[{"_id":"63498","user_id":"83383","department":[{"_id":"52"}],"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"],"type":"journal_article","publication":"IEEE Transactions on Power Electronics","status":"public","date_updated":"2026-01-06T08:08:01Z","date_created":"2026-01-06T08:07:13Z","author":[{"last_name":"Kirchgässner","full_name":"Kirchgässner, Wilhelm","first_name":"Wilhelm"},{"full_name":"Förster, Nikolas","last_name":"Förster","first_name":"Nikolas"},{"first_name":"Till","last_name":"Piepenbrock","full_name":"Piepenbrock, Till"},{"first_name":"Oliver","full_name":"Schweins, Oliver","last_name":"Schweins"},{"last_name":"Wallscheid","full_name":"Wallscheid, Oliver","first_name":"Oliver"}],"volume":40,"title":"HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores","doi":"10.1109/TPEL.2024.3488174","issue":"2","year":"2025","citation":{"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>.","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>","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>","short":"W. Kirchgässner, N. Förster, T. Piepenbrock, O. Schweins, O. Wallscheid, IEEE Transactions on Power Electronics 40 (2025) 3326–3335.","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>.","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} }"},"intvolume":"        40","page":"3326-3335"},{"date_created":"2025-11-04T12:47:06Z","author":[{"first_name":"Johannes","last_name":"Gerritzen","orcid":"0000-0002-0169-8602","id":"105344","full_name":"Gerritzen, Johannes"},{"first_name":"Andreas","last_name":"Hornig","full_name":"Hornig, Andreas"},{"first_name":"Peter","last_name":"Winkler","full_name":"Winkler, Peter"},{"full_name":"Gude, Maik","last_name":"Gude","first_name":"Maik"}],"volume":3,"date_updated":"2026-02-27T06:46:21Z","publisher":"European Society for Composite Materials (ESCM)","doi":"10.60691/yj56-np80","title":"Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning","publication_identifier":{"isbn":["978-2-912985-01-9"]},"citation":{"mla":"Gerritzen, Johannes, et al. “Direct Parameter Identification for Highly Nonlinear Strain Rate Dependent Constitutive Models Using Machine Learning.” <i>ECCM21 - Proceedings of the 21st European Conference on Composite Materials</i>, vol. 3, European Society for Composite Materials (ESCM), 2024, pp. 1252–1259, doi:<a href=\"https://doi.org/10.60691/yj56-np80\">10.60691/yj56-np80</a>.","bibtex":"@inproceedings{Gerritzen_Hornig_Winkler_Gude_2024, title={Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning}, volume={3}, DOI={<a href=\"https://doi.org/10.60691/yj56-np80\">10.60691/yj56-np80</a>}, booktitle={ECCM21 - Proceedings of the 21st European Conference on Composite Materials}, publisher={European Society for Composite Materials (ESCM)}, author={Gerritzen, Johannes and Hornig, Andreas and Winkler, Peter and Gude, Maik}, year={2024}, pages={1252–1259} }","short":"J. Gerritzen, A. Hornig, P. Winkler, M. Gude, in: ECCM21 - Proceedings of the 21st European Conference on Composite Materials, European Society for Composite Materials (ESCM), 2024, pp. 1252–1259.","ama":"Gerritzen J, Hornig A, Winkler P, Gude M. Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning. In: <i>ECCM21 - Proceedings of the 21st European Conference on Composite Materials</i>. Vol 3. European Society for Composite Materials (ESCM); 2024:1252–1259. doi:<a href=\"https://doi.org/10.60691/yj56-np80\">10.60691/yj56-np80</a>","apa":"Gerritzen, J., Hornig, A., Winkler, P., &#38; Gude, M. (2024). Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning. <i>ECCM21 - Proceedings of the 21st European Conference on Composite Materials</i>, <i>3</i>, 1252–1259. <a href=\"https://doi.org/10.60691/yj56-np80\">https://doi.org/10.60691/yj56-np80</a>","ieee":"J. Gerritzen, A. Hornig, P. Winkler, and M. Gude, “Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning,” in <i>ECCM21 - Proceedings of the 21st European Conference on Composite Materials</i>, 2024, vol. 3, pp. 1252–1259, doi: <a href=\"https://doi.org/10.60691/yj56-np80\">10.60691/yj56-np80</a>.","chicago":"Gerritzen, Johannes, Andreas Hornig, Peter Winkler, and Maik Gude. “Direct Parameter Identification for Highly Nonlinear Strain Rate Dependent Constitutive Models Using Machine Learning.” In <i>ECCM21 - Proceedings of the 21st European Conference on Composite Materials</i>, 3:1252–1259. European Society for Composite Materials (ESCM), 2024. <a href=\"https://doi.org/10.60691/yj56-np80\">https://doi.org/10.60691/yj56-np80</a>."},"intvolume":"         3","page":"1252–1259","year":"2024","user_id":"105344","project":[{"name":"TRR 285:  Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen Prozessketten","_id":"130"},{"_id":"137","name":"TRR 285 - Subproject A03"},{"_id":"131","name":"TRR 285 - Project Area A"}],"_id":"62078","language":[{"iso":"eng"}],"keyword":["Direct parameter identification","Machine learning","Convolutional neural networks","Strain rate dependency","Fiber reinforced plastics","woven composites","segmentation","synthetic training data","x-ray computed tomography"],"type":"conference","publication":"ECCM21 - Proceedings of the 21st European Conference on Composite Materials","status":"public","abstract":[{"text":"Fiber reinforced plastics (FRP) exhibit strongly non-linear deformation behavior. To capture this in simulations, intricate models with a variety of parameters are typically used. The identification of values for such parameters is highly challenging and requires in depth understanding of the model itself. Machine learning (ML) is a promising approach for alleviating this challenge by directly predicting parameters based on experimental results. So far, this works mostly for purely artificial data. In this work, two approaches to generalize to experimental data are investigated: a sequential approach, leveraging understanding of the constitutive model and a direct, purely data driven approach. This is exemplary carried out for a highly non-linear strain rate dependent constitutive model for the shear behavior of FRP.The sequential model is found to work better on both artificial and experimental data. It is capable of extracting well suited parameters from the artificial data under realistic conditions. For the experimental data, the model performance depends on the composition of the experimental curves, varying between excellently suiting and reasonable predictions. Taking the expert knowledge into account for ML-model training led to far better results than the purely data driven approach. Robustifying the model predictions on experimental data promises further improvement. ","lang":"eng"}]},{"status":"public","abstract":[{"lang":"eng","text":"Artificial