@inproceedings{KuehnelEissmannWiedeetal.2024, author = {K{\"u}hnel, Johannes and Eißmann, Timo and Wiede, Christian and Schwung, Dorothea and Grabmaier, Anton}, title = {Semi-Supervised Anomaly Detection in the TinyML Domain Through Multi-Target Few-Shot Domain Adaptation}, series = {2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)}, volume = {133}, booktitle = {2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)}, publisher = {IEEE}, doi = {10.1109/ETFA61755.2024.10710811}, pages = {8}, year = {2024}, subject = {Maschinelles Lernen}, language = {en} } @unpublished{WolfBraunUlrich2024, author = {Wolf, Dominik Werner and Braun, Alexander and Ulrich, Markus}, title = {Optical aberrations in autonomous driving: Physics-informed parameterized temperature scaling for neural network uncertainty calibration}, series = {International Journal of Computer Vision (IJCV)}, journal = {International Journal of Computer Vision (IJCV)}, publisher = {arXiv}, doi = {10.48550/arXiv.2412.13695}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-50120}, year = {2024}, abstract = {'A trustworthy representation of uncertainty is desirable and should be considered as a key feature of any machine learning method' (Huellermeier and Waegeman, 2021). This conclusion of Huellermeier et al. underpins the importance of calibrated uncertainties. Since AI-based algorithms are heavily impacted by dataset shifts, the automotive industry needs to safeguard its system against all possible contingencies. One important but often neglected dataset shift is caused by optical aberrations induced by the windshield. For the verification of the perception system performance, requirements on the AI performance need to be translated into optical metrics by a bijective mapping (Braun, 2023). Given this bijective mapping it is evident that the optical system characteristics add additional information about the magnitude of the dataset shift. As a consequence, we propose to incorporate a physical inductive bias into the neural network calibration architecture to enhance the robustness and the trustworthiness of the AI target application, which we demonstrate by using a semantic segmentation task as an example. By utilizing the Zernike coefficient vector of the optical system as a physical prior we can significantly reduce the mean expected calibration error in case of optical aberrations. As a result, we pave the way for a trustworthy uncertainty representation and for a holistic verification strategy of the perception chain.}, language = {en} } @unpublished{WolfBalajiBraunetal.2024, author = {Wolf, Dominik Werner and Balaji, Prasannavenkatesh and Braun, Alexander and Ulrich, Markus}, title = {Decoupling of neural network calibration measures}, series = {German Conference on Pattern Recognition (GCPR) 2024}, journal = {German Conference on Pattern Recognition (GCPR) 2024}, publisher = {arXiv}, doi = {10.48550/arXiv.2406.02411}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-50104}, year = {2024}, abstract = {A lot of effort is currently invested in safeguarding autonomous driving systems, which heavily rely on deep neural networks for computer vision. We investigate the coupling of different neural network calibration measures with a special focus on the Area Under the Sparsification Error curve (AUSE) metric. We elaborate on the well-known inconsistency in determining optimal calibration using the Expected Calibration Error (ECE) and we demonstrate similar issues for the AUSE, the Uncertainty Calibration Score (UCS), as well as the Uncertainty Calibration Error (UCE). We conclude that the current methodologies leave a degree of freedom, which prevents a unique model calibration for the homologation of safety-critical functionalities. Furthermore, we propose the AUSE as an indirect measure for the residual uncertainty, which is irreducible for a fixed network architecture and is driven by the stochasticity in the underlying data generation process (aleatoric contribution) as well as the limitation in the hypothesis space (epistemic contribution).}, language = {en} } @article{JakabBraunAgnewetal.2024, author = {Jakab, Daniel and Braun, Alexander and Agnew, Cathaoir and Mohandas, Reenu and Deegan, Brian Michael and Molloy, Dara and Ward, Enda and Scanlan, Anthony and Eising, Ciar{\´a}n}, title = {SS-SFR: synthetic scenes spatial frequency response on Virtual KITTI and degraded automotive simulations for object detection}, series = {IET Conference Proceedings}, volume = {2024}, journal = {IET Conference Proceedings}, number = {10}, publisher = {Institution of Engineering and Technology (IET)}, issn = {2732-4494}, doi = {10.1049/icp.2024.3292}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-50080}, pages = {110 -- 117}, year = {2024}, language = {en} } @inproceedings{DiprasetyaPullaniSchwungetal.2024, author = {Diprasetya, Mochammad Rizky and Pullani, Ali Nafih and Schwung, Andreas and