Prof. Dr. rer. nat. Bernhard Sick

Group Leader; Team Leader: Collaborative Interactive Learning (CIL); Team Leader: AI for Computationally Intelligent Systems (AI4CIS)

Telephone: +49 561 804-6020
Fax: +49 561 804-6022
Email: bsick[at]uni-kassel[dot]de

Site: Wilhelmshöher Allee 73
34121 Kassel

Room: WA-altes Gebäude (WA 73), ohne Raumangabe

Publications

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[ 2021 ]
[ 2020 ]
[ 2019 ]
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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.
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 ]

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.
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.
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.
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.
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.
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%.
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.
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.
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.
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.
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.
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
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
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
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
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
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.
- [ Abstract ]
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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.
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
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
Deist, S., Schreiber, J., Bieshaar, M., Sick, B.: Extended Coopetitive Soft Gating Ensemble arXiv e-prints. arXiv:2004.14026 (2020).
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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).
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@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
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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@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
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.
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@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
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
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
Kress, V., Jung, J., Zernetsch, S., Doll, K., Sick, B.: Start Intention Detection of Cyclists using an LSTM Network In: INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beiträge). bll 219–228. Gesellschaft für Informatik e.V., Bonn (2019). https://doi.org/10.18420/inf2019_ws25.
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- [ EndNote ]
@inproceedings{kress2019start, address = {Bonn}, author = {Kress, Viktor and Jung, Janis and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, booktitle = {INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (Workshop-Beiträge)}, keywords = {imported}, pages = {219–228}, publisher = {Gesellschaft für Informatik e.V.}, title = {Start Intention Detection of Cyclists using an {LSTM} Network}, year = 2019 }
%0 Conference Paper %1 kress2019start %A Kress, Viktor %A Jung, Janis %A Zernetsch, Stefan %A Doll, Konrad %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 219--228 %R 10.18420/inf2019_ws25 %T Start Intention Detection of Cyclists using an LSTM Network
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
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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@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.
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@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
Schreiber, J., Buschin, A., Sick, B.: Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models In: INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft. bll 585–598. Gesellschaft für Informatik e.V., Bonn (2019). https://doi.org/10.18420/inf2019_74.
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@inproceedings{schreiber2019influences, address = {Bonn}, author = {Schreiber, Jens and Buschin, Artjom and Sick, Bernhard}, booktitle = {INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft}, keywords = {imported}, pages = {585–598}, publisher = {Gesellschaft für Informatik e.V.}, title = {Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models}, year = 2019 }
%0 Conference Paper %1 schreiber2019influences %A Schreiber, Jens %A Buschin, Artjom %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 585--598 %R 10.18420/inf2019_74 %T Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models
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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@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., 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
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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@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
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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- [ EndNote ]
@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
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 ]

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
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
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.
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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.
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.
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@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.
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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).
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@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).
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- [ 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 ]

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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@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
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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@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
Lang, D., Kottke, D., Krempl, G., Sick, B.: Probabilistic Active Learning with Structure-Sensitive Kernels In: Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. bll 37–48 (2017).
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@inproceedings{lang2017probabilistic, author = {Lang, Dominik and Kottke, Daniel and Krempl, Georg and Sick, Bernhard}, booktitle = {Workshop on Interactive Adaptive Learning (IAL), ECML PKDD}, keywords = {imported}, pages = {37–48}, series = {CEUR Workshop Proceedings}, title = {Probabilistic Active Learning with Structure-Sensitive Kernels}, year = 2017 }
%0 Conference Paper %1 lang2017probabilistic %A Lang, Dominik %A Kottke, Daniel %A Krempl, Georg %A Sick, Bernhard %B Workshop on Interactive Adaptive Learning (IAL), ECML PKDD %D 2017 %P 37--48 %T Probabilistic Active Learning with Structure-Sensitive Kernels %U http://ceur-ws.org/Vol-1924/ialatecml_paper3.pdf
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
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
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
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).
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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 ]
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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.
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).
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@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
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).
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@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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@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).
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@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
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).
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@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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@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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@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.
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@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
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).
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@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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@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
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).
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@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
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).
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@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
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.
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@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
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
Schlegel, B., Sick, B.: Design and optimization of an autonomous feature selection pipeline for high dimensional, heterogeneous feature spaces In: IEEE Symposium Series on Computational Intelligence (SSCI). bll 1–9. IEEE, Athens, Greece (2016). https://doi.org/10.1109/ssci.2016.7850092.
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@inproceedings{schlegel2016design, address = {Athens, Greece}, author = {Schlegel, B. and Sick, B.}, booktitle = {IEEE Symposium Series on Computational Intelligence (SSCI)}, keywords = {imported}, pages = {1–9}, publisher = {IEEE}, title = {Design and optimization of an autonomous feature selection pipeline for high dimensional, heterogeneous feature spaces}, year = 2016 }
%0 Conference Paper %1 schlegel2016design %A Schlegel, B. %A Sick, B. %B IEEE Symposium Series on Computational Intelligence (SSCI) %C Athens, Greece %D 2016 %I IEEE %P 1--9 %R 10.1109/ssci.2016.7850092 %T Design and optimization of an autonomous feature selection pipeline for high dimensional, heterogeneous feature spaces
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
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
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
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
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
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.
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@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).
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@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 ]

