[{"publication_status":"accepted","quality_controlled":"1","year":"2026","citation":{"apa":"Malena, K., Link, C., Gausemeier, S., &#38; Trächtler, A. (n.d.). ML-based Prediction Framework for varying Traffic Signal Control Strategies and its GLOSA-application. <i>2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)</i>, <i>28</i>.","short":"K. Malena, C. Link, S. Gausemeier, A. Trächtler, in: 2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), IEEE, n.d.","bibtex":"@inproceedings{Malena_Link_Gausemeier_Trächtler, title={ML-based Prediction Framework for varying Traffic Signal Control Strategies and its GLOSA-application}, volume={28}, booktitle={2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)}, publisher={IEEE}, author={Malena, Kevin and Link, Christopher and Gausemeier, Sandra and Trächtler, Ansgar} }","mla":"Malena, Kevin, et al. “ML-Based Prediction Framework for Varying Traffic Signal Control Strategies and Its GLOSA-Application.” <i>2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)</i>, vol. 28, IEEE.","ama":"Malena K, Link C, Gausemeier S, Trächtler A. ML-based Prediction Framework for varying Traffic Signal Control Strategies and its GLOSA-application. In: <i>2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)</i>. Vol 28. IEEE.","ieee":"K. Malena, C. Link, S. Gausemeier, and A. Trächtler, “ML-based Prediction Framework for varying Traffic Signal Control Strategies and its GLOSA-application,” in <i>2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)</i>, Gold Coast (Australia), vol. 28.","chicago":"Malena, Kevin, Christopher Link, Sandra Gausemeier, and Ansgar Trächtler. “ML-Based Prediction Framework for Varying Traffic Signal Control Strategies and Its GLOSA-Application.” In <i>2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)</i>, Vol. 28. IEEE, n.d."},"intvolume":"        28","date_updated":"2026-01-26T08:50:37Z","publisher":"IEEE","date_created":"2025-10-01T11:20:34Z","author":[{"full_name":"Malena, Kevin","id":"36303","orcid":"0000-0003-1183-4679","last_name":"Malena","first_name":"Kevin"},{"last_name":"Link","id":"38249","full_name":"Link, Christopher","first_name":"Christopher"},{"first_name":"Sandra","last_name":"Gausemeier","full_name":"Gausemeier, Sandra","id":"17793"},{"first_name":"Ansgar","full_name":"Trächtler, Ansgar","id":"552","last_name":"Trächtler"}],"volume":28,"title":"ML-based Prediction Framework for varying Traffic Signal Control Strategies and its GLOSA-application","conference":{"start_date":"2025-11-18","name":"28th International Conference on Intelligent Transportation Systems (ITSC)","location":"Gold Coast (Australia)","end_date":"2025-11-21"},"type":"conference","publication":"2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)","abstract":[{"lang":"eng","text":"This paper deals with the development and results of a prediction framework for traffic light control systems as well as the usage and benefits of such predictions in green light optimal speed advisory (GLOSA) scenarios.\r\nVarious machine learning methods like support vector machines, neural networks or reinforcement learning were evaluated for their applicability in the prediction context and compared based on their efficiency and most importantly accuracy. The resulting prediction framework uses decision tree ensemble models combined with certain model knowledge to forecast different control strategies. This method was chosen due to its best performance in various test scenarios. Very high accuracy and fidelity were achieved for standard control methods like fixed-time, time-of-day-based and 'ordinary' traffic-based programs. Only for the more sophisticated model predictive control which was tested lower accuracies were achieved.