TY - CHAP A1 - Fatemi, Armin A1 - Tischbein, Franziska A1 - Wirtz, Frank A1 - Schmoger, Christin A1 - Dorendorf, Stefan A1 - Schurtz, Annika A1 - Echternacht, David A1 - Ulbig, Andreas T1 - On the Impact of Smartification Strategies for the State Estimation of Low Voltage Grids T2 - 13th "Internationale Energiewirtschaftstagung" (IEWT2023), February 15-17, 2023, Vienna, Austria N2 - The decarbonization of for example the energy or heat sector leads to the transformation of distribution grids. The expansion of decentralized energy resources and the integration of new consumers due to sector coupling (e.g. heat pumps or electric vehicles) into low voltage grids increases the need for grid expansion and usage of flexibilities in the grid. A high observability of the current grid status is needed to perform these tasks efficiently and effectively. Therefore, there is a need to increase the observability of low voltage grids by installing measurement technologies (e.g. smart meters). Multiple different measurement technologies are available for low voltage grids which can vary in their benefit to observation quality and their installation costs. Therefore, Bayernwerk Netz GmbH and E.DIS AG in cooperation with E-Bridge Consulting GmbH and the Institute for High Voltage Equipment and Grids, Digitalization and Energy Economics (IAEW) investigated the effectiveness of different strategies for the smartification of low voltage grids. This paper presents the methodology used for the investigation and exemplary results focusing on the impact of intelligent cable distribution cabinets and smart meters on the quality of the state estimation. KW - State Estimation KW - Smart Meter KW - Grid Observability KW - Measurement Technologies Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-41282 SP - 1 EP - 15 PB - arXiv CY - Wien ER - TY - JOUR A1 - Wohlers, Luis Constantin A1 - Müller, Patrick A1 - Braun, Alexander T1 - Original image noise reconstruction for spatially-varying filtered driving scenes JF - Electronic Imaging: Society for Imaging Science and Technology Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-41479 SN - 2470-1173 VL - 34 IS - 16 SP - 1 EP - 7 PB - Society for Imaging Science and Technology ER - TY - JOUR A1 - Wolf, Dominik Werner A1 - Ulrich, Markus A1 - Braun, Alexander T1 - Novel developments of refractive power measurement techniques in the automotive world JF - Metrologia N2 - Refractive power measurements serve as the primary quality standard in the automotive glazing industry. In the light of autonomous driving new optical metrics are becoming more and more popular for specifying optical quality requirements for the windshield. Nevertheless, the link between those quantities and the refractive power needs to be established in order to ensure a holistic requirement profile for the windshield. As a consequence, traceable high-resolution refractive power measurements are still required for the glass quality assessment. Standard measurement systems using Moiré patterns for refractive power monitoring in the automotive industry are highly resolution limited, wherefore they are insufficient for evaluating the camera window area. Consequently, there is a need for more sophisticated refractive power measurement systems that provide a higher spatial resolution. In addition, a calibration procedure has to be developed in order to guarantee for comparability of the measurement results. For increasing the resolution, a measurement setup based on an auto-correlation algorithm is tested in this paper. Furthermore, a calibration procedure is established by using a single reference lens with a nominal refractive power of 100 km-1. For the calibration of the entire measurement range of the system, the lens is tilted by an inclination angle orthogonal to the optical axis. The effective refractive power is then given by the Kerkhof model. By adopting the measurement and calibration procedure presented in this paper, glass suppliers in the automotive industry will be able to detect relevant manufacturing defects within the camera window area more accurately paving the way for a holistic quality assurance of the windshield for future advanced driver-assistance system (ADAS) functionalities. Concurrently, the traceability of the measurement results is ensured by establishing a calibration chain based on a single reference lens, which is traced back to international standards. Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-41484 UR - http://iopscience.iop.org/article/10.1088/1681-7575/acf1a4 SN - 1681-7575 VL - 60 IS - 064001 PB - IOP Publishing ER - TY - CHAP A1 - Müller, Patrick A1 - Braun, Alexander A1 - Keuper, Margret T1 - Impact of realistic properties of the point spread function on classification tasks to reveal a possible distribution shift T2 - NeurIPS 2022: Workshop on Distribution Shifts: Connecting Methods and Applications, December 3rd, 2022, New Orleans, USA KW - chromatic aberration KW - corruption KW - Deep Neural Networks KW - Distribution Shift KW - MTF Y1 - 2022 UR - https://openreview.net/forum?id=r7WJpE3oy0 PB - NeurIPS CY - New Orleans ER - TY - CHAP A1 - Müller, Patrick A1 - Braun, Alexander T1 - Simulating optical properties to access novel metrological parameter ranges and the impact of different model approximations T2 - 2022 IEEE International Workshop on Metrology for Automotive (MetroAutomotive), 4-6 July 2022 Y1 - 2022 SN - 978-1-6654-6689-9 U6 - https://doi.org/10.1109/MetroAutomotive54295.2022.9855079 SP - 133 EP - 138 PB - IEEE ER - TY - JOUR A1 - Müller, Patrick A1 - Braun, Alexander T1 - Local performance evaluation of AI-algorithms with the generalized spatial recall index JF - tm - Technisches Messen Y1 - 2023 U6 - https://doi.org/10.1515/teme-2023-0013 SN - 2196-7113 VL - 90 IS - 7-8 SP - 464 EP - 477 PB - De Gruyter ER - TY - JOUR A1 - Müller, Patrick A1 - Braun, Alexander T1 - MTF as a performance indicator for AI algorithms? JF - Electronic Imaging: Society for Imaging Science and Technology N2 - Abstract The modulation-transfer function (MTF) is a fundamental optical metric to measure the optical quality of an imaging system. In the automotive industry it is used to qualify camera systems for ADAS/AD. Each modern ADAS/AD system includes evaluation algorithms for environment perception and decision making that are based on AI/ML methods and neural networks. The performance of these AI algorithms is measured by established metrics like Average Precision (AP) or precision-recall-curves. In this article we research the robustness of the link between the optical quality metric and the AI performance metric. A series of numerical experiments were performed with object detection and instance segmentation algorithms (cars, pedestrians) evaluated on image databases with varying optical quality. We demonstrate with these that for strong optical aberrations a distinct performance loss is apparent, but that for subtle optical quality differences – as might arise during production tolerances – this link does not exhibit a satisfactory correlation. This calls into question how reliable the current industry practice is where a produced camera is tested end-of-line (EOL) with the MTF, and fixed MTF thresholds are used to qualify the performance of the camera-under-test. KW - AI performance metrics KW - Artificial Intelligence KW - Modulation Transfer Function (MTF) KW - Optical Quality KW - Perception Y1 - 2023 U6 - https://doi.org/10.2352/EI.2023.35.16.AVM-125 SN - 2470-1173 VL - 35 IS - 16 SP - 1 EP - 7 PB - Society for Imaging Science and Technology ER - TY - JOUR A1 - Brummel, Mattis A1 - Müller, Patrick A1 - Braun, Alexander T1 - Spatial precision and recall indices to assess the performance of instance segmentation algorithms JF - Electronic Imaging KW - Space-Variance KW - Computer Vision KW - IoU KW - image perturbations Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-41552 SN - 2470-1173 VL - 34 IS - 16 SP - 1 EP - 6 PB - Society for Imaging Science and Technology ER - TY - PAT A1 - Braun, Alexander A1 - Richwin, Matthias A1 - Weber, Thomas T1 - Optoelektronische Sensoreinrichtung für ein Kraftfahrzeug N2 - Beschrieben wird eine optoelektronische Sensoreinrichtung für ein Kraftfahrzeug zur unterscheidenden Detektion von Feuchtigkeit und Salzlösungen auf einer Fahrzeugscheibe, mit einer Beleuchtungseinheit zur Einstrahlung von Licht in eine Fahrzeugscheibe und mit einem Lichtempfänger, welcher von der Fahrzeugscheibe reflektiertes Licht erfasst, sowie mit einem Koppelelement zur Ein- und Auskopplung von Licht in bzw. aus der Fahrzeugscheibe, wobei die Beleuchtungseinrichtung Licht über einen Einfallswinkelbereich in die Fahrzeugscheibe einstrahlt, der sich über mehrere Winkelgrade erstreckt, wobei der Lichtempfänger eine Vielzahl von Empfangselementen aufweist, wobei ein optisches System das von der Fahrzeugscheibe in einen Ausfallswinkelbereich reflektierte Licht auf Empfangselemente des Lichtempfängers abbildet und wobei der Lichtempfänger ein Signal generiert, aus dem eine Auswertevorrichtung den Grenzwinkel der Totalreflexion an der Fahrzeugscheibe ermittelt. Y1 - 2009 UR - https://depatisnet.dpma.de/DepatisNet/depatisnet?action=bibdat&docid=DE102007052704A1 PB - Deutsches Patent- und Markenamt ER - TY - GEN T1 - 887 - Berichtigung der Zweiten Neubekanntmachung der Prüfungsordnung für den Masterstudiengang Elektro- und Informationstechnik an der Hochschule Düsseldorf vom 17.05.2023 N2 - Die Zweite Neubekanntmachung der Prüfungsordnung für den Masterstudiengang Elektro- und Informationstechnik an der Hochschule Düsseldorf vom 02.05.2023 (Verkündungsblatt der Hochschule Düsseldorf, Amtliche Mitteilung Nr. 882) ist wie folgt zu berichtigen: In § 20 Abs. 1 S. 1 werden die Wörter „dokumentenecht gebundener Ausfertigung und“ gestrichen. T3 - Amtliche Mitteilungen : Verkündungsblatt der Hochschule Düsseldorf - 887 KW - Amtliche Mitteilungen KW - Master Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-41031 CY - Düsseldorf 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 -