Kalkowski, E., Sick, B.: Using Ontology-Based Similarity Measures to Find Training Data for Problems with Sparse Data In: IEEE International Conference on Systems, Man and Cybernetics (SMC). bll 1693–1699. IEEE, Hongkong, China (2015). https://doi.org/10.1109/smc.2015.298.
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@inproceedings{kalkowski2015using, address = {Hongkong, China}, author = {Kalkowski, E. and Sick, B.}, booktitle = {IEEE International Conference on Systems, Man and Cybernetics (SMC)}, keywords = {imported}, pages = {1693–1699}, publisher = {IEEE}, title = {Using Ontology-Based Similarity Measures to Find Training Data for Problems with Sparse Data}, year = 2015 }
%0 Conference Paper %1 kalkowski2015using %A Kalkowski, E. %A Sick, B. %B IEEE International Conference on Systems, Man and Cybernetics (SMC) %C Hongkong, China %D 2015 %I IEEE %P 1693--1699 %R 10.1109/smc.2015.298 %T Using Ontology-Based Similarity Measures to Find Training Data for Problems with Sparse Data
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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@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.
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@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.
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@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
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).
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@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
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).
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@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.
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@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.
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- [ 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).
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- [ 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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@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.
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- [ 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.
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- [ 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
Stone, T.C., Huber, A., Siwy, R., Sick, B.: Analyse des Fahrerverhaltens zur Entwicklung von intelligenten Komfortfunktionen Elektronik automotive. 2, 32–36 (2015).
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- [ 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).
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- [ 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
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).
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- [ 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.
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@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).
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- [ 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.
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@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

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.
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@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).
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@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
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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@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
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).
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@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.
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@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
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.
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@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).
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- [ 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).
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- [ 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.
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@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.
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@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).
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@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.
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@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
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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@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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@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 ]

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
Hähner, J., Rudolph, S., Tomforde, S., Fisch, D., Sick, B., Kopal, N., Wacker, A.: A Concept for Securing Cyber-Physical Systems with Organic Computing Techniques In: Workshop on Self-Optimisation in Autonomic and Organic Computing Systems (SAOS), ARCS. bll 1–13. VDE, Prague, Czech Republic (2013).
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- [ URL ]
@inproceedings{haehner2013concept, address = {Prague, Czech Republic}, author = {H{\"a}hner, J. and Rudolph, S. and Tomforde, S. and Fisch, D. and Sick, B. and Kopal, N. and Wacker, A.}, booktitle = {Workshop on Self-Optimisation in Autonomic and Organic Computing Systems (SAOS), ARCS}, keywords = {imported}, pages = {1–13}, publisher = {VDE}, title = {A Concept for Securing Cyber-Physical Systems with Organic Computing Techniques}, year = 2013 }
%0 Conference Paper %1 haehner2013concept %A Hähner, J. %A Rudolph, S. %A Tomforde, S. %A Fisch, D. %A Sick, B. %A Kopal, N. %A Wacker, A. %B Workshop on Self-Optimisation in Autonomic and Organic Computing Systems (SAOS), ARCS %C Prague, Czech Republic %D 2013 %I VDE %P 1--13 %T A Concept for Securing Cyber-Physical Systems with Organic Computing Techniques %U http://www.vde-verlag.de/proceedings-de/563492001.html

