TY - CHAP A1 - Kühnel, Johannes A1 - Eißmann, Timo A1 - Wiede, Christian A1 - Schwung, Dorothea A1 - Grabmaier, Anton T1 - Semi-Supervised Anomaly Detection in the TinyML Domain Through Multi-Target Few-Shot Domain Adaptation T2 - 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA) KW - Maschinelles Lernen KW - Intelligente Fertigung KW - Automation KW - Sensortechnik KW - Anomalieerkennung Y1 - 2024 U6 - https://doi.org/10.1109/ETFA61755.2024.10710811 VL - 133 PB - IEEE ER - TY - INPR A1 - Wolf, Dominik Werner A1 - Braun, Alexander A1 - Ulrich, Markus T1 - Optical aberrations in autonomous driving: Physics-informed parameterized temperature scaling for neural network uncertainty calibration T2 - International Journal of Computer Vision (IJCV) N2 - '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. Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-50120 PB - arXiv ER - TY - INPR A1 - Wolf, Dominik Werner A1 - Balaji, Prasannavenkatesh A1 - Braun, Alexander A1 - Ulrich, Markus T1 - Decoupling of neural network calibration measures T2 - German Conference on Pattern Recognition (GCPR) 2024 N2 - 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). Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-50104 PB - arXiv ER - TY - JOUR A1 - Jakab, Daniel A1 - Braun, Alexander A1 - Agnew, Cathaoir A1 - Mohandas, Reenu A1 - Deegan, Brian Michael A1 - Molloy, Dara A1 - Ward, Enda A1 - Scanlan, Anthony A1 - Eising, Ciarán T1 - SS-SFR: synthetic scenes spatial frequency response on Virtual KITTI and degraded automotive simulations for object detection JF - IET Conference Proceedings Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-50080 SN - 2732-4494 VL - 2024 IS - 10 SP - 110 EP - 117 PB - Institution of Engineering and Technology (IET) ER - TY - CHAP A1 - Diprasetya, Mochammad Rizky A1 - Pullani, Ali Nafih A1 - Schwung, Andreas A1 - Schwung, Dorothea T1 - Model-based Reinforcement Learning for Sim-to-Real Transfer in Robotics using HTM neural networks T2 - 2024 10th International Conference on Control, Decision and Information Technologies (CoDIT), 1-4 July 2024 KW - Maschinelles Lernen KW - Bestärkendes Lernen (Künstliche Intelligenz) KW - Industrieroboter KW - Simulationsmodell KW - Neuronales Netz Y1 - 2024 U6 - https://doi.org/10.1109/CoDIT62066.2024.10708424 VL - 97 SP - 43 EP - 48 PB - IEEE ER - TY - INPR A1 - Yuwono, Steve A1 - Schwung, Dorothea A1 - Schwung, Andreas T1 - Distributed Stackelberg Strategies in State-based Potential Games for Autonomous Decentralized Learning Manufacturing Systems N2 - 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. KW - Maschinelles Lernen KW - Produktionssystem KW - Spieltheorie KW - Optimierung Y1 - 2024 U6 - https://doi.org/10.48550/arXiv.2408.06397 PB - arXiv ER - TY - INPR A1 - Yuwono, Steve A1 - Schwung, Dorothea A1 - Schwung, Andreas T1 - Transfer learning of state-based potential games for process optimization in decentralized manufacturing systems T2 - arXiv N2 - 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. KW - Maschinelles Lernen KW - Spieltheorie KW - Produktionssystem KW - Optimierung Y1 - 2024 U6 - https://doi.org/10.48550/arXiv.2408.05992 PB - arXiv ER - TY - CHAP A1 - Zeller, Marc A1 - Waschulzik, Thomas A1 - Carlan, Carmen A1 - Serahlazau, Marat A1 - Bahlmann, Claus A1 - Wu, Zhiliang A1 - Spieckermann, Sigurd A1 - Krompass, Denis A1 - Geerkens, Simon A1 - Sieberichs, Christian A1 - Kirchheim, Konstantin A1 - Özen, Batu Kaan A1 - Robles, Lucia Diez ED - Ceccarelli, Andrea ED - Trapp, Mario ED - Bondavalli, Andrea ED - Schoitsch, Erwin ED - Gallina, Barbara ED - Bitsch, Friedemann T1 - Continuous Development and Safety Assurance Pipeline for ML-Based Systems in the Railway Domain T2 - 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 KW - Maschinelles Lernen KW - Sicherheit KW - Eisenbahn KW - Autonomes Fahrzeug Y1 - 2024 SN - 9783031687372 U6 - https://doi.org/10.1007/978-3-031-68738-9_36 SN - 0302-9743 SP - 446 EP - 459 PB - Springer Nature CY - Cham ER - TY - JOUR A1 - Wolf, Dominik Werner A1 - Thielbeer, Boris A1 - Ulrich, Markus A1 - Braun, Alexander T1 - Wavefront aberration measurements based on the Background Oriented Schlieren method JF - Measurement: Sensors N2 - 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. KW - Wavefront aberration measurements KW - Deep optics KW - Dataset shifts KW - Data augmentation