benchmark functions are commonly used in optimization research because of their ability to rapidly evaluate potential solutions, making them a preferred substitute for real-world problems. However, these benchmark functions have faced criticism for their limited resemblance to real-world problems. In response, recent research has focused on automatically generating new benchmark functions for areas where established test suites are inadequate. These approaches have limitations, such as the difficulty of generating new benchmark functions that exhibit exploratory landscape analysis (ELA) features beyond those of existing benchmarks.The objective of this work is to develop a method for generating benchmark functions for single-objective continuous optimization with user-specified structural properties. Specifically, we aim to demonstrate a proof of concept for a method that uses an ELA feature vector to specify these properties in advance. To achieve this, we begin by generating a random sample of decision space variables and objective values. We then adjust the objective values using CMA-ES until the corresponding features of our new problem match the predefined ELA features within a specified threshold. By iteratively transforming the landscape in this way, we ensure that the resulting function exhibits the desired properties. To create the final function, we use the resulting point cloud as training data for a simple neural network that produces a function exhibiting the target ELA features. We demonstrate the effectiveness of this approach by replicating the existing functions of the well-known BBOB suite and creating new functions with ELA feature values that are not present in BBOB."}],"publication":"Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms","type":"conference","language":[{"iso":"eng"}],"keyword":["Benchmarking","Instance Generator","Black-Box Continuous Optimization","Exploratory Landscape Analysis","Neural Networks"],"department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","series_title":"FOGA ’23","_id":"47522","page":"129–139","citation":{"chicago":"Prager, Raphael Patrick, Konstantin Dietrich, Lennart Schneider, Lennart Schäpermeier, Bernd Bischl, Pascal Kerschke, Heike Trautmann, and Olaf Mersmann. “Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features.” In <i>Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 129–139. FOGA ’23. New York, NY, USA: Association for Computing Machinery, 2023. <a href=\"https://doi.org/10.1145/3594805.3607136\">https://doi.org/10.1145/3594805.3607136</a>.","ieee":"R. P. Prager <i>et al.</i>, “Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features,” in <i>Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 2023, pp. 129–139, doi: <a href=\"https://doi.org/10.1145/3594805.3607136\">10.1145/3594805.3607136</a>.","ama":"Prager RP, Dietrich K, Schneider L, et al. Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features. In: <i>Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>. FOGA ’23. Association for Computing Machinery; 2023:129–139. doi:<a href=\"https://doi.org/10.1145/3594805.3607136\">10.1145/3594805.3607136</a>","apa":"Prager, R. P., Dietrich, K., Schneider, L., Schäpermeier, L., Bischl, B., Kerschke, P., Trautmann, H., &#38; Mersmann, O. (2023). Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features. <i>Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 129–139. <a href=\"https://doi.org/10.1145/3594805.3607136\">https://doi.org/10.1145/3594805.3607136</a>","bibtex":"@inproceedings{Prager_Dietrich_Schneider_Schäpermeier_Bischl_Kerschke_Trautmann_Mersmann_2023, place={New York, NY, USA}, series={FOGA ’23}, title={Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features}, DOI={<a href=\"https://doi.org/10.1145/3594805.3607136\">10.1145/3594805.3607136</a>}, booktitle={Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}, publisher={Association for Computing Machinery}, author={Prager, Raphael Patrick and Dietrich, Konstantin and Schneider, Lennart and Schäpermeier, Lennart and Bischl, Bernd and Kerschke, Pascal and Trautmann, Heike and Mersmann, Olaf}, year={2023}, pages={129–139}, collection={FOGA ’23} }","short":"R.P. Prager, K. Dietrich, L. Schneider, L. Schäpermeier, B. Bischl, P. Kerschke, H. Trautmann, O. Mersmann, in: Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, New York, NY, USA, 2023, pp. 129–139.","mla":"Prager, Raphael Patrick, et al. “Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features.” <i>Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, Association for Computing Machinery, 2023, pp. 129–139, doi:<a href=\"https://doi.org/10.1145/3594805.3607136\">10.1145/3594805.3607136</a>."},"year":"2023","place":"New York, NY, USA","publication_identifier":{"isbn":["9798400702020"]},"doi":"10.1145/3594805.3607136","title":"Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features","author":[{"first_name":"Raphael Patrick","last_name":"Prager","full_name":"Prager, Raphael Patrick"},{"first_name":"Konstantin","full_name":"Dietrich, Konstantin","last_name":"Dietrich"},{"last_name":"Schneider","full_name":"Schneider, Lennart","first_name":"Lennart"},{"last_name":"Schäpermeier","full_name":"Schäpermeier, Lennart","first_name":"Lennart"},{"first_name":"Bernd","full_name":"Bischl, Bernd","last_name":"Bischl"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"full_name":"Trautmann, Heike","id":"100740","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike"},{"last_name":"Mersmann","full_name":"Mersmann, Olaf","first_name":"Olaf"}],"date_created":"2023-09-27T15:43:17Z","publisher":"Association for Computing Machinery","date_updated":"2023-10-16T12:33:02Z"},{"doi":"10.1007/978-3-031-47896-3_6","title":"Lost in Transformation: Rediscovering LLM-Generated Campaigns in Social Media","author":[{"last_name":"Grimme","full_name":"Grimme, Britta","first_name":"Britta"},{"first_name":"Janina","last_name":"Pohl","full_name":"Pohl, Janina"},{"full_name":"Winkelmann, Hendrik","last_name":"Winkelmann","first_name":"Hendrik"},{"first_name":"Lucas","last_name":"Stampe","full_name":"Stampe, Lucas"},{"full_name":"Grimme, Christian","last_name":"Grimme","first_name":"Christian"}],"date_created":"2024-03-25T14:38:01Z","publisher":"Springer-Verlag","date_updated":"2026-03-19T07:48:51Z","page":"72–87","citation":{"ieee":"B. Grimme, J. Pohl, H. Winkelmann, L. Stampe, and C. Grimme, “Lost in Transformation: Rediscovering LLM-Generated Campaigns in Social Media,” in <i>Disinformation in Open Online Media: 5th Multidisciplinary International Symposium, MISDOOM 2023, Amsterdam, The Netherlands, November 