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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- [ EndNote ]
@article{fisch2012techniques, author = {Fisch, D. and J{\"a}nicke, M. and Kalkowski, E. and Sick, B.}, journal = {{ACM} Transactions on Autonomous and Adaptive Systems}, keywords = {imported}, number = 1, pages = 16, publisher = {ACM}, title = {Techniques for knowledge acquisition in dynamically changing environments}, volume = 7, year = 2012 }
%0 Journal Article %1 fisch2012techniques %A Fisch, D. %A Jänicke, M. %A Kalkowski, E. %A Sick, B. %D 2012 %I ACM %J ACM Transactions on Autonomous and Adaptive Systems %N 1 %P 16 %R 10.1145/2168260.2168276 %T Techniques for knowledge acquisition in dynamically changing environments %V 7
Fisch, D., Jänicke, M., Kalkowski, E., Sick, B.: Learning from others: Exchange of classification rules in intelligent distributed systems Artificial Intelligence. 187--188, 90–114 (2012). https://doi.org/10.1016/j.artint.2012.04.002.
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@article{fisch2012learning, author = {Fisch, D. and J{\"a}nicke, M. and Kalkowski, E. and Sick, B.}, journal = {Artificial Intelligence}, keywords = {imported}, pages = {90–114}, publisher = {Elsevier}, title = {Learning from others: Exchange of classification rules in intelligent distributed systems}, volume = {187–188}, year = 2012 }
%0 Journal Article %1 fisch2012learning %A Fisch, D. %A Jänicke, M. %A Kalkowski, E. %A Sick, B. %D 2012 %I Elsevier %J Artificial Intelligence %P 90--114 %R 10.1016/j.artint.2012.04.002 %T Learning from others: Exchange of classification rules in intelligent distributed systems %V 187--188
Gruber, T., Meixner, B., Prosser, J., Sick, B.: Handedness Tests for Preschool Children: A Novel Approach Based on Graphics Tablets and Support Vector Machines Applied Soft Computing. 12, 1390–1398 (2012). https://doi.org/10.1016/j.asoc.2011.11.022.
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@article{gruber2012handedness, author = {Gruber, T. and Meixner, B. and Prosser, J. and Sick, B.}, journal = {Applied Soft Computing}, keywords = {imported}, number = 4, pages = {1390–1398}, publisher = {Elsevier}, title = {Handedness Tests for Preschool Children: A Novel Approach Based on Graphics Tablets and Support Vector Machines}, volume = 12, year = 2012 }
%0 Journal Article %1 gruber2012handedness %A Gruber, T. %A Meixner, B. %A Prosser, J. %A Sick, B. %D 2012 %I Elsevier %J Applied Soft Computing %N 4 %P 1390--1398 %R 10.1016/j.asoc.2011.11.022 %T Handedness Tests for Preschool Children: A Novel Approach Based on Graphics Tablets and Support Vector Machines %V 12
Embrechts, M.J., Gatti, C.J., Linton, J.D., Gruber, T., Sick, B.: Forecasting exchange rates with ensemble neural networks and ensemble K-PLS: A case study for the US Dollar per Indian Rupee In: International Joint Conference on Neural Networks (IJCNN). bll 1–8. IEEE, Brisbane, Australia (2012). https://doi.org/10.1109/ijcnn.2012.6252739.
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@inproceedings{embrechts2012forecasting, address = {Brisbane, Australia}, author = {Embrechts, M. J. and Gatti, C. J. and Linton, J. D. and Gruber, T. and Sick, B.}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {imported}, pages = {1–8}, publisher = {IEEE}, title = {Forecasting exchange rates with ensemble neural networks and ensemble {K-PLS}: A case study for the {US Dollar per Indian Rupee}}, year = 2012 }
%0 Conference Paper %1 embrechts2012forecasting %A Embrechts, M. J. %A Gatti, C. J. %A Linton, J. D. %A Gruber, T. %A Sick, B. %B International Joint Conference on Neural Networks (IJCNN) %C Brisbane, Australia %D 2012 %I IEEE %P 1--8 %R 10.1109/ijcnn.2012.6252739 %T Forecasting exchange rates with ensemble neural networks and ensemble K-PLS: A case study for the US Dollar per Indian Rupee
Giedl-Wagner, R., Miller, T., Sick, B.: Determination of Optimal CT Scan Parameters Using Radial Basis Function Neural Networks In: Conference on Industrial Computed Tomography (iCT). bll 221–228. , Wels, Austria (2012).
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- [ EndNote ]
- [ URL ]
@inproceedings{giedlwagner2012determination, address = {Wels, Austria}, author = {Giedl-Wagner, R. and Miller, T. and Sick, B.}, booktitle = {Conference on Industrial Computed Tomography (iCT)}, keywords = {imported}, pages = {221–228}, title = {Determination of Optimal {CT} Scan Parameters Using Radial Basis Function Neural Networks}, year = 2012 }
%0 Conference Paper %1 giedlwagner2012determination %A Giedl-Wagner, R. %A Miller, T. %A Sick, B. %B Conference on Industrial Computed Tomography (iCT) %C Wels, Austria %D 2012 %P 221--228 %T Determination of Optimal CT Scan Parameters Using Radial Basis Function Neural Networks %U https://www.ndt.net/article/ctc2012/papers/235.pdf

