Publikationen

Wissenschaftliche Publikationen des Instituts der letzten zwei Jahre

2023

Spatio-temporal LPV model of 2D workpiece temperature for Direct Laser Deposition. Pereira, Guilherme; Jelicic, Goran; Kroll, Andreas. In 22nd IFAC World Congress. IFAC, Yokohama, 2023.
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@inproceedings{Pereira-IFAC-2023, address = {Yokohama}, author = {Pereira, Guilherme and Jelicic, Goran and Kroll, Andreas}, booktitle = {22nd IFAC World Congress}, keywords = {isac-www}, month = {Juli}, note = {accepted}, organization = {IFAC}, title = {Spatio-temporal LPV model of 2D workpiece temperature for Direct Laser Deposition}, year = 2023 }
An Airborne Measurement System to Detect, Locate and Quantify Methane Emissions. Kistner, Lars; Schmoll, Robert; Kroll, Andreas. In Sensor and Measurement Science International Conference (SMSI) 2023. AMA Verband für Sensorik und Messtechnik e.V., Nürnberg, 2023.
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@inproceedings{Kistner2023, address = {Nürnberg}, author = {Kistner, Lars and Schmoll, Robert and Kroll, Andreas}, booktitle = {Sensor and Measurement Science International Conference (SMSI) 2023}, keywords = {isac-www}, month = {May}, note = {accepted}, organization = {AMA Verband für Sensorik und Messtechnik e.V.}, title = {An Airborne Measurement System to Detect, Locate and Quantify Methane Emissions}, year = 2023 }
Integration of a High-Precision 3D Sensor into a 3D Thermography System. Mendez, Miguel; Schramm, Sebastian; Schmoll, Robert; Kroll, Andreas. In Sensor and Measurement Science International Conference (SMSI) 2023. AMA Verband für Sensorik und Messtechnik e.V., Nürnberg, 2023.
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@inproceedings{Mendez2023, address = {Nürnberg}, author = {Mendez, Miguel and Schramm, Sebastian and Schmoll, Robert and Kroll, Andreas}, booktitle = {Sensor and Measurement Science International Conference (SMSI) 2023}, keywords = {isac-www}, month = {May}, note = {accepted}, organization = {AMA Verband für Sensorik und Messtechnik e.V.}, title = {Integration of a High-Precision 3D Sensor into a 3D Thermography System}, year = 2023 }
A Digital Twin for Part QualityPrediction and Control in Plastic Injection Molding. Rehmer, Alexander; Klute, Marco; Kroll, Andreas; Heim, Hans-Peter. In Modeling, Identification, and Control for Cyber-Physical Systems Towards Industry 4.0, 1st , P. Mercorelli, W. Zhang, H. Nemati, Y. Zhang (reds.). Elsevier, 2023.
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@inbook{EmergMeth_Rehmer_2022, author = {Rehmer, Alexander and Klute, Marco and Kroll, Andreas and Heim, Hans-Peter}, booktitle = {Modeling, Identification, and Control for Cyber-Physical Systems Towards Industry 4.0}, edition = {1st}, editor = {Mercorelli, Paolo and Zhang, Weicun and Nemati, Hamidreza and Zhang, YuMing}, keywords = {isac-www}, note = {accepted}, publisher = {Elsevier}, series = {Emerging Methodologies and Applications in Modelling, Identification and Control}, title = {A Digital Twin for Part QualityPrediction and Control in Plastic Injection Molding}, year = 2023 }
Model-Based Optimization of Vaccination Strategies in Different Phases of Pandemic Virus Spread. Liu, Z.; Omayrat, M.; Stursberg, O. In Informatics in Control, Automation and Robotics. ICINCO 2021. Lecture Notes in Electrical Engineering, 1006, bll 185–208. 2023.
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This paper aims at applying optimal control principles to investigate optimal vaccination strategies in different phases of a pandemic. Background of the study is that many countries have started their vaccination procedures against the COVID-19 disease in the beginning of 2021, but supply shortages for the vaccines prevented that everyone could be vaccinated immediately. At the beginning of 2022, in contrast, the vaccine supply was ample, but the effectiveness of different existing vaccines to avoid infection by new virus variants was in doubt, as well as the acceptance of booster doses decreased over time. To account for these effects, two formulations of optimization tasks based on different epidemic models are proposed in this paper. The solution of these tasks determines optimal distribution strategies for available vaccines, and optimized vaccination schemes to reduce the need of booster doses for later phase. Effectiveness of these strategies compared with other popular strategies (as applied in practice) is demonstrated through a series of simulations.
@article{liu2023modelbased, abstract = {This paper aims at applying optimal control principles to investigate optimal vaccination strategies in different phases of a pandemic. Background of the study is that many countries have started their vaccination procedures against the COVID-19 disease in the beginning of 2021, but supply shortages for the vaccines prevented that everyone could be vaccinated immediately. At the beginning of 2022, in contrast, the vaccine supply was ample, but the effectiveness of different existing vaccines to avoid infection by new virus variants was in doubt, as well as the acceptance of booster doses decreased over time. To account for these effects, two formulations of optimization tasks based on different epidemic models are proposed in this paper. The solution of these tasks determines optimal distribution strategies for available vaccines, and optimized vaccination schemes to reduce the need of booster doses for later phase. Effectiveness of these strategies compared with other popular strategies (as applied in practice) is demonstrated through a series of simulations.}, author = {Liu, Z. and Omayrat, M. and Stursberg, O.}, journal = {Informatics in Control, Automation and Robotics. ICINCO 2021. Lecture Notes in Electrical Engineering}, keywords = {isac-www}, pages = {185–208}, title = {Model-Based Optimization of Vaccination Strategies in Different Phases of Pandemic Virus Spread}, volume = 1006, year = 2023 }
Context-aware recommendations for extended electric vehicle battery lifetime. Eider, Markus; Sick, Bernhard; Berl, Andreas. In Sustainable Computing: Informatics and Systems (SUSCOM), 37, bl 100845. Elsevier, 2023.
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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 = {isac-www}, pages = 100845, publisher = {Elsevier}, title = {Context-aware recommendations for extended electric vehicle battery lifetime}, volume = 37, year = 2023 }

2022

Approximation of the Feasible Parameter Set in Bounded-Error Parameter Estimation of Takagi-Sugeno Fuzzy Models for Large Problems by Using a Ray Shooting Method. Wittich, Felix; Kroll, Andras. In Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2022). Padua, Italy, 2022.
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@inproceedings{Wittich_WCCI_2022, address = {Padua, Italy}, author = {Wittich, Felix and Kroll, Andras}, booktitle = {Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2022)}, keywords = {isac-www}, month = {18.-23. July}, title = {Approximation of the Feasible Parameter Set in Bounded-Error Parameter Estimation of Takagi-Sugeno Fuzzy Models for Large Problems by Using a Ray Shooting Method}, year = 2022 }
Transfer Learning as an Essential Tool for Digital Twins in Renewable Energy Systems. Nivarthi, Chandana Priya. In Organic Computing -- Doctoral Dissertation Colloquium 2021, S. Tomforde, C. Krupitzer (reds.), bll 47–59. kassel university press, 2022.
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Transfer learning (TL), the next frontier in machine learning (ML), has gained much popularity in recent years, due to the various challenges faced in ML, like the requirement of vast amounts of training data, expensive and time-consuming labelling processes for data samples, and long training duration for models. TL is useful in tackling these problems, as it focuses on transferring knowledge from previously solved tasks to new tasks. Digital twins and other intelligent systems need to utilise TL to use the previously gained knowledge and solve new tasks in a more self-reliant way, and to incrementally increase their knowledge base. Therefore, in this article, the critical challenges in power forecasting and anomaly detection in the context of renewable energy systems are identified, and a potential TL framework to meet these challenges is proposed. This article also proposes a feature embedding approach to handle the missing sensors data. The proposed TL methods help to make a system more autonomous in the context of organic computing.
@incollection{nivarthi2022transfer, abstract = {Transfer learning (TL), the next frontier in machine learning (ML), has gained much popularity in recent years, due to the various challenges faced in ML, like the requirement of vast amounts of training data, expensive and time-consuming labelling processes for data samples, and long training duration for models. TL is useful in tackling these problems, as it focuses on transferring knowledge from previously solved tasks to new tasks. Digital twins and other intelligent systems need to utilise TL to use the previously gained knowledge and solve new tasks in a more self-reliant way, and to incrementally increase their knowledge base. Therefore, in this article, the critical challenges in power forecasting and anomaly detection in the context of renewable energy systems are identified, and a potential TL framework to meet these challenges is proposed. This article also proposes a feature embedding approach to handle the missing sensors data. The proposed TL methods help to make a system more autonomous in the context of organic computing.}, author = {Nivarthi, Chandana Priya}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2021}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {isac-www}, pages = {47–59}, publisher = {kassel university press}, title = {Transfer Learning as an Essential Tool for Digital Twins in Renewable Energy Systems}, year = 2022 }
FDGNN: Fully Dynamic Graph Neural Network. Moallemy-Oureh, Alice; Beddar-Wiesing, Silcia; Nather, Rüdiger; Thomas, Josephine M. In arXiv e-prints, bl arXiv:2206.03469. 2022.
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Dynamic Graph Neural Networks recently became more and more important as graphs from many scientific fields, ranging from mathematics, biology, social sciences, and physics to computer science, are dynamic by nature. While temporal changes (dynamics) play an essential role in many real-world applications, most of the models in the literature on Graph Neural Networks (GNN) process static graphs. The few GNN models on dynamic graphs only consider exceptional cases of dynamics, e.g., node attribute-dynamic graphs or structure-dynamic graphs limited to additions or changes to the graph's edges, etc. Therefore, we present a novel Fully Dynamic Graph Neural Network (FDGNN) that can handle fully-dynamic graphs in continuous time. The proposed method provides a node and an edge embedding that includes their activity to address added and deleted nodes or edges, and possible attributes. Furthermore, the embeddings specify Temporal Point Processes for each event to encode the distributions of the structure- and attribute-related incoming graph events. In addition, our model can be updated efficiently by considering single events for local retraining.
@article{moallemyoureh2022fdgnn, abstract = {Dynamic Graph Neural Networks recently became more and more important as graphs from many scientific fields, ranging from mathematics, biology, social sciences, and physics to computer science, are dynamic by nature. While temporal changes (dynamics) play an essential role in many real-world applications, most of the models in the literature on Graph Neural Networks (GNN) process static graphs. The few GNN models on dynamic graphs only consider exceptional cases of dynamics, e.g., node attribute-dynamic graphs or structure-dynamic graphs limited to additions or changes to the graph's edges, etc. Therefore, we present a novel Fully Dynamic Graph Neural Network (FDGNN) that can handle fully-dynamic graphs in continuous time. The proposed method provides a node and an edge embedding that includes their activity to address added and deleted nodes or edges, and possible attributes. Furthermore, the embeddings specify Temporal Point Processes for each event to encode the distributions of the structure- and attribute-related incoming graph events. In addition, our model can be updated efficiently by considering single events for local retraining.}, author = {Moallemy-Oureh, Alice and Beddar-Wiesing, Silcia and Nather, Rüdiger and Thomas, Josephine M.}, journal = {arXiv e-prints}, keywords = {isac-www}, pages = {arXiv:2206.03469}, title = {FDGNN: Fully Dynamic Graph Neural Network}, year = 2022 }
A Review of Uncertainty Calibration in Pretrained Object Detectors. Huseljic, Denis; Herde, Marek; Muejde, Mehmet; Sick, Bernhard. In arXiv e-prints, bl arXiv:2210.02935. 2022.