21–22, 2023, Proceedings</i>, 2023, pp. 72–87, doi: <a href=\"https://doi.org/10.1007/978-3-031-47896-3_6\">10.1007/978-3-031-47896-3_6</a>.","chicago":"Grimme, Britta, Janina Pohl, Hendrik Winkelmann, Lucas Stampe, and Christian Grimme. “Lost in Transformation: Rediscovering LLM-Generated Campaigns in Social Media.” In <i>Disinformation in Open Online Media: 5th Multidisciplinary International Symposium, MISDOOM 2023, Amsterdam, The Netherlands, November 21–22, 2023, Proceedings</i>, 72–87. Berlin, Heidelberg: Springer-Verlag, 2023. <a href=\"https://doi.org/10.1007/978-3-031-47896-3_6\">https://doi.org/10.1007/978-3-031-47896-3_6</a>.","ama":"Grimme B, Pohl J, Winkelmann H, Stampe L, Grimme C. Lost in Transformation: Rediscovering LLM-Generated Campaigns in Social Media. In: <i>Disinformation in Open Online Media: 5th Multidisciplinary International Symposium, MISDOOM 2023, Amsterdam, The Netherlands, November 21–22, 2023, Proceedings</i>. Springer-Verlag; 2023:72–87. doi:<a href=\"https://doi.org/10.1007/978-3-031-47896-3_6\">10.1007/978-3-031-47896-3_6</a>","apa":"Grimme, B., Pohl, J., Winkelmann, H., Stampe, L., &#38; Grimme, C. (2023). Lost in Transformation: Rediscovering LLM-Generated Campaigns in Social Media. <i>Disinformation in Open Online Media: 5th Multidisciplinary International Symposium, MISDOOM 2023, Amsterdam, The Netherlands, November 21–22, 2023, Proceedings</i>, 72–87. <a href=\"https://doi.org/10.1007/978-3-031-47896-3_6\">https://doi.org/10.1007/978-3-031-47896-3_6</a>","short":"B. Grimme, J. Pohl, H. Winkelmann, L. Stampe, C. Grimme, in: Disinformation in Open Online Media: 5th Multidisciplinary International Symposium, MISDOOM 2023, Amsterdam, The Netherlands, November 21–22, 2023, Proceedings, Springer-Verlag, Berlin, Heidelberg, 2023, pp. 72–87.","bibtex":"@inproceedings{Grimme_Pohl_Winkelmann_Stampe_Grimme_2023, place={Berlin, Heidelberg}, title={Lost in Transformation: Rediscovering LLM-Generated Campaigns in Social Media}, DOI={<a href=\"https://doi.org/10.1007/978-3-031-47896-3_6\">10.1007/978-3-031-47896-3_6</a>}, booktitle={Disinformation in Open Online Media: 5th Multidisciplinary International Symposium, MISDOOM 2023, Amsterdam, The Netherlands, November 21–22, 2023, Proceedings}, publisher={Springer-Verlag}, author={Grimme, Britta and Pohl, Janina and Winkelmann, Hendrik and Stampe, Lucas and Grimme, Christian}, year={2023}, pages={72–87} }","mla":"Grimme, Britta, et al. “Lost in Transformation: Rediscovering LLM-Generated Campaigns in Social Media.” <i>Disinformation in Open Online Media: 5th Multidisciplinary International Symposium, MISDOOM 2023, Amsterdam, The Netherlands, November 21–22, 2023, Proceedings</i>, Springer-Verlag, 2023, pp. 72–87, doi:<a href=\"https://doi.org/10.1007/978-3-031-47896-3_6\">10.1007/978-3-031-47896-3_6</a>."},"place":"Berlin, Heidelberg","year":"2023","publication_identifier":{"isbn":["978-3-031-47895-6"]},"keyword":["Social Media","Campaign Detection","Large Language Models","Siamese Neural Networks"],"user_id":"103682","_id":"52865","status":"public","abstract":[{"lang":"eng","text":"This paper addresses new challenges of detecting campaigns in social media, which emerged with the rise of Large Language Models (LLMs). LLMs particularly challenge algorithms focused on the temporal analysis of topical clusters. Simple similarity measures can no longer capture and map campaigns that were previously broadly similar in content. Herein, we analyze whether the classification of messages over time can be profitably used to rediscover poorly detectable campaigns at the content level. Thus, we evaluate classical classifiers and a new method based on siamese neural networks. Our results show that campaigns can be detected despite the limited reliability of the classifiers as long as they are based on a large amount of simultaneously spread artificial content."}],"publication":"Disinformation in Open Online Media: 5th Multidisciplinary International Symposium, MISDOOM 2023, Amsterdam, The Netherlands, November 21–22, 2023, Proceedings","type":"conference"},{"related_material":{"link":[{"url":"https://ieeexplore.ieee.org/document/9921520","relation":"confirmation"}]},"citation":{"chicago":"Deppe, Sahar, Lukas Brandt, Marc Brünninghaus, Jörg Papenkordt, Stefan Heindorf, and Gudrun Tschirner-Vinke. “AI-Based Assistance System for Manufacturing.” 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), 2022. <a href=\"https://doi.org/10.1109/ETFA52439.2022.9921520\">https://doi.org/10.1109/ETFA52439.2022.9921520</a>.","ieee":"S. Deppe, L. Brandt, M. Brünninghaus, J. Papenkordt, S. Heindorf, and G. Tschirner-Vinke, “AI-Based Assistance System for Manufacturing.” 2022, doi: <a href=\"https://doi.org/10.1109/ETFA52439.2022.9921520\">10.1109/ETFA52439.2022.9921520</a>.","ama":"Deppe S, Brandt L, Brünninghaus M, Papenkordt J, Heindorf S, Tschirner-Vinke G. AI-Based Assistance System for Manufacturing. Published online 2022. doi:<a href=\"https://doi.org/10.1109/ETFA52439.2022.9921520\">10.1109/ETFA52439.2022.9921520</a>","mla":"Deppe, Sahar, et al. <i>AI-Based Assistance System for Manufacturing</i>. 2022, doi:<a href=\"https://doi.org/10.1109/ETFA52439.2022.9921520\">10.1109/ETFA52439.2022.9921520</a>.","bibtex":"@article{Deppe_Brandt_Brünninghaus_Papenkordt_Heindorf_Tschirner-Vinke_2022, series={2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)}, title={AI-Based Assistance System for Manufacturing}, DOI={<a href=\"https://doi.org/10.1109/ETFA52439.2022.9921520\">10.1109/ETFA52439.2022.9921520</a>}, author={Deppe, Sahar and Brandt, Lukas and Brünninghaus, Marc and Papenkordt, Jörg and Heindorf, Stefan and Tschirner-Vinke, Gudrun}, year={2022}, collection={2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)} }","short":"S. Deppe, L. Brandt, M. Brünninghaus, J. Papenkordt, S. Heindorf, G. Tschirner-Vinke, (2022).","apa":"Deppe, S., Brandt, L., Brünninghaus, M., Papenkordt, J., Heindorf, S., &#38; Tschirner-Vinke, G. (2022). <i>AI-Based Assistance System for Manufacturing</i>. ETFA, Stuttgart. <a href=\"https://doi.org/10.1109/ETFA52439.2022.9921520\">https://doi.org/10.1109/ETFA52439.2022.9921520</a>"},"year":"2022","date_created":"2022-10-28T11:43:49Z","author":[{"last_name":"Deppe","full_name":"Deppe, Sahar","first_name":"Sahar"},{"last_name":"Brandt","full_name":"Brandt, Lukas","first_name":"Lukas"},{"last_name":"Brünninghaus","full_name":"Brünninghaus, Marc","first_name":"Marc"},{"last_name":"Papenkordt","full_name":"Papenkordt, Jörg","id":"44648","first_name":"Jörg"},{"first_name":"Stefan","last_name":"Heindorf","orcid":"0000-0002-4525-6865","full_name":"Heindorf, Stefan","id":"11871"},{"full_name":"Tschirner-Vinke, Gudrun","last_name":"Tschirner-Vinke","first_name":"Gudrun"}],"date_updated":"2023-11-23T08:07:51Z","conference":{"name":"ETFA","start_date":"2022-09-06","end_date":"2022-09-09","location":"Stuttgart"},"doi":"10.1109/ETFA52439.2022.9921520","title":"AI-Based Assistance System for Manufacturing","type":"conference","status":"public","abstract":[{"lang":"eng","text":"Manufacturing companies are challenged to make the increasingly complex work processes equally manageable for all employees to prevent an impending loss of competence. In this contribution, an intelligent assistance system is proposed enabling employees to help themselves in the workplace and provide them with competence-related support. This results in increasing the short- and long-term efficiency of problem solving in companies."