[{"language":[{"iso":"eng"}],"article_number":"306","user_id":"93461","department":[{"_id":"52"}],"_id":"65253","status":"public","type":"journal_article","publication":"Journal of Open Source Education","doi":"10.21105/jose.00306","title":"Reinforcement Learning: A Comprehensive Open-Source Course","author":[{"full_name":"Abdelwanis, Ali Hassan Ali","last_name":"Abdelwanis","first_name":"Ali Hassan Ali"},{"full_name":"Haucke-Korber, Barnabas","id":"93461","orcid":"0000-0003-0862-2069","last_name":"Haucke-Korber","first_name":"Barnabas"},{"first_name":"Darius","last_name":"Jakobeit","full_name":"Jakobeit, Darius"},{"first_name":"Wilhelm","last_name":"Kirchgässner","orcid":"0000-0001-9490-1843","id":"49265","full_name":"Kirchgässner, Wilhelm"},{"full_name":"Meyer, Marvin","last_name":"Meyer","first_name":"Marvin"},{"first_name":"Maximilian","id":"52638","full_name":"Schenke, Maximilian","orcid":"0000-0001-5427-9527","last_name":"Schenke"},{"first_name":"Hendrik","last_name":"Vater","id":"63220","full_name":"Vater, Hendrik"},{"first_name":"Oliver","last_name":"Wallscheid","orcid":"https://orcid.org/0000-0001-9362-8777","id":"11291","full_name":"Wallscheid, Oliver"},{"first_name":"Daniel","last_name":"Weber","orcid":"0000-0003-3367-5998","id":"24041","full_name":"Weber, Daniel"}],"date_created":"2026-03-31T07:30:04Z","volume":9,"publisher":"The Open Journal","date_updated":"2026-03-31T07:32:23Z","citation":{"bibtex":"@article{Abdelwanis_Haucke-Korber_Jakobeit_Kirchgässner_Meyer_Schenke_Vater_Wallscheid_Weber_2026, title={Reinforcement Learning: A Comprehensive Open-Source Course}, volume={9}, DOI={<a href=\"https://doi.org/10.21105/jose.00306\">10.21105/jose.00306</a>}, number={97306}, journal={Journal of Open Source Education}, publisher={The Open Journal}, author={Abdelwanis, Ali Hassan Ali and Haucke-Korber, Barnabas and Jakobeit, Darius and Kirchgässner, Wilhelm and Meyer, Marvin and Schenke, Maximilian and Vater, Hendrik and Wallscheid, Oliver and Weber, Daniel}, year={2026} }","mla":"Abdelwanis, Ali Hassan Ali, et al. “Reinforcement Learning: A Comprehensive Open-Source Course.” <i>Journal of Open Source Education</i>, vol. 9, no. 97, 306, The Open Journal, 2026, doi:<a href=\"https://doi.org/10.21105/jose.00306\">10.21105/jose.00306</a>.","short":"A.H.A. Abdelwanis, B. Haucke-Korber, D. Jakobeit, W. Kirchgässner, M. Meyer, M. Schenke, H. Vater, O. Wallscheid, D. Weber, Journal of Open Source Education 9 (2026).","apa":"Abdelwanis, A. H. A., Haucke-Korber, B., Jakobeit, D., Kirchgässner, W., Meyer, M., Schenke, M., Vater, H., Wallscheid, O., &#38; Weber, D. (2026). Reinforcement Learning: A Comprehensive Open-Source Course. <i>Journal of Open Source Education</i>, <i>9</i>(97), Article 306. <a href=\"https://doi.org/10.21105/jose.00306\">https://doi.org/10.21105/jose.00306</a>","ama":"Abdelwanis AHA, Haucke-Korber B, Jakobeit D, et al. Reinforcement Learning: A Comprehensive Open-Source Course. <i>Journal of Open Source Education</i>. 2026;9(97). doi:<a href=\"https://doi.org/10.21105/jose.00306\">10.21105/jose.00306</a>","chicago":"Abdelwanis, Ali Hassan Ali, Barnabas Haucke-Korber, Darius Jakobeit, Wilhelm Kirchgässner, Marvin Meyer, Maximilian Schenke, Hendrik Vater, Oliver Wallscheid, and Daniel Weber. “Reinforcement Learning: A Comprehensive Open-Source Course.” <i>Journal of Open Source Education</i> 9, no. 97 (2026). <a href=\"https://doi.org/10.21105/jose.00306\">https://doi.org/10.21105/jose.00306</a>.","ieee":"A. H. A. Abdelwanis <i>et al.