2011[ to top ]

Gottfried, T., Fliege, R., Frömberg, J., Heckmann, G., Sick, B., Triller, U., Wunsch, M.: Wissenschaftspropädeutisches Arbeiten im W-Seminar: Grundlagen -- Chancen -- Herausforderungen, (2011).
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@misc{gottfried2011wissenschaftspropadeutisches, address = {Munich, Germany}, author = {Gottfried, T. and Fliege, R. and Fr{\"o}mberg, J. and Heckmann, G. and Sick, B. and Triller, U. and Wunsch, M.}, keywords = {imported}, title = {Wissenschaftsprop{\"a}deutisches {Arbeiten} im {W-Seminar}: Grundlagen – {Chancen} – {Herausforderungen}}, year = 2011 }
%0 Generic %1 gottfried2011wissenschaftspropadeutisches %A Gottfried, T. %A Fliege, R. %A Frömberg, J. %A Heckmann, G. %A Sick, B. %A Triller, U. %A Wunsch, M. %C Munich, Germany %D 2011 %T Wissenschaftspropädeutisches Arbeiten im W-Seminar: Grundlagen -- Chancen -- Herausforderungen
Fisch, D., Gruber, T., Sick, B.: SwiftRule: Mining Comprehensible Classification Rules for Time Series Analysis IEEE Transactions on Knowledge and Data Engineering. 23, 774–787 (2011). https://doi.org/10.1109/tkde.2010.161.
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@article{fisch2011swiftrule, author = {Fisch, D. and Gruber, T. and Sick, B.}, journal = {IEEE Transactions on Knowledge and Data Engineering}, keywords = {imported}, number = 5, pages = {774–787}, publisher = {IEEE}, title = {{SwiftRule}: Mining Comprehensible Classification Rules for Time Series Analysis}, volume = 23, year = 2011 }
%0 Journal Article %1 fisch2011swiftrule %A Fisch, D. %A Gruber, T. %A Sick, B. %D 2011 %I IEEE %J IEEE Transactions on Knowledge and Data Engineering %N 5 %P 774--787 %R 10.1109/tkde.2010.161 %T SwiftRule: Mining Comprehensible Classification Rules for Time Series Analysis %V 23
Hofmann, A., Sick, B.: On-Line Intrusion Alert Aggregation With Generative Data Stream Modeling IEEE Transactions on Dependable and Secure Computing. 8, 282–294 (2011). https://doi.org/10.1109/tdsc.2009.36.
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@article{hofmann2011line, author = {Hofmann, A. and Sick, B.}, journal = {IEEE Transactions on Dependable and Secure Computing}, keywords = {imported}, number = 2, pages = {282–294}, publisher = {IEEE}, title = {On-Line Intrusion Alert Aggregation With Generative Data Stream Modeling}, volume = 8, year = 2011 }
%0 Journal Article %1 hofmann2011line %A Hofmann, A. %A Sick, B. %D 2011 %I IEEE %J IEEE Transactions on Dependable and Secure Computing %N 2 %P 282--294 %R 10.1109/tdsc.2009.36 %T On-Line Intrusion Alert Aggregation With Generative Data Stream Modeling %V 8
Sick, B.: Learning: Preface In: Müller-Schloer, C., Schmeck, H., en Ungerer, T. (reds) Organic Computing -- A Paradigm Shift for Complex Systems. bll 235–236. Springer (2011). https://doi.org/10.1007/978-3-0348-0130-0.
- [ BibTeX ]
- [ EndNote ]
@inbook{sick2011learning, author = {Sick, B.}, booktitle = {Organic Computing – A Paradigm Shift for Complex Systems}, chapter = 3, editor = {M{\"u}ller-Schloer, C. and Schmeck, H. and Ungerer, T.}, keywords = {imported}, pages = {235–236}, publisher = {Springer}, title = {Learning: Preface}, year = 2011 }
%0 Book Section %1 sick2011learning %A Sick, B. %B Organic Computing -- A Paradigm Shift for Complex Systems %D 2011 %E Müller-Schloer, C. %E Schmeck, H. %E Ungerer, T. %I Springer %P 235--236 %R 10.1007/978-3-0348-0130-0 %T Learning: Preface %& 3
Fisch, D., Kalkowski, E., Sick, B., Ovaska, S.: In your interest: Objective interestingness measures for a generative classifier In: International Conference on Agents and Artificial Intelligence (ICAART). bll 414–423. , Rome, Italy (2011). https://doi.org/10.5220/0003186404140423.
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@inproceedings{fisch2011your, address = {Rome, Italy}, author = {Fisch, D. and Kalkowski, E. and Sick, B. and Ovaska, S.}, booktitle = {International Conference on Agents and Artificial Intelligence (ICAART)}, keywords = {imported}, pages = {414–423}, title = {In your interest: Objective interestingness measures for a generative classifier}, year = 2011 }