}],"user_id":"44648","series_title":"2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)","department":[{"_id":"178"},{"_id":"574"},{"_id":"184"}],"project":[{"_id":"409","name":"KIAM: KIAM: Kompetenzzentrum KI in der Arbeitswelt des industriellen Mittelstands in OstWestfalenLippe","grant_number":"02L19C115"}],"_id":"33957","language":[{"iso":"eng"}],"keyword":["Assistance system","Knowledge graph","Information retrieval","Neural networks","AR"]},{"keyword":["data-driven","physics-based","physics-informed","neural networks","system identification","hybrid modelling"],"language":[{"iso":"eng"}],"_id":"26539","department":[{"_id":"153"},{"_id":"880"}],"user_id":"43992","abstract":[{"lang":"eng","text":"In control design most control strategies are model-based and require accurate models to be applied successfully. Due to simplifications and the model-reality-gap physics-derived models frequently exhibit deviations from real-world-systems. Likewise, purely data-driven methods often do not generalise well enough and may violate physical laws. Recently Physics-Guided Neural Networks (PGNN) and physics-inspired loss functions separately have shown promising results to conquer these drawbacks. In this contribution we extend existing methods towards the identification of non-autonomous systems and propose a combined approach PGNN-L, which uses a PGNN and a physics-inspired loss term (-L) to successfully identify the system's dynamics, while maintaining the consistency with physical laws. The proposed method is demonstrated on two real-world nonlinear systems and outperforms existing techniques regarding complexity and reliability."}],"status":"public","publication":"2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)","type":"conference","title":"Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering","doi":"10.1109/AIRC56195.2022.9836982","conference":{"name":"3rd International Conference on Artificial Intelligence, Robotics and Control","start_date":"2021-12-08","end_date":"2021-12-10","location":"Cairo, Egypt"},"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2112.08148"}],"oa":"1","date_updated":"2024-11-13T08:43:28Z","author":[{"id":"43992","full_name":"Götte, Ricarda-Samantha","last_name":"Götte","first_name":"Ricarda-Samantha"},{"first_name":"Julia","id":"15402","full_name":"Timmermann, Julia","last_name":"Timmermann"}],"date_created":"2021-10-19T14:47:17Z","year":"2022","page":"67-76","citation":{"chicago":"Götte, Ricarda-Samantha, and Julia Timmermann. “Composed Physics- and Data-Driven System Identification for Non-Autonomous Systems in Control Engineering.” In <i>2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)</i>, 67–76, 2022. <a href=\"https://doi.org/10.1109/AIRC56195.2022.9836982\">https://doi.org/10.1109/AIRC56195.2022.9836982</a>.","ieee":"R.-S. Götte and J. Timmermann, “Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering,” in <i>2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)</i>, Cairo, Egypt, 2022, pp. 67–76, doi: <a href=\"https://doi.org/10.1109/AIRC56195.2022.9836982\">10.1109/AIRC56195.2022.9836982</a>.","ama":"Götte R-S, Timmermann J. Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering. In: <i>2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)</i>. ; 2022:67-76. doi:<a href=\"https://doi.org/10.1109/AIRC56195.2022.9836982\">10.1109/AIRC56195.2022.9836982</a>","mla":"Götte, Ricarda-Samantha, and Julia Timmermann. “Composed Physics- and Data-Driven System Identification for Non-Autonomous Systems in Control Engineering.” <i>2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)</i>, 2022, pp. 67–76, doi:<a href=\"https://doi.org/10.1109/AIRC56195.2022.9836982\">10.1109/AIRC56195.2022.9836982</a>.","bibtex":"@inproceedings{Götte_Timmermann_2022, title={Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering}, DOI={<a href=\"https://doi.org/10.1109/AIRC56195.2022.9836982\">10.1109/AIRC56195.2022.9836982</a>}, booktitle={2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)}, author={Götte, Ricarda-Samantha and Timmermann, Julia}, year={2022}, pages={67–76} }","short":"R.-S. Götte, J. Timmermann, in: 2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC), 2022, pp. 67–76.","apa":"Götte, R.-S., &#38; Timmermann, J. (2022). Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering. <i>2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)</i>, 67–76. <a href=\"https://doi.org/10.1109/AIRC56195.2022.9836982\">https://doi.org/10.1109/AIRC56195.2022.9836982</a>"},"quality_controlled":"1"},{"keyword":["neural networks","physics-guided","data-driven","multi-objective optimization","system identification","machine learning","dynamical systems"],"language":[{"iso":"eng"}],"abstract":[{"lang":"eng","text":"While trade-offs between modeling effort and model accuracy remain a major concern with system identification, resorting to data-driven methods often leads to a complete disregard for physical plausibility. To address this issue, we propose a physics-guided hybrid approach for modeling non-autonomous systems under control. Starting from a traditional physics-based model, this is extended by a recurrent neural network and trained using a sophisticated multi-objective strategy yielding physically plausible models. While purely data-driven methods fail to produce satisfying results, experiments conducted on real data reveal substantial accuracy improvements by our approach compared to a physics-based model. "}],"publication":"14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)","title":"Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems","date_created":"2022-05-05T06:22:55Z","year":"2022","quality_controlled":"1","issue":"12","_id":"31066","user_id":"43992","department":[{"_id":"153"},{"_id":"880"}],"status":"public","type":"conference","conference":{"location":"Casablanca, Morocco","end_date":"2022-07-01","start_date":"2022-06-29","name":"14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)"},"doi":"https://doi.org/10.1016/j.ifacol.2022.07.282","date_updated":"2024-11-13T08:43:16Z","author":[{"full_name":"Schön, Oliver","last_name":"Schön","first_name":"Oliver"},{"first_name":"Ricarda-Samantha","full_name":"Götte, Ricarda-Samantha","id":"43992","last_name":"Götte"},{"first_name":"Julia","last_name":"Timmermann","full_name":"Timmermann, Julia","id":"15402"}],"volume":55,"citation":{"ieee":"O. Schön, R.