@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} } @article{OrzadaFiedlerBitzetal.2020, author = {Orzada, Stephan and Fiedler, Thomas M. and Bitz, Andreas and Ladd, Mark E. and Quick, Harald H.}, title = {Local SAR compression with overestimation control to reduce maximum relative SAR overestimation and improve multi-channel RF array performance}, series = {Magnetic Resonance Materials in Physics, Biology and Medicine}, journal = {Magnetic Resonance Materials in Physics, Biology and Medicine}, number = {34 (2021)}, publisher = {Springer}, address = {Heidelberg}, isbn = {1352-8661}, doi = {10.1007/s10334-020-00890-0}, pages = {153 -- 164}, year = {2020}, abstract = {Objective In local SAR compression algorithms, the overestimation is generally not linearly dependent on actual local SAR. This can lead to large relative overestimation at low actual SAR values, unnecessarily constraining transmit array performance. Method Two strategies are proposed to reduce maximum relative overestimation for a given number of VOPs. The first strategy uses an overestimation matrix that roughly approximates actual local SAR; the second strategy uses a small set of pre-calculated VOPs as the overestimation term for the compression. Result Comparison with a previous method shows that for a given maximum relative overestimation the number of VOPs can be reduced by around 20\% at the cost of a higher absolute overestimation at high actual local SAR values. Conclusion The proposed strategies outperform a previously published strategy and can improve the SAR compression where maximum relative overestimation constrains the performance of parallel transmission.}, language = {en} } @inproceedings{ChajanSchulteTiggesRekeetal.2021, author = {Chajan, Eduard and Schulte-Tigges, Joschua and Reke, Michael and Ferrein, Alexander and Matheis, Dominik and Walter, Thomas}, title = {GPU based model-predictive path control for self-driving vehicles}, series = {IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, isbn = {978-1-7281-5394-0}, doi = {10.1109/IV48863.2021.9575619}, pages = {1243 -- 1248}, year = {2021}, abstract = {One central challenge for self-driving cars is a proper path-planning. Once a trajectory has been found, the next challenge is to accurately and safely follow the precalculated path. The model-predictive controller (MPC) is a common approach for the lateral control of autonomous vehicles. The MPC uses a vehicle dynamics model to predict the future states of the vehicle for a given prediction horizon. However, in order to achieve real-time path control, the computational load is usually large, which leads to short prediction horizons. To deal with the computational load, the control algorithm can be parallelized on the graphics processing unit (GPU). In contrast to the widely used stochastic methods, in this paper we propose a deterministic approach based on grid search. Our approach focuses on systematically discovering the search area with different levels of granularity. To achieve this, we split the optimization algorithm into multiple iterations. The best sequence of each iteration is then used as an initial solution to the next iteration. The granularity increases, resulting in smooth and predictable steering angle sequences. We present a novel GPU-based algorithm and show its accuracy and realtime abilities with a number of real-world experiments.}, language = {en} } @inproceedings{LorenzAltherrPelz2020, author = {Lorenz, Imke-Sophie and Altherr, Lena and Pelz, Peter F.}, title = {Assessing and Optimizing the Resilience of Water Distribution Systems Using Graph-Theoretical Metrics}, series = {Operations Research Proceedings 2019}, booktitle = {Operations Research Proceedings 2019}, editor = {Neufeld, Janis S. and Buscher, Udo and Lasch, Rainer and M{\"o}st, Dominik and Sch{\"o}nberger, J{\"o}rn}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-48439-2}, doi = {10.1007/978-3-030-48439-2_63}, pages = {521 -- 527}, year = {2020}, abstract = {Water distribution systems are an essential supply infrastructure for cities. Given that climatic and demographic influences will pose further challenges for these infrastructures in the future, the resilience of water supply systems, i.e. their ability to withstand and recover from disruptions, has recently become a subject of research. To assess the resilience of a WDS, different graph-theoretical approaches exist. Next to general metrics characterizing the network topology, also hydraulic and technical restrictions have to be taken into account. In this work, the resilience of an exemplary water distribution network of a major German city is assessed, and a Mixed-Integer Program is presented which allows to assess the impact of capacity adaptations on its resilience.}, language = {en} } @inproceedings{LeiseSimonAltherr2020, author = {Leise, Philipp and Simon, Nicolai and Altherr, Lena}, title = {Comparison of Piecewise Linearization Techniques to Model Electric Motor Efficiency Maps: A Computational Study}, series = {Operations Research Proceedings 2019}, booktitle = {Operations Research Proceedings 2019}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-48439-2}, doi = {10.1007/978-3-030-48439-2_55}, pages = {457 -- 463}, year = {2020}, abstract = {To maximize the travel distances of battery electric vehicles such as cars or buses for a given amount of stored energy, their powertrains are optimized energetically. One key part within optimization models for electric powertrains is the efficiency map of the electric motor. The underlying function is usually highly nonlinear and nonconvex and leads to major challenges within a global optimization process. To enable faster solution times, one possibility is the usage of piecewise linearization techniques to approximate the nonlinear efficiency map with linear constraints. Therefore, we evaluate the influence of different piecewise linearization modeling techniques on the overall solution process and compare the solution time and accuracy for methods with and without explicitly used binary variables.}, language = {en} }