%0 Conference Paper %1 fisch2011your %A Fisch, D. %A Kalkowski, E. %A Sick, B. %A Ovaska, S. %B International Conference on Agents and Artificial Intelligence (ICAART) %C Rome, Italy %D 2011 %P 414--423 %R 10.5220/0003186404140423 %T In your interest: Objective interestingness measures for a generative classifier
Fisch, D., Jänicke, M., Müller-Schloer, C., Sick, B.: Divergence Measures as a Generalised Approach to Quantitative Emergence In: Müller-Schloer, C., Schmeck, H., en Ungerer, T. (reds) Organic Computing -- A Paradigm Shift for Complex Systems. bll 53–66. Springer (2011). https://doi.org/10.1007/978-3-0348-0130-0_3.
- [ BibTeX ]
- [ EndNote ]
@inbook{fisch2011divergence, author = {Fisch, D. and J{\"a}nicke, M. and M{\"u}ller-Schloer, C. and Sick, B.}, booktitle = {Organic Computing – A Paradigm Shift for Complex Systems}, chapter = 1, editor = {M{\"u}ller-Schloer, C. and Schmeck, H. and Ungerer, T.}, keywords = {imported}, pages = {53–66}, publisher = {Springer}, title = {Divergence Measures as a Generalised Approach to Quantitative Emergence}, year = 2011 }
%0 Book Section %1 fisch2011divergence %A Fisch, D. %A Jänicke, M. %A Müller-Schloer, C. %A Sick, B. %B Organic Computing -- A Paradigm Shift for Complex Systems %D 2011 %E Müller-Schloer, C. %E Schmeck, H. %E Ungerer, T. %I Springer %P 53--66 %R 10.1007/978-3-0348-0130-0_3 %T Divergence Measures as a Generalised Approach to Quantitative Emergence %& 1
Fisch, D., Kalkowski, E., Sick, B.: Collaborative Learning by Knowledge Exchange In: Müller-Schloer, C., Schmeck, H., en Ungerer, T. (reds) Organic Computing -- A Paradigm Shift for Complex Systems. bll 267–280. Springer (2011). https://doi.org/10.1007/978-3-0348-0130-0_17.
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- [ EndNote ]
@inbook{fisch2011collaborative, author = {Fisch, D. and Kalkowski, E. and Sick, B.}, booktitle = {Organic Computing – A Paradigm Shift for Complex Systems}, chapter = 3, editor = {M{\"u}ller-Schloer, C. and Schmeck, H. and Ungerer, T.}, keywords = {imported}, pages = {267–280}, publisher = {Springer}, title = {Collaborative Learning by Knowledge Exchange}, year = 2011 }
%0 Book Section %1 fisch2011collaborative %A Fisch, D. %A Kalkowski, E. %A Sick, B. %B Organic Computing -- A Paradigm Shift for Complex Systems %D 2011 %E Müller-Schloer, C. %E Schmeck, H. %E Ungerer, T. %I Springer %P 267--280 %R 10.1007/978-3-0348-0130-0_17 %T Collaborative Learning by Knowledge Exchange %& 3
Bannach, D., Sick, B., Lukowicz, P.: Automatic Adaptation of Mobile Activity Recognition Systems to New Sensors In: Workshop Mobile Sensing: Challenges, Opportunities, and Future Directions, UbiComp. bll 1–5. ACM, Beijing, China (2011).
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- [ EndNote ]
@inproceedings{bannach2011automatic, address = {Beijing, China}, author = {Bannach, D. and Sick, B. and Lukowicz, P.}, booktitle = {Workshop Mobile Sensing: Challenges, Opportunities, and Future Directions, UbiComp}, keywords = {imported}, pages = {1–5}, publisher = {ACM}, title = {Automatic Adaptation of Mobile Activity Recognition Systems to New Sensors}, year = 2011 }
%0 Conference Paper %1 bannach2011automatic %A Bannach, D. %A Sick, B. %A Lukowicz, P. %B Workshop Mobile Sensing: Challenges, Opportunities, and Future Directions, UbiComp %C Beijing, China %D 2011 %I ACM %P 1--5 %T Automatic Adaptation of Mobile Activity Recognition Systems to New Sensors
Reitmaier, T., Sick, B.: Active classifier training with the 3DS strategy In: IEEE Symposium on Computational Intelligence and Data Mining (CIDM). bll 88–95. IEEE, Paris, France (2011). https://doi.org/10.1109/cidm.2011.5949421.
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- [ EndNote ]
@inproceedings{reitmaier2011active, address = {Paris, France}, author = {Reitmaier, T. and Sick, B.}, booktitle = {IEEE Symposium on Computational Intelligence and Data Mining (CIDM)}, keywords = {imported}, pages = {88–95}, publisher = {IEEE}, title = {Active classifier training with the {3DS} strategy}, year = 2011 }
%0 Conference Paper %1 reitmaier2011active %A Reitmaier, T. %A Sick, B. %B IEEE Symposium on Computational Intelligence and Data Mining (CIDM) %C Paris, France %D 2011 %I IEEE %P 88--95 %R 10.1109/cidm.2011.5949421 %T Active classifier training with the 3DS strategy