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In the field of deep learning based computer vision, the development of deep object detection has led to unique paradigms (e.g., two-stage or set-based) and architectures (e.g., Faster-RCNN or DETR) which enable outstanding performance on challenging benchmark datasets. Despite this, the trained object detectors typically do not reliably assess uncertainty regarding their own knowledge, and the quality of their probabilistic predictions is usually poor. As these are often used to make subsequent decisions, such inaccurate probabilistic predictions must be avoided. In this work, we investigate the uncertainty calibration properties of different pretrained object detection architectures in a multi-class setting. We propose a framework to ensure a fair, unbiased, and repeatable evaluation and conduct detailed analyses assessing the calibration under distributional changes (e.g., distributional shift and application to out-of-distribution data). Furthermore, by investigating the influence of different detector paradigms, post-processing steps, and suitable choices of metrics, we deliver novel insights into why poor detector calibration emerges. Based on these insights, we are able to improve the calibration of a detector by simply finetuning its last layer.
@article{huseljic2022review, abstract = {In the field of deep learning based computer vision, the development of deep object detection has led to unique paradigms (e.g., two-stage or set-based) and architectures (e.g., Faster-RCNN or DETR) which enable outstanding performance on challenging benchmark datasets. Despite this, the trained object detectors typically do not reliably assess uncertainty regarding their own knowledge, and the quality of their probabilistic predictions is usually poor. As these are often used to make subsequent decisions, such inaccurate probabilistic predictions must be avoided. In this work, we investigate the uncertainty calibration properties of different pretrained object detection architectures in a multi-class setting. We propose a framework to ensure a fair, unbiased, and repeatable evaluation and conduct detailed analyses assessing the calibration under distributional changes (e.g., distributional shift and application to out-of-distribution data). Furthermore, by investigating the influence of different detector paradigms, post-processing steps, and suitable choices of metrics, we deliver novel insights into why poor detector calibration emerges. Based on these insights, we are able to improve the calibration of a detector by simply finetuning its last layer.}, author = {Huseljic, Denis and Herde, Marek and Muejde, Mehmet and Sick, Bernhard}, journal = {arXiv e-prints}, keywords = {isac-www}, pages = {arXiv:2210.02935}, title = {A Review of Uncertainty Calibration in Pretrained Object Detectors}, year = 2022 }
A Holistic View on Probabilistic Trajectory Forecasting -- Case Study: Cyclist Intention Detection. Zernetsch, Stefan; Reichert, Hannes; Kress, Viktor; Doll, Konrad; Sick, Bernhard. In IEEE Intelligent Vehicles Symposium (IV), bll 265–272. IEEE, 2022.
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This article presents a holistic approach for probabilistic cyclist intention detection. By combining probabilities for the current cyclist motion states with a probabilistic ensemble trajectory forecast, we are able to generate reliable estimates of cyclists’ future positions. The probabilities are used as weights in the probabilistic ensemble trajectory forecast. The ensemble consists of specialized models, which produce individual forecasts in the form of Gaussian distributions under the assumption of a certain motion state of the cyclist (e.g. cyclist is starting). By weighting the specialized models, we create forecasts in the form of Gaussian mixtures that define regions within which the cyclists will reside with a certain probability. To evaluate our method, we rate the reliability, sharpness, and positional accuracy of our forecasted distributions. We compare our method to a widely used unimodal approach which produces forecasts in the form of Gaussian distributions and show that our method is able to produce more reliable and sharper outputs while retaining comparable positional accuracy. By comparing two different methods for basic movement detection, one based on the past cyclist trajectory and one using past image sequences, we demonstrate that the results can be further improved by incorporating video information. Our methods are evaluated using a dataset created at a public intersection. The code and the dataset are publicly available.
@inproceedings{zernetsch2022holistic, abstract = {This article presents a holistic approach for probabilistic cyclist intention detection. By combining probabilities for the current cyclist motion states with a probabilistic ensemble trajectory forecast, we are able to generate reliable estimates of cyclists’ future positions. The probabilities are used as weights in the probabilistic ensemble trajectory forecast. The ensemble consists of specialized models, which produce individual forecasts in the form of Gaussian distributions under the assumption of a certain motion state of the cyclist (e.g. cyclist is starting). By weighting the specialized models, we create forecasts in the form of Gaussian mixtures that define regions within which the cyclists will reside with a certain probability. To evaluate our method, we rate the reliability, sharpness, and positional accuracy of our forecasted distributions. We compare our method to a widely used unimodal approach which produces forecasts in the form of Gaussian distributions and show that our method is able to produce more reliable and sharper outputs while retaining comparable positional accuracy. By comparing two different methods for basic movement detection, one based on the past cyclist trajectory and one using past image sequences, we demonstrate that the results can be further improved by incorporating video information. Our methods are evaluated using a dataset created at a public intersection. The code and the dataset are publicly available.}, author = {Zernetsch, Stefan and Reichert, Hannes and Kress, Viktor and Doll, Konrad and Sick, Bernhard}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, keywords = {isac-www}, pages = {265–272}, publisher = {IEEE}, title = {A Holistic View on Probabilistic Trajectory Forecasting – Case Study: Cyclist Intention Detection}, year = 2022 }
Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning. He, Yujiang; Huang, Zhixin; Sick, Bernhard. In Workshop on Interactive Machine Learning Workshop (IMLW), AAAI, bll 1–6. 2022.
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Continual learning can enable neural networks to evolve by learning new tasks sequentially in task-changing scenarios. However, two general challenges should be overcome in further research before we apply this technique to real-world applications. Firstly, newly collected novelties from the data stream in applications could contain anomalies that are meaningless for continual learning. Instead of viewing them as a new task for updating, we have to filter out such anomalies to reduce the disturbance of extremely high-entropy data for the progression of convergence. Secondly, fewer efforts have been put into research regarding the explainability of continual learning, which leads to a lack of transparency and credibility of the updated neural networks. Elaborated explanations about the process and result of continual learning can help experts in judgment and making decisions. Therefore, we propose the conceptual design of an explainability module with experts in the loop based on techniques, such as dimension reduction, visualization, and evaluation strategies. This work aims to overcome the mentioned challenges by sufficiently explaining and visualizing the identified anomalies and the updated neural network. With the help of this module, experts can be more confident in decision-making regarding anomaly filtering, dynamic adjustment of hyperparameters, data backup, etc.
@inproceedings{he2022design, abstract = {Continual learning can enable neural networks to evolve by learning new tasks sequentially in task-changing scenarios. However, two general challenges should be overcome in further research before we apply this technique to real-world applications. Firstly, newly collected novelties from the data stream in applications could contain anomalies that are meaningless for continual learning. Instead of viewing them as a new task for updating, we have to filter out such anomalies to reduce the disturbance of extremely high-entropy data for the progression of convergence. Secondly, fewer efforts have been put into research regarding the explainability of continual learning, which leads to a lack of transparency and credibility of the updated neural networks. Elaborated explanations about the process and result of continual learning can help experts in judgment and making decisions. Therefore, we propose the conceptual design of an explainability module with experts in the loop based on techniques, such as dimension reduction, visualization, and evaluation strategies. This work aims to overcome the mentioned challenges by sufficiently explaining and visualizing the identified anomalies and the updated neural network. With the help of this module, experts can be more confident in decision-making regarding anomaly filtering, dynamic adjustment of hyperparameters, data backup, etc.}, author = {He, Yujiang and Huang, Zhixin and Sick, Bernhard}, booktitle = {Workshop on Interactive Machine Learning Workshop (IMLW), AAAI}, keywords = {Evaluation}, pages = {1–6}, title = {Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning}, year = 2022 }
Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving. Rösch, Kevin; Heidecker, Florian; Truetsch, Julian; Kowol, Kamil; Schicktanz, Clemens; Bieshaar, Maarten; Sick, Bernhard; Stiller, Christoph. In IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (IEEE CIVTS), IEEE SSCI. 2022.
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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 = {isac-www}, note = {(accepted)}, title = {Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving}, year = 2022 }
Unified Autoencoder with Task Embeddings for Multi-Task Learning in Renewable Power Forecasting. Nivarthi, Chandana Priya; Vogt, Stephan; Sick, Bernhard. In International Conference on Machine Learning and Applications (ICMLA). IEEE, 2022.
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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 = {isac-www}, note = {(accepted)}, publisher = {IEEE}, title = {Unified Autoencoder with Task Embeddings for Multi-Task Learning in Renewable Power Forecasting}, year = 2022 }
On Stability of Network Control Systems with Switching Transmission Probabilities. Hahn, Jannik; Stursberg, Olaf. In IFAC-PapersOnLine, 55(13), bll 276–281. 2022.
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The paper addresses modeling, stability analysis, and control of a class of networked control systems, in which the communication network, and thus the probability for successful transmission of sensor data, varies over time. This variation is modeled probabilistically by Markov chains with time-varying transition probabilities. The offline synthesis of output feedback controllers for this system class is addressed with focus on criteria for mean square stability.
@article{HAHN2022276, abstract = {The paper addresses modeling, stability analysis, and control of a class of networked control systems, in which the communication network, and thus the probability for successful transmission of sensor data, varies over time. This variation is modeled probabilistically by Markov chains with time-varying transition probabilities. The offline synthesis of output feedback controllers for this system class is addressed with focus on criteria for mean square stability.}, author = {Hahn, Jannik and Stursberg, Olaf}, journal = {IFAC-PapersOnLine}, keywords = {isac-www}, note = {9th IFAC Conference on Networked Systems NECSYS 2022}, number = 13, pages = {276-281}, title = {On Stability of Network Control Systems with Switching Transmission Probabilities}, volume = 55, year = 2022 }
Distributed Solution of Mixed-Integer Programs by ADMM with Closed Duality Gap. Liu, Zonglin; Stursberg, Olaf. In 61st IEEE Conference on Decision and Control (CDC). 2022.
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@article{liu2022mixedinteger, author = {Liu, Zonglin and Stursberg, Olaf}, journal = {61st IEEE Conference on Decision and Control (CDC)}, keywords = {isac-www}, title = {Distributed Solution of Mixed-Integer Programs by ADMM with Closed Duality Gap}, year = 2022 }
Efficient Fault Detection for Discrete-Time PWA Systems,. Liu, Xinyang; Liu, Zonglin; Wang, Zhenhua; Stursberg, Olaf. In IEEE Control Systems Letters, 6, bll 3361–3366. 2022.
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This letter considers a class of fault detection problems for discrete-time piecewise-affine (PWA) systems. Inspired by the state-of-the-art of set-membership methods to estimate the state of PWA systems, a novel detection method based on zonotopes and over-approximation of sets is proposed to overcome high computational complexity caused by unknown discrete states. The efficiency and high accuracy of detection of the new method is confirmed through both theoretical analysis and numerical examples.
@article{liu2022efficient, abstract = {This letter considers a class of fault detection problems for discrete-time piecewise-affine (PWA) systems. Inspired by the state-of-the-art of set-membership methods to estimate the state of PWA systems, a novel detection method based on zonotopes and over-approximation of sets is proposed to overcome high computational complexity caused by unknown discrete states. The efficiency and high accuracy of detection of the new method is confirmed through both theoretical analysis and numerical examples.}, author = {Liu, Xinyang and Liu, Zonglin and Wang, Zhenhua and Stursberg, Olaf}, journal = {IEEE Control Systems Letters}, keywords = {isac-www}, pages = {3361-3366}, title = {Efficient Fault Detection for Discrete-Time PWA Systems,}, volume = 6, year = 2022 }
Distributed Optimization for Mixed-Integer Consensus in Multi-Agent Networks. Liu, Zonglin; Stursberg, Olaf. In 2022 European Control Conference (ECC), bll 2196–2202. 2022.
