TY - CHAP A1 - Nikolovski, Gjorgji A1 - Reke, Michael A1 - Elsen, Ingo A1 - Schiffer, Stefan T1 - Machine learning based 3D object detection for navigation in unstructured environments T2 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) N2 - 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. KW - 3D object detection KW - LiDAR KW - autonomous driving KW - Deep learning KW - Three-dimensional displays Y1 - 2021 SN - 978-1-6654-7921-9 U6 - https://doi.org/10.1109/IVWorkshops54471.2021.9669218 N1 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops), 11-17 July 2021, Nagoya, Japan. SP - 236 EP - 242 PB - IEEE ER - TY - CHAP A1 - Stenger, David A1 - Altherr, Lena A1 - Abel, Dirk T1 - Machine learning and metaheuristics for black-box optimization of product families: a case-study investigating solution quality vs. computational overhead T2 - Operations Research Proceedings 2018 N2 - 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. KW - Product family optimization KW - Mixed-integer nonlinear black-box optimization KW - Engineering optimization KW - Machine learning Y1 - 2019 SN - 978-3-030-18499-5 (Print) SN - 978-3-030-18500-8 (Online) U6 - https://doi.org/10.1007/978-3-030-18500-8_47 SP - 379 EP - 385 PB - Springer CY - Cham ER - TY - JOUR A1 - Heuermann, Holger T1 - LZY: A Self-Calibration Approach in Competition to the LRM Method for On-Wafer Measurements Y1 - 1995 N1 - 45th ARFTG Conference digest, Spring 1995 : [conference topic: Testing and design of RFIC'S], May 19, 1995, Orange County Convention Center, Orlando, Florida / Automatic RF Techniques Group. Publications chairman: Ed Godshalk; ARFTG Conference digest ; 4 SP - 129 EP - 136 ER - TY - JOUR A1 - Sommer, Angela M. A1 - Bitz, Andreas A1 - Streckert, Joachim A1 - Hansen, Volkert W. A1 - Lerchl, Alexander T1 - Lymphoma development in mice chronically exposed to UMTS-modulated radiofrequency electromagnetic fields JF - Radiation Research Y1 - 2007 U6 - https://doi.org/10.1667/RR0857.1 SN - 1938-5404 VL - 168 IS - 1 SP - 72 EP - 80 ER - TY - CHAP A1 - Becker, Tim A1 - Bragard, Michael T1 - Low-Voltage DC Training Lab for Electric Drives - Optimizing the Balancing Act Between High Student Throughput and Individual Learning Speed T2 - 2024 IEEE Global Engineering Education Conference (EDUCON) N2 - 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. KW - Synchronous machines KW - Power dissipation KW - Throughput KW - Low voltage KW - DC machines KW - Manifolds KW - Training Y1 - 2024 U6 - https://doi.org/10.1109/EDUCON60312.2024.10578902 SN - 2165-9559 SN - 2165-9567 (eISSN) N1 - 2024 IEEE Global Engineering Education Conference (EDUCON), 08-11 May 2024, Kos Island, Greece PB - IEEE CY - New York, NY ER - TY - JOUR A1 - Hagemann, Hans-Jürgen T1 - Loss mechanisms and domain stabilization in doped BaTiO₃ JF - Journal of Physics C: Solid State Physics Y1 - 1978 SN - 0022-3719 U6 - https://doi.org/10.1088/0022-3719/11/15/031 VL - 11 IS - 15 SP - 3333 EP - 3344 PB - n.a. CY - London ER - TY - JOUR A1 - Ferrein, Alexander A1 - Steinbauer, Gerald T1 - Looking back on 20 Years of RoboCup JF - KI - Künstliche Intelligenz Y1 - 2016 U6 - https://doi.org/10.1007/s13218-016-0443-y SN - 1610-1987 VL - 30 IS - 3-4 SP - 321 EP - 323 PB - Springer CY - Berlin ER - TY - JOUR A1 - Ferrein, Alexander T1 - Logic-based robot control in highly dynamic domains / Ferrein, Alexander ; Lakemeyer, Gerhard JF - Robotics and Autonomous Systems. 56 (2008), H. 11 Y1 - 2008 SN - 0921-8890 SP - 980 EP - 991 ER - TY - JOUR A1 - Orzada, Stephan A1 - Fiedler, Thomas M. A1 - Bitz, Andreas A1 - Ladd, Mark E. A1 - Quick, Harald H. T1 - Local SAR compression with overestimation control to reduce maximum relative SAR overestimation and improve multi-channel RF array performance JF - Magnetic Resonance Materials in Physics, Biology and Medicine N2 - 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. Y1 - 2020 SN - 1352-8661 U6 - https://doi.org/10.1007/s10334-020-00890-0 IS - 34 (2021) SP - 153 EP - 164 PB - Springer CY - Heidelberg ER - TY - JOUR A1 - Heuermann, Holger A1 - Schiek, Burkhard T1 - LNN( Line-Network-Network): The In-Fixture Calibration Procedure Y1 - 1993 N1 - XXIVth General Assembly of the International URSI, Kyoto SP - 149 EP - 149 ER -