2010[ to top ]

Fuchs, E., Gruber, T., Pree, H., Sick, B.: Temporal Data Mining Using Shape Space Representations of Time Series Neurocomputing. 74, 379–393 (2010). https://doi.org/10.1016/j.neucom.2010.03.022.
- [ BibTeX ]
- [ EndNote ]
@article{fuchs2010temporal, author = {Fuchs, E. and Gruber, T. and Pree, H. and Sick, B.}, journal = {Neurocomputing}, keywords = {imported}, number = {1–3}, pages = {379–393}, publisher = {Elsevier}, title = {Temporal Data Mining Using Shape Space Representations of Time Series}, volume = 74, year = 2010 }
%0 Journal Article %1 fuchs2010temporal %A Fuchs, E. %A Gruber, T. %A Pree, H. %A Sick, B. %D 2010 %I Elsevier %J Neurocomputing %N 1--3 %P 379--393 %R 10.1016/j.neucom.2010.03.022 %T Temporal Data Mining Using Shape Space Representations of Time Series %V 74
Fisch, D., Jänicke, M., Sick, B., Müller-Schloer, C.: Quantitative Emergence -- A Refined Approach Based on Divergence Measures In: IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO). bll 94–103. IEEE, Budapest, Hungary (2010). https://doi.org/10.1109/saso.2010.31.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{fisch2010quantitative, address = {Budapest, Hungary}, author = {Fisch, D. and J{\"a}nicke, M. and Sick, B. and M{\"u}ller-Schloer, C.}, booktitle = {IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO)}, keywords = {imported}, pages = {94–103}, publisher = {IEEE}, title = {Quantitative Emergence – A Refined Approach Based on Divergence Measures}, year = 2010 }
%0 Conference Paper %1 fisch2010quantitative %A Fisch, D. %A Jänicke, M. %A Sick, B. %A Müller-Schloer, C. %B IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO) %C Budapest, Hungary %D 2010 %I IEEE %P 94--103 %R 10.1109/saso.2010.31 %T Quantitative Emergence -- A Refined Approach Based on Divergence Measures
Gruber, C., Gruber, T., Krinninger, S., Sick, B.: Online Signature Verification With Support Vector Machines Based on LCSS Kernel Functions IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 40, 1088–1100 (2010). https://doi.org/10.1109/tsmcb.2009.2034382.
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- [ EndNote ]
@article{gruber2010online, author = {Gruber, C. and Gruber, T. and Krinninger, S. and Sick, B.}, journal = {IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)}, keywords = {imported}, number = 4, pages = {1088–1100}, publisher = {IEEE}, title = {Online Signature Verification With Support Vector Machines Based on {LCSS} Kernel Functions}, volume = 40, year = 2010 }
%0 Journal Article %1 gruber2010online %A Gruber, C. %A Gruber, T. %A Krinninger, S. %A Sick, B. %D 2010 %I IEEE %J IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) %N 4 %P 1088--1100 %R 10.1109/tsmcb.2009.2034382 %T Online Signature Verification With Support Vector Machines Based on LCSS Kernel Functions %V 40
Fuchs, E., Gruber, T., Nitschke, J., Sick, B.: Online Segmentation of Time Series Based on Polynomial Least-Squares Approximations IEEE Transactions on Pattern Analysis and Machine Intelligence. 32, 2232–2245 (2010). https://doi.org/10.1109/tpami.2010.44.
- [ BibTeX ]
- [ EndNote ]
@article{fuchs2010online, author = {Fuchs, E. and Gruber, T. and Nitschke, J. and Sick, B.}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, keywords = {imported}, number = 12, pages = {2232–2245}, publisher = {IEEE}, title = {Online Segmentation of Time Series Based on Polynomial Least-Squares Approximations}, volume = 32, year = 2010 }
%0 Journal Article %1 fuchs2010online %A Fuchs, E. %A Gruber, T. %A Nitschke, J. %A Sick, B. %D 2010 %I IEEE %J IEEE Transactions on Pattern Analysis and Machine Intelligence %N 12 %P 2232--2245 %R 10.1109/tpami.2010.44 %T Online Segmentation of Time Series Based on Polynomial Least-Squares Approximations %V 32