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This paper considers the consensus of mixed-integer linear programming (MILP) problems as occurring in distributed control and machine learning of multi-agent networks. Unlike existing work on consensus problems, in which the agents only have to agree on the continuous part of their decision variables, this paper proposes a new method to enable them to also agree on the integer part. This mixed-integer setting may arise from distributed control problems of hybrid dynamical systems, or distributed machine learning problems using support vector machines. It is shown in this paper that the consensus of mixed-integer variables is guaranteed to be achieved by a tailored series of continuous consensus problems.
@article{liu2022distributed, abstract = {This paper considers the consensus of mixed-integer linear programming (MILP) problems as occurring in distributed control and machine learning of multi-agent networks. Unlike existing work on consensus problems, in which the agents only have to agree on the continuous part of their decision variables, this paper proposes a new method to enable them to also agree on the integer part. This mixed-integer setting may arise from distributed control problems of hybrid dynamical systems, or distributed machine learning problems using support vector machines. It is shown in this paper that the consensus of mixed-integer variables is guaranteed to be achieved by a tailored series of continuous consensus problems.}, author = {Liu, Zonglin and Stursberg, Olaf}, journal = {2022 European Control Conference (ECC)}, keywords = {isac-www}, pages = {2196-2202}, title = {Distributed Optimization for Mixed-Integer Consensus in Multi-Agent Networks}, year = 2022 }
Active Learning in Multivariate Time Series Anomaly Detection. Huang, Zhixin. In Organic Computing -- Doctoral Dissertation Colloquium 2021, S. Tomforde, C. Krupitzer (reds.), bll 113–124. kassel university press, 2022.
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Multivariate time series anomaly detection is an important research field of industry, science, and finances. In practical applications, unlabeled data is abundant, and labeled data is rare. Experts’ experience plays a key role in data labeling, while experts often have limited time, are expensive, and are concerned primarily with finding the potential anomalies. Active learning, which has been widely used to select the most informative data for labeling through experts, can improve training with less labeling effort. However, most active learning algorithms have not been evaluated on datasets with imbalanced class frequencies, such as anomaly detection. Although some semi-supervised and active learning methods were proposed to handle univariate time series data, not much work has been done on multivariate time series classification. Therefore, this article identifies critical challenges and proposes potential solutions for active learning in multivariate time series anomaly detection. We combine all possible solutions into a framework that conforms to the scope of the observation/control system in organic computing.
@incollection{huang2022active, abstract = {Multivariate time series anomaly detection is an important research field of industry, science, and finances. In practical applications, unlabeled data is abundant, and labeled data is rare. Experts’ experience plays a key role in data labeling, while experts often have limited time, are expensive, and are concerned primarily with finding the potential anomalies. Active learning, which has been widely used to select the most informative data for labeling through experts, can improve training with less labeling effort. However, most active learning algorithms have not been evaluated on datasets with imbalanced class frequencies, such as anomaly detection. Although some semi-supervised and active learning methods were proposed to handle univariate time series data, not much work has been done on multivariate time series classification. Therefore, this article identifies critical challenges and proposes potential solutions for active learning in multivariate time series anomaly detection. We combine all possible solutions into a framework that conforms to the scope of the observation/control system in organic computing.}, author = {Huang, Zhixin}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2021}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {isac-www}, pages = {113–124}, publisher = {kassel university press}, title = {Active Learning in Multivariate Time Series Anomaly Detection}, year = 2022 }
Actively Controlling and Redesigning Experiments using the Application Case of Free-Electron Laser Pulse Characterization. Dingel, Kristina. In Organic Computing -- Doctoral Dissertation Colloquium 2021, S. Tomforde, C. Krupitzer (reds.), bll 86–98. kassel university press, 2022.
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Experiments are the elective tool for developing new technologies, testing hypotheses, and designing new materials in many scientific fields. Thereby, it is state-of-the-art to create experiments traditionally, starting with a hypothesis leading to the product or relevant data after several experiment cycles. There are some techniques such as automation, parallelization, or inverse design to accelerate experiments. Particularly interesting, however, is the approach to integrate immediate data analysis into the experiment cycle. By analyzing experiment output data on-site using, e.g., neural networks, one can make inferences regarding the experiment's current settings at run-time and furthermore improve them. In the following research project, the goal is to develop and incorporate a data analysis stage into an experimental setup, which can be actively adapted and designed on-site. To demonstrate the developed method, the experimental design of a self-amplification of spontaneous emission pulse characterization experiment at a free-electron laser will be used to show to what extent the experiment can be adapted and optimized.
@incollection{dingel2022actively, abstract = {Experiments are the elective tool for developing new technologies, testing hypotheses, and designing new materials in many scientific fields. Thereby, it is state-of-the-art to create experiments traditionally, starting with a hypothesis leading to the product or relevant data after several experiment cycles. There are some techniques such as automation, parallelization, or inverse design to accelerate experiments. Particularly interesting, however, is the approach to integrate immediate data analysis into the experiment cycle. By analyzing experiment output data on-site using, e.g., neural networks, one can make inferences regarding the experiment's current settings at run-time and furthermore improve them. In the following research project, the goal is to develop and incorporate a data analysis stage into an experimental setup, which can be actively adapted and designed on-site. To demonstrate the developed method, the experimental design of a self-amplification of spontaneous emission pulse characterization experiment at a free-electron laser will be used to show to what extent the experiment can be adapted and optimized.}, author = {Dingel, Kristina}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2021}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {isac-www}, pages = {86–98}, publisher = {kassel university press}, title = {Actively Controlling and Redesigning Experiments using the Application Case of Free-Electron Laser Pulse Characterization}, year = 2022 }
Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts. He, Yujiang. In Organic Computing -- Doctoral Dissertation Colloquium 2021, S. Tomforde, C. Krupitzer (reds.), bll 125–140. kassel university press, 2022.
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Compared with traditional deep learning techniques, continual learning enables deep neural networks to learn continually and adaptively. Deep neural networks have to learn unseen tasks and overcome forgetting the knowledge obtained from previously learned tasks as the amount of data keeps increasing in applications. This article proposes two continual learning application scenarios, i.e., the target-domain incremental scenario and the data-domain incremental scenario, to describe the potential challenges in this context. Based on our previous work regarding the CLeaR framework, which is short for continual learning for regression tasks, models will be enabled to extend themselves and to learn data successively. Research topics are related, but not limited, to developing continual deep learning algorithms, strategies for non-stationarity detection in data streams, explainable and visualizable artificial intelligence, etc. Moreover, the framework- and algorithm-related hyperparameters should be dynamically updated in applications. Forecasting experiments will be conducted based on power generation and consumption data collected from real-world applications. A series of comprehensive evaluation metrics and visualization tools are applied to access the experimental results. The proposed framework is expected to be generally applied to other constantly changing scenarios.
@incollection{he2022adaptive, abstract = {Compared with traditional deep learning techniques, continual learning enables deep neural networks to learn continually and adaptively. Deep neural networks have to learn unseen tasks and overcome forgetting the knowledge obtained from previously learned tasks as the amount of data keeps increasing in applications. This article proposes two continual learning application scenarios, i.e., the target-domain incremental scenario and the data-domain incremental scenario, to describe the potential challenges in this context. Based on our previous work regarding the CLeaR framework, which is short for continual learning for regression tasks, models will be enabled to extend themselves and to learn data successively. Research topics are related, but not limited, to developing continual deep learning algorithms, strategies for non-stationarity detection in data streams, explainable and visualizable artificial intelligence, etc. Moreover, the framework- and algorithm-related hyperparameters should be dynamically updated in applications. Forecasting experiments will be conducted based on power generation and consumption data collected from real-world applications. A series of comprehensive evaluation metrics and visualization tools are applied to access the experimental results. The proposed framework is expected to be generally applied to other constantly changing scenarios.}, author = {He, Yujiang}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2021}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {isac-www}, pages = {125–140}, publisher = {kassel university press}, title = {Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts}, year = 2022 }
Deep Adaptive Knowledge-Aware Learning for E-Mobility. Ali, Mohammad Wazed. In Organic Computing - Doctoral Dissertation Colloquium 2022. kassel university press, 2022.
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Electromobility can reduce global energy demand by reducing the dependence on fossil fuels. Many different types of electric vehicles (EVs) have been produced recently. They are in high demand, and EV usage is also rising. However, there is no fixed charging behavior or energy demand for these different types of EV users. Moreover, most renewable energy sources are stochastic and highly dependent on the weather. Thus, it is essential that EV charging demand is controlled and adapted in advance to prevent catastrophic failures such as blackouts. Deep Learning (DL) models are standard technology for power forecasting in many scenarios. However, there is a lack of real-world heterogeneous multi-source data in the e-mobility domain to train a deep learning model from scratch. Moreover, DL models lack model explainability. Therefore, EV stakeholders can benefit from a framework that coordinates deep transfer learning, interactive machine learning, and heterogeneous graph representation learning (to ensure self-explainability). This article aims to develop a novel framework to solve e-mobility-related tasks such as forecasting EV loads efficiently in a resource-constrained environment (data, computing power, etc.).
@incollection{ali2022deep, abstract = {Electromobility can reduce global energy demand by reducing the dependence on fossil fuels. Many different types of electric vehicles (EVs) have been produced recently. They are in high demand, and EV usage is also rising. However, there is no fixed charging behavior or energy demand for these different types of EV users. Moreover, most renewable energy sources are stochastic and highly dependent on the weather. Thus, it is essential that EV charging demand is controlled and adapted in advance to prevent catastrophic failures such as blackouts. Deep Learning (DL) models are standard technology for power forecasting in many scenarios. However, there is a lack of real-world heterogeneous multi-source data in the e-mobility domain to train a deep learning model from scratch. Moreover, DL models lack model explainability. Therefore, EV stakeholders can benefit from a framework that coordinates deep transfer learning, interactive machine learning, and heterogeneous graph representation learning (to ensure self-explainability). This article aims to develop a novel framework to solve e-mobility-related tasks such as forecasting EV loads efficiently in a resource-constrained environment (data, computing power, etc.).}, author = {Ali, Mohammad Wazed}, booktitle = {Organic Computing - Doctoral Dissertation Colloquium 2022}, keywords = {isac-www}, note = {(accepted)}, publisher = {kassel university press}, title = {Deep Adaptive Knowledge-Aware Learning for E-Mobility}, year = 2022 }
Stream-Based Active Learning in Changing Environments under Verification Latency. Pham, Tuan. In Organic Computing -- Doctoral Dissertation Colloquium 2021, S. Tomforde, C. Krupitzer (reds.), bll 152–164. kassel university press, 2022.
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Labeling data often requires human interaction, which is slow and costly depending on the required expertise. Stream-based active learning reduces the cost of labeling for data stream mining by only querying the labels for instances that improve the classifier the most. However, state-of-the-art stream-based active learning strategies assume that verification latency, i.e., the time between a query and the availability of the queried label, is negligible. In reality, this is usually not the case as the experts who provide such labels are not available all the time, and the labeling process itself requires time. This article investigates the impact of verification latency within a stream-based active learning scenario for changing data streams. We focus on two different types of changes that occur over time, i.e., changes in the number of features and changes in the distribution of instances and/or labels. In particular, we identify challenges that occur due to verification latency and propose ideas to overcome these problems within a stream-based active learning strategy.
