@article{17390, author = {{Chantakit, Teanchai and Schlickriede, Christian and Sain, Basudeb and Meyer, Fabian and Weiss, Thomas and Chattham, Nattaporn and Zentgraf, Thomas}}, issn = {{2327-9125}}, journal = {{Photonics Research}}, number = {{9}}, pages = {{1435--1440}}, publisher = {{OSA}}, title = {{{All-dielectric silicon metalens for two-dimensional particle manipulation in optical tweezers}}}, doi = {{10.1364/prj.389200}}, volume = {{8}}, year = {{2020}}, } @inproceedings{17398, author = {{Turcanu, Ion and Engel, Thomas and Sommer, Christoph}}, booktitle = {{2019 IEEE Vehicular Networking Conference (VNC)}}, isbn = {{9781728145716}}, title = {{{Fog Seeding Strategies for Information-Centric Heterogeneous Vehicular Networks}}}, doi = {{10.1109/vnc48660.2019.9062816}}, year = {{2020}}, } @inproceedings{17405, author = {{Frank, Maximilian and Gausemeier, Juergen and Hennig-Cardinal von Widdern, Nils and Koldewey, Christian and Menzefricke, Joern Steffen and Reinhold, Jannik}}, booktitle = {{Proceedings of the ISPIM connects}}, publisher = {{International Society for Professional Innovation Management (ISPIM)}}, title = {{{A reference process for the Smart Service business: development and practical implications}}}, year = {{2020}}, } @inproceedings{17406, author = {{Becker, Julia-Kristin and Joachim, Klemens and Koldewey, Christian and Reinhold, Jannik and Dumitrescu, Roman}}, booktitle = {{Proceedings of the 2020 ISPIM Innovation Conference (Virtual) Event "Innovating in Times of Crisis"}}, publisher = {{ISPIM Innovation Conference}}, title = {{{Scaling Digital Business Models: A Case from the Automotive Industry}}}, year = {{2020}}, } @inproceedings{17407, author = {{Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}}, booktitle = {{Discovery Science}}, title = {{{Extreme Algorithm Selection with Dyadic Feature Representation}}}, year = {{2020}}, } @inproceedings{17408, author = {{Hanselle, Jonas Manuel and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}}, booktitle = {{KI 2020: Advances in Artificial Intelligence}}, title = {{{Hybrid Ranking and Regression for Algorithm Selection}}}, year = {{2020}}, } @inbook{17411, abstract = {{Many dynamical systems possess symmetries, e.g. rotational and translational invariances of mechanical systems. These can be beneficially exploited in the design of numerical optimal control methods. We present a model predictive control scheme which is based on a library of precomputed motion primitives. The primitives are equivalence classes w.r.t. the symmetry of the optimal control problems. Trim primitives as relative equilibria w.r.t. this symmetry, play a crucial role in the algorithm. The approach is illustrated using an academic mobile robot example.}}, author = {{Flaßkamp, Kathrin and Ober-Blöbaum, Sina and Peitz, Sebastian}}, booktitle = {{Advances in Dynamics, Optimization and Computation}}, editor = {{Junge, Oliver and Schütze, Oliver and Froyland, Gary and Ober-Blöbaum, Sina and Padberg-Gehle, Kathrin}}, isbn = {{9783030512637}}, issn = {{2198-4182}}, publisher = {{Springer}}, title = {{{Symmetry in Optimal Control: A Multiobjective Model Predictive Control Approach}}}, doi = {{10.1007/978-3-030-51264-4_9}}, year = {{2020}}, } @inproceedings{17424, author = {{Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}}, booktitle = {{Proceedings of the ECMLPKDD 2020}}, title = {{{AutoML for Predictive Maintenance: One Tool to RUL Them All}}}, doi = {{10.1007/978-3-030-66770-2_8}}, year = {{2020}}, } @article{17426, abstract = {{The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale.}}, author = {{Hoffmann, Martin W. and Wildermuth, Stephan and Gitzel, Ralf and Boyaci, Aydin and Gebhardt, Jörg and Kaul, Holger and Amihai, Ido and Forg, Bodo and Suriyah, Michael and Leibfried, Thomas and Stich, Volker and Hicking, Jan and Bremer, Martin and Kaminski, Lars and Beverungen, Daniel and zur Heiden, Philipp and Tornede, Tanja}}, issn = {{1424-8220}}, journal = {{Sensors}}, title = {{{Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions}}}, doi = {{10.3390/s20072099}}, year = {{2020}}, } @article{17433, author = {{Wang, D. Q. and Reuter, Dirk and Wieck, A. D. and Hamilton, A. R. and Klochan, O.}}, issn = {{0003-6951}}, journal = {{Applied Physics Letters}}, title = {{{Two-dimensional lateral surface superlattices in GaAs heterostructures with independent control of carrier density and modulation potential}}}, doi = {{10.1063/5.0009462}}, year = {{2020}}, }