Schwung, Dorothea}, title = {Model-based Reinforcement Learning for Sim-to-Real Transfer in Robotics using HTM neural networks}, series = {2024 10th International Conference on Control, Decision and Information Technologies (CoDIT), 1-4 July 2024}, volume = {97}, booktitle = {2024 10th International Conference on Control, Decision and Information Technologies (CoDIT), 1-4 July 2024}, publisher = {IEEE}, doi = {10.1109/CoDIT62066.2024.10708424}, pages = {43 -- 48}, year = {2024}, subject = {Maschinelles Lernen}, language = {en} } @unpublished{YuwonoSchwungSchwung2024, author = {Yuwono, Steve and Schwung, Dorothea and Schwung, Andreas}, title = {Distributed Stackelberg Strategies in State-based Potential Games for Autonomous Decentralized Learning Manufacturing Systems}, publisher = {arXiv}, doi = {10.48550/arXiv.2408.06397}, pages = {14}, year = {2024}, abstract = {This article describes a novel game structure for autonomously optimizing decentralized manufacturing systems with multi-objective optimization challenges, namely Distributed Stackelberg Strategies in State-Based Potential Games (DS2-SbPG). DS2-SbPG integrates potential games and Stackelberg games, which improves the cooperative trade-off capabilities of potential games and the multi-objective optimization handling by Stackelberg games. Notably, all training procedures remain conducted in a fully distributed manner. DS2-SbPG offers a promising solution to finding optimal trade-offs between objectives by eliminating the complexities of setting up combined objective optimization functions for individual players in self- learning domains, particularly in real-world industrial settings with diverse and numerous objectives between the sub-systems. We further prove that DS2-SbPG constitutes a dynamic potential game that results in corresponding converge guarantees. Experimental validation conducted on a laboratory-scale testbed highlights the efficacy of DS2-SbPG and its two variants, such as DS2-SbPG for single-leader-follower and Stack DS2-SbPG for multi-leader-follower. The results show significant reductions in power consumption and improvements in overall performance, which signals the potential of DS2-SbPG in real-world applications.}, subject = {Maschinelles Lernen}, language = {en} } @unpublished{YuwonoSchwungSchwung2024, author = {Yuwono, Steve and Schwung, Dorothea and Schwung, Andreas}, title = {Transfer learning of state-based potential games for process optimization in decentralized manufacturing systems}, series = {arXiv}, journal = {arXiv}, publisher = {arXiv}, doi = {10.48550/arXiv.2408.05992}, pages = {48}, year = {2024}, abstract = {This paper presents a novel transfer learning approach in state-based potential games (TL-SbPGs) for enhancing distributed self-optimization in manufacturing systems. The approach focuses on the practical relevant industrial setting where sharing and transferring gained knowledge among similar-behaved players improves the self-learning mechanism in large-scale systems. With TL-SbPGs, the gained knowledge can be reused by other players to optimize their policies, thereby improving the learning outcomes of the players and accelerating the learning process. To accomplish this goal, we develop transfer learning concepts and similarity criteria for players, which offer two distinct settings: (a) predefined similarities between players and (b) dynamically inferred similarities between players during training. We formally prove the applicability of the SbPG framework in transfer learning. Additionally, we introduce an efficient method to determine the optimal timing and weighting of the transfer learning procedure during the training phase. Through experiments on a laboratory-scale testbed, we demonstrate that TL-SbPGs significantly boost production efficiency while reducing power consumption of the production schedules while also outperforming native SbPGs.}, subject = {Maschinelles Lernen}, language = {en} } @inproceedings{ZellerWaschulzikCarlanetal.2024, author = {Zeller, Marc and Waschulzik, Thomas and Carlan, Carmen and Serahlazau, Marat and Bahlmann, Claus and Wu, Zhiliang and Spieckermann, Sigurd and Krompass, Denis and Geerkens, Simon and Sieberichs, Christian and Kirchheim, Konstantin and {\"O}zen, Batu Kaan and Robles, Lucia Diez}, title = {Continuous Development and Safety Assurance Pipeline for ML-Based Systems in the Railway Domain}, series = {Computer Safety, Reliability, and Security. SAFECOMP 2024 Workshops. DECSoS, SASSUR, TOASTS, and WAISE, Florence, Italy, September 17, 2024, Proceedings. Lecture Notes in Computer Science, vol 14989}, booktitle = {Computer Safety, Reliability, and Security. SAFECOMP 2024 Workshops. DECSoS, SASSUR, TOASTS, and WAISE, Florence, Italy, September 17, 2024, Proceedings. Lecture Notes in Computer Science, vol 14989}, editor = {Ceccarelli, Andrea and Trapp, Mario and Bondavalli, Andrea and Schoitsch, Erwin and Gallina, Barbara and