@incollection{pham2022stream-based, abstract = {Labeling data often requires human interaction, which is slow and costly depending on the required expertise. Stream-based active learning reduces the cost of labeling for data stream mining by only querying the labels for instances that improve the classifier the most. However, state-of-the-art stream-based active learning strategies assume that verification latency, i.e., the time between a query and the availability of the queried label, is negligible. In reality, this is usually not the case as the experts who provide such labels are not available all the time, and the labeling process itself requires time. This article investigates the impact of verification latency within a stream-based active learning scenario for changing data streams. We focus on two different types of changes that occur over time, i.e., changes in the number of features and changes in the distribution of instances and/or labels. In particular, we identify challenges that occur due to verification latency and propose ideas to overcome these problems within a stream-based active learning strategy.}, author = {Pham, Tuan}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2021}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {isac-www}, pages = {152–164}, publisher = {kassel university press}, title = {Stream-Based Active Learning in Changing Environments under Verification Latency}, year = 2022 }
Student Research Abstract: Using Local Activity Encoding for Dynamic Graph Pooling in Stuctural-Dynamic Graphs. Beddar-Wiesing, Silvia. In ACM/SIGAPP Symposium on Applied Computing (SAC), bll 604–609. ACM, 2022.
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Graphs have recently become more popular for modeling real-world as well as theoretical problems, and several Machine Learning algorithms for learning on them have developed. For graphs that change over time, however, research is still at an early stage. This article proposes a framework DynHeatmaP that generates a constant-size graph sequence representation of graphs whose structure changes over time (structural-dynamic, SDG) in linear time. The framework uses a representation of additions and deletions of nodes and edges (events) to perform dynamic graph pooling. Our algorithm translates the dynamic changes of the nodes and edges to a heatmap representation of the current node activity, regarding additions and deletions in the neighborhood, respectively. The created activity heatmap is utilized as probabilities of the nodes to be sampled in the subsequent pooling step. In addition, the neighborhood of selected nodes has been sampled such that the ratio of additions and deletions is represented accordingly. The obtained downsampled graph sequence can then be used representatively as input to a GNN for learning on the original stream. Focussing on the active regions in a graph, the fast pooling process can reduce the training time significantly and addresses the problem of unbalanced datasets in real-world applications modeled as sparse graphs.
@inproceedings{beddarwiesing2022student, abstract = {Graphs have recently become more popular for modeling real-world as well as theoretical problems, and several Machine Learning algorithms for learning on them have developed. For graphs that change over time, however, research is still at an early stage. This article proposes a framework DynHeatmaP that generates a constant-size graph sequence representation of graphs whose structure changes over time (structural-dynamic, SDG) in linear time. The framework uses a representation of additions and deletions of nodes and edges (events) to perform dynamic graph pooling. Our algorithm translates the dynamic changes of the nodes and edges to a heatmap representation of the current node activity, regarding additions and deletions in the neighborhood, respectively. The created activity heatmap is utilized as probabilities of the nodes to be sampled in the subsequent pooling step. In addition, the neighborhood of selected nodes has been sampled such that the ratio of additions and deletions is represented accordingly. The obtained downsampled graph sequence can then be used representatively as input to a GNN for learning on the original stream. Focussing on the active regions in a graph, the fast pooling process can reduce the training time significantly and addresses the problem of unbalanced datasets in real-world applications modeled as sparse graphs.}, author = {Beddar-Wiesing, Silvia}, booktitle = {ACM/SIGAPP Symposium on Applied Computing (SAC)}, keywords = {isac-www}, pages = {604–609}, publisher = {ACM}, title = {Student Research Abstract: Using Local Activity Encoding for Dynamic Graph Pooling in Stuctural-Dynamic Graphs}, year = 2022 }
A Stopping Criterion for Transductive Active Learning. Kottke, Daniel; Sandrock, Christoph; Krempl, Georg; Sick, Bernhard. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD). Springer, 2022.
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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 = {isac-www}, note = {(accepted)}, publisher = {Springer}, title = {A Stopping Criterion for Transductive Active Learning}, year = 2022 }
Student Research Abstract: Continuous-Time Generative Graph Neural Network for Attributed Dynamic Graphs. Moallemy-Oureh, Alice. In ACM/SIGAPP Symposium on Applied Computing (SAC), bll 600–603. ACM, 2022.
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The history of neural networks dates back to the early 1940s and has not only evolved rapidly over time but they are now amongst one of the most popular and powerful machine learning techniques. In this area, graph representation learning (GRL) using graph neural networks (GNNs) has emerged during the early 90s and developed into an impactful approach for modeling graph-structured real-world data, such as social or biological networks. There are a variety of successful applications of GRL in computational neuroscience, chemistry, mathematics, and so on. While temporal changes (dynamics) play an essential role in many real-world applications, most of the literature on GNNs and GRL, deals with static graphs. Since the few GNN models on dynamic graphs only consider exceptional cases of dynamics (i.e., attribute-dynamic graphs in discrete-time representation or structure-dynamic graphs in continuous-time representation), we aim to present a novel GNN model that can handle attribute-dynamic graphs in continuous time. This model learns any kind of graph information embedding, performs node/edge attribute forecasts, and allows for attribute-dynamic graph generation in both discrete and continuous-time.
@inproceedings{moallemyoureh2022student, abstract = {The history of neural networks dates back to the early 1940s and has not only evolved rapidly over time but they are now amongst one of the most popular and powerful machine learning techniques. In this area, graph representation learning (GRL) using graph neural networks (GNNs) has emerged during the early 90s and developed into an impactful approach for modeling graph-structured real-world data, such as social or biological networks. There are a variety of successful applications of GRL in computational neuroscience, chemistry, mathematics, and so on. While temporal changes (dynamics) play an essential role in many real-world applications, most of the literature on GNNs and GRL, deals with static graphs. Since the few GNN models on dynamic graphs only consider exceptional cases of dynamics (i.e., attribute-dynamic graphs in discrete-time representation or structure-dynamic graphs in continuous-time representation), we aim to present a novel GNN model that can handle attribute-dynamic graphs in continuous time. This model learns any kind of graph information embedding, performs node/edge attribute forecasts, and allows for attribute-dynamic graph generation in both discrete and continuous-time.}, author = {Moallemy-Oureh, Alice}, booktitle = {ACM/SIGAPP Symposium on Applied Computing (SAC)}, keywords = {isac-www}, pages = {600–603}, publisher = {ACM}, title = {Student Research Abstract: Continuous-Time Generative Graph Neural Network for Attributed Dynamic Graphs}, year = 2022 }
Using AoI Forecasts in Communicating and Robust Distributed Model-Predictive Control. Hahn, Jannik; Schoeffauer, Richard; Wunder, Gerhard; Stursberg, Olaf. In IEEE Transactions on Control of Network Systems, 9(2), bll 742–752. 2022.
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In order to enhance the performance of cyber-physical systems, this paper proposes the integrated design of distributed controllers for distributed plants together with the control of the communication network. Conventional design methods use static interfaces between both entities and therefore rely on worst-case estimations of communication delay, often leading to conservative behavior of the overall system. By contrast, the present approach establishes a robust distributed model-predictive control scheme, in which the local subsystem controllers operate under the assumption of a variable communication schedule that is predicted by a network controller. Using appropriate models for the communication network, the network controller applies a predictive network policy for scheduling the communication among the subsystem controllers across the network. Given the resulting time-varying predictions of the age of information, the paper shows under which conditions the subsystem controllers can robustly stabilize the distributed system. To illustrate the approach, the paper also reports on the application to a vehicle platooning scenario.
@article{hahn2022using, abstract = {In order to enhance the performance of cyber-physical systems, this paper proposes the integrated design of distributed controllers for distributed plants together with the control of the communication network. Conventional design methods use static interfaces between both entities and therefore rely on worst-case estimations of communication delay, often leading to conservative behavior of the overall system. By contrast, the present approach establishes a robust distributed model-predictive control scheme, in which the local subsystem controllers operate under the assumption of a variable communication schedule that is predicted by a network controller. Using appropriate models for the communication network, the network controller applies a predictive network policy for scheduling the communication among the subsystem controllers across the network. Given the resulting time-varying predictions of the age of information, the paper shows under which conditions the subsystem controllers can robustly stabilize the distributed system. To illustrate the approach, the paper also reports on the application to a vehicle platooning scenario.}, author = {Hahn, Jannik and Schoeffauer, Richard and Wunder, Gerhard and Stursberg, Olaf}, journal = {IEEE Transactions on Control of Network Systems}, keywords = {isac-www}, number = 2, pages = {742-752}, title = {Using AoI Forecasts in Communicating and Robust Distributed Model-Predictive Control}, volume = 9, year = 2022 }
Uncertain AoI in stochastic optimal control of constrained LTI systems. Hahn, Jannik; Stursberg, Olaf. In at - Automatisierungstechnik, 70(4), bll 343–354. 2022.
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This paper addresses finite-time horizon optimal control of control structures with shared communication network. To cope with the uncertainties, induced by network imperfections and exogenous disturbances at the same time, an optimization-based control scheme is proposed. It uses a disturbance feedback and the Age of Information (AoI), a receiver-based measure of communication delays, as central aspects. The disturbance feedback is an extension of the control law used for balanced stochastic optimal control. Balanced optimality is understood as a compromise between minimizing expected deviations from the reference and the minimization of the uncertainty of future states. Time-varying state constraints as well as time-invariant input constraints are considered, and the controllers are synthesized by semi-definite programming.
@article{hahn2022uncertain, abstract = {This paper addresses finite-time horizon optimal control of control structures with shared communication network. To cope with the uncertainties, induced by network imperfections and exogenous disturbances at the same time, an optimization-based control scheme is proposed. It uses a disturbance feedback and the Age of Information (AoI), a receiver-based measure of communication delays, as central aspects. The disturbance feedback is an extension of the control law used for balanced stochastic optimal control. Balanced optimality is understood as a compromise between minimizing expected deviations from the reference and the minimization of the uncertainty of future states. Time-varying state constraints as well as time-invariant input constraints are considered, and the controllers are synthesized by semi-definite programming.}, author = {Hahn, Jannik and Stursberg, Olaf}, journal = {at - Automatisierungstechnik}, keywords = {isac-www}, pages = {343–354}, title = {Uncertain AoI in stochastic optimal control of constrained LTI systems}, volume = {70(4)}, year = 2022 }
Zum strukturvorwissensbasierten Testsignalentwurf mittels Vorsteuerung für die Identifikation von Takagi-Sugeno-Modellen. Himmelsbach, Matthias; Kroll, Andreas. In at -- Automatisierungstechnik, 70(2), bll 119–133. 2022.
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In diesem Beitrag wird eine Methode zum Entwurf von Testsignalen für die Identifikation lokal-affiner dynamischer Takagi-Sugeno-SISO-Modelle vorgestellt. Diese Methode verwendet ein zuvor identifizierten Prozessmodell und nutzt so erlangtes Wissen über die Struktur mittels modellprädiktiver Regelung aus. Im Rahmen einer akademischen Fallstudie wird die Methode demonstriert.
@article{2022-MH-at-MPC, abstract = {In diesem Beitrag wird eine Methode zum Entwurf von Testsignalen für die Identifikation lokal-affiner dynamischer Takagi-Sugeno-SISO-Modelle vorgestellt. Diese Methode verwendet ein zuvor identifizierten Prozessmodell und nutzt so erlangtes Wissen über die Struktur mittels modellprädiktiver Regelung aus. Im Rahmen einer akademischen Fallstudie wird die Methode demonstriert.}, author = {Himmelsbach, Matthias and Kroll, Andreas}, journal = {at – Automatisierungstechnik}, keywords = {optimaler}, number = 2, pages = {119–133}, title = {Zum strukturvorwissensbasierten Testsignalentwurf mittels Vorsteuerung für die Identifikation von Takagi-Sugeno-Modellen}, volume = 70, year = 2022 }
A Deep Recurrent Neural Network model for affine quasi-LPV System identification. Rehmer, Alexander; Kroll, Andreas. In Preprints of the 20th European Control Conference (ECC). London, UK, 2022.
