@inproceedings{CordesGligorevicBlicharski2019, author = {Cordes, Sven and Gligorevic, Snjezana and Blicharski, Peter}, title = {Analysis of sine precision influence on DOA estimation using the MUSIC algorithm}, series = {2019 20th International Radar Symposium (IRS)}, booktitle = {2019 20th International Radar Symposium (IRS)}, isbn = {978-3-7369-9860-5}, doi = {10.23919/IRS.2019.8768162}, pages = {1 -- 10}, year = {2019}, language = {en} } @article{SchmidtForkmannSchultzetal.2019, author = {Schmidt, Katharina and Forkmann, Katarina and Schultz, Heidrun and Gratz, Marcel and Bitz, Andreas and Wiech, Katja and Bingel, Ulrike}, title = {Enhanced Neural Reinstatement for Evoked Facial Pain Compared With Evoked Hand Pain}, series = {The Journal of Pain}, journal = {The Journal of Pain}, number = {In Press, Corrected Proof}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1526-5900}, doi = {10.1016/j.jpain.2019.03.003}, year = {2019}, language = {en} } @article{ClaerFerreinSchiffer2019, author = {Claer, Mario and Ferrein, Alexander and Schiffer, Stefan}, title = {Calibration of a Rotating or Revolving Platform with a LiDAR Sensor}, series = {Applied Sciences}, volume = {Volume 9}, journal = {Applied Sciences}, number = {issue 11, 2238}, publisher = {MDPI}, address = {Basel}, issn = {2076-3417}, doi = {10.3390/app9112238}, pages = {18 Seiten}, year = {2019}, language = {en} } @inproceedings{SteinbauerFerrein2019, author = {Steinbauer, Gerald and Ferrein, Alexander}, title = {CogRob 2018 : Cognitive Robotics Workshop. Proceedings of the 11th Cognitive Robotics Workshop 2018 co-located with 16th International Conference on Principles of Knowledge Representation and Reasoning (KR 2018). Tempe, AZ, USA, October 27th, 2018.}, series = {CEUR workshop proceedings}, booktitle = {CEUR workshop proceedings}, number = {Vol-2325}, issn = {1613-0073}, pages = {46 Seiten}, year = {2019}, language = {en} } @inproceedings{SchifferBragard2019, author = {Schiffer, Fabian and Bragard, Michael}, title = {Cascaded LQ and Field-Oriented Control of a Mobile Inverse Pendulum (Segway) with Permanent Magnet Synchronous Machines}, series = {2019 20th International Conference on Research and Education in Mechatronics (REM)}, booktitle = {2019 20th International Conference on Research and Education in Mechatronics (REM)}, isbn = {978-1-5386-9257-8}, doi = {10.1109/REM.2019.8744101}, pages = {1 -- 8}, year = {2019}, language = {en} } @inproceedings{BragardSubeSchneideretal.2019, author = {Bragard, Michael and Sube, Maike and Schneider, Maike and Jungemann, Christoph}, title = {Introducing a Cross-University Bachelor's Programme with Orientation Semester - Enabling a Permeable Academic Education System}, series = {2019 20th International Conference on Research and Education in Mechatronics (REM)}, booktitle = {2019 20th International Conference on Research and Education in Mechatronics (REM)}, isbn = {978-1-5386-9257-8}, doi = {10.1109/REM.2019.8744132}, pages = {1 -- 6}, year = {2019}, language = {en} } @article{OrzadaSolbachGratzetal.2019, author = {Orzada, Stephan and Solbach, Klaus and Gratz, Marcel and Brunheim, Sascha and Fiedler, Thomas M. and Johst, S{\"o}ren and Bitz, Andreas and Shooshtary, Samaneh and Abuelhaija, Asjraf and Voelker, Maximilian N. and Rietsch, Stefan H. G. and Kraff, Oliver and Maderwald, Stefan and Fl{\"o}ser, Martina and Oehmingen, Mark and Quick, Harald H. and Ladd, Mark E.