Bitsch, Friedemann}, publisher = {Springer Nature}, address = {Cham}, isbn = {9783031687372}, issn = {0302-9743}, doi = {10.1007/978-3-031-68738-9_36}, pages = {446 -- 459}, year = {2024}, subject = {Maschinelles Lernen}, language = {en} } @article{WolfThielbeerUlrichetal.2024, author = {Wolf, Dominik Werner and Thielbeer, Boris and Ulrich, Markus and Braun, Alexander}, title = {Wavefront aberration measurements based on the Background Oriented Schlieren method}, series = {Measurement: Sensors}, journal = {Measurement: Sensors}, publisher = {Elsevier}, issn = {2665-9174}, doi = {10.1016/j.measen.2024.101509}, year = {2024}, abstract = {Applications based on neural networks tend to be very sensitive to dataset shifts. Hence, the perception chain for autonomous driving is safeguarded against perturbations by imposing exaggerated optical quality requirements. Due to the non-linear coupling of optical elements, system requirements of camera-based Advanced Driver Assistance Systems (ADAS) can not be easily decomposed into individual part tolerances of the objective lens and the windscreen. This holds true for intensity-based part measurements, e.g. slanted edge measurements according to ISO12233, which can fundamentally not capture interference effects of the complex light field. Instead wavefront-based part measurements are required. Unfortunately, state-of-the-art wavefront measurement techniques are limited by the spanned sensitive area of the Shack-Hartmann lenslet array or the aperture stop of a corresponding interferometrical setup, respectively. Further, both measurements are limited by using collimated light only, whereas the target application has a large (angular) field of view, requiring many different measurements. We address those bottlenecks by proposing a novel wavefront aberration measurement procedure based on the Background Oriented Schlieren (BOS) method utilizing image auto-correlation. We analytically derive the governing equations for determining the Zernike coefficients of a wavefront aberration map in the knowledge of the local refractive power map obtained by a high-resolution BOS measurement. Furthermore, we experimentally demonstrate the feasibility of the measurement technique. Applying this novel method yields the promise of affordable wavefront aberration measurements only requiring a high-resolution camera and a sophisticated alignment strategy.}, language = {en} } @unpublished{YuwonoLoeppenbergSchwungetal.2024, author = {Yuwono, Steve and L{\"o}ppenberg, Marlon and Schwung, Dorothea and Schwung, Andreas}, title = {Gradient-based Learning in State-based Potential Games for Self-Learning Production Systems}, series = {arxiv}, journal = {arxiv}, publisher = {arxiv}, doi = {10.48550/arXiv.2406.10015}, year = {2024}, abstract = {In this paper, we introduce novel gradient-based optimization methods for state-based potential games (SbPGs) within self-learning distributed production systems. SbPGs are recognised for their efficacy in enabling self-optimizing distributed multi-agent systems and offer a proven convergence guarantee, which facilitates collaborative player efforts towards global objectives. Our study strives to replace conventional ad-hoc random exploration-based learning in SbPGs with contemporary gradient-based approaches, which aim for faster convergence and smoother exploration dynamics, thereby shortening training duration while upholding the efficacy of SbPGs. Moreover, we propose three distinct variants for estimating the objective function of gradient-based learning, each developed to suit the unique characteristics of the systems under consideration. To validate our methodology, we apply it to a laboratory testbed, namely Bulk Good Laboratory Plant, which represents a smart and flexible distributed multi-agent production system. The incorporation of gradient-based learning in SbPGs reduces training times and achieves more optimal policies than its baseline.}, subject = {Maschinelles Lernen}, language = {en} } @book{RiessWallraff2024, author = {Rieß, Bernhard and Wallraff, Christoph}, title = {{\"U}bungsbuch Signale und Systeme}, edition = {4. Auflage}, publisher = {Springer Nature}, address = {Wiesbaden}, isbn = {9783658424244}, doi = {10.1007/978-3-658-42425-1}, year = {2024}, subject = {Aufgabensammlung}, language = {de} } @misc{OPUS4-4671, title = {976 - Erste Satzung zur {\"A}nderung der Pr{\"u}fungsordnung (studiengangspezifische Bestimmungen) f{\"u}r den Masterstudiengang Elektro- und Informationstechnik an der Hochschule D{\"u}sseldorf vom 06.11.2024}, address = {D{\"u}sseldorf}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-46713}, pages = {2}, year = {2024}, abstract = {Aufgrund der \S\S 2 Abs. 4, 64 Abs. 1 des Gesetzes {\"u}ber die Hochschulen des Landes Nordrhein-Westfalen (Hochschulgesetz - HG) vom 16.09.2014 (GV. NRW. S. 547) in der aktuell g{\"u}ltigen Fassung hat die Hochschule D{\"u}sseldorf die folgende Ordnung als Satzung erlassen.