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@inproceedings{RehmerECC2022_2, address = {London, UK}, author = {Rehmer, Alexander and Kroll, Andreas}, booktitle = {Preprints of the 20th European Control Conference (ECC)}, keywords = {mrt,}, month = {12.-15. July}, note = {accepted}, title = {A Deep Recurrent Neural Network model for affine quasi-LPV System identification}, year = 2022 }
Method and Experimental Investigation of Surface Heat Dissipation Measurement using 3D Thermography. Schmoll, Robert; Schramm, Sebastian; Breitenstein, Tom; Kroll, Andreas. In Journal of Sensors and Sensor Systems (JSSS) - Sensors and Measurement Science International (SMSI) 2021 Special Issue, November, bll 41–49. 2022.
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@article{SchmollJSSS2021, author = {Schmoll, Robert and Schramm, Sebastian and Breitenstein, Tom and Kroll, Andreas}, journal = {Journal of Sensors and Sensor Systems (JSSS) - Sensors and Measurement Science International (SMSI) 2021 Special Issue}, keywords = {mrt,}, pages = {41–49}, title = {Method and Experimental Investigation of Surface Heat Dissipation Measurement using 3D Thermography}, volume = {November}, year = 2022 }
Compensating the Size-of-Source Effect: Relationship between the MTF and a Data-Driven Convolution Filter Approach. Schramm, Sebastian; Ebert, Jannik; Schmoll, Robert; Kroll, Andreas. In 16th Quantitative InfraRed Thermography Conference (QIRT). Paris, Frankreich, 2022.
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@inproceedings{SchrammQIRT2022, address = {Paris, Frankreich}, author = {Schramm, Sebastian and Ebert, Jannik and Schmoll, Robert and Kroll, Andreas}, booktitle = {16th Quantitative InfraRed Thermography Conference (QIRT)}, keywords = {mrt,}, month = {4.-8. Juli}, note = {accepted}, title = {Compensating the Size-of-Source Effect: Relationship between the MTF and a Data-Driven Convolution Filter Approach}, year = 2022 }
Calibration and validation of micromagnetic data for non-destructive analysis of near-surface properties after hard turning. Wegener, Thomas; Liehr, Alexander; Bolender, Artjom; Degener, Sebastian; Wittich, Felix; Kroll, Andreas; Niendorf, Thomas. In HTM Journal of Heat Treatment and Materials. 2022.
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@article{Wegener_HTM2021, author = {Wegener, Thomas and Liehr, Alexander and Bolender, Artjom and Degener, Sebastian and Wittich, Felix and Kroll, Andreas and Niendorf, Thomas}, booktitle = {HTM Journal of Heat Treatment and Materials}, keywords = {mrt,}, note = {accepted}, title = {Calibration and validation of micromagnetic data for non-destructive analysis of near-surface properties after hard turning}, year = 2022 }
Control of Jump Markov Uncertain Linear Systems With General Probability Distributions. Flues, Patrick; Stursberg, Olaf. In Frontiers of Control Eng., Vol 3. 2022.
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This paper introduces a method to control a class of jump Markov linear systems with uncertain initialization of the continuous state and affected by disturbances. Both types of uncertainties are modeled as stochastic processes with arbitrarily chosen probability distributions, for which however, the expected values and (co-)variances are known. The paper elaborates on the control task of steering the uncertain system into a target set by use of continuous controls, while chance constraints have to be satisfied for all possible state sequences of the Markov chain. The proposed approach uses a stochastic model predictive control approach on moving finite-time horizons with tailored constraints to achieve the control goal with prescribed confidence. Key steps of the procedure are (i) to over-approximate probabilistic reachable sets by use of the Chebyshev inequality, and (ii) to embed a tightened version of the original constraints into the optimization problem, in order to obtain a control strategy satisfying the specifications. Convergence of the probabilistic reachable sets is attained by suitable bounding of the state covariance matrices for arbitrary Markov chain sequences. The paper presents the main steps of the solution approach, discusses its properties, and illustrates the principle for a numeric example.
@article{flues2022control, abstract = {This paper introduces a method to control a class of jump Markov linear systems with uncertain initialization of the continuous state and affected by disturbances. Both types of uncertainties are modeled as stochastic processes with arbitrarily chosen probability distributions, for which however, the expected values and (co-)variances are known. The paper elaborates on the control task of steering the uncertain system into a target set by use of continuous controls, while chance constraints have to be satisfied for all possible state sequences of the Markov chain. The proposed approach uses a stochastic model predictive control approach on moving finite-time horizons with tailored constraints to achieve the control goal with prescribed confidence. Key steps of the procedure are (i) to over-approximate probabilistic reachable sets by use of the Chebyshev inequality, and (ii) to embed a tightened version of the original constraints into the optimization problem, in order to obtain a control strategy satisfying the specifications. Convergence of the probabilistic reachable sets is attained by suitable bounding of the state covariance matrices for arbitrary Markov chain sequences. The paper presents the main steps of the solution approach, discusses its properties, and illustrates the principle for a numeric example.}, author = {Flues, Patrick and Stursberg, Olaf}, journal = {Frontiers of Control Eng.}, keywords = {isac-www}, title = {Control of Jump Markov Uncertain Linear Systems With General Probability Distributions}, volume = {Vol 3.}, year = 2022 }
Proactive hybrid learning and optimisation in self-adaptive systems: The swarm-fleet infrastructure scenario. Krupitzer, Christian; Gruhl, Christian; Sick, Bernhard; Tomforde, Sven. In Information and Software Technology, 145, bl 106826. Elsevier, 2022.
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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 = {isac-www}, pages = 106826, publisher = {Elsevier}, title = {Proactive hybrid learning and optimisation in self-adaptive systems: The swarm-fleet infrastructure scenario}, volume = 145, year = 2022 }
Fast Bayesian Updates for Deep Learning with a Use Case in Active Learning. Herde, Marek; Huang, Zhixin; Huseljic, Denis; Kottke, Daniel; Vogt, Stephan; Sick, Bernhard. In arXiv e-prints, bl arXiv:2210.06112. 2022.
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Retraining deep neural networks when new data arrives is typically computationally expensive. Moreover, certain applications do not allow such costly retraining due to time or computational constraints. Fast Bayesian updates are a possible solution to this issue. Therefore, we propose a Bayesian update based on Monte-Carlo samples and a last-layer Laplace approximation for different Bayesian neural network types, i.e., Dropout, Ensemble, and Spectral Normalized Neural Gaussian Process (SNGP). In a large-scale evaluation study, we show that our updates combined with SNGP represent a fast and competitive alternative to costly retraining. As a use case, we combine the Bayesian updates for SNGP with different sequential query strategies to exemplarily demonstrate their improved selection performance in active learning.
@article{herde2022fast, abstract = {Retraining deep neural networks when new data arrives is typically computationally expensive. Moreover, certain applications do not allow such costly retraining due to time or computational constraints. Fast Bayesian updates are a possible solution to this issue. Therefore, we propose a Bayesian update based on Monte-Carlo samples and a last-layer Laplace approximation for different Bayesian neural network types, i.e., Dropout, Ensemble, and Spectral Normalized Neural Gaussian Process (SNGP). In a large-scale evaluation study, we show that our updates combined with SNGP represent a fast and competitive alternative to costly retraining. As a use case, we combine the Bayesian updates for SNGP with different sequential query strategies to exemplarily demonstrate their improved selection performance in active learning.}, author = {Herde, Marek and Huang, Zhixin and Huseljic, Denis and Kottke, Daniel and Vogt, Stephan and Sick, Bernhard}, journal = {arXiv e-prints}, keywords = {ActiveLearning}, pages = {arXiv:2210.06112}, title = {Fast Bayesian Updates for Deep Learning with a Use Case in Active Learning}, year = 2022 }
Predicting flow stress behavior of an AA7075 alloy using machine learning methods. Decke, Jens; Engelhardt, Anna; Rauch, Lukas; Degener, Sebastian; Sajjadifar, Seyedvahid; Scharifi, Emad; Steinhoff, Kurt; Niendorf, Thomas; Sick, Bernhard. In Crystals, 9(12), bll 1–19. MDPI, 2022.
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This work focuses on the prediction of hot deformation behavior of thermo-mechanically processed precipitation hardenable aluminum alloy AA7075. Data available are focusing on a novel hot forming process at different tool temperatures ranging from 24°C to 350°C to set different cooling rates after solution-heat-treatment. Isothermal uniaxial tensile tests in the temperature range from 200°C to 400°C and at strain rates ranging from 0.001 s^-1 to 0.1 s^-1 were carried out on four different material conditions. The present paper mainly focuses on a comparative study of modeling techniques based on Machine Learning (ML) and the well-known Zerilli-Armstrong model (Z-A) as an empirically based reference. Work focused on predicting single data points of curves that the model was trained on. Due to the novel way data were split with respect to training and testing data, it becomes possible to predict entire stress-strain curves which leads to a reduction in the number of required laboratory experiments, finally saving costs and time in future experiments. While all investigated ML methods showed a higher performance than the Z-A model, the extreme Gradient Boosting model (XGB) showed the superior results, i.e., highest error reduction of 91% with respect to the Mean Squared Error.
@article{decke2022predicting, abstract = {This work focuses on the prediction of hot deformation behavior of thermo-mechanically processed precipitation hardenable aluminum alloy AA7075. Data available are focusing on a novel hot forming process at different tool temperatures ranging from 24°C to 350°C to set different cooling rates after solution-heat-treatment. Isothermal uniaxial tensile tests in the temperature range from 200°C to 400°C and at strain rates ranging from 0.001 s^{-1} to 0.1 s^{-1} were carried out on four different material conditions. The present paper mainly focuses on a comparative study of modeling techniques based on Machine Learning (ML) and the well-known Zerilli-Armstrong model (Z-A) as an empirically based reference. Work focused on predicting single data points of curves that the model was trained on. Due to the novel way data were split with respect to training and testing data, it becomes possible to predict entire stress-strain curves which leads to a reduction in the number of required laboratory experiments, finally saving costs and time in future experiments. While all investigated ML methods showed a higher performance than the Z-A model, the extreme Gradient Boosting model (XGB) showed the superior results, i.e., highest error reduction of 91% with respect to the Mean Squared Error.}, author = {Decke, Jens and Engelhardt, Anna and Rauch, Lukas and Degener, Sebastian and Sajjadifar, Seyedvahid and Scharifi, Emad and Steinhoff, Kurt and Niendorf, Thomas and Sick, Bernhard}, journal = {Crystals}, keywords = {AA7075}, number = 12, pages = {1–19}, publisher = {MDPI}, title = {Predicting flow stress behavior of an AA7075 alloy using machine learning methods}, volume = 9, year = 2022 }
Method and Experimental Investigation of Surface Heat Dissipation Measurement using 3D Thermography. Schmoll, Robert; Schramm, Sebastian; Breitenstein, Tom; Kroll, Andreas. In Journal of Sensors and Sensor Systems (JSSS), 11, bll 41–49. 2022.