</i>, “Reinforcement Learning: A Comprehensive Open-Source Course,” <i>Journal of Open Source Education</i>, vol. 9, no. 97, Art. no. 306, 2026, doi: <a href=\"https://doi.org/10.21105/jose.00306\">10.21105/jose.00306</a>."},"intvolume":"         9","year":"2026","issue":"97","publication_status":"published","publication_identifier":{"issn":["2577-3569"]}},{"title":"Data-driven thermal modeling of a permanent magnet synchronous motor with machine learning","doi":"10.17619/UNIPB/1-2068","date_updated":"2025-02-21T11:41:01Z","publisher":"LibreCat University","date_created":"2025-02-21T11:38:22Z","author":[{"first_name":"Wilhelm","orcid":"0000-0001-9490-1843","last_name":"Kirchgässner","full_name":"Kirchgässner, Wilhelm","id":"49265"}],"year":"2024","citation":{"apa":"Kirchgässner, W. (2024). <i>Data-driven thermal modeling of a permanent magnet synchronous motor with machine learning</i>. LibreCat University. <a href=\"https://doi.org/10.17619/UNIPB/1-2068\">https://doi.org/10.17619/UNIPB/1-2068</a>","bibtex":"@book{Kirchgässner_2024, title={Data-driven thermal modeling of a permanent magnet synchronous motor with machine learning}, DOI={<a href=\"https://doi.org/10.17619/UNIPB/1-2068\">10.17619/UNIPB/1-2068</a>}, publisher={LibreCat University}, author={Kirchgässner, Wilhelm}, year={2024} }","short":"W. Kirchgässner, Data-Driven Thermal Modeling of a Permanent Magnet Synchronous Motor with Machine Learning, LibreCat University, 2024.","mla":"Kirchgässner, Wilhelm. <i>Data-Driven Thermal Modeling of a Permanent Magnet Synchronous Motor with Machine Learning</i>. LibreCat University, 2024, doi:<a href=\"https://doi.org/10.17619/UNIPB/1-2068\">10.17619/UNIPB/1-2068</a>.","ama":"Kirchgässner W. <i>Data-Driven Thermal Modeling of a Permanent Magnet Synchronous Motor with Machine Learning</i>. LibreCat University; 2024. doi:<a href=\"https://doi.org/10.17619/UNIPB/1-2068\">10.17619/UNIPB/1-2068</a>","ieee":"W. Kirchgässner, <i>Data-driven thermal modeling of a permanent magnet synchronous motor with machine learning</i>. LibreCat University, 2024.","chicago":"Kirchgässner, Wilhelm. <i>Data-Driven Thermal Modeling of a Permanent Magnet Synchronous Motor with Machine Learning</i>. LibreCat University, 2024. <a href=\"https://doi.org/10.17619/UNIPB/1-2068\">https://doi.org/10.17619/UNIPB/1-2068</a>."},"language":[{"iso":"eng"}],"_id":"58756","department":[{"_id":"52"}],"user_id":"71353","abstract":[{"text":"Der Permanentmagnet-Synchronmotor (PMSM) ist aufgrund seiner hohen Leistungs- und Drehmomentdichte bezogen auf Volumen und Gewicht ein häufig verwendeter Traktionsmotor in Automobilanwendungen. Jene Charakteristika werden jedoch maßgeblich durch Temperaturhöchstwerte begrenzt. Hinzu kommt, dass die Temperatur wichtiger Rotorkomponenten nicht wirtschaftlich messbar ist. Temperaturschätzverfahren wie modellbasierte Ansätze sind potentiell in der Lage, das Problem der fehlenden Temperaturinformation zu relativieren, ohne zusätzliche Geräte zu erfordern. Diese Arbeit stellt ein Portfolio von thermischen Modellen aus dem Bereich des maschinellen Lernens zusammen. Die Untersuchung basiert auf einem PMSM-Datensatz, der auf einem Prüfstand aufgezeichnet wurde. Neben dem durchschnittlichen Schätzfehler diktiert die erforderliche Anzahl von Modellparametern zahlreiche Auslegungsentscheidungen. Der gesamte Entwurfsprozess eines Modells aus dem maschinellen Lernen wird beleuchtet und für verschiedene lineare, sowie baumbasierte Modelle; vorschiebende, rekurrente und faltende neuronale Netze als auch für verschiedene hybride Modellierungsansätze