords = {imported}, number = 1, pages = {46–63}, publisher = {IEEE}, title = {Classification of Electromyographic Signals: Comparing Evolvable Hardware to Conventional Classifiers}, volume = 17, year = 2013 }
%0 Journal Article %1 kaufmann2013classification %A Kaufmann, P. %A Glette, K. %A Gruber, T. %A Platzner, M. %A Torresen, J. %A Sick, B. %D 2013 %I IEEE %J IEEE Transactions on Evolutionary Computation %N 1 %P 46--63 %R 10.1109/tevc.2012.2185845 %T Classification of Electromyographic Signals: Comparing Evolvable Hardware to Conventional Classifiers %V 17
Gensler, A., Gruber, T., Sick, B.: Blazing Fast Time Series Segmentation Based on Update Techniques for Polynomial Approximations In: International Workshop on Spatial and Spatio-Temporal Data Mining (SSTDM), ICDM. bll 1002–1011. IEEE, Dallas, TX (2013). https://doi.org/10.1109/icdmw.2013.90.
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@inproceedings{gensler2013blazing, address = {Dallas, TX}, author = {Gensler, A. and Gruber, T. and Sick, B.}, booktitle = {International Workshop on Spatial and Spatio-Temporal Data Mining (SSTDM), ICDM}, keywords = {imported}, pages = {1002–1011}, publisher = {IEEE}, title = {Blazing Fast Time Series Segmentation Based on Update Techniques for Polynomial Approximations}, year = 2013 }
%0 Conference Paper %1 gensler2013blazing %A Gensler, A. %A Gruber, T. %A Sick, B. %B International Workshop on Spatial and Spatio-Temporal Data Mining (SSTDM), ICDM %C Dallas, TX %D 2013 %I IEEE %P 1002--1011 %R 10.1109/icdmw.2013.90 %T Blazing Fast Time Series Segmentation Based on Update Techniques for Polynomial Approximations
Kubátová, H., Hochberger, C., Danue}k, M., Sick, B. reds: Architecture of Computing Systems -- ARCS 2013 Springer, Heidelberg, Germany (2013). https://doi.org/10.1007/978-3-642-36424-2.
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- [ EndNote ]
@book{kubatova2013architecture, address = {Heidelberg, Germany}, editor = {Kub{\'a}tov{\'a}, H. and Hochberger, C. and Dan{\ue}k, M. and Sick, B.}, keywords = {imported}, publisher = {Springer}, title = {Architecture of Computing Systems – ARCS 2013}, year = 2013 }
%0 Book %1 kubatova2013architecture %C Heidelberg, Germany %D 2013 %E Kubátová, H. %E Hochberger, C. %E Danue}k, M. %E Sick, B. %I Springer %R 10.1007/978-3-642-36424-2 %T Architecture of Computing Systems -- ARCS 2013
Reitmaier, T.: Active Learning of Generative and Discriminative Classifiers for Organic Computing In: Tomforde, S. (red) Organic Computing -- Doctoral Dissertation Colloquium 2013. bll 27–30. kassel university press, Augsburg, Germany (2013).
- [ BibTeX ]
- [ EndNote ]
@incollection{reitmaier2013active, address = {Augsburg, Germany}, author = {Reitmaier, T.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2013}, editor = {Tomforde, S.}, keywords = {imported}, pages = {27–30}, publisher = {kassel university press}, title = {Active Learning of Generative and Discriminative Classifiers for {Organic Computing}}, year = 2013 }
%0 Book Section %1 reitmaier2013active %A Reitmaier, T. %B Organic Computing -- Doctoral Dissertation Colloquium 2013 %C Augsburg, Germany %D 2013 %E Tomforde, S. %I kassel university press %P 27--30 %T Active Learning of Generative and Discriminative Classifiers for Organic Computing

2012[ to top ]

Fisch, D., Jänicke, M., Kalkowski, E., Sick, B.: Techniques for knowledge acquisition in dynamically changing environments ACM Transactions on Autonomous and Adaptive Systems. 7, 16 (2012). https://doi.org/10.1145/2168260.2168276.
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