@book{BuchholzJerominAsgariehetal.2018, author = {Buchholz, B. and Jeromin, I. and Asgarieh, L. and Echternacht, David and Fan, M. and Repo, S. and Dedekind, K. and Ma, Z. and Gramme, E.}, title = {Asset management for distribution networks with high penetration of distributed energy resources}, address = {Cigr{\´e}}, year = {2018}, language = {en} } @misc{Echternacht2018, author = {Echternacht, David}, title = {Spitzenkappung erfordert intelligente Netzsteuerung}, series = {e|m|w}, journal = {e|m|w}, number = {2}, publisher = {Energate}, organization = {Energate}, year = {2018}, language = {de} } @inproceedings{EchternachtElHaouatiSchermulyetal.2018, author = {Echternacht, David and El Haouati, I. and Schermuly, Rainer and Meyer, F.}, title = {Simulating the impact of e-mobility charging infrastructure on urban low-voltage networks. Postersession}, series = {NEIS 2018: Conference on Sustainable Energy Supply and Energy Storage Systems. Hamburg, Deutschland, 20.09.-21.09.2018}, booktitle = {NEIS 2018: Conference on Sustainable Energy Supply and Energy Storage Systems. Hamburg, Deutschland, 20.09.-21.09.2018}, editor = {Schulz, Detlef}, publisher = {VDE}, address = {Frankfurt am Main; Piscataway, NJ}, isbn = {978-3-8007-4821-1}, pages = {208 -- 213}, year = {2018}, language = {en} } @inproceedings{EchternachtSchultze2018, author = {Echternacht, David and Schultze, Nico}, title = {Integriertes Asset Management als Basis einer Energiewendegerechten Instandhaltungs- und Investitionsstrategie}, series = {Anlagentechnik 2018: Anlagentechnik f{\"u}r elektrische Verteilungsnetze}, volume = {Anlagentechnik f{\"u}r elektrische Verteilungsnetze}, booktitle = {Anlagentechnik 2018: Anlagentechnik f{\"u}r elektrische Verteilungsnetze}, editor = {Cichowski, Rolf R{\"u}diger}, publisher = {EW Medien und Kongresse}, address = {Frankfurt am Main}, isbn = {9783802211669}, pages = {167 -- 173}, year = {2018}, language = {de} } @article{WittpahlZakourLehmannetal.2018, author = {Wittpahl, Christian and Zakour, Hatem Ben and Lehmann, Matthias and Braun, Alexander}, title = {Realistic Image Degradation with Measured PSF}, series = {Electronic Imaging, Autonomous Vehicles and Machines 2018}, journal = {Electronic Imaging, Autonomous Vehicles and Machines 2018}, number = {17}, publisher = {Society for Imaging Science and Technology}, issn = {2470-1173}, doi = {10.2352/ISSN.2470-1173.2018.17.AVM-149}, pages = {149}, year = {2018}, language = {en} } @article{DiGioiaBrownNieetal.2018, author = {Di Gioia, Antonio and Brown, Ian P. and Nie, Yue and Knippel, Ryan and Ludois, Daniel C. and Dai, Jiejian and Hagen, Skyler and Alteheld, Christian}, title = {Design and Demonstration of a Wound Field Synchronous Machine for Electric Vehicle Traction With Brushless Capacitive Field Excitation}, series = {IEEE Transactions on Industry Applications}, volume = {54}, journal = {IEEE Transactions on Industry Applications}, number = {2}, publisher = {IEEE}, doi = {10.1109/tia.2017.2784799}, pages = {1390 -- 1403}, year = {2018}, abstract = {This paper describes the modeling, optimization, mechanical design, and experimental characterization of a high power density wound field synchronous machine (WFSM) for electric vehicle traction applications. The WFSM is designed for brushless rotor field excitation using an axial flux hydrodynamic capacitive power coupler (CPC). The flexible design environment, which was used for large-scale multiobjective optimization of the WFSM, is described. A prototype WFSM, spray cooled with automatic transmission fluid, with an 80-kW output at a base speed of 4000 r/min has been tested. The prototyped WFSM achieves volumetric torque and power densities of 17.22 N·m/L and 7.19 kW/L referred to the envelope cylindrical volume of the active materials plus spray cooling rings. The specific torque and power densities are 4.69 N·m/kg and 1.95 kW/kg referred to the mass of the active materials and the shaft. The prototyped CPC provided up to 1-kW excitation power with a mass 600 g and volume of 0.271 L.}, language = {en} } @inproceedings{LehmannWittpahlZakouretal.2018, author = {Lehmann, Matthias and Wittpahl, Christian and Zakour, Hatem Ben and Braun, Alexander}, title = {Resolution and accuracy of non-linear regression of PSF with artificial neural networks}, series = {SPIE Optical Systems Design: Optical Instrument Science, Technology, and Applications, 2018, Frankfurt, Germany}, volume = {Proc. SPIE, Vol. 10695}, booktitle = {SPIE Optical Systems Design: Optical Instrument Science, Technology, and Applications, 2018, Frankfurt, Germany}, number = {106950C}, editor = {Haverkamp, Nils and Youngworth, Richard N.}, publisher = {International Society for Optics and Photonics}, address = {Frankfurt}, organization = {International Society for Optics and Photonics}, doi = {10.1117/12.2313144}, pages = {52 -- 63}, year = {2018}, abstract = {In a previous work we have demonstrated a novel numerical model for the point spread function (PSF) of an optical system that can efficiently model both experimental measurements and lens design simulations of the PSF. The novelty lies in the portability and the parameterization of this model, which allows for completely new ways to validate optical systems, which is especially interesting for mass production optics like in the automotive industry, but also for ophtalmology. The numerical basis for this model is a non-linear regression of the PSF with an artificial neural network (ANN). In this work we examine two important aspects of this model: the spatial resolution and the accuracy of the model. Measurement and simulation of a PSF can have a much higher resolution then the typical pixel size used in current camera sensors, especially those for the automotive industry. We discuss the influence this has on on the topology of the ANN and the final application where the modeled PSF is actually used. Another important influence on the accuracy of the trained ANN is the error metric which is used during training. The PSF is a distinctly non-linear function, which varies strongly over field and defocus, but nonetheless exhibits strong symmetries and spatial relations. Therefore we examine different distance and similarity measures and discuss its influence on the modeling performance of the ANN.}, language = {en} }