durchgeführt. Desweiteren wird der hybride Modellierungsansatz über thermische neuronale Netze besonders hervorgehoben. Sie setzen sich aus neuronalen Netzen und einem thermischen Ersatzschaltbild zusammen und wurden erstmals vom Autor dieser Arbeit veröffentlicht. Schließlich wird ein von Experten entworfenes, datengetriebenes thermisches Netz mit konzentrierten Parametern über verschiedene Algorithmen optimiert und als Stand der Technik herangezogen.","lang":"eng"},{"text":"The permanent magnet synchronous motor (PMSM) is a commonly used traction motor in automotive applications due to its high power and torque density with respect to volume and weight. These characteristics are constrained by the maximum temperature at which vital components can still operate without harm. Moreover, important rotor component temperatures cannot be measured economically. Temperature estimation methods such as model-based approaches can alleviate the problem of missing thermal information at potentially no additionally required equipment. This work collates a portfolio of data-driven thermal models from the domain of machine learning and investigates their feasibility for the task of accurate thermal modeling on the example of a PMSM data set recorded on a test bench. Aside from the average estimation error, the required amount of model parameters as an approximation for the computational demand dictates design decisions throughout. The whole process of designing a machine learning model is illuminated and carried out for varying linear models; tree-based models; feed-forward, recurrent, and convolutional neural networks, as well as various hybrid gray-box modeling approaches. Moreover, a hybrid modeling paradigm with thermal neural networks is highlighted, which was first introduced by this work's author. Eventually, an expert-designed, data-driven lumped-parameter thermal network is optimized under different algorithms in order to put machine learning models to the test against the state of the art of thermal modeling.","lang":"eng"}],"status":"public","type":"dissertation"},{"type":"journal_article","publication":"Engineering Applications of Artificial Intelligence","status":"public","project":[{"name":"PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"_id":"34065","user_id":"49265","department":[{"_id":"52"}],"article_number":"105537","language":[{"iso":"eng"}],"publication_status":"published","publication_identifier":{"issn":["0952-1976"]},"year":"2022","citation":{"bibtex":"@article{Kirchgässner_Wallscheid_Böcker_2022, title={Thermal neural networks: Lumped-parameter thermal modeling with state-space machine learning}, volume={117}, DOI={<a href=\"https://doi.org/10.1016/j.engappai.2022.105537\">10.1016/j.engappai.2022.105537</a>}, number={105537}, journal={Engineering Applications of Artificial Intelligence}, publisher={Elsevier BV}, author={Kirchgässner, Wilhelm and Wallscheid, Oliver and Böcker, Joachim}, year={2022} }","short":"W. Kirchgässner, O. Wallscheid, J. Böcker, Engineering Applications of Artificial Intelligence 117 (2022).","mla":"Kirchgässner, Wilhelm, et al. “Thermal Neural Networks: Lumped-Parameter Thermal Modeling with State-Space Machine Learning.” <i>Engineering Applications of Artificial Intelligence</i>, vol. 117, 105537, Elsevier BV, 2022, doi:<a href=\"https://doi.org/10.1016/j.engappai.2022.105537\">10.1016/j.engappai.2022.105537</a>.","apa":"Kirchgässner, W., Wallscheid, O., &#38; Böcker, J. (2022). Thermal neural networks: Lumped-parameter thermal modeling with state-space machine