}, title = {A 32-channel parallel transmit system add-on for 7T MRI}, series = {Plos one}, journal = {Plos one}, doi = {10.1371/journal.pone.0222452}, year = {2019}, language = {en} } @article{NoureddineKraffLaddetal.2019, author = {Noureddine, Yacine and Kraff, Oliver and Ladd, Mark E. and Wrede, Karsten and Chen, Bixia and Quick, Harald H. and Schaefers, Georg and Bitz, Andreas}, title = {Radiofrequency induced heating around aneurysm clips using a generic birdcage head coil at 7 Tesla under consideration of the minimum distance to decouple multiple aneurysm clips}, series = {Magnetic Resonance in Medicine}, journal = {Magnetic Resonance in Medicine}, number = {Early view}, publisher = {Wiley}, address = {Weinheim}, issn = {1522-2594}, doi = {10.1002/mrm.27835}, pages = {1 -- 17}, year = {2019}, language = {en} } @incollection{LeiseAltherrSimonetal.2019, author = {Leise, Philipp and Altherr, Lena and Simon, Nicolai and Pelz, Peter F.}, title = {Finding global-optimal gearbox designs for battery electric vehicles}, series = {Optimization of complex systems - theory, models, algorithms and applications : WCGO 2019}, booktitle = {Optimization of complex systems - theory, models, algorithms and applications : WCGO 2019}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-21802-7}, doi = {10.1007/978-3-030-21803-4_91}, pages = {916 -- 925}, year = {2019}, abstract = {In order to maximize the possible travel distance of battery electric vehicles with one battery charge, it is mandatory to adjust all components of the powertrain carefully to each other. While current vehicle designs mostly simplify the powertrain rigorously and use an electric motor in combination with a gearbox with only one fixed transmission ratio, the use of multi-gear systems has great potential. First, a multi-speed system is able to improve the overall energy efficiency. Secondly, it is able to reduce the maximum momentum and therefore to reduce the maximum current provided by the traction battery, which results in a longer battery lifetime. In this paper, we present a systematic way to generate multi-gear gearbox designs that—combined with a certain electric motor—lead to the most efficient fulfillment of predefined load scenarios and are at the same time robust to uncertainties in the load. Therefore, we model the electric motor and the gearbox within a Mixed-Integer Nonlinear Program, and optimize the efficiency of the mechanical parts of the powertrain. By combining this mathematical optimization program with an unsupervised machine learning algorithm, we are able to derive global-optimal gearbox designs for practically relevant momentum and speed requirements.}, language = {en} } @incollection{StengerAltherrAbel2019, author = {Stenger, David and Altherr, Lena and Abel, Dirk}, title = {Machine learning and metaheuristics for black-box optimization of product families: a case-study investigating solution quality vs. computational overhead}, series = {Operations Research Proceedings 2018}, booktitle = {Operations Research Proceedings 2018}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-18499-5 (Print)}, doi = {10.1007/978-3-030-18500-8_47}, pages = {379 -- 385}, year = {2019}, abstract = {In product development, numerous design decisions have to be made. Multi-domain virtual prototyping provides a variety of tools to assess technical feasibility of design options, however often requires substantial computational effort for just a single evaluation. A special challenge is therefore the optimal design of product families, which consist of a group of products derived from a common platform. Finding an optimal platform configuration (stating what is shared and what is individually designed for each product) and an optimal design of all products simultaneously leads to a mixed-integer nonlinear black-box optimization model. We present an optimization approach based on metamodels and a metaheuristic. To increase computational efficiency and solution quality, we compare different types of Gaussian process regression metamodels adapted from the domain of machine learning, and combine them with a genetic algorithm. We illustrate our approach on the example of a product family of electrical drives, and investigate the trade-off between solution quality and computational overhead.}, language = {en} }