-S. Götte, and J. Timmermann, “Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems,” in <i>14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)</i>, Casablanca, Morocco, 2022, vol. 55, no. 12, pp. 19–24, doi: <a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">https://doi.org/10.1016/j.ifacol.2022.07.282</a>.","chicago":"Schön, Oliver, Ricarda-Samantha Götte, and Julia Timmermann. “Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems.” In <i>14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)</i>, 55:19–24, 2022. <a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">https://doi.org/10.1016/j.ifacol.2022.07.282</a>.","ama":"Schön O, Götte R-S, Timmermann J. Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems. In: <i>14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)</i>. Vol 55. ; 2022:19-24. doi:<a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">https://doi.org/10.1016/j.ifacol.2022.07.282</a>","apa":"Schön, O., Götte, R.-S., &#38; Timmermann, J. (2022). Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems. <i>14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)</i>, <i>55</i>(12), 19–24. <a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">https://doi.org/10.1016/j.ifacol.2022.07.282</a>","short":"O. Schön, R.-S. Götte, J. Timmermann, in: 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022), 2022, pp. 19–24.","mla":"Schön, Oliver, et al. “Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems.” <i>14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)</i>, vol. 55, no. 12, 2022, pp. 19–24, doi:<a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">https://doi.org/10.1016/j.ifacol.2022.07.282</a>.","bibtex":"@inproceedings{Schön_Götte_Timmermann_2022, title={Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems}, volume={55}, DOI={<a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">https://doi.org/10.1016/j.ifacol.2022.07.282</a>}, number={12}, booktitle={14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)}, author={Schön, Oliver and Götte, Ricarda-Samantha and Timmermann, Julia}, year={2022}, pages={19–24} }"},"intvolume":"        55","page":"19-24"},{"date_created":"2021-06-20T23:25:54Z","author":[{"first_name":"Sven","last_name":"Lange","id":"38240","full_name":"Lange, Sven"},{"full_name":"Schröder, Dominik","last_name":"Schröder","first_name":"Dominik"},{"last_name":"Hedayat","full_name":"Hedayat, Christian","first_name":"Christian"},{"full_name":"Kuhn, Harald","last_name":"Kuhn","first_name":"Harald"},{"first_name":"Ulrich","last_name":"Hilleringmann","full_name":"Hilleringmann, Ulrich"}],"date_updated":"2022-01-06T06:55:33Z","publisher":"IEEE","main_file_link":[{"url":"https://ieeexplore.ieee.org/document/9453609"}],"conference":{"name":"22nd IEEE International Conference on Industrial Technology (ICIT)","start_date":"2021-03-10","end_date":"2021-03-12","location":"Valencia, Spain "},"doi":"10.1109/icit46573.2021.9453609","title":"Development of Methods for Coil-Based Localization by Magnetic Fields of Miniaturized Sensor Platforms in Bioprocesses","publication_status":"published","publication_identifier":{"isbn":["9781728157306"]},"citation":{"apa":"Lange, S., Schröder, D., Hedayat, C., Kuhn, H., &#38; Hilleringmann, U. (2021). Development of Methods for Coil-Based Localization by Magnetic Fields of Miniaturized Sensor Platforms in Bioprocesses. In <i>22nd IEEE International Conference on Industrial Technology (ICIT)</i>.  Valencia, Spain : IEEE. <a href=\"https://doi.org/10.1109/icit46573.2021.9453609\">https://doi.org/10.1109/icit46573.2021.9453609</a>","bibtex":"@inproceedings{Lange_Schröder_Hedayat_Kuhn_Hilleringmann_2021, place={ Valencia, Spain }, title={Development of Methods for Coil-Based Localization by Magnetic Fields of Miniaturized Sensor Platforms in Bioprocesses}, DOI={<a href=\"https://doi.org/10.1109/icit46573.2021.9453609\">10.1109/icit46573.2021.9453609</a>}, booktitle={22nd IEEE International Conference on Industrial Technology (ICIT)}, publisher={IEEE}, author={Lange, Sven and Schröder, Dominik and Hedayat, Christian and Kuhn, Harald and Hilleringmann, Ulrich}, year={2021} }","short":"S. Lange, D. Schröder, C. Hedayat, H. Kuhn, U. Hilleringmann, in: 22nd IEEE International Conference on Industrial Technology (ICIT), IEEE,  Valencia, Spain , 2021.","mla":"Lange, Sven, et al. “Development of Methods for Coil-Based Localization by Magnetic Fields of Miniaturized Sensor Platforms in Bioprocesses.” <i>22nd IEEE International Conference on Industrial Technology (ICIT)</i>, IEEE, 2021, doi:<a href=\"https://doi.org/10.1109/icit46573.2021.9453609\">10.1109/icit46573.2021.9453609</a>.","ama":"Lange S, Schröder D, Hedayat C, Kuhn H, Hilleringmann U. Development of Methods for Coil-Based Localization by Magnetic Fields of Miniaturized Sensor Platforms in Bioprocesses. In: <i>22nd IEEE International Conference on Industrial Technology (ICIT)</i>.  Valencia, Spain : IEEE; 2021. doi:<a href=\"https://doi.org/10.1109/icit46573.2021.9453609\">10.1109/icit46573.2021.9453609</a>","ieee":"S. Lange, D. Schröder, C. Hedayat, H. Kuhn, and U. Hilleringmann, “Development of Methods for Coil-Based Localization by Magnetic Fields of Miniaturized Sensor Platforms in Bioprocesses,” in <i>22nd IEEE International Conference on Industrial Technology (ICIT)</i>, Valencia, Spain , 2021.","chicago":"Lange, Sven, Dominik Schröder, Christian Hedayat, Harald Kuhn, and Ulrich Hilleringmann. “Development of Methods for Coil-Based Localization by Magnetic Fields of Miniaturized Sensor Platforms in Bioprocesses.” In <i>22nd IEEE International Conference on Industrial Technology (ICIT)</i>.  Valencia, Spain : IEEE, 2021. <a href=\"https://doi.org/10.1109/icit46573.2021.9453609\">https://doi.org/10.1109/icit46573.2021.9453609</a>."