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@article{SchmollJSSS2021, author = {Schmoll, Robert and Schramm, Sebastian and Breitenstein, Tom and Kroll, Andreas}, journal = {Journal of Sensors and Sensor Systems (JSSS)}, keywords = {isac-www}, pages = {41–49}, title = {Method and Experimental Investigation of Surface Heat Dissipation Measurement using 3D Thermography}, volume = 11, year = 2022 }
On the Extension of the Weisfeiler-Lehman Hierarchy by WL Tests for Arbitrary Graphs. Beddar-Wiesing, Silvia; D’Inverno, Giuseppe Alessio; Graziani, Caterina; Lachi, Veronica; Moallemy-Oureh, Alice; Scarselli, Franco. In Workshp on Mining and Learning on Graphs (MLG), ECML PKDD, bll 1–13. 2022.
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Graph isomorphism (GI) has occupied both theoreticians and applied scientists since the early 1950s. Over the years, several approaches and algorithms with which an isomorphism between two graphs can be tested have been developed. A general approach is the Weisfeiler–Lehman (WL) test, which is based on a coloring algorithm and provides a necessary criterion for graph isomorphism. However, the WL test is restricted to examining graphs with only node attributes. Therefore, this paper presents two extensions of the WL algorithm to allow for testing on arbitrary graphs. One considers edge attributes, and the other tests the isomorphism on dynamic graphs. Additionally, we extend the WL-hierarchy by the attributed and dynamic WL tests and show that it is a partial order, which induces a lattice. In the future, this may allow for practical implications coming from lattice theory.
@inproceedings{beddarwiesing2022extension, abstract = {Graph isomorphism (GI) has occupied both theoreticians and applied scientists since the early 1950s. Over the years, several approaches and algorithms with which an isomorphism between two graphs can be tested have been developed. A general approach is the Weisfeiler–Lehman (WL) test, which is based on a coloring algorithm and provides a necessary criterion for graph isomorphism. However, the WL test is restricted to examining graphs with only node attributes. Therefore, this paper presents two extensions of the WL algorithm to allow for testing on arbitrary graphs. One considers edge attributes, and the other tests the isomorphism on dynamic graphs. Additionally, we extend the WL-hierarchy by the attributed and dynamic WL tests and show that it is a partial order, which induces a lattice. In the future, this may allow for practical implications coming from lattice theory.}, author = {Beddar-Wiesing, Silvia and D'Inverno, Giuseppe Alessio and Graziani, Caterina and Lachi, Veronica and Moallemy-Oureh, Alice and Scarselli, Franco}, booktitle = {Workshp on Mining and Learning on Graphs (MLG), ECML PKDD}, keywords = {isac-www}, pages = {1–13}, title = {On the Extension of the {W}eisfeiler-{L}ehman Hierarchy by WL Tests for Arbitrary Graphs}, year = 2022 }
Predicting the compressive strength of concrete up to 28 days-ahead: Comparison of machine learning algorithms on benchmark datasets. Rezazadeh, Farzad; Kroll, Andreas. In 32. Workshop Computational Intelligence, bll 53–75. KIT Scientific Publishing, Berlin, 2022.
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@inproceedings{RezazadehGMACI2022, author = {Rezazadeh, Farzad and Kroll, Andreas}, booktitle = {32. Workshop Computational Intelligence}, keywords = {isac-www}, month = {1.-2. December}, organization = {GMA-FA 5.14}, pages = {53-75}, publisher = {KIT Scientific Publishing}, title = {Predicting the compressive strength of concrete up to 28 days-ahead: Comparison of machine learning algorithms on benchmark datasets}, year = 2022 }
Investigation of processing windows in additive manufacturing of AlSi10Mg for faster production utilizing data-driven modelling. Engelhardt, A; Kahl, M; Richter, J; Krooß, P; Kroll, A; Niendorf, T. In Additive Manufacturing, 55. Elsevier, 2022.
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@article{ADDMA2021, author = {Engelhardt, A and Kahl, M and Richter, J and Krooß, P and Kroll, A and Niendorf, T}, journal = {Additive Manufacturing}, keywords = {isac-www}, note = 102858, publisher = {Elsevier}, title = {Investigation of processing windows in additive manufacturing of AlSi10Mg for faster production utilizing data-driven modelling}, volume = 55, year = 2022 }
The effect of the forget gate on bifurcation boundaries and dynamics in Recurrent Neural Networks and its implications for gradient-based optimization. Rehmer, Alexander; Kroll, Andras. In Preprints of the International Joint Conference on Neural Networks (IJCNN 2022), bll 1–8. Padua, Italy, 2022.
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@inproceedings{Rehmer_WCCI_2022, address = {Padua, Italy}, author = {Rehmer, Alexander and Kroll, Andras}, booktitle = {Preprints of the International Joint Conference on Neural Networks (IJCNN 2022)}, keywords = {isac-www}, month = {18.-23. July}, pages = {1-8}, title = {The effect of the forget gate on bifurcation boundaries and dynamics in Recurrent Neural Networks and its implications for gradient-based optimization}, year = 2022 }
An internal dynamics approach to predicting batch-end product quality in plastic injection molding using Recurrent Neural Networks. Rehmer, Alexander; Klute, Marco; Kroll, Andreas; Heim, Hans-Peter. In IFAC-PapersOnLine, Vol. 53, bll 1427–1432. Elsevier, Trieste, Italy, 2022.
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Recurrent Neural Networks are applied in areas such as speech recognition, natural language and video processing, and the identification of nonlinear state space models. Conventional Recurrent Neural Networks, e.g. the Elman Network, are hard to train. A more recently developed class of recurrent neural networks, so-called Gated Units, outperform their counterparts on virtually every task. This paper aims to provide additional insights into the differences between RNNs and Gated Units in order to explain the superior perfomance of gated recurrent units. It is argued, that Gated Units are easier to optimize not because they solve the vanishing gradient problem, but because they circumvent the emergence of large local gradients.
@inproceedings{Rehmer-CCTA-2022, abstract = {Recurrent Neural Networks are applied in areas such as speech recognition, natural language and video processing, and the identification of nonlinear state space models. Conventional Recurrent Neural Networks, e.g. the Elman Network, are hard to train. A more recently developed class of recurrent neural networks, so-called Gated Units, outperform their counterparts on virtually every task. This paper aims to provide additional insights into the differences between RNNs and Gated Units in order to explain the superior perfomance of gated recurrent units. It is argued, that Gated Units are easier to optimize not because they solve the vanishing gradient problem, but because they circumvent the emergence of large local gradients.}, address = {Trieste, Italy}, author = {Rehmer, Alexander and Klute, Marco and Kroll, Andreas and Heim, Hans-Peter}, booktitle = {6th IEEE Conference on Control Technology and Applications (CCTA)}, journal = {IFAC-PapersOnLine}, keywords = {isac-www}, month = {22.-25- August}, organization = {IFAC}, pages = {1427-1432}, publisher = {Elsevier}, title = {An internal dynamics approach to predicting batch-end product quality in plastic injection molding using Recurrent Neural Networks}, volume = 53, year = 2022 }
Eine Python-Toolbox zur datengetriebenen Modellierung des Spritzgieprozsses und Lösung von Optimalsteuerungsproblemen zur Steuerung der Bauteilqualität. Rehmer, Alexander; Kroll, Andreas. In 32. Workshop Computational Intelligence, bll 133–150. KIT Scientific Publishing, Berlin, 2022.
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@inproceedings{RehmerGMACI2022, address = {Berlin}, author = {Rehmer, Alexander and Kroll, Andreas}, booktitle = {32. Workshop Computational Intelligence}, keywords = {isac-www}, month = {1. - 2. Dezember 2022}, organization = {GMA-FA 5.14}, pages = {133-150}, publisher = {KIT Scientific Publishing}, title = {Eine Python-Toolbox zur datengetriebenen Modellierung des Spritzgieprozsses und Lösung von Optimalsteuerungsproblemen zur Steuerung der Bauteilqualität}, year = 2022 }
On affine quasi-LPV System Identification with unknown state-scheduling using (deep) Recurrent Neural Networks. Rehmer, Alexander; Kroll, Andreas. In IFAC-PapersOnLine, bll 446–451. Sinaia, Romania, 2022.
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Recurrent Neural Networks are applied in areas such as speech recognition, natural language and video processing, and the identification of nonlinear state space models. Conventional Recurrent Neural Networks, e.g. the Elman Network, are hard to train. A more recently developed class of recurrent neural networks, so-called Gated Units, outperform their counterparts on virtually every task. This paper aims to provide additional insights into the differences between RNNs and Gated Units in order to explain the superior perfomance of gated recurrent units. It is argued, that Gated Units are easier to optimize not because they solve the vanishing gradient problem, but because they circumvent the emergence of large local gradients.
@inproceedings{Rehmer-ICSTCC-2022, abstract = {Recurrent Neural Networks are applied in areas such as speech recognition, natural language and video processing, and the identification of nonlinear state space models. Conventional Recurrent Neural Networks, e.g. the Elman Network, are hard to train. A more recently developed class of recurrent neural networks, so-called Gated Units, outperform their counterparts on virtually every task. This paper aims to provide additional insights into the differences between RNNs and Gated Units in order to explain the superior perfomance of gated recurrent units. It is argued, that Gated Units are easier to optimize not because they solve the vanishing gradient problem, but because they circumvent the emergence of large local gradients.}, address = {Sinaia, Romania}, author = {Rehmer, Alexander and Kroll, Andreas}, booktitle = {Proceedings of the 26th International Conference on System Theory, Control and Computing (ICSTCC)}, journal = {IFAC-PapersOnLine}, keywords = {isac-www}, month = {19.-21- October}, pages = {446-451}, title = {On affine quasi-LPV System Identification with unknown state-scheduling using (deep) Recurrent Neural Networks}, year = 2022 }
Calibration and validation of micromagnetic data for non-destructive analysis of near-surface properties after hard turning. Wegener, Thomas; Liehr, Alexander; Bolender, Artjom; Degener, Sebastian; Wittich, Felix; Kroll, Andreas; Niendorf, Thomas. In HTM Journal of Heat Treatment and Materials, 77(2), bll 156–172. 2022.
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@article{Wegener_HTM2021, author = {Wegener, Thomas and Liehr, Alexander and Bolender, Artjom and Degener, Sebastian and Wittich, Felix and Kroll, Andreas and Niendorf, Thomas}, journal = {HTM Journal of Heat Treatment and Materials}, keywords = {isac-www}, number = 2, pages = {156 – 172}, title = {Calibration and validation of micromagnetic data for non-destructive analysis of near-surface properties after hard turning}, volume = 77, year = 2022 }
Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users Trajectories. Kress, Viktor; Jeske, Fabian; Zernetsch, Stefan; Doll, Konrad; Sick, Bernhard. In IEEE Transactions on Intelligent Vehicles. IEEE, 2022.
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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 = {isac-www}, note = {(accepted)}, publisher = {IEEE}, title = {Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users Trajectories}, year = 2022 }
Organic Computing -- Doctoral Dissertation Colloquium 2021. Tomforde, Sven; Krupitzer, Christian. In Vol. 20Intelligent Embedded Systems. kassel university press, 2022.
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@book{tomforde2022organic, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {isac-www}, publisher = {kassel university press}, series = {Intelligent Embedded Systems}, title = {Organic Computing – Doctoral Dissertation Colloquium 2021}, volume = 20, year = 2022 }
LwTool: A data processing toolkit for building a real-time pressure mapping smart textile software system. Guo, Tao; Huang, Zhixin; Cheng, Jingyuan. In Pervasive and Mobile Computing, 80, bl 101540. Elsevier, 2022.
