[{"citation":{"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>.","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>","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} }","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>","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>.","short":"W. Kirchgässner, N. Förster, T. Piepenbrock, O. Schweins, O. Wallscheid, IEEE Transactions on Power Electronics 40 (2025) 3326–3335."},"issue":"2","publication":"IEEE Transactions on Power Electronics","date_created":"2026-01-06T08:07:13Z","department":[{"_id":"52"}],"type":"journal_article","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"],"author":[{"first_name":"Wilhelm","last_name":"Kirchgässner","full_name":"Kirchgässner, Wilhelm"},{"full_name":"Förster, Nikolas","last_name":"Förster","first_name":"Nikolas"},{"first_name":"Till","last_name":"Piepenbrock","full_name":"Piepenbrock, Till"},{"last_name":"Schweins","first_name":"Oliver","full_name":"Schweins, Oliver"},{"full_name":"Wallscheid, Oliver","first_name":"Oliver","last_name":"Wallscheid"}],"status":"public","title":"HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores","year":"2025","intvolume":"        40","date_updated":"2026-01-06T08:08:01Z","_id":"63498","page":"3326-3335","volume":40,"user_id":"83383","doi":"10.1109/TPEL.2024.3488174"},{"project":[{"_id":"52","name":"PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"citation":{"mla":"Sander, Tom, et al. “Detection of Defects on Irregularly Structured Surfaces Using Supervised and Semi-Supervised Learning Methods.” <i>2022 Smart Systems Integration (SSI)</i>, IEEE, 2022, doi:<a href=\"https://doi.org/10.1109/ssi56489.2022.9901433\">10.1109/ssi56489.2022.9901433</a>.","ama":"Sander T, Lange S, Hilleringmann U, Geneiß V, Hedayat C, Kuhn H. Detection of Defects on Irregularly Structured Surfaces using Supervised and Semi-Supervised Learning Methods. In: <i>2022 Smart Systems Integration (SSI)</i>. IEEE; 2022. doi:<a href=\"https://doi.org/10.1109/ssi56489.2022.9901433\">10.1109/ssi56489.2022.9901433</a>","bibtex":"@inproceedings{Sander_Lange_Hilleringmann_Geneiß_Hedayat_Kuhn_2022, place={Grenoble, France}, title={Detection of Defects on Irregularly Structured Surfaces using Supervised and Semi-Supervised Learning Methods}, DOI={<a href=\"https://doi.org/10.1109/ssi56489.2022.9901433\">10.1109/ssi56489.2022.9901433</a>}, booktitle={2022 Smart Systems Integration (SSI)}, publisher={IEEE}, author={Sander, Tom and Lange, Sven and Hilleringmann, Ulrich and Geneiß, Volker and Hedayat, Christian and Kuhn, Harald}, year={2022} }","apa":"Sander, T., Lange, S., Hilleringmann, U., Geneiß, V., Hedayat, C., &#38; Kuhn, H. (2022). Detection of Defects on Irregularly Structured Surfaces using Supervised and Semi-Supervised Learning Methods. <i>2022 Smart Systems Integration (SSI)</i>. 2022 Smart Systems Integration (SSI), Grenoble, France. <a href=\"https://doi.org/10.1109/ssi56489.2022.9901433\">https://doi.org/10.1109/ssi56489.2022.9901433</a>","ieee":"T. Sander, S. Lange, U. Hilleringmann, V. Geneiß, C. Hedayat, and H. Kuhn, “Detection of Defects on Irregularly Structured Surfaces using Supervised and Semi-Supervised Learning Methods,” presented at the 2022 Smart Systems Integration (SSI), Grenoble, France, 2022, doi: <a href=\"https://doi.org/10.1109/ssi56489.2022.9901433\">10.1109/ssi56489.2022.9901433</a>.","chicago":"Sander, Tom, Sven Lange, Ulrich Hilleringmann, Volker Geneiß, Christian Hedayat, and Harald Kuhn. “Detection of Defects on Irregularly Structured Surfaces Using Supervised and Semi-Supervised Learning Methods.” In <i>2022 Smart Systems Integration (SSI)</i>. Grenoble, France: IEEE, 2022. <a href=\"https://doi.org/10.1109/ssi56489.2022.9901433\">https://doi.org/10.1109/ssi56489.2022.9901433</a>.","short":"T. Sander, S. Lange, U. Hilleringmann, V. Geneiß, C. Hedayat, H. Kuhn, in: 2022 Smart Systems Integration (SSI), IEEE, Grenoble, France, 2022."},"place":"Grenoble, France","conference":{"end_date":"2022-04-28","start_date":"2022-04-27","name":"2022 Smart Systems Integration (SSI)","location":"Grenoble, France"},"status":"public","user_id":"38240","_id":"33510","publisher":"IEEE","abstract":[{"lang":"eng","text":"In the manufacture of real wood products, defects can quickly occur during the production process. To quickly sort out these defects, a system is needed that finds damage in the irregularly structured surfaces of the product. The difficulty in this task is that each surface is visually different and no standard defects can be defined. Thus, damage detection using correlation does not work, so this paper will test different machine learning methods. To evaluate different machine learning methods, a data set is needed. For this reason, the available samples were recorded manually using a static fixed camera. Subsequently, the images were divided into sub-images, which resulted in a relatively small data set. Next, a convolutional neural network (CNN) was constructed to classify the images. However, this approach did not lead to a generalized solution, so the dataset was hashed using the a- and pHash. These hash values were then trained with a fully supervised system that will later serve as a reference model, in the semi-supervised learning procedures. To improve the supervised model and not have to label every data point, semi-supervised learning methods are used in the following. For this purpose, the CEAL method (wrapper method) is considered in the first and then the Π-Model (intrinsically semi-supervised)."}],"publication":"2022 Smart Systems Integration (SSI)","department":[{"_id":"59"},{"_id":"485"}],"type":"conference","keyword":["Machine Learning","CNN","Hashing","semi-supervised learning"],"date_created":"2022-10-04T11:35:55Z","publication_status":"published","date_updated":"2022-10-04T11:37:39Z","author":[{"last_name":"Sander","first_name":"Tom","full_name":"Sander, Tom"},{"id":"38240","full_name":"Lange, Sven","last_name":"Lange","first_name":"Sven"},{"full_name":"Hilleringmann, Ulrich","last_name":"Hilleringmann","first_name":"Ulrich"},{"full_name":"Geneiß, Volker","last_name":"Geneiß","first_name":"Volker"},{"first_name":"Christian","last_name":"Hedayat","full_name":"Hedayat, Christian"},{"full_name":"Kuhn, Harald","first_name":"Harald","last_name":"Kuhn"}],"year":"2022","title":"Detection of Defects on Irregularly Structured Surfaces using Supervised and Semi-Supervised Learning Methods","doi":"10.1109/ssi56489.2022.9901433","language":[{"iso":"eng"}],"main_file_link":[{"url":"https://ieeexplore.ieee.org/document/9901433"}]}]