TY - CHAP A1 - Kippelt, Stefan A1 - Rehtanz, Christian A1 - Gitis, Alexander Mihailovic A1 - Leuthold, Matthias A1 - Sauer, Dirk Uwe A1 - Echternacht, David A1 - Rotering, Niklas A1 - Moser, Albert A1 - Kleimaier, Martin A1 - Loges, Hauke A1 - Becker, Andreas A1 - Aundrup, Thomas A1 - Berthold, Andreas A1 - Pokojski, Martin T1 - Dezentrale Energiespeicher: Studie zu Stromspeichern in der Nieder- und Mittelspannungsebene T2 - Von Smart Grids zu Smart Markets 2015 - Beiträge der ETG-Fachtagung 25.03.2015 - 26.03.2015 in Kassel, Deutschland Y1 - 2015 PB - ETG CY - Kassel ER - TY - CHAP A1 - Linnemann, Christian A1 - Echternacht, David A1 - Breuer, Christopher A1 - Heuberger, Daniel A1 - Moser, Albert T1 - Ermittlung vorteilhafter HGÜ-Anschlussknoten T2 - Smart Grid: Intelligente Energieversorgung der Zukunft ; Kongressbeitäge ; 5. - 6. November 2012, Internationales Congresscenter Stuttgart (ICS) Y1 - 2012 SN - 978-3-8007-3446-7 PB - VDE-Verl. CY - Berlin ER - TY - CHAP A1 - Linnemann, Christian A1 - Echternacht, David A1 - Breuer, Christopher A1 - Moser, Albert T1 - Modeling optimal redispatch for the European Transmission grid T2 - 2011 IEEE Trondheim PowerTech,19.06.2011 - 23.06.2011, Trondheim Y1 - 2011 UR - http://ieeexplore.ieee.org/document/6019442/ SN - 978-1-4244-8418-8 U6 - https://doi.org/10.1109/PTC.2011.6019442 SP - 1 EP - 8 PB - IEEE ER - TY - CHAP A1 - Neme, Christopher A1 - Seefeldt, Friedrich A1 - Gottstein, Meg A1 - Echternacht, David A1 - Moser, Albert A1 - Weston, Frederick T1 - The Positive Effects of Energy Efficiency on the German Electricity Sector: Peer-reviewed paper presented at IEPPEC 2014 International Energy Policies & Programmes Evaluation Conference T2 - 15. International Energy Policies & Programmes Evaluation Conference (IEPEC), Berlin, Deutschland, 09.09-11.09.2014 Y1 - 2014 UR - https://www.agora-energiewende.de/en/publications/the-positive-effects-of-energy-efficiency-on-the-german-electricity-sector/ SP - 1 EP - 16 CY - Berlin ER - TY - JOUR A1 - Braun, Alexander T1 - Automotive mass production of camera systems: Linking image quality to AI performance JF - tm - Technisches Messen N2 - Artificial intelligence methods based on machine learning or artificial neural networks have become indispensable in camera-based driver assistance systems, and also represent an essential building block for future autonomous driving. However, the great successes of these evaluation methods in environment perception and also driving planning are accompanied by equally great challenges in the validation and verification of these systems. One of the essential aspects for this is the required guaranteed safety of the functions under mass production conditions of the vehicles. This article explains this point of view using a detailed example from the field of camera-based driver assistance systems: the determination of inspection limits at the end of the production line. The camera is one of the most important sensor modalities for vehicle environment sensing and as such, the quality of the camera systems plays a key role in the safety argumentation of the overall system. Several illustrative application examples (role of simulations, calibration, influence of the windshield) will be presented. The basic ideas presented can be well transferred to the other sensor modalities (lidar, radar, ToF, etc.). The investigations/evidence show that doubts are allowed whether or how fast autonomous driving on level L4/5 will take hold as robotaxis or – even more challenging – in private ownership on a larger scale. KW - Electrical and Electronic Engineering KW - Instrumentation Y1 - 2022 U6 - https://doi.org/10.1515/teme-2022-0029 SN - 2196-7113 PB - Walter de Gruyter ER - TY - CHAP A1 - Balzer, Christoph A1 - Hannemann, Th. A1 - Wunderlich, Chr. A1 - Braun, Alexander A1 - Ettler, M. A1 - Paape, Chr. A1 - Neuhauser, W. T1 - Efficient photoionisation, preparation and coherent manipulation of trapped /sup 171/Yb/sup +/-ions T2 - EQEC '05. European Quantum Electronics Conference, 12-17 June 2005, Munich Y1 - 2005 UR - http://ieeexplore.ieee.org/document/1567474/ SN - 0-7803-8973-5 U6 - https://doi.org/10.1109/EQEC.2005.1567474 SP - 308 PB - IEEE ER - TY - THES A1 - Braun, Alexander T1 - Addressing Single Yb+ Ions: A new scheme for quantum computing in linear ion traps Y1 - 2007 SN - 978-3-86727-276-6 PB - Cuvillier Verlag CY - Göttingen ER - TY - JOUR A1 - Wittpahl, Christian A1 - Zakour, Hatem Ben A1 - Lehmann, Matthias A1 - Braun, Alexander T1 - Realistic Image Degradation with Measured PSF JF - Electronic Imaging, Autonomous Vehicles and Machines 2018 KW - SiL/HiL Y1 - 2018 U6 - https://doi.org/10.2352/ISSN.2470-1173.2018.17.AVM-149 SN - 2470-1173 IS - 17 PB - Society for Imaging Science and Technology ER - TY - JOUR A1 - Tseng, Ethan A1 - Mosleh, Ali A1 - Mannan, Fahim A1 - St-Arnaud, Karl A1 - Sharma, Avinash A1 - Peng, Yifan A1 - Braun, Alexander A1 - Nowrouzezahrai, Derek A1 - Lalonde, Jean-François A1 - Heide, Felix T1 - Differentiable Compound Optics and Processing Pipeline Optimization for End-to-end Camera Design JF - ACM Transactions on Graphics N2 - Most modern commodity imaging systems we use directly for photography‐-or indirectly rely on for downstream applications‐-employ optical systems of multiple lenses that must balance deviations from perfect optics, manufacturing constraints, tolerances, cost, and footprint. Although optical designs often have complex interactions with downstream image processing or analysis tasks, today’s compound optics are designed in isolation from these interactions. Existing optical design tools aim to minimize optical aberrations, such as deviations from Gauss’ linear model of optics, instead of application-specific losses, precluding joint optimization with hardware image signal processing (ISP) and highly parameterized neural network processing. In this article, we propose an optimization method for compound optics that lifts these limitations. We optimize entire lens systems jointly with hardware and software image processing pipelines, downstream neural network processing, and application-specific end-to-end losses. To this end, we propose a learned, differentiable forward model for compound optics and an alternating proximal optimization method that handles function compositions with highly varying parameter dimensions for optics, hardware ISP, and neural nets. Our method integrates seamlessly atop existing optical design tools, such as Zemax. We can thus assess our method across many camera system designs and end-to-end applications. We validate our approach in an automotive camera optics setting‐-together with hardware ISP post processing and detection‐-outperforming classical optics designs for automotive object detection and traffic light state detection. For human viewing tasks, we optimize optics and processing pipelines for dynamic outdoor scenarios and dynamic low-light imaging. We outperform existing compartmentalized design or fine-tuning methods qualitatively and quantitatively, across all domain-specific applications tested. Y1 - 2021 U6 - https://doi.org/10.1145/3446791 SN - 1557-7368 VL - 40 IS - 2 SP - 1 EP - 19 PB - Association for Computing Machinery CY - New York, NY, USA ER - TY - CHAP A1 - Mueller, Patrick A1 - Lehmann, Matthias A1 - Braun, Alexander T1 - Simulating tests to test simulation T2 - IS&T International Symposium on Electronic Imaging 2020: Autonomous Vehicles and Machines, 26 January 2020 — 30 January 2020, Burlingame, CA, USA Y1 - 2020 U6 - https://doi.org/10.2352/ISSN.2470-1173.2020.16.AVM-149 VL - 2020 IS - 16 PB - Society for Imaging Science and Technology ER -