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Pressure mapping smart textile is a new type of sensing modality that transforms the pressure distribution over surfaces into digital ”image” and ”video”, that has rich application scenarios in Human Activity Recognition (HAR), because all human activities are linked with force change over certain surfaces. To speed up its application exploration, we propose a toolkit named LwTool for the data processing, including: (a) a feature library, including 1830 ready-to-use temporal and spatial features, (b) a hierarchical feature selection framework that automatically picks out the best features for a new application from the feature library. As real-time processing capability is important for instant user feedback, we emphasize not only on good recognition result but also on reducing time cost when selecting features. Our library and algorithms are validated on Smart-Toy and Smart-Bedsheet applications, an 89.7% accuracy for Smart-Toy and an 83.8% accuracy for Smart-Bedsheet can be achieved (10-fold cross-validation) using our feature library. Adopting the feature selection algorithm, the processing speed is increased by more than 3 times while maintaining high accuracy for both two applications. We believe our method could be a general and powerful toolkit in building real-time recognition software systems for pressure mapping smart textile.
@article{guo2022lwtool, abstract = {Pressure mapping smart textile is a new type of sensing modality that transforms the pressure distribution over surfaces into digital ”image” and ”video”, that has rich application scenarios in Human Activity Recognition (HAR), because all human activities are linked with force change over certain surfaces. To speed up its application exploration, we propose a toolkit named LwTool for the data processing, including: (a) a feature library, including 1830 ready-to-use temporal and spatial features, (b) a hierarchical feature selection framework that automatically picks out the best features for a new application from the feature library. As real-time processing capability is important for instant user feedback, we emphasize not only on good recognition result but also on reducing time cost when selecting features. Our library and algorithms are validated on Smart-Toy and Smart-Bedsheet applications, an 89.7% accuracy for Smart-Toy and an 83.8% accuracy for Smart-Bedsheet can be achieved (10-fold cross-validation) using our feature library. Adopting the feature selection algorithm, the processing speed is increased by more than 3 times while maintaining high accuracy for both two applications. We believe our method could be a general and powerful toolkit in building real-time recognition software systems for pressure mapping smart textile.}, author = {Guo, Tao and Huang, Zhixin and Cheng, Jingyuan}, journal = {Pervasive and Mobile Computing}, keywords = {MachineLearning}, pages = 101540, publisher = {Elsevier}, title = {LwTool: A data processing toolkit for building a real-time pressure mapping smart textile software system}, volume = 80, year = 2022 }
Multi-Task Autoencoders and Transfer Learning for Day-Ahead Wind and Photovoltaic Power Forecasts. Schreiber, Jens; Sick, Bernhard. In Energies, 15(21), bl 8062. MDPI, 2022.
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Integrating new renewable energy resources requires robust and reliable forecasts to ensure a stable electrical grid and avoid blackouts. Sophisticated representation learning techniques, such as autoencoders, play an essential role, as they allow for the extraction of latent features to forecast the expected generated wind and photovoltaic power for the next seconds up to days. Thereby, autoencoders reduce the required training time and the time spent in manual feature engineering and often improve the forecast error. However, most current renewable energy forecasting research on autoencoders focuses on smaller forecast horizons for the following seconds and hours based on meteorological measurements. At the same time, larger forecast horizons, such as day-ahead power forecasts based on numerical weather predictions, are crucial for planning loads and demands within the electrical grid to prevent power failures. There is little evidence on the ability of autoencoders and their respective forecasting models to improve through multi-task learning and time series autoencoders for day-ahead power forecasts. We can close these gaps by proposing a multi-task learning autoencoder based on the recently introduced temporal convolution network. This approach reduces the number of trainable parameters by 38 for photovoltaic data and 202 for wind data while having the best reconstruction error compared to nine other representation learning techniques. At the same time, this model decreases the day-ahead forecast error up to 18.3% for photovoltaic parks and 1.5% for wind parks. We round off these results by analyzing the influences of the latent size and the number of layers to fine-tune the encoder for wind and photovoltaic power forecasts.
@article{schreiber2022multi, abstract = {Integrating new renewable energy resources requires robust and reliable forecasts to ensure a stable electrical grid and avoid blackouts. Sophisticated representation learning techniques, such as autoencoders, play an essential role, as they allow for the extraction of latent features to forecast the expected generated wind and photovoltaic power for the next seconds up to days. Thereby, autoencoders reduce the required training time and the time spent in manual feature engineering and often improve the forecast error. However, most current renewable energy forecasting research on autoencoders focuses on smaller forecast horizons for the following seconds and hours based on meteorological measurements. At the same time, larger forecast horizons, such as day-ahead power forecasts based on numerical weather predictions, are crucial for planning loads and demands within the electrical grid to prevent power failures. There is little evidence on the ability of autoencoders and their respective forecasting models to improve through multi-task learning and time series autoencoders for day-ahead power forecasts. We can close these gaps by proposing a multi-task learning autoencoder based on the recently introduced temporal convolution network. This approach reduces the number of trainable parameters by 38 for photovoltaic data and 202 for wind data while having the best reconstruction error compared to nine other representation learning techniques. At the same time, this model decreases the day-ahead forecast error up to 18.3% for photovoltaic parks and 1.5% for wind parks. We round off these results by analyzing the influences of the latent size and the number of layers to fine-tune the encoder for wind and photovoltaic power forecasts.}, author = {Schreiber, Jens and Sick, Bernhard}, journal = {Energies}, keywords = {isac-www}, number = 21, pages = 8062, publisher = {MDPI}, title = {Multi-Task Autoencoders and Transfer Learning for Day-Ahead Wind and Photovoltaic Power Forecasts}, volume = 15, year = 2022 }
Detecting Corner Case in the Context of Highly Automated Driving. Heidecker, Florian. In Organic Computing -- Doctoral Dissertation Colloquium 2021, S. Tomforde, C. Krupitzer (reds.), bll 60–73. kassel university press, 2022.
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This article introduces a research proposal that aims to deﬁne, detect, and evaluate corner cases in the context of machine learning (ML) systems and highly automated driving. Corner cases are scenarios or data in a dataset that are recorded only very rarely and therefore only available in small quantities or not at all in the dataset. However, corner cases are very important for model training, as they are needed to improve the model performance in these rare scenarios. On the other hand, in safety- and security-critical applications, such as highly automated driving, corner case data is extremely important for testing and verifying the ML system. The research proposal addresses these challenges and focuses on the deﬁnition and categorization of corner cases. Based on this, ML methods for corner case detection are developed, focusing on object detection for automated driving and trajectory data for intention detection of pedestrians and cyclists. Finally, the research proposal addresses corner case metrics and the evaluation of the developed detection models.
@incollection{florianheidecker2022detecting, abstract = {This article introduces a research proposal that aims to deﬁne, detect, and evaluate corner cases in the context of machine learning (ML) systems and highly automated driving. Corner cases are scenarios or data in a dataset that are recorded only very rarely and therefore only available in small quantities or not at all in the dataset. However, corner cases are very important for model training, as they are needed to improve the model performance in these rare scenarios. On the other hand, in safety- and security-critical applications, such as highly automated driving, corner case data is extremely important for testing and verifying the ML system. The research proposal addresses these challenges and focuses on the deﬁnition and categorization of corner cases. Based on this, ML methods for corner case detection are developed, focusing on object detection for automated driving and trajectory data for intention detection of pedestrians and cyclists. Finally, the research proposal addresses corner case metrics and the evaluation of the developed detection models.}, author = {Heidecker, Florian}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2021}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {isac-www}, pages = {60–73}, publisher = {kassel university press}, title = {Detecting Corner Case in the Context of Highly Automated Driving}, year = 2022 }
Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs. Beddar-Wiesing, Silvia; D’Inverno, Giuseppe Alessio; Graziani, Caterina; Lachi, Veronica; Moallemy-Oureh, Alice; Scarselli, Franco; Thomas, Josephine. In arXiv e-prints, bl arXiv:2210.03990. 2022.
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Graph Neural Networks (GNNs) are a large class of relational models for graph processing. Recent theoretical studies on the expressive power of GNNs have focused on two issues. On the one hand, it has been proven that GNNs are as powerful as the Weisfeiler-Lehman test (1-WL) in their ability to distinguish graphs. Moreover, it has been shown that the equivalence enforced by 1-WL equals unfolding equivalence. On the other hand, GNNs turned out to be universal approximators on graphs modulo the constraints enforced by 1-WL/unfolding equivalence. However, these results only apply to Static Undirected Homogeneous Graphs with node attributes. In contrast, real-life applications often involve a variety of graph properties, such as, e.g., dynamics or node and edge attributes. In this paper, we conduct a theoretical analysis of the expressive power of GNNs for these two graph types that are particularly of interest. Dynamic graphs are widely used in modern applications, and its theoretical analysis requires new approaches. The attributed type acts as a standard form for all graph types since it has been shown that all graph types can be transformed without loss to Static Undirected Homogeneous Graphs with attributes on nodes and edges (SAUHG). The study considers generic GNN models and proposes appropriate 1-WL tests for those domains. Then, the results on the expressive power of GNNs are extended by proving that GNNs have the same capability as the 1-WL test in distinguishing dynamic and attributed graphs, the 1-WL equivalence equals unfolding equivalence and that GNNs are universal approximators modulo 1-WL/unfolding equivalence. Moreover, the proof of the approximation capability holds for SAUHGs, which include most of those used in practical applications, and it is constructive in nature allowing to deduce hints on the architecture of GNNs that can achieve the desired accuracy.
@article{beddarwiesing2022weisfeiler, abstract = {Graph Neural Networks (GNNs) are a large class of relational models for graph processing. Recent theoretical studies on the expressive power of GNNs have focused on two issues. On the one hand, it has been proven that GNNs are as powerful as the Weisfeiler-Lehman test (1-WL) in their ability to distinguish graphs. Moreover, it has been shown that the equivalence enforced by 1-WL equals unfolding equivalence. On the other hand, GNNs turned out to be universal approximators on graphs modulo the constraints enforced by 1-WL/unfolding equivalence. However, these results only apply to Static Undirected Homogeneous Graphs with node attributes. In contrast, real-life applications often involve a variety of graph properties, such as, e.g., dynamics or node and edge attributes. In this paper, we conduct a theoretical analysis of the expressive power of GNNs for these two graph types that are particularly of interest. Dynamic graphs are widely used in modern applications, and its theoretical analysis requires new approaches. The attributed type acts as a standard form for all graph types since it has been shown that all graph types can be transformed without loss to Static Undirected Homogeneous Graphs with attributes on nodes and edges (SAUHG). The study considers generic GNN models and proposes appropriate 1-WL tests for those domains. Then, the results on the expressive power of GNNs are extended by proving that GNNs have the same capability as the 1-WL test in distinguishing dynamic and attributed graphs, the 1-WL equivalence equals unfolding equivalence and that GNNs are universal approximators modulo 1-WL/unfolding equivalence. Moreover, the proof of the approximation capability holds for SAUHGs, which include most of those used in practical applications, and it is constructive in nature allowing to deduce hints on the architecture of GNNs that can achieve the desired accuracy.}, author = {Beddar-Wiesing, Silvia and D'Inverno, Giuseppe Alessio and Graziani, Caterina and Lachi, Veronica and Moallemy-Oureh, Alice and Scarselli, Franco and Thomas, Josephine}, journal = {arXiv e-prints}, keywords = {isac-www}, pages = {arXiv:2210.03990}, title = {{W}eisfeiler-{L}ehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs}, year = 2022 }
Graph Neural Networks Designed for Different Graph Types: A Survey. Thomas, Josephine M.; Moallemy-Oureh, Alice; Beddar-Wiesing, Silvia; Holzhüter, Clara. In arXiv e-prints, bl arXiv:2204.03080. 2022.