\r\nFor the upcoming GLOSA application the penetration of equipped vehicles was varied for different traffic scenarios and control strategies. Results showcase high potentials for enhancing urban mobility and reducing environmental impact by lower emissions and waiting times. However, it is also clear from the studies presented in this contribution that the coordination of the control strategy with the GLOSA vehicles is of enormous importance."}],"status":"public","_id":"61492","user_id":"36303","department":[{"_id":"153"}],"keyword":["ML","Prediction","Tree Ensembles","GLOSA"],"language":[{"iso":"eng"}]},{"issue":"2","citation":{"apa":"Kirchgässner, W., Förster, N., Piepenbrock, T., Schweins, O., &#38; Wallscheid, O. (2025). HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores. <i>IEEE Transactions on Power Electronics</i>, <i>40</i>(2), 3326–3335. <a href=\"https://doi.org/10.1109/TPEL.2024.3488174\">https://doi.org/10.1109/TPEL.2024.3488174</a>","mla":"Kirchgässner, Wilhelm, et al. “HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores.” <i>IEEE Transactions on Power Electronics</i>, vol. 40, no. 2, 2025, pp. 3326–35, doi:<a href=\"https://doi.org/10.1109/TPEL.2024.3488174\">10.1109/TPEL.2024.3488174</a>.","short":"W. Kirchgässner, N. Förster, T. Piepenbrock, O. Schweins, O. Wallscheid, IEEE Transactions on Power Electronics 40 (2025) 3326–3335.","bibtex":"@article{Kirchgässner_Förster_Piepenbrock_Schweins_Wallscheid_2025, title={HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores}, volume={40}, DOI={<a href=\"https://doi.org/10.1109/TPEL.2024.3488174\">10.1109/TPEL.2024.3488174</a>}, number={2}, journal={IEEE Transactions on Power Electronics}, author={Kirchgässner, Wilhelm and Förster, Nikolas and Piepenbrock, Till and Schweins, Oliver and Wallscheid, Oliver}, year={2025}, pages={3326–3335} }","ama":"Kirchgässner W, Förster N, Piepenbrock T, Schweins O, Wallscheid O. HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores. <i>IEEE Transactions on Power Electronics</i>. 2025;40(2):3326-3335. doi:<a href=\"https://doi.org/10.1109/TPEL.2024.3488174\">10.1109/TPEL.2024.3488174</a>","ieee":"W. Kirchgässner, N. Förster, T. Piepenbrock, O. Schweins, and O. Wallscheid, “HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores,” <i>IEEE Transactions on Power Electronics</i>, vol. 40, no. 2, pp. 3326–3335, 2025, doi: <a href=\"https://doi.org/10.1109/TPEL.2024.3488174\">10.1109/TPEL.2024.3488174</a>.","chicago":"Kirchgässner, Wilhelm, Nikolas Förster, Till Piepenbrock, Oliver Schweins, and Oliver Wallscheid. “HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores.” <i>IEEE Transactions on Power Electronics</i> 40, no. 2 (2025): 3326–35. <a href=\"https://doi.org/10.1109/TPEL.2024.3488174\">https://doi.org/10.1109/TPEL.2024.3488174</a>."},"intvolume":"        40","page":"3326-3335","year":"2025","date_created":"2026-01-06T08:07:13Z","author":[{"first_name":"Wilhelm","full_name":"Kirchgässner, Wilhelm","last_name":"Kirchgässner"},{"first_name":"Nikolas","last_name":"Förster","full_name":"Förster, Nikolas"},{"first_name":"Till","full_name":"Piepenbrock, Till","last_name":"Piepenbrock"},{"first_name":"Oliver","last_name":"Schweins","full_name":"Schweins, Oliver"},{"first_name":"Oliver","last_name":"Wallscheid","full_name":"Wallscheid, Oliver"}],"volume":40,"date_updated":"2026-01-06T08:08:01Z","doi":"10.1109/TPEL.2024.3488174","title":"HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores","type":"journal_article","publication":"IEEE Transactions on Power Electronics","status":"public","user_id":"83383","department":[{"_id":"52"}],"_id":"63498","keyword":["Mathematical models","Estimation","Data models","Convolutional neural networks","Accuracy","Magnetic hysteresis","Magnetic cores","Temperature measurement","Magnetic domains","Temperature distribution","Convolutional neural network (CNN)","machine learning (ML)","magnetics"]},{"project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"_id":"3510","user_id":"5786","department":[{"_id":"355"},{"_id":"34"},{"_id":"7"},{"_id":"26"}],"article_type":"original","file_date_updated":"2018-11-02T15:32:16Z","type":"journal_article","status":"public","date_updated":"2022-01-06T06:59:21Z","oa":"1","author":[{"first_name":"Felix","full_name":"Mohr, Felix","last_name":"Mohr"},{"id":"33176","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","first_name":"Marcel