},"place":" Valencia, Spain ","year":"2021","user_id":"38240","department":[{"_id":"59"},{"_id":"485"}],"project":[{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"_id":"22480","language":[{"iso":"eng"}],"keyword":["Location awareness","Coils","Couplings","Nonuniform electric fields","Magnetic separation","Neural networks","Training data"],"type":"conference","publication":"22nd IEEE International Conference on Industrial Technology (ICIT)","status":"public","abstract":[{"text":"In this publication important aspects for the implementation of inductive locating are explained. The miniaturized sensor platform called Sens-o-Spheres is used as an application of this locating method. The sensor platform is applied in bioreactors in order to obtain the environmental parameters, which makes a localization by magnetic fields necessary. Since the properties of magnetic fields in the localization area are very different from the wave characteristics, the principle of inductive localization is investigated in this publication and explained by using electrical equivalent circuit diagrams. Thereby, inductive localization uses the coupling or the mutual inductivities between coils, which is noticeable by an induced voltage. Therefore some properties and procedures are explained to extract the location of Sens-o-Spheres or other industrial sensor platforms from the couplings of the coils. One method calculates the location from an adapted ratio calculation and the other method uses neural networks and stochastic filters to obtain the results. In the end, these results are evaluated and compared.","lang":"eng"}]},{"citation":{"bibtex":"@inproceedings{Sander_Lange_Hilleringmann_Geneis_Hedayat_Kuhn_Gockel_2021, place={Valencia, Spain }, title={Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction}, DOI={<a href=\"https://doi.org/10.1109/icit46573.2021.9453646\">10.1109/icit46573.2021.9453646</a>}, booktitle={22nd IEEE International Conference on Industrial Technology (ICIT)}, publisher={IEEE}, author={Sander, Tom and Lange, Sven and Hilleringmann, Ulrich and Geneis, Volker and Hedayat, Christian and Kuhn, Harald and Gockel, Franz-Barthold}, year={2021} }","short":"T. Sander, S. Lange, U. Hilleringmann, V. Geneis, C. Hedayat, H. Kuhn, F.-B. Gockel, in: 22nd IEEE International Conference on Industrial Technology (ICIT), IEEE, Valencia, Spain , 2021.","mla":"Sander, Tom, et al. “Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction.” <i>22nd IEEE International Conference on Industrial Technology (ICIT)</i>, IEEE, 2021, doi:<a href=\"https://doi.org/10.1109/icit46573.2021.9453646\">10.1109/icit46573.2021.9453646</a>.","apa":"Sander, T., Lange, S., Hilleringmann, U., Geneis, V., Hedayat, C., Kuhn, H., &#38; Gockel, F.-B. (2021). Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction. In <i>22nd IEEE International Conference on Industrial Technology (ICIT)</i>. Valencia, Spain : IEEE. <a href=\"https://doi.org/10.1109/icit46573.2021.9453646\">https://doi.org/10.1109/icit46573.2021.9453646</a>","chicago":"Sander, Tom, Sven Lange, Ulrich Hilleringmann, Volker Geneis, Christian Hedayat, Harald Kuhn, and Franz-Barthold Gockel. “Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction.” In <i>22nd IEEE International Conference on Industrial Technology (ICIT)</i>. Valencia, Spain : IEEE, 2021. <a href=\"https://doi.org/10.1109/icit46573.2021.9453646\">https://doi.org/10.1109/icit46573.2021.9453646</a>.","ieee":"T. Sander <i>et al.</i>, “Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction,” in <i>22nd IEEE International Conference on Industrial Technology (ICIT)</i>, Valencia, Spain , 2021.","ama":"Sander T, Lange S, Hilleringmann U, et al. Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction. In: <i>22nd IEEE International Conference on Industrial Technology (ICIT)</i>. Valencia, Spain : IEEE; 2021. doi:<a href=\"https://doi.org/10.1109/icit46573.2021.9453646\">10.1109/icit46573.2021.9453646</a>"},"place":"Valencia, Spain ","year":"2021","publication_status":"published","publication_identifier":{"isbn":["9781728157306"]},"main_file_link":[{"url":"https://ieeexplore.ieee.org/document/9453646"}],"doi":"10.1109/icit46573.2021.9453646","conference":{"end_date":"2021-03-12","location":"Valencia, Spain ","name":"22nd IEEE International Conference on Industrial Technology (ICIT)","start_date":"2021-03-10"},"title":"Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction","author":[{"first_name":"Tom","full_name":"Sander, Tom","last_name":"Sander"},{"last_name":"Lange","id":"38240","full_name":"Lange, Sven","first_name":"Sven"},{"first_name":"Ulrich","last_name":"Hilleringmann","full_name":"Hilleringmann, Ulrich"},{"first_name":"Volker","full_name":"Geneis, Volker","last_name":"Geneis"},{"first_name":"Christian","full_name":"Hedayat, Christian","last_name":"Hedayat"},{"full_name":"Kuhn, Harald","last_name":"Kuhn","first_name":"Harald"},{"full_name":"Gockel, Franz-Barthold","last_name":"Gockel","first_name":"Franz-Barthold"}],"date_created":"2021-06-20T23:32:11Z","publisher":"IEEE","date_updated":"2022-01-06T06:55:33Z","status":"public","abstract":[{"text":"During the industrial processing of materials for the manufacture of new products, surface defects can quickly occur. In order to achieve high quality without a long time delay, it makes sense to inspect the work pieces so that defective work pieces can be sorted out right at the beginning of the process. At the same time, the evaluation unit should come close the perception of the human eye regarding detection of defects in surfaces. Such defects often manifest themselves by a deviation of the existing structure. The only restriction should be that only matt surfaces should be considered here. Therefore in this work, different classification and image processing algorithms are applied to surface data to identify possible surface damages. For this purpose, the Gabor filter and the FST (Fused Structure and Texture) features generated with it, as well as the salience metric are used on the image processing side. On the classification side, however, deep neural networks, Convolutional Neural Networks (CNN), and autoencoders are used to make a decision. A distinction is also made between training using class labels and without. It turns out later that the salience metric are best performed by CNN. On the other hand, if there is no labeled training data available, a novelty classification can easily be achieved by using autoencoders as well as the salience metric and some filters.","lang":"eng"}],"type":"conference","publication":"22nd IEEE International Conference on Industrial Technology (ICIT)","language":[{"iso":"eng"}],"keyword":["Image Processing","Defect Detection","wooden surfaces","Machine Learning","Neural Networks"],"user_id":"38240","department":[{"_id":"59"},{"_id":"485"}],"_id":"22481"},{"publication":"Proceedings of The 24th International Conference on Discovery Science (DS 