learning. <i>Engineering Applications of Artificial Intelligence</i>, <i>117</i>, Article 105537. <a href=\"https://doi.org/10.1016/j.engappai.2022.105537\">https://doi.org/10.1016/j.engappai.2022.105537</a>","chicago":"Kirchgässner, Wilhelm, Oliver Wallscheid, and Joachim Böcker. “Thermal Neural Networks: Lumped-Parameter Thermal Modeling with State-Space Machine Learning.” <i>Engineering Applications of Artificial Intelligence</i> 117 (2022). <a href=\"https://doi.org/10.1016/j.engappai.2022.105537\">https://doi.org/10.1016/j.engappai.2022.105537</a>.","ieee":"W. Kirchgässner, O. Wallscheid, and J. Böcker, “Thermal neural networks: Lumped-parameter thermal modeling with state-space machine learning,” <i>Engineering Applications of Artificial Intelligence</i>, vol. 117, Art. no. 105537, 2022, doi: <a href=\"https://doi.org/10.1016/j.engappai.2022.105537\">10.1016/j.engappai.2022.105537</a>.","ama":"Kirchgässner W, Wallscheid O, Böcker J. Thermal neural networks: Lumped-parameter thermal modeling with state-space machine learning. <i>Engineering Applications of Artificial Intelligence</i>. 2022;117. doi:<a href=\"https://doi.org/10.1016/j.engappai.2022.105537\">10.1016/j.engappai.2022.105537</a>"},"intvolume":"       117","date_updated":"2023-03-09T10:08:12Z","publisher":"Elsevier BV","date_created":"2022-11-14T08:13:11Z","author":[{"first_name":"Wilhelm","full_name":"Kirchgässner, Wilhelm","id":"49265","orcid":"0000-0001-9490-1843","last_name":"Kirchgässner"},{"first_name":"Oliver","last_name":"Wallscheid","orcid":"https://orcid.org/0000-0001-9362-8777","id":"11291","full_name":"Wallscheid, Oliver"},{"first_name":"Joachim","full_name":"Böcker, Joachim","id":"66","orcid":"0000-0002-8480-7295","last_name":"Böcker"}],"volume":117,"title":"Thermal neural networks: Lumped-parameter thermal modeling with state-space machine learning","doi":"10.1016/j.engappai.2022.105537"},{"year":"2022","citation":{"ieee":"W. Kirchgässner, O. Wallscheid, and J. Böcker, “Learning Thermal Properties and Temperature Models of Electric Motors with Neural Ordinary Differential Equations,” 2022, doi: <a href=\"https://doi.org/10.23919/ipec-himeji2022-ecce53331.2022.9807209\">10.23919/ipec-himeji2022-ecce53331.2022.9807209</a>.","chicago":"Kirchgässner, Wilhelm, Oliver Wallscheid, and Joachim Böcker. “Learning Thermal Properties and Temperature Models of Electric Motors with Neural Ordinary Differential Equations.” In <i>2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia)</i>. IEEE, 2022. <a href=\"https://doi.org/10.23919/ipec-himeji2022-ecce53331.2022.9807209\">https://doi.org/10.23919/ipec-himeji2022-ecce53331.2022.9807209</a>.","ama":"Kirchgässner W, Wallscheid O, Böcker J. Learning Thermal Properties and Temperature Models of Electric Motors with Neural Ordinary Differential Equations. In: <i>2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia)</i>. IEEE; 2022. doi:<a href=\"https://doi.org/10.23919/ipec-himeji2022-ecce53331.2022.9807209\">10.23919/ipec-himeji2022-ecce53331.2022.9807209</a>","mla":"Kirchgässner, Wilhelm, et al. “Learning Thermal Properties and Temperature Models of Electric Motors with Neural Ordinary Differential Equations.” <i>2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia)</i>, IEEE, 2022, doi:<a href=\"https://doi.org/10.23919/ipec-himeji2022-ecce53331.2022.9807209\">10.23919/ipec-himeji2022-ecce53331.2022.9807209</a>.","short":"W. Kirchgässner, O. Wallscheid, J. Böcker, in: 2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia), IEEE, 