Dominik"},{"last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"main_file_link":[{"open_access":"1","url":"https://rdcu.be/3Nc2"}],"doi":"10.1007/s10994-018-5735-z","conference":{"start_date":"2018-09-10","name":"European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases","location":"Dublin, Ireland","end_date":"2018-09-14"},"publication_status":"epub_ahead","publication_identifier":{"issn":["0885-6125"],"eissn":["1573-0565"]},"has_accepted_license":"1","citation":{"ama":"Mohr F, Wever MD, Hüllermeier E. ML-Plan: Automated Machine Learning via Hierarchical Planning. <i>Machine Learning</i>. Published online 2018:1495-1515. doi:<a href=\"https://doi.org/10.1007/s10994-018-5735-z\">10.1007/s10994-018-5735-z</a>","chicago":"Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “ML-Plan: Automated Machine Learning via Hierarchical Planning.” <i>Machine Learning</i>, 2018, 1495–1515. <a href=\"https://doi.org/10.1007/s10994-018-5735-z\">https://doi.org/10.1007/s10994-018-5735-z</a>.","ieee":"F. Mohr, M. D. Wever, and E. Hüllermeier, “ML-Plan: Automated Machine Learning via Hierarchical Planning,” <i>Machine Learning</i>, pp. 1495–1515, 2018, doi: <a href=\"https://doi.org/10.1007/s10994-018-5735-z\">10.1007/s10994-018-5735-z</a>.","apa":"Mohr, F., Wever, M. D., &#38; Hüllermeier, E. (2018). ML-Plan: Automated Machine Learning via Hierarchical Planning. <i>Machine Learning</i>, 1495–1515. <a href=\"https://doi.org/10.1007/s10994-018-5735-z\">https://doi.org/10.1007/s10994-018-5735-z</a>","short":"F. Mohr, M.D. Wever, E. Hüllermeier, Machine Learning (2018) 1495–1515.","bibtex":"@article{Mohr_Wever_Hüllermeier_2018, title={ML-Plan: Automated Machine Learning via Hierarchical Planning}, DOI={<a href=\"https://doi.org/10.1007/s10994-018-5735-z\">10.1007/s10994-018-5735-z</a>}, journal={Machine Learning}, publisher={Springer}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2018}, pages={1495–1515} }","mla":"Mohr, Felix, et al. “ML-Plan: Automated Machine Learning via Hierarchical Planning.” <i>Machine Learning</i>, Springer, 2018, pp. 1495–515, doi:<a href=\"https://doi.org/10.1007/s10994-018-5735-z\">10.1007/s10994-018-5735-z</a>."},"page":"1495-1515","ddc":["000"],"keyword":["AutoML","Hierarchical Planning","HTN planning","ML-Plan"],"language":[{"iso":"eng"}],"publication":"Machine Learning","abstract":[{"text":"Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset). Although current approaches to AutoML have already produced impressive results, the field is still far from mature, and new techniques are still being developed. In this paper, we present ML-Plan, a new approach to AutoML based on hierarchical planning. To highlight the potential of this approach, we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn, and TPOT. In an extensive series of experiments, we show that ML-Plan is highly competitive and often outperforms existing approaches.","lang":"eng"}],"file":[{"relation":"main_file","success":1,"content_type":"application/pdf","access_level":"closed","file_id":"5306","file_name":"ML-PlanAutomatedMachineLearnin.pdf","file_size":1070937,"date_created":"2018-11-02T15:32:16Z","creator":"ups","date_updated":"2018-11-02T15:32:16Z"}],"publisher":"Springer","date_created":"2018-07-08T14:06:14Z","title":"ML-Plan: Automated Machine Learning via Hierarchical Planning","year":"2018"},{"abstract":[{"text":"In this paper, we consider the Maximum Likelihood (ML) estimation of the parameters of a GAUSSIAN in the presence of censored, i.e., clipped data. We show that the resulting Expectation Maximization (EM) algorithm delivers virtually biasfree and efficient estimates, and we discuss its convergence properties. We also discuss optimal