2007[ to top ]

Dose, M., Gruber, C., Grunz, A., Hook, C., Kempf, J., Scharfenberg, G., Sick, B.: Towards an Automated Analysis of Neuroleptics’ Impact on Human Hand Motor Skills In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), SSCI. bll 494–501. IEEE (2007).
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
@inproceedings{dose2007towards, author = {Dose, Matthias and Gruber, Christian and Grunz, Ariane and Hook, Christian and Kempf, J{\"{u}}rgen and Scharfenberg, Georg and Sick, Bernhard}, booktitle = {IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), SSCI}, keywords = {imported}, pages = {494–501}, publisher = {IEEE}, title = {Towards an Automated Analysis of Neuroleptics' Impact on Human Hand Motor Skills}, year = 2007 }
%0 Conference Paper %1 dose2007towards %A Dose, Matthias %A Gruber, Christian %A Grunz, Ariane %A Hook, Christian %A Kempf, Jürgen %A Scharfenberg, Georg %A Sick, Bernhard %B IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), SSCI %D 2007 %I IEEE %P 494--501 %T Towards an Automated Analysis of Neuroleptics' Impact on Human Hand Motor Skills %U http://ieeexplore.ieee.org/document/4221261/

2006[ to top ]

Hofer, J., Gruber, C., Sick, B.: Biometric Analysis of Handwriting Dynamics Using a Script Generator Model In: IEEE Mountain Workshop on Adaptive and Learning Systems. bll 36–41. IEEE, Logan (2006). https://doi.org/10.1109/smcals.2006.250689.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{hofer2006biometric, address = {Logan}, author = {Hofer, J. and Gruber, C. and Sick, B.}, booktitle = {IEEE Mountain Workshop on Adaptive and Learning Systems}, keywords = {imported}, pages = {36–41}, publisher = {IEEE}, title = {Biometric Analysis of Handwriting Dynamics Using a Script Generator Model}, year = 2006 }
%0 Conference Paper %1 hofer2006biometric %A Hofer, J. %A Gruber, C. %A Sick, B. %B IEEE Mountain Workshop on Adaptive and Learning Systems %C Logan %D 2006 %I IEEE %P 36--41 %R 10.1109/smcals.2006.250689 %T Biometric Analysis of Handwriting Dynamics Using a Script Generator Model