2022.","bibtex":"@inproceedings{Kirchgässner_Wallscheid_Böcker_2022, title={Learning Thermal Properties and Temperature Models of Electric Motors with Neural Ordinary Differential Equations}, DOI={<a href=\"https://doi.org/10.23919/ipec-himeji2022-ecce53331.2022.9807209\">10.23919/ipec-himeji2022-ecce53331.2022.9807209</a>}, booktitle={2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia)}, publisher={IEEE}, author={Kirchgässner, Wilhelm and Wallscheid, Oliver and Böcker, Joachim}, year={2022} }","apa":"Kirchgässner, W., Wallscheid, O., &#38; Böcker, J. (2022). Learning Thermal Properties and Temperature Models of Electric Motors with Neural Ordinary Differential Equations. <i>2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia)</i>. <a href=\"https://doi.org/10.23919/ipec-himeji2022-ecce53331.2022.9807209\">https://doi.org/10.23919/ipec-himeji2022-ecce53331.2022.9807209</a>"},"publication_status":"published","title":"Learning Thermal Properties and Temperature Models of Electric Motors with Neural Ordinary Differential Equations","doi":"10.23919/ipec-himeji2022-ecce53331.2022.9807209","date_updated":"2023-03-09T10:08:29Z","publisher":"IEEE","date_created":"2022-08-16T15:38:35Z","author":[{"first_name":"Wilhelm","last_name":"Kirchgässner","orcid":"0000-0001-9490-1843","id":"49265","full_name":"Kirchgässner, Wilhelm"},{"first_name":"Oliver","last_name":"Wallscheid","orcid":"https://orcid.org/0000-0001-9362-8777","full_name":"Wallscheid, Oliver","id":"11291"},{"full_name":"Böcker, Joachim","id":"66","orcid":"0000-0002-8480-7295","last_name":"Böcker","first_name":"Joachim"}],"status":"public","publication":"2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia)","type":"conference","language":[{"iso":"eng"}],"_id":"32859","project":[{"name":"PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"department":[{"_id":"52"}],"user_id":"49265"},{"date_created":"2023-03-09T12:10:01Z","author":[{"id":"49265","full_name":"Kirchgässner, Wilhelm","orcid":"0000-0001-9490-1843","last_name":"Kirchgässner","first_name":"Wilhelm"},{"full_name":"Wöckinger, Daniel","last_name":"Wöckinger","first_name":"Daniel"},{"first_name":"Oliver","full_name":"Wallscheid, Oliver","id":"11291","last_name":"Wallscheid","orcid":"https://orcid.org/0000-0001-9362-8777"},{"first_name":"Gerd","last_name":"Bramerdorfer","full_name":"Bramerdorfer, Gerd"},{"orcid":"0000-0002-8480-7295","last_name":"Böcker","full_name":"Böcker, Joachim","id":"66","first_name":"Joachim"}],"date_updated":"2023-03-09T12:11:43Z","title":"Application of Thermal Neural Networks on a Small-Scale Electric Motor","page":"1-6","citation":{"chicago":"Kirchgässner, Wilhelm, Daniel Wöckinger, Oliver Wallscheid, Gerd Bramerdorfer, and Joachim Böcker. “Application of Thermal Neural Networks on a Small-Scale Electric Motor.” In <i>IKMT 2022; 13. GMM/ETG-Symposium</i>, 1–6, 2022.","ieee":"W. Kirchgässner, D. Wöckinger, O. Wallscheid, G. Bramerdorfer, and J. Böcker, “Application of Thermal Neural Networks on a Small-Scale Electric Motor,” in <i>IKMT 2022; 13. GMM/ETG-Symposium</i>, 2022, pp. 1–6.","ama":"Kirchgässner W, Wöckinger D, Wallscheid O, Bramerdorfer G, Böcker J. Application of Thermal Neural Networks on a Small-Scale Electric Motor. In: <i>IKMT 2022; 13. GMM/ETG-Symposium</i>. ; 2022:1-6.","apa":"Kirchgässner, W., Wöckinger, D., Wallscheid, O., Bramerdorfer, G., &#38; Böcker, J. (2022). Application of Thermal Neural Networks on a Small-Scale Electric Motor. <i>IKMT 2022; 13. GMM/ETG-Symposium</i>, 1–6.","mla":"Kirchgässner, Wilhelm, et al. “Application of Thermal Neural Networks on a Small-Scale