@inproceedings{NikolovskiRekeElsenetal.2021, author = {Nikolovski, Gjorgji and Reke, Michael and Elsen, Ingo and Schiffer, Stefan}, title = {Machine learning based 3D object detection for navigation in unstructured environments}, series = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)}, booktitle = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)}, publisher = {IEEE}, isbn = {978-1-6654-7921-9}, doi = {10.1109/IVWorkshops54471.2021.9669218}, pages = {236 -- 242}, year = {2021}, abstract = {In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine.}, 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} } @article{Heuermann1995, author = {Heuermann, Holger}, title = {LZY: A Self-Calibration Approach in Competition to the LRM Method for On-Wafer Measurements}, pages = {129 -- 136}, year = {1995}, language = {en} } @article{SommerBitzStreckertetal.2007, author = {Sommer, Angela M. and Bitz, Andreas and Streckert, Joachim and Hansen, Volkert W. and Lerchl, Alexander}, title = {Lymphoma development in mice chronically exposed to UMTS-modulated radiofrequency electromagnetic fields}, series = {Radiation Research}, volume = {168}, journal = {Radiation Research}, number = {1}, issn = {1938-5404}, doi = {10.1667/RR0857.1}, pages = {72 -- 80}, year = {2007}, language = {en} } @inproceedings{BeckerBragard2024, author = {Becker, Tim and Bragard, Michael}, title = {Low-Voltage DC Training Lab for Electric Drives - Optimizing the Balancing Act Between High Student Throughput and Individual Learning Speed}, series = {2024 IEEE Global Engineering Education Conference (EDUCON)}, booktitle = {2024 IEEE Global Engineering Education Conference (EDUCON)}, publisher = {IEEE}, address = {New York, NY}, issn = {2165-9559}, doi = {10.1109/EDUCON60312.2024.10578902}, pages = {8 Seiten}, year = {2024}, abstract = {After a brief introduction of conventional laboratory structures, this work focuses on an innovative and universal approach for a setup of a training laboratory for electric machines and drive systems. The novel approach employs a central 48 V DC bus, which forms the backbone of the structure. Several sets of DC machine, asynchronous machine and synchronous machine are connected to this bus. The advantages of the novel system structure are manifold, both from a didactic and a technical point of view: Student groups can work on their own performance level in a highly parallelized and at the same time individualized way. Additional training setups (similar or different) can easily be added. Only the total power dissipation has to be provided, i.e. the DC bus balances the power flow between the student groups. Comparative results of course evaluations of several cohorts of students are shown.}, language = {en} } @article{Hagemann1978, author = {Hagemann, Hans-J{\"u}rgen}, title = {Loss mechanisms and domain stabilization in doped BaTiO₃}, series = {Journal of Physics C: Solid State Physics}, volume = {11}, journal = {Journal of Physics C: Solid State Physics}, number = {15}, publisher = {n.a.}, address = {London}, isbn = {0022-3719}, doi = {10.1088/0022-3719/11/15/031}, pages = {3333 -- 3344}, year = {1978}, language = {en} } @article{FerreinSteinbauer2016, author = {Ferrein, Alexander and Steinbauer, Gerald}, title = {Looking back on 20 Years of RoboCup}, series = {KI - K{\"u}nstliche Intelligenz}, volume = {30}, journal = {KI - K{\"u}nstliche Intelligenz}, number = {3-4}, publisher = {Springer}, address = {Berlin}, issn = {1610-1987}, doi = {10.1007/s13218-016-0443-y}, pages = {321 -- 323}, year = {2016}, language = {en} } @article{Ferrein2008, author = {Ferrein, Alexander}, title = {Logic-based robot control in highly dynamic domains / Ferrein, Alexander ; Lakemeyer, Gerhard}, series = {Robotics and Autonomous Systems. 56 (2008), H. 11}, journal = {Robotics and Autonomous Systems. 56 (2008), H. 11}, isbn = {0921-8890}, pages = {980 -- 991}, year = {2008}, 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} } @article{HeuermannSchiek1993, author = {Heuermann, Holger and Schiek, Burkhard}, title = {LNN( Line-Network-Network): The In-Fixture Calibration Procedure}, pages = {149 -- 149}, year = {1993}, language = {en} }