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Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. Based on this, the young research field of Graph Neural Networks (GNNs) has emerged. Despite the youth of the field and the speed in which new models are developed, many good surveys have been published in the last years. Nevertheless, an overview on which graph types can be modeled by GNNs is missing. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static as well as on dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover in the dynamic case, we separate the models in discrete-time and continuous-time dynamic graphs based on their architecture. While ordering the existing GNN models, we find, that there are still graph types, that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.
@article{thomas2022graph, abstract = {Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. Based on this, the young research field of Graph Neural Networks (GNNs) has emerged. Despite the youth of the field and the speed in which new models are developed, many good surveys have been published in the last years. Nevertheless, an overview on which graph types can be modeled by GNNs is missing. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static as well as on dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover in the dynamic case, we separate the models in discrete-time and continuous-time dynamic graphs based on their architecture. While ordering the existing GNN models, we find, that there are still graph types, that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.}, author = {Thomas, Josephine M. and Moallemy-Oureh, Alice and Beddar-Wiesing, Silvia and Holzhüter, Clara}, journal = {arXiv e-prints}, keywords = {Survey}, pages = {arXiv:2204.03080}, title = {Graph Neural Networks Designed for Different Graph Types: A Survey}, year = 2022 }
Self-Adaptive Charging Management in Electric Vehicle Infrastructures based on Reinforcement Learning. Hassouna, Mohamed. In Organic Computing -- Doctoral Dissertation Colloquium 2022. kassel university press, 2022.
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Electromobility plays an increasingly important role in the energy transition. Electric vehicle charging poses challenges to the electricity grid due to high peak demand situations. Therefore, it is crucial to leverage the vehicle's flexibility when charging can be delayed. Conventional charging management systems cannot adapt to frequently changing conditions such as fluctuating renewable energy output, electricity prices and user behavior. In this article, we investigate the application of reinforcement learning approaches for self-adaptive charging management. In particular, we identify challenges regarding realistic environments and adaption to varying topologies and connections among charging stations. We describe related approaches and propose ideas and planned experiments to overcome these problems by utilizing generative models and graph neural networks.
@incollection{hassouna2022self, abstract = {Electromobility plays an increasingly important role in the energy transition. Electric vehicle charging poses challenges to the electricity grid due to high peak demand situations. Therefore, it is crucial to leverage the vehicle's flexibility when charging can be delayed. Conventional charging management systems cannot adapt to frequently changing conditions such as fluctuating renewable energy output, electricity prices and user behavior. In this article, we investigate the application of reinforcement learning approaches for self-adaptive charging management. In particular, we identify challenges regarding realistic environments and adaption to varying topologies and connections among charging stations. We describe related approaches and propose ideas and planned experiments to overcome these problems by utilizing generative models and graph neural networks.}, author = {Hassouna, Mohamed}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2022}, keywords = {GraphNeuralNetwork}, note = {(accepted)}, publisher = {kassel university press}, title = {Self-Adaptive Charging Management in Electric Vehicle Infrastructures based on Reinforcement Learning}, year = 2022 }
Social Machines. Draude, Claude; Gruhl, Christian; Hornung, Gerrit; Kropf, Jonathan; Lamla, Jörn; Leimeister, Jan Marco; Sick, Bernhard; Stumme, Gerd. In Informatik Spektrum, 45(1), bll 38–42. Springer, 2022.
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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 = {social-machine}, number = 1, pages = {38–42}, publisher = {Springer}, title = {Social Machines}, volume = 45, year = 2022 }
NDNET: A Unified Framework for Anomaly and Novelty Detection. Decke, Jens; Schmeißing, Jörn; Botache, Diego; Bieshaar, Maarten; Sick, Bernhard; Gruhl, Christian. In International Conference on Architecture of Computing Systems (ARCS), bll 197–210. Springer, 2022.
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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 = {AnomalyDetection}, pages = {197–210}, publisher = {Springer}, title = {{NDNET}: {A} Unified Framework for Anomaly and Novelty Detection}, year = 2022 }
Self-Aware Microsystems. Gruhl, Christian; Tomforde, Sven; Sick, Bernhard. In Workshop on Self-Improving System Integration (SISSY), ACSOS, bll 126–127. IEEE, 2022.
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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 = {Online}, pages = {126–127}, publisher = {IEEE}, title = {Self-Aware Microsystems}, year = 2022 }
Three-dimensional close-to-substrate trajectories of magnetic microparticles in dynamically changing magnetic field landscapes. Huhnstock, Rico; Reginka, Meike; Sonntag, Claudius; Merkel, Maximilian; Dingel, Kristina; Sick, Bernhard; Vogel, Michael; Ehresmann, Arno. In Scientific Reports, 12(1), bll 1–10. Springer Nature, 2022.
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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 = {isac-www}, 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 }
Heterogeneous Multi-Source Deep Adaptive Knowledge-Aware Learning for E-Mobility. Ali, Mohammad Wazed. In IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), bll 57–60. IEEE, 2022.
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Electromobility (e-mobility) reduces global fossil fuel-based energy demand and promotes the usage of renewable energy sources. Different types of electric vehicle (EVs) production have increased recently due to their need. However, there is no fixed charging behavior, and demand exists. Uncontrolled EV charging may lead to a harmful impact on the power grid. Besides, most renewable energy sources depend highly on the weather and location. Thus, analyzing, forecasting, and adapting EV charging demand is essential to prevent power outages. Deep Learning (DL) models are a de facto standard technology for energy forecasting in many scenarios. Still, there is a lack of real-world data in the e-mobility domain to train a DL model from scratch. Besides, DL models have explainability issues, which can hinder decision-making. This article proposes a novel framework for addressing data scarcity issues, enabling continuous adaptation to change, and improving explainability.
@inproceedings{ali2022heterogeneous, abstract = {Electromobility (e-mobility) reduces global fossil fuel-based energy demand and promotes the usage of renewable energy sources. Different types of electric vehicle (EVs) production have increased recently due to their need. However, there is no fixed charging behavior, and demand exists. Uncontrolled EV charging may lead to a harmful impact on the power grid. Besides, most renewable energy sources depend highly on the weather and location. Thus, analyzing, forecasting, and adapting EV charging demand is essential to prevent power outages. Deep Learning (DL) models are a de facto standard technology for energy forecasting in many scenarios. Still, there is a lack of real-world data in the e-mobility domain to train a DL model from scratch. Besides, DL models have explainability issues, which can hinder decision-making. This article proposes a novel framework for addressing data scarcity issues, enabling continuous adaptation to change, and improving explainability.}, author = {Ali, Mohammad Wazed}, booktitle = {IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)}, keywords = {isac-www}, pages = {57–60}, publisher = {IEEE}, title = {Heterogeneous Multi-Source Deep Adaptive Knowledge-Aware Learning for E-Mobility}, year = 2022 }
Receding-Horizon Control of Constrained Switched Systems with Neural Networks as Parametric Function Approximators. Markolf, Lukas; Stursberg, Olaf. In SN Computer Science, 4(62). 2022.
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This work studies receding-horizon control of discrete-time switched linear systems subject to polytopic constraints for the continuous states and inputs. The objective is to approximate the optimal receding-horizon control strategy for cases in which the online computation is intractable due to the necessity of solving mixed-integer quadratic programs in each discrete time instant. The proposed approach builds upon an approximated optimal finite-horizon control law in closed-loop form with guaranteed constraint satisfaction. The paper derives the properties of recursive feasibility and asymptotic stability for the proposed approach. A numerical example is provided for illustration and evaluation of the approach.
@article{markolf2022recedinghorizon, abstract = {This work studies receding-horizon control of discrete-time switched linear systems subject to polytopic constraints for the continuous states and inputs. The objective is to approximate the optimal receding-horizon control strategy for cases in which the online computation is intractable due to the necessity of solving mixed-integer quadratic programs in each discrete time instant. The proposed approach builds upon an approximated optimal finite-horizon control law in closed-loop form with guaranteed constraint satisfaction. The paper derives the properties of recursive feasibility and asymptotic stability for the proposed approach. A numerical example is provided for illustration and evaluation of the approach.}, author = {Markolf, Lukas and Stursberg, Olaf}, journal = {SN Computer Science}, keywords = {isac-www}, number = 62, title = {Receding-Horizon Control of Constrained Switched Systems with Neural Networks as Parametric Function Approximators}, volume = 4, year = 2022 }
Artificial intelligence for online characterization of ultrashort X‑ray free‑electron laser pulses. Dingel, Kristina; Otto, Thorsten; Marder, Lutz; Funke, Lars; Held, Arne; Savio, Sara; Hans, Andreas; Hartmann, Gregor; Meier, David; Viefhaus, Jens; Sick, Bernhard; Ehresmann, Arno; Ilchen, Markus; Helml, Wolfram. In Scientific Reports, 12(1), bll 1–14. Nature Publishing Group, 2022.
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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 = {isac-www}, 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 }
A Practical Evaluation of Active Learning Approaches for Object Detection. Schneegans, Jan; Bieshaar, Maarten; Sick, Bernhard. 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 = {isac-www}, pages = {49–67}, title = {A Practical Evaluation of Active Learning Approaches for Object Detection}, year = 2022 }
Enhancing Active Learning with Weak Supervision and Transfer Learning by Leveraging Information and Knowledge Sources. Rauch, Lukas; Huseljic, Denis; Sick, Bernhard. 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 = {isac-www}, pages = {27–42}, title = {Enhancing Active Learning with Weak Supervision and Transfer Learning by Leveraging Information and Knowledge Sources}, year = 2022 }
A Concept for Automated Polarized Web Content Annotation based on Multimodal Active Learning. Herde, Marek; Huseljic, Denis; Mitrovic, Jelena; Granitzer, Michael; Sick, Bernhard. 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 = {HatefulMemes}, pages = {1–6}, title = {A Concept for Automated Polarized Web Content Annotation based on Multimodal Active Learning}, year = 2022 }
Optimizing a superconducting radio-frequency gun using deep reinforcement learning. Meier, David; Ramirez, Luis Vera; Völker, Jens; Viefhaus, Jens; Sick, Bernhard; Hartmann, Gregor. In Physical Review Accelerators and Beams, 25(10), bl 104604. American Physical Society, 2022.
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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 = {SuperconductingRF}, number = 10, pages = 104604, publisher = {American Physical Society}, title = {Optimizing a superconducting radio-frequency gun using deep reinforcement learning}, volume = 25, year = 2022 }
Efficient SVDD sampling with approximation guarantees for the decision boundary. Englhardt, Adrian; Trittenbach, Holger; Kottke, Daniel; Sick, Bernhard; Böhm, Klemens. In Machine Learning, 111(4), bll 1349–1375. Springer, 2022.
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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 = {isac-www}, number = 4, pages = {1349–1375}, publisher = {Springer}, title = {Efficient {SVDD} sampling with approximation guarantees for the decision boundary}, volume = 111, year = 2022 }
The Vision of Self-Management in Cognitive Organic Power Distribution Systems. Loeser, Inga; Braun, Martin; Gruhl, Christian; Menke, Jan-Hendrik; Sick, Bernhard; Tomforde, Sven. In Energies, 15(3), bl 881. MDPI, 2022.
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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 = {isac-www}, number = 3, pages = 881, publisher = {MDPI}, title = {The Vision of Self-Management in Cognitive Organic Power Distribution Systems}, volume = 15, year = 2022 }

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