Electric Motor.” <i>IKMT 2022; 13. GMM/ETG-Symposium</i>, 2022, pp. 1–6.","short":"W. Kirchgässner, D. Wöckinger, O. Wallscheid, G. Bramerdorfer, J. Böcker, in: IKMT 2022; 13. GMM/ETG-Symposium, 2022, pp. 1–6.","bibtex":"@inproceedings{Kirchgässner_Wöckinger_Wallscheid_Bramerdorfer_Böcker_2022, title={Application of Thermal Neural Networks on a Small-Scale Electric Motor}, booktitle={IKMT 2022; 13. GMM/ETG-Symposium}, author={Kirchgässner, Wilhelm and Wöckinger, Daniel and Wallscheid, Oliver and Bramerdorfer, Gerd and Böcker, Joachim}, year={2022}, pages={1–6} }"},"year":"2022","department":[{"_id":"52"}],"user_id":"49265","_id":"42894","language":[{"iso":"eng"}],"publication":"IKMT 2022; 13. GMM/ETG-Symposium","type":"conference","status":"public"},{"status":"public","type":"journal_article","publication":"IEEE Open Journal of Power Electronics","language":[{"iso":"eng"}],"user_id":"66","department":[{"_id":"52"}],"_id":"22162","citation":{"apa":"Book, G., Traue, A., Balakrishna, P., Brosch, A., Schenke, M., Hanke, S., Kirchgässner, W., &#38; Wallscheid, O. (2021). Transferring Online Reinforcement Learning for Electric Motor Control From Simulation to Real-World Experiments. <i>IEEE Open Journal of Power Electronics</i>, 187–201. <a href=\"https://doi.org/10.1109/ojpel.2021.3065877\">https://doi.org/10.1109/ojpel.2021.3065877</a>","short":"G. Book, A. Traue, P. Balakrishna, A. Brosch, M. Schenke, S. Hanke, W. Kirchgässner, O. Wallscheid, IEEE Open Journal of Power Electronics (2021) 187–201.","bibtex":"@article{Book_Traue_Balakrishna_Brosch_Schenke_Hanke_Kirchgässner_Wallscheid_2021, title={Transferring Online Reinforcement Learning for Electric Motor Control From Simulation to Real-World Experiments}, DOI={<a href=\"https://doi.org/10.1109/ojpel.2021.3065877\">10.1109/ojpel.2021.3065877</a>}, journal={IEEE Open Journal of Power Electronics}, author={Book, Gerrit and Traue, Arne and Balakrishna, Praneeth and Brosch, Anian and Schenke, Maximilian and Hanke, Sören and Kirchgässner, Wilhelm and Wallscheid, Oliver}, year={2021}, pages={187–201} }","mla":"Book, Gerrit, et al. “Transferring Online Reinforcement Learning for Electric Motor Control From Simulation to Real-World Experiments.” <i>IEEE Open Journal of Power Electronics</i>, 2021, pp. 187–201, doi:<a href=\"https://doi.org/10.1109/ojpel.2021.3065877\">10.1109/ojpel.2021.3065877</a>.","chicago":"Book, Gerrit, Arne Traue, Praneeth Balakrishna, Anian Brosch, Maximilian Schenke, Sören Hanke, Wilhelm Kirchgässner, and Oliver Wallscheid. “Transferring Online Reinforcement Learning for Electric Motor Control From Simulation to Real-World Experiments.” <i>IEEE Open Journal of Power Electronics</i>, 2021, 187–201. <a href=\"https://doi.org/10.1109/ojpel.2021.3065877\">https://doi.org/10.1109/ojpel.2021.3065877</a>.","ieee":"G. Book <i>et al.</i>, “Transferring Online Reinforcement Learning for Electric Motor Control From Simulation to Real-World Experiments,” <i>IEEE Open Journal of Power Electronics</i>, pp. 187–201, 2021, doi: <a href=\"https://doi.org/10.1109/ojpel.2021.3065877\">10.1109/ojpel.2021.3065877</a>.","ama":"Book G, Traue A, Balakrishna P, et al. Transferring Online Reinforcement Learning for Electric Motor Control From Simulation to Real-World Experiments. <i>IEEE Open Journal of Power Electronics</i>. 