classification in the presence of censored data. Censored data are frequently encountered in wireless LAN positioning systems based on the fingerprinting method employing signal strength measurements, due to the limited sensitivity of the portable devices. Experiments both on simulated and real-world data demonstrate the effectiveness of the proposed algorithms.","lang":"eng"}],"status":"public","publication":"38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)","type":"conference","keyword":["Gaussian processes","Global Positioning System","convergence","expectation-maximisation algorithm","fingerprint identification","indoor radio","signal classification","wireless LAN","EM algorithm","ML estimation","WiFi indoor positioning","censored Gaussian data classification","clipped data","convergence properties","expectation maximization algorithm","fingerprinting method","maximum likelihood estimation","optimal classification","parameters estimation","portable devices sensitivity","signal strength measurements","wireless LAN positioning systems","Convergence","IEEE 802.11 Standards","Maximum likelihood estimation","Parameter estimation","Position measurement","Training","Indoor positioning","censored data","expectation maximization","signal strength","wireless LAN"],"language":[{"iso":"eng"}],"_id":"11816","department":[{"_id":"54"}],"user_id":"44006","year":"2013","page":"3721-3725","citation":{"mla":"Hoang, Manh Kha, and Reinhold Haeb-Umbach. “Parameter Estimation and Classification of Censored Gaussian Data with Application to WiFi Indoor Positioning.” <i>38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>, 2013, pp. 3721–25, doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6638353\">10.1109/ICASSP.2013.6638353</a>.","bibtex":"@inproceedings{Hoang_Haeb-Umbach_2013, title={Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2013.6638353\">10.1109/ICASSP.2013.6638353</a>}, booktitle={38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)}, author={Hoang, Manh Kha and Haeb-Umbach, Reinhold}, year={2013}, pages={3721–3725} }","short":"M.K. Hoang, R. Haeb-Umbach, in: 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), 2013, pp. 3721–3725.","apa":"Hoang, M. K., &#38; Haeb-Umbach, R. (2013). Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning. In <i>38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i> (pp. 3721–3725). <a href=\"https://doi.org/10.1109/ICASSP.2013.6638353\">https://doi.org/10.1109/ICASSP.2013.6638353</a>","ama":"Hoang MK, Haeb-Umbach R. Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning. In: <i>38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>. ; 2013:3721-3725. doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6638353\">10.1109/ICASSP.2013.6638353</a>","ieee":"M. K. Hoang and R. Haeb-Umbach, “Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning,” in <i>38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>, 2013, pp. 3721–3725.","chicago":"Hoang, Manh Kha, and Reinhold Haeb-Umbach. “Parameter Estimation and Classification of Censored Gaussian Data with Application to WiFi Indoor Positioning.” In <i>38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>, 3721–25, 2013. <a href=\"https://doi.org/10.1109/ICASSP.2013.6638353\">https://doi.org/10.1109/ICASSP.2013.6638353</a>."},"publication_identifier":{"issn":["1520-6149"]},"related_material":{"link":[{"description":"Poster","relation":"supplementary_material","url":"https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013_Poster.pdf"}]},"title":"Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning","doi":"10.1109/ICASSP.2013.6638353","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013.pdf"}],"oa":"1","date_updated":"2022-01-06T06:51:09Z","author":[{"first_name":"Manh Kha","full_name":"Hoang, Manh Kha","last_name":"Hoang"},{"id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach","first_name":"Reinhold"}],"date_created":"2019-07-12T05:28:48Z"}]