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2184 Publications
2020 | Conference Paper | LibreCat-ID: 16947
Weinzierl, S., Wolf, V., Pauli, T., Beverungen, D., & Matzner, M. (2020). Detecting Workarounds in Business Processes — A Deep Learning Method for Analyzing Event Logs. In Proceedings of the 28th European Conference on Information Systems. Marrakech, Morroco.
LibreCat
2020 | Conference Paper | LibreCat-ID: 16968
Braun, M., Castenow, J., & Meyer auf der Heide, F. (2020). Local Gathering of Mobile Robots in Three Dimensions. In Proceedings of the 27th Conference on Structural Information and Communication Complexity (SIROCCO). Paderborn: Springer. https://doi.org/10.1007/978-3-030-54921-3_4
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| DOI
| arXiv
2020 | Working Paper | LibreCat-ID: 17019 | 
Schryen, G., Wagner, G., & Benlian, A. (2020). Distinguishing Knowledge Impact from Citation Impact: A Methodology for Analysing Knowledge Impact for the Literature Review Genre.
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2020 | Conference Paper | LibreCat-ID: 17063
Hansmeier, T., Kaufmann, P., & Platzner, M. (2020). An Adaption Mechanism for the Error Threshold of XCSF. GECCO ’20: Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1756–1764. https://doi.org/10.1145/3377929.3398106
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| DOI
2020 | Conference Paper | LibreCat-ID: 15580
Kersting, J., & Geierhos, M. (2020). Aspect Phrase Extraction in Sentiment Analysis with Deep Learning. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) -- Special Session on Natural Language Processing in Artificial Intelligence (NLPinAI 2020) (pp. 391--400). Setúbal, Portugal: SCITEPRESS.
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2020 | Journal Article | LibreCat-ID: 15628
Xu, X., Port, A., Wiebeler, C., Zhao, K.-H., Schapiro, I., & Gärtner, W. (2020). Structural Elements Regulating the Photochromicity in a Cyanobacteriochrome. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1910208117
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| DOI
2020 | Conference Paper | LibreCat-ID: 15629
Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (n.d.). LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. Symposium on Intelligent Data Analysis, Konstanz, Germany.
LibreCat
2020 | Conference Paper | LibreCat-ID: 15635
Kersting, J., & Geierhos, M. (2020). Neural Learning for Aspect Phrase Extraction and Classification in Sentiment Analysis. In Proceedings of the 33rd International Florida Artificial Intelligence Research Symposium (FLAIRS) Conference (pp. 282--285). North Miami Beach, FL, USA: AAAI.
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2020 | Journal Article | LibreCat-ID: 15025
Wever, M. D., van Rooijen, L., & Hamann, H. (2020). Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. Evolutionary Computation, 28(2), 165–193. https://doi.org/10.1162/evco_a_00266
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2020 | Conference Paper | LibreCat-ID: 16219 |
Schneider, S. B., Satheeschandran, N. P., Peuster, M., & Karl, H. (2020). Machine Learning for Dynamic Resource Allocation in Network Function Virtualization. In IEEE Conference on Network Softwarization (NetSoft). Ghent, Belgium: IEEE.
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2020 | Journal Article | LibreCat-ID: 16280
Li, F., Yu, D., Yang, H., Yu, J., Karl, H., & Cheng, X. (2020). Multi-Armed-Bandit-Based Spectrum Scheduling Algorithms in Wireless Networks: A Survey. IEEE Wireless Communications, 24–30. https://doi.org/10.1109/mwc.001.1900280
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| DOI
2020 | Journal Article | LibreCat-ID: 16288
Klus, S., Nüske, F., Peitz, S., Niemann, J.-H., Clementi, C., & Schütte, C. (2020). Data-driven approximation of the Koopman generator: Model reduction, system identification, and control. Physica D: Nonlinear Phenomena, 406. https://doi.org/10.1016/j.physd.2020.132416
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2020 | Journal Article | LibreCat-ID: 16290 |
Bieker, K., Peitz, S., Brunton, S. L., Kutz, J. N., & Dellnitz, M. (2020). Deep model predictive flow control with limited sensor data and online learning. Theoretical and Computational Fluid Dynamics, 34, 577–591. https://doi.org/10.1007/s00162-020-00520-4
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2020 | Journal Article | LibreCat-ID: 21252
Traue, A., Book, G., Kirchgässner, W., & Wallscheid, O. (2020). Toward a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control. IEEE Transactions on Neural Networks and Learning Systems, 1–10. https://doi.org/10.1109/tnnls.2020.3029573
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2020 | Journal Article | LibreCat-ID: 21250
Kirchgässner, W., Wallscheid, O., & Böcker, J. (2020). Estimating Electric Motor Temperatures with Deep Residual Machine Learning. IEEE Transactions on Power Electronics, 36(7), 7480–7488. https://doi.org/10.1109/tpel.2020.3045596
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| DOI
2020 | Conference Paper | LibreCat-ID: 29939
Unruh, R., Schafmeister, F., & Böcker, J. (2020). Evaluation of MMCs for High-Power Low-Voltage DC-Applications in Combination with the Module LLC-Design. 2020 22nd European Conference on Power Electronics and Applications (EPE’20 ECCE Europe). 22nd European Conference on Power Electronics and Applications (EPE’20 ECCE Europe), Lyon, France. https://doi.org/10.23919/epe20ecceeurope43536.2020.9215687
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2020 | Preprint | LibreCat-ID: 21623
Bode, H., Heid, S., Weber, D., Hüllermeier, E., & Wallscheid, O. (2020). Towards a Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid Control. In arXiv:2005.04869.
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2020 | Journal Article | LibreCat-ID: 29642
Hanke, S., Wallscheid, O., & Böcker, J. (2020). Data Set Description: Identifying the Physics Behind an Electric Motor–Data-Driven Learning of the Electrical Behavior (Part II). ArXiv Preprint ArXiv:2003.06268.
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2020 | Journal Article | LibreCat-ID: 29640
Kirchgässner, W., Wallscheid, O., & Böcker, J. (2020). Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning. ArXiv Preprint ArXiv:2001.06246.
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