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Empirical Evaluation of Exponentially Weighted Moving Averages for Simple Linear Thermal Modeling of Permanent Magnet Synchronous Machines. <i>2019 IEEE 28th International Symposium on Industrial Electronics (ISIE)</i>. <a href=\"https://doi.org/10.1109/isie.2019.8781195\">https://doi.org/10.1109/isie.2019.8781195</a>","chicago":"Kirchgässner, Wilhelm, Oliver Wallscheid, and Joachim Böcker. “Empirical Evaluation of Exponentially Weighted Moving Averages for Simple Linear Thermal Modeling of Permanent Magnet Synchronous Machines.” In <i>2019 IEEE 28th International Symposium on Industrial Electronics (ISIE)</i>, 2019. <a href=\"https://doi.org/10.1109/isie.2019.8781195\">https://doi.org/10.1109/isie.2019.8781195</a>.","ieee":"W. Kirchgässner, O. Wallscheid, and J. 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In: <i>2019 IEEE 28th International Symposium on Industrial Electronics (ISIE)</i>. ; 2019. doi:<a href=\"https://doi.org/10.1109/isie.2019.8781195\">10.1109/isie.2019.8781195</a>"},"year":"2019"},{"date_updated":"2022-02-19T16:51:49Z","date_created":"2021-02-16T21:22:06Z","author":[{"first_name":"Wilhelm","full_name":"Kirchgässner, Wilhelm","id":"49265","orcid":"0000-0001-9490-1843","last_name":"Kirchgässner"},{"first_name":"Oliver","last_name":"Wallscheid","orcid":"https://orcid.org/0000-0001-9362-8777","id":"11291","full_name":"Wallscheid, Oliver"},{"full_name":"Böcker, Joachim","id":"66","orcid":"0000-0002-8480-7295","last_name":"Böcker","first_name":"Joachim"}],"title":"Deep Residual Convolutional and Recurrent Neural Networks for Temperature Estimation in Permanent Magnet Synchronous Motors","doi":"10.1109/iemdc.2019.8785109","publication_identifier":{"isbn":["9781538693506"]},"publication_status":"published","year":"2019","citation":{"bibtex":"@inproceedings{Kirchgässner_Wallscheid_Böcker_2019, title={Deep Residual Convolutional and Recurrent Neural Networks for Temperature Estimation in Permanent Magnet Synchronous Motors}, DOI={<a href=\"https://doi.org/10.1109/iemdc.2019.8785109\">10.1109/iemdc.2019.8785109</a>}, booktitle={2019 IEEE International Electric Machines &#38; Drives Conference (IEMDC)}, author={Kirchgässner, Wilhelm and Wallscheid, Oliver and Böcker, Joachim}, year={2019} }","short":"W. Kirchgässner, O. Wallscheid, J. Böcker, in: 2019 IEEE International Electric Machines &#38; Drives Conference (IEMDC), 2019.","mla":"Kirchgässner, Wilhelm, et al. “Deep Residual Convolutional and Recurrent Neural Networks for Temperature Estimation in Permanent Magnet Synchronous Motors.” <i>2019 IEEE International Electric Machines &#38; Drives Conference (IEMDC)</i>, 2019, doi:<a href=\"https://doi.org/10.1109/iemdc.2019.8785109\">10.1109/iemdc.2019.8785109</a>.","apa":"Kirchgässner, W., Wallscheid, O., &#38; Böcker, J. (2019). Deep Residual Convolutional and Recurrent Neural Networks for Temperature Estimation in Permanent Magnet Synchronous Motors. <i>2019 IEEE International Electric Machines &#38; Drives Conference (IEMDC)</i>. <a href=\"https://doi.org/10.1109/iemdc.2019.8785109\">https://doi.org/10.1109/iemdc.2019.8785109</a>","ieee":"W. Kirchgässner, O. Wallscheid, and J. 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In: <i>2019 IEEE International Electric Machines &#38; Drives Conference (IEMDC)</i>. ; 2019. doi:<a href=\"https://doi.org/10.1109/iemdc.2019.8785109\">10.1109/iemdc.2019.8785109</a>"},"_id":"21249","department":[{"_id":"52"}],"user_id":"49265","language":[{"iso":"eng"}],"publication":"2019 IEEE International Electric Machines & Drives Conference (IEMDC)","type":"conference","status":"public"},{"citation":{"apa":"Wallscheid, O., Kirchgässner, W., &#38; Böcker, J. (2017). 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