2021)","abstract":[{"text":"Graph neural networks (GNNs) have been successfully applied in many structured data domains, with applications ranging from molecular property prediction to the analysis of social networks. Motivated by the broad applicability of GNNs, we propose the family of so-called RankGNNs, a combination of neural Learning to Rank (LtR) methods and GNNs. RankGNNs are trained with a set of pair-wise preferences between graphs, suggesting that one of them is preferred over the other. One practical application of this problem is drug screening, where an expert wants to find the most promising molecules in a large collection of drug candidates. We empirically demonstrate that our proposed pair-wise RankGNN approach either significantly outperforms or at least matches the ranking performance of the naive point-wise baseline approach, in which the LtR problem is solved via GNN-based graph regression.","lang":"eng"}],"external_id":{"arxiv":["2104.08869"]},"language":[{"iso":"eng"}],"keyword":["Graph-structured data","Graph neural networks","Preference learning","Learning to rank"],"quality_controlled":"1","year":"2021","date_created":"2021-11-11T14:15:18Z","publisher":"Springer","title":"Ranking Structured Objects with Graph Neural Networks","type":"conference","status":"public","editor":[{"first_name":"Carlos","full_name":"Soares, Carlos","last_name":"Soares"},{"last_name":"Torgo","full_name":"Torgo, Luis","first_name":"Luis"}],"series_title":"Lecture Notes in Computer Science","user_id":"48192","department":[{"_id":"355"}],"_id":"27381","publication_status":"published","publication_identifier":{"issn":["0302-9743","1611-3349"],"isbn":["9783030889418","9783030889425"]},"citation":{"apa":"Damke, C., &#38; Hüllermeier, E. (2021). Ranking Structured Objects with Graph Neural Networks. In C. Soares &#38; L. Torgo (Eds.), <i>Proceedings of The 24th International Conference on Discovery Science (DS 2021)</i> (Vol. 12986, pp. 166–180). Springer. <a href=\"https://doi.org/10.1007/978-3-030-88942-5\">https://doi.org/10.1007/978-3-030-88942-5</a>","mla":"Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with Graph Neural Networks.” <i>Proceedings of The 24th International Conference on Discovery Science (DS 2021)</i>, edited by Carlos Soares and Luis Torgo, vol. 12986, Springer, 2021, pp. 166–80, doi:<a href=\"https://doi.org/10.1007/978-3-030-88942-5\">10.1007/978-3-030-88942-5</a>.","bibtex":"@inproceedings{Damke_Hüllermeier_2021, series={Lecture Notes in Computer Science}, title={Ranking Structured Objects with Graph Neural Networks}, volume={12986}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-88942-5\">10.1007/978-3-030-88942-5</a>}, booktitle={Proceedings of The 24th International Conference on Discovery Science (DS 2021)}, publisher={Springer}, author={Damke, Clemens and Hüllermeier, Eyke}, editor={Soares, Carlos and Torgo, Luis}, year={2021}, pages={166–180}, collection={Lecture Notes in Computer Science} }","short":"C. Damke, E. Hüllermeier, in: C. Soares, L. Torgo (Eds.), Proceedings of The 24th International Conference on Discovery Science (DS 2021), Springer, 2021, pp. 166–180.","chicago":"Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with Graph Neural Networks.” In <i>Proceedings of The 24th International Conference on Discovery Science (DS 2021)</i>, edited by Carlos Soares and Luis Torgo, 12986:166–80. Lecture Notes in Computer Science. Springer, 2021. <a href=\"https://doi.org/10.1007/978-3-030-88942-5\">https://doi.org/10.1007/978-3-030-88942-5</a>.","ieee":"C. Damke and E. Hüllermeier, “Ranking Structured Objects with Graph Neural Networks,” in <i>Proceedings of The 24th International Conference on Discovery Science (DS 2021)</i>, Halifax, Canada, 2021, vol. 12986, pp. 166–180, doi: <a href=\"https://doi.org/10.1007/978-3-030-88942-5\">10.1007/978-3-030-88942-5</a>.","ama":"Damke C, Hüllermeier E. Ranking Structured Objects with Graph Neural Networks. In: Soares C, Torgo L, eds. <i>Proceedings of The 24th International Conference on Discovery Science (DS 2021)</i>. Vol 12986. Lecture Notes in Computer Science. Springer; 2021:166-180. doi:<a href=\"https://doi.org/10.1007/978-3-030-88942-5\">10.1007/978-3-030-88942-5</a>"},"page":"166-180","intvolume":"     12986","author":[{"last_name":"Damke","orcid":"0000-0002-0455-0048","id":"48192","full_name":"Damke, Clemens","first_name":"Clemens"},{"first_name":"Eyke","last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke"}],"volume":12986,"date_updated":"2022-04-11T22:08:12Z","doi":"10.1007/978-3-030-88942-5","conference":{"end_date":"2021-10-13","location":"Halifax, Canada","name":"24th International Conference on Discovery Science","start_date":"2021-10-11"}},{"editor":[{"last_name":"Jialin Pan","full_name":"Jialin Pan, Sinno","first_name":"Sinno"},{"last_name":"Sugiyama","full_name":"Sugiyama, Masashi","first_name":"Masashi"}],"status":"public","type":"conference","file_date_updated":"2020-10-08T11:24:29Z","_id":"19953","user_id":"48192","series_title":"Proceedings of Machine Learning Research","department":[{"_id":"355"}],"place":"Bangkok, Thailand","citation":{"chicago":"Damke, Clemens, Vitaly Melnikov, and Eyke Hüllermeier. “A Novel Higher-Order Weisfeiler-Lehman Graph Convolution.” In <i>Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)</i>, edited by Sinno Jialin Pan and Masashi Sugiyama, 129:49–64. Proceedings of Machine Learning Research. Bangkok, Thailand: PMLR, 2020.","ieee":"C. Damke, V. Melnikov, and E. Hüllermeier, “A Novel Higher-order Weisfeiler-Lehman Graph Convolution,” in <i>Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)</i>, Bangkok, Thailand, 2020, vol. 129, pp. 49–64.","ama":"Damke C, Melnikov V, Hüllermeier E. A Novel Higher-order Weisfeiler-Lehman Graph Convolution. In: Jialin Pan S, Sugiyama M, eds. <i>Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)</i>. Vol 129. Proceedings of Machine Learning Research. Bangkok, Thailand: PMLR; 2020:49-64.","apa":"Damke, C., Melnikov, V., &#38; Hüllermeier, E. (2020). A Novel Higher-order Weisfeiler-Lehman Graph Convolution. In S. Jialin Pan &#38; M. Sugiyama (Eds.), <i>Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)</i> (Vol. 129, pp. 49–64). Bangkok, Thailand: PMLR.","bibtex":"@inproceedings{Damke_Melnikov_Hüllermeier_2020, place={Bangkok, Thailand}, series={Proceedings of Machine Learning Research}, title={A Novel Higher-order Weisfeiler-Lehman Graph Convolution}, volume={129}, booktitle={Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)}, publisher={PMLR}, author={Damke, Clemens and Melnikov, Vitaly and Hüllermeier, Eyke}, editor={Jialin Pan, Sinno and Sugiyama, MasashiEditors}, year={2020}, pages={49–64}, collection={Proceedings of Machine Learning Research} }","short":"C. Damke, V. Melnikov, E. Hüllermeier, in: S. Jialin Pan, M. Sugiyama (Eds.), Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), PMLR, Bangkok, Thailand, 2020, pp. 49–64.","mla":"Damke, Clemens, et al. “A Novel Higher-Order Weisfeiler-Lehman Graph Convolution.” <i>Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)</i>, edited by Sinno Jialin Pan and Masashi Sugiyama, vol. 129, PMLR, 2020, pp. 49–64."