KW - Autonomous driving Y1 - 2024 U6 - https://doi.org/10.1016/j.measen.2024.101509 SN - 2665-9174 N1 - In Press, Corrected Proof PB - Elsevier ER - TY - INPR A1 - Yuwono, Steve A1 - Löppenberg, Marlon A1 - Schwung, Dorothea A1 - Schwung, Andreas T1 - Gradient-based Learning in State-based Potential Games for Self-Learning Production Systems T2 - arxiv N2 - 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. KW - Machine Learning KW - Artificial Intelligence KW - Computer Science and Game Theory KW - Maschinelles Lernen KW - Künstliche Intelligenz KW - Spieltheorie Y1 - 2024 U6 - https://doi.org/10.48550/arXiv.2406.10015 PB - arxiv ER - TY - BOOK A1 - Rieß, Bernhard A1 - Wallraff, Christoph T1 - Übungsbuch Signale und Systeme BT - Aufgaben und Lösungen KW - Aufgabensammlung KW - Fourier-Reihe KW - Differentialgleichung KW - Impulsantwort KW - Übergangsfunktion KW - Faltung (Mathematik) KW - Fourier-Transformation KW - Laplace-Transformation Y1 - 2024 SN - 9783658424244 U6 - https://doi.org/10.1007/978-3-658-42425-1 PB - Springer Nature CY - Wiesbaden ET - 4. Auflage ER - TY - GEN T1 - 976 - Erste Satzung zur Änderung der Prüfungsordnung (studiengangspezifische Bestimmungen) für den Masterstudiengang Elektro- und Informationstechnik an der Hochschule Düsseldorf vom 06.11.2024 N2 - Aufgrund der §§ 2 Abs. 4, 64 Abs. 1 des Gesetzes über die Hochschulen des Landes Nordrhein-Westfalen (Hochschulgesetz - HG) vom 16.09.2014 (GV. NRW. S. 547) in der aktuell gültigen Fassung hat die Hochschule Düsseldorf die folgende Ordnung als Satzung erlassen. T3 - Amtliche Mitteilungen : Verkündungsblatt der Hochschule Düsseldorf - 976 KW - Amtliche Mitteilungen Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-46713 CY - Düsseldorf ER - TY - GEN T1 - 975 - Erste Satzung zur Änderung der Rahmenprüfungsordnung des Fachbereichs Elektro- und Informationstechnik (RahmenPO EI) an der Hochschule Düsseldorf vom 06.11.2024 N2 - Aufgrund der §§ 2 Abs. 4, 64 Abs. 1 des Gesetzes über die Hochschulen des Landes Nordrhein-Westfalen (Hochschulgesetz - HG) vom 16.09.2014 (GV. NRW. S. 547) in der aktuell gültigen Fassung hat die Hochschule Düsseldorf die folgende Ordnung als Satzung erlassen. T3 - Amtliche Mitteilungen : Verkündungsblatt der Hochschule Düsseldorf - 975 KW - Amtliche Mitteilungen Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-46703 CY - Düsseldorf ER - TY - JOUR A1 - Molloy, Dara A1 - Müller, Patrick A1 - Deegan, Brian A1 - Mullins, Darragh A1 - Horgan, Jonathan A1 - Ward, Enda A1 - Jones, Edward A1 - Braun, Alexander A1 - Glavin, Martin T1 - Analysis of the Impact of Lens Blur on Safety-Critical Automotive Object Detection JF - IEEE Access N2 - 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 KW - DOAJ KW - Object detection KW - intelligent transportation system KW - autonomous vehicles KW - ADAS KW - lens blur Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-42999 SN - 2169-3536 VL - 12 SP - 3554 EP - 3569 PB - IEEE ER - TY - JOUR A1 - Geerkens, Simon A1 - Sieberichs, Christian A1 - Braun, Alexander A1 - Waschulzik, Thomas T1 - QI²: an interactive tool for data quality assurance JF - AI and Ethics N2 - 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. KW - Performance metrics KW - HSD Publikationsfonds KW - DEAL KW - DFG Publikationskosten KW - Machine learning KW - Quality assurance KW - Data integrity KW - Data quality Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-42945 SN - 2730-5961 N1 - Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 532148125 and supported by the central publication fund of Hochschule Düsseldorf University of Applied Sciences VL - 4 SP - 141 EP - 149 PB - Springer Nature ER - TY - JOUR A1 - Sieberichs, Christian A1 - Geerkens, Simon A1 - Braun, Alexander A1 - Waschulzik, Thomas T1 - ECS: an interactive tool for data quality assurance JF - AI and Ethics N2 - 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. KW - DEAL KW - HSD Publikationsfonds KW - Data visulization KW - Distance based KW - Data quality assurance KW - Equivalence class sets KW - DFG Publikationskosten Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-42908 SN - 2730-5961 N1 - Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 532148125 and supported by the central publication fund of Hochschule Düsseldorf University of Applied Sciences VL - 4 SP - 131 EP - 139 PB - Springer Nature ER -