}, language = {de} } @misc{OPUS4-4670, title = {975 - Erste Satzung zur {\"A}nderung der Rahmenpr{\"u}fungsordnung des Fachbereichs Elektro- und Informationstechnik (RahmenPO EI) an der Hochschule D{\"u}sseldorf vom 06.11.2024}, address = {D{\"u}sseldorf}, organization = {Hochschule D{\"u}sseldorf}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-46703}, pages = {2}, year = {2024}, abstract = {Aufgrund der \S\S 2 Abs. 4, 64 Abs. 1 des Gesetzes {\"u}ber die Hochschulen des Landes Nordrhein-Westfalen (Hochschulgesetz - HG) vom 16.09.2014 (GV. NRW. S. 547) in der aktuell g{\"u}ltigen Fassung hat die Hochschule D{\"u}sseldorf die folgende Ordnung als Satzung erlassen.}, language = {de} } @article{MolloyMuellerDeeganetal.2024, author = {Molloy, Dara and M{\"u}ller, Patrick and Deegan, Brian and Mullins, Darragh and Horgan, Jonathan and Ward, Enda and Jones, Edward and Braun, Alexander and Glavin, Martin}, title = {Analysis of the Impact of Lens Blur on Safety-Critical Automotive Object Detection}, series = {IEEE Access}, volume = {12}, journal = {IEEE Access}, publisher = {IEEE}, issn = {2169-3536}, doi = {10.1109/ACCESS.2023.3348663}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-42999}, pages = {3554 -- 3569}, year = {2024}, abstract = {Camera-based object detection is widely used in safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous vehicle research. Road infrastructure has been designed for human vision, so computer vision, with RGB cameras, is a vital source of semantic information from the environment. Sensors, such as LIDAR and RADAR, are also often utilized for these applications; however, cameras provide a higher spatial resolution and color information. The spatial frequency response (SFR), or sharpness of a camera, utilized in object detection systems must be sufficient to allow a detection algorithm to localize objects in the environment over its lifetime reliably. This study explores the relationship between object detection performance and SFR. Six state-of-the-art object detection models are evaluated with varying levels of lens defocus. A novel raw image dataset is created and utilized, containing pedestrians and cars over a range of distances up to 100-m from the sensor. Object detection performance for each defocused dataset is analyzed over a range of distances to determine the minimum SFR necessary in each case. Results show that the relationship between object detection performance and lens blur is much more complex than previous studies have found due to lens field curvature, chromatic aberration, and astigmatisms. We have found that smaller objects are disproportionately impacted by lens blur, and different object detection models have differing levels of robustness to lens blur}, language = {en} } @article{GeerkensSieberichsBraunetal.2024, author = {Geerkens, Simon and Sieberichs, Christian and Braun, Alexander and Waschulzik, Thomas}, title = {QI²: an interactive tool for data quality assurance}, series = {AI and Ethics}, volume = {4}, journal = {AI and Ethics}, publisher = {Springer Nature}, issn = {2730-5961}, doi = {10.1007/s43681-023-00390-6}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-42945}, pages = {141 -- 149}, year = {2024}, abstract = {The importance of high data quality is increasing with the growing impact and distribution of ML systems and big data. Also, the planned AI Act from the European commission defines challenging legal requirements for data quality especially for the market introduction of safety relevant ML systems. In this paper, we introduce a novel approach that supports the data quality assurance process of multiple data quality aspects. This approach enables the verification of quantitative data quality requirements. The concept and benefits are introduced and explained on small example data sets. How the method is applied is demonstrated on the well-known MNIST data set based an handwritten digits.}, language = {en} } @article{SieberichsGeerkensBraunetal.2024, author = {Sieberichs, Christian and Geerkens, Simon and Braun, Alexander and Waschulzik, Thomas}, title = {ECS: an interactive tool for data quality assurance}, series = {AI and Ethics}, volume = {4}, journal = {AI and Ethics}, publisher = {Springer Nature}, issn = {2730-5961}, doi = {10.1007/s43681-023-00393-3}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-42908}, pages = {131 -- 139}, year = {2024}, abstract = {With the increasing capabilities of machine learning systems and their potential use in safety-critical systems, ensuring high-quality data is becoming increasingly important. In this paper, we present a novel approach for the assurance of data quality. For this purpose, the mathematical basics are first discussed and the approach is presented using multiple examples. This results in the detection of data points with potentially harmful properties for the use in safety-critical systems.}, language = {en} }