},"page":"49-64","intvolume":"       129","publication_status":"published","has_accepted_license":"1","conference":{"location":"Bangkok, Thailand","end_date":"2020-11-20","start_date":"2020-11-18","name":"Asian Conference on Machine Learning"},"oa":"1","date_updated":"2022-01-06T06:54:17Z","author":[{"first_name":"Clemens","full_name":"Damke, Clemens","id":"48192","last_name":"Damke","orcid":"0000-0002-0455-0048"},{"first_name":"Vitaly","last_name":"Melnikov","full_name":"Melnikov, Vitaly","id":"58747"},{"id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"}],"volume":129,"abstract":[{"lang":"eng","text":"Current GNN architectures use a vertex neighborhood aggregation scheme, which limits their discriminative power to that of the 1-dimensional Weisfeiler-Lehman (WL) graph isomorphism test. Here, we propose a novel graph convolution operator that is based on the 2-dimensional WL test. We formally show that the resulting 2-WL-GNN architecture is more discriminative than existing GNN approaches. This theoretical result is complemented by experimental studies using synthetic and real data. On multiple common graph classification benchmarks, we demonstrate that the proposed model is competitive with state-of-the-art graph kernels and GNNs."}],"file":[{"relation":"main_file","content_type":"application/pdf","file_name":"damke20.pdf","file_id":"19954","access_level":"open_access","file_size":771137,"creator":"cdamke","date_created":"2020-10-08T10:54:48Z","date_updated":"2020-10-08T11:21:00Z"},{"relation":"supplementary_material","content_type":"application/pdf","file_name":"damke20-supp.pdf","access_level":"open_access","file_id":"19955","file_size":613163,"creator":"cdamke","date_created":"2020-10-08T10:54:59Z","date_updated":"2020-10-08T11:24:29Z"}],"publication":"Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)","ddc":["006"],"keyword":["graph neural networks","Weisfeiler-Lehman test","cycle detection"],"language":[{"iso":"eng"}],"external_id":{"arxiv":["2007.00346"]},"year":"2020","quality_controlled":"1","title":"A Novel Higher-order Weisfeiler-Lehman Graph Convolution","publisher":"PMLR","date_created":"2020-10-08T10:48:38Z"},{"type":"conference","publication":"20. GMA/ITG-Fachtagung. Sensoren und Messsysteme 2019","abstract":[{"lang":"eng","text":"The continuous refinement of sensor technologies enables the manufacturing industry to capture increasing amounts of data during the production process. As processes take time to complete, sensors register large amounts of time-series-like data for each product. In order to make this data usable, a feature extraction is mandatory. In this work, we discuss and evaluate different network architectures, input pre-processing and cost functions regarding, among other aspects, their suitability for time series of different lengths."}],"status":"public","_id":"15488","user_id":"11829","department":[{"_id":"49"}],"keyword":["Dynamic Time Warping","Feature Extraction","Masking","Neural Networks"],"language":[{"iso":"eng"}],"publication_identifier":{"isbn":["978-3-9819376-0-2"]},"year":"2019","place":"Von-Münchhausen-Str. 49, 31515 Wunstorf","citation":{"ama":"Thiel C, Steidl C, Henning B. P2.9 Comparison of deep feature extraction techniques for varying-length time series from an industrial piercing press. In: AMA Service GmbH, ed. <i>20. GMA/ITG-Fachtagung. Sensoren Und Messsysteme 2019</i>. Von-Münchhausen-Str. 49, 31515 Wunstorf; 2019. doi:<a href=\"https://doi.org/10.5162/SENSOREN2019/P2.9\">10.5162/SENSOREN2019/P2.9</a>","ieee":"C. Thiel, C. Steidl, and B. Henning, “P2.9 Comparison of deep feature extraction techniques for varying-length time series from an industrial piercing press,” in <i>20. GMA/ITG-Fachtagung. Sensoren und Messsysteme 2019</i>, 2019.","chicago":"Thiel, Christian, Carolin Steidl, and Bernd Henning. “P2.9 Comparison of Deep Feature Extraction Techniques for Varying-Length Time Series from an Industrial Piercing Press.” In <i>20. GMA/ITG-Fachtagung. Sensoren Und Messsysteme 2019</i>, edited by AMA Service GmbH. Von-Münchhausen-Str. 49, 31515 Wunstorf, 2019. <a href=\"https://doi.org/10.5162/SENSOREN2019/P2.9\">https://doi.org/10.5162/SENSOREN2019/P2.9</a>.","short":"C. Thiel, C. Steidl, B. Henning, in: AMA Service GmbH (Ed.), 20. GMA/ITG-Fachtagung. Sensoren Und Messsysteme 2019, Von-Münchhausen-Str. 49, 31515 Wunstorf, 2019.","bibtex":"@inproceedings{Thiel_Steidl_Henning_2019, place={Von-Münchhausen-Str. 49, 31515 Wunstorf}, title={P2.9 Comparison of deep feature extraction techniques for varying-length time series from an industrial piercing press}, DOI={<a href=\"https://doi.org/10.5162/SENSOREN2019/P2.9\">10.5162/SENSOREN2019/P2.9</a>}, booktitle={20. GMA/ITG-Fachtagung. Sensoren und Messsysteme 2019}, author={Thiel, Christian and Steidl, Carolin and Henning, Bernd}, editor={AMA Service GmbHEditor}, year={2019} }","mla":"Thiel, Christian, et al. “P2.9 Comparison of Deep Feature Extraction Techniques for Varying-Length Time Series from an Industrial Piercing Press.” <i>20. GMA/ITG-Fachtagung. Sensoren Und Messsysteme 2019</i>, edited by AMA Service GmbH, 2019, doi:<a href=\"https://doi.org/10.5162/SENSOREN2019/P2.9\">10.5162/SENSOREN2019/P2.9</a>.","apa":"Thiel, C., Steidl, C., &#38; Henning, B. (2019). P2.9 Comparison of deep feature extraction techniques for varying-length time series from an industrial piercing press. In AMA Service GmbH (Ed.), <i>20. GMA/ITG-Fachtagung. Sensoren und Messsysteme 2019</i>. Von-Münchhausen-Str. 49, 31515 Wunstorf. <a href=\"https://doi.org/10.5162/SENSOREN2019/P2.9\">https://doi.org/10.5162/SENSOREN2019/P2.9</a>"},"corporate_editor":["AMA Service GmbH"],"date_updated":"2022-01-06T06:52:27Z","date_created":"2020-01-10T16:03:58Z","author":[{"last_name":"Thiel","full_name":"Thiel, Christian","first_name":"Christian"},{"full_name":"Steidl, Carolin","last_name":"Steidl","first_name":"Carolin"},{"full_name":"Henning, Bernd","id":"213","last_name":"Henning","first_name":"Bernd"}],"title":"P2.9 Comparison of deep feature extraction techniques for varying-length time series from an industrial piercing press","doi":"10.5162/SENSOREN2019/P2.9"}]