[{"oa":"1","project":[{"name":"5G Development and validation platform for global industry-specific network services and Apps","grant_number":"761493","_id":"28"},{"_id":"1","name":"SFB 901"},{"_id":"4","name":"SFB 901 - Project Area C"},{"name":"SFB 901 - Subproject C4","_id":"16"}],"citation":{"ieee":"S. B. Schneider, N. P. Satheeschandran, M. Peuster, and H. Karl, “Machine Learning for Dynamic Resource Allocation in Network Function Virtualization,” in <i>IEEE Conference on Network Softwarization (NetSoft)</i>, Ghent, Belgium, 2020.","apa":"Schneider, S. B., Satheeschandran, N. P., Peuster, M., &#38; Karl, H. (2020). Machine Learning for Dynamic Resource Allocation in Network Function Virtualization. In <i>IEEE Conference on Network Softwarization (NetSoft)</i>. Ghent, Belgium: IEEE.","chicago":"Schneider, Stefan Balthasar, Narayanan Puthenpurayil Satheeschandran, Manuel Peuster, and Holger Karl. “Machine Learning for Dynamic Resource Allocation in Network Function Virtualization.” In <i>IEEE Conference on Network Softwarization (NetSoft)</i>. IEEE, 2020.","short":"S.B. Schneider, N.P. Satheeschandran, M. Peuster, H. Karl, in: IEEE Conference on Network Softwarization (NetSoft), IEEE, 2020.","mla":"Schneider, Stefan Balthasar, et al. “Machine Learning for Dynamic Resource Allocation in Network Function Virtualization.” <i>IEEE Conference on Network Softwarization (NetSoft)</i>, IEEE, 2020.","bibtex":"@inproceedings{Schneider_Satheeschandran_Peuster_Karl_2020, title={Machine Learning for Dynamic Resource Allocation in Network Function Virtualization}, booktitle={IEEE Conference on Network Softwarization (NetSoft)}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Satheeschandran, Narayanan Puthenpurayil and Peuster, Manuel and Karl, Holger}, year={2020} }","ama":"Schneider SB, Satheeschandran NP, Peuster M, Karl H. Machine Learning for Dynamic Resource Allocation in Network Function Virtualization. In: <i>IEEE Conference on Network Softwarization (NetSoft)</i>. IEEE; 2020."},"file_date_updated":"2020-03-03T11:42:16Z","user_id":"35343","ddc":["000"],"_id":"16219","publisher":"IEEE","has_accepted_license":"1","conference":{"name":"IEEE Conference on Network Softwarization (NetSoft)","location":"Ghent, Belgium"},"status":"public","department":[{"_id":"75"}],"type":"conference","date_created":"2020-03-03T11:42:22Z","file":[{"file_id":"16220","content_type":"application/pdf","file_name":"ris_preprint.pdf","file_size":476590,"access_level":"open_access","relation":"main_file","date_updated":"2020-03-03T11:42:16Z","date_created":"2020-03-03T11:42:16Z","creator":"stschn"}],"abstract":[{"text":"Network function virtualization (NFV) proposes\r\nto replace physical middleboxes with more flexible virtual\r\nnetwork functions (VNFs). To dynamically adjust to everchanging\r\ntraffic demands, VNFs have to be instantiated and\r\ntheir allocated resources have to be adjusted on demand.\r\nDeciding the amount of allocated resources is non-trivial.\r\nExisting optimization approaches often assume fixed resource\r\nrequirements for each VNF instance. However, this can easily\r\nlead to either waste of resources or bad service quality if too\r\nmany or too few resources are allocated.\r\n\r\nTo solve this problem, we train machine learning models\r\non real VNF data, containing measurements of performance\r\nand resource requirements. For each VNF, the trained models\r\ncan then accurately predict the required resources to handle\r\na certain traffic load. We integrate these machine learning\r\nmodels into an algorithm for joint VNF scaling and placement\r\nand evaluate their impact on resulting VNF placements. Our\r\nevaluation based on real-world data shows that using suitable\r\nmachine learning models effectively avoids over- and underallocation\r\nof resources, leading to up to 12 times lower resource\r\nconsumption and better service quality with up to 4.5 times\r\nlower total delay than using standard fixed resource allocation.","lang":"eng"}],"publication":"IEEE Conference on Network Softwarization (NetSoft)","language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:52:46Z","author":[{"orcid":"0000-0001-8210-4011","first_name":"Stefan Balthasar","last_name":"Schneider","full_name":"Schneider, Stefan Balthasar","id":"35343"},{"full_name":"Satheeschandran, Narayanan Puthenpurayil","first_name":"Narayanan Puthenpurayil","last_name":"Satheeschandran"},{"last_name":"Peuster","first_name":"Manuel","full_name":"Peuster, Manuel","id":"13271"},{"id":"126","first_name":"Holger","last_name":"Karl","full_name":"Karl, Holger"}],"title":"Machine Learning for Dynamic Resource Allocation in Network Function Virtualization","year":"2020"},{"department":[{"_id":"75"}],"type":"conference","date_created":"2020-03-03T11:51:22Z","project":[{"grant_number":"761493","_id":"28","name":"5G Development and validation platform for global industry-specific network services and Apps"}],"citation":{"ieee":"A. Zafeiropoulos <i>et al.</i>, “Benchmarking and Profiling 5G Verticals’ Applications: An Industrial IoT Use Case,” in <i>IEEE Conference on Network Softwarization (NetSoft)</i>, 2020.","apa":"Zafeiropoulos, A., Fotopoulou, E., Peuster, M., Schneider, S. B., Gouvas, P., Behnke, D., … Karl, H. (2020). Benchmarking and Profiling 5G Verticals’ Applications: An Industrial IoT Use Case. In <i>IEEE Conference on Network Softwarization (NetSoft)</i>.","mla":"Zafeiropoulos, A., et al. “Benchmarking and Profiling 5G Verticals’ Applications: An Industrial IoT Use Case.” <i>IEEE Conference on Network Softwarization (NetSoft)</i>, 2020.","bibtex":"@inproceedings{Zafeiropoulos_Fotopoulou_Peuster_Schneider_Gouvas_Behnke_Müller_Bök_Trakadas_Karkazis_et al._2020, title={Benchmarking and Profiling 5G Verticals’ Applications: An Industrial IoT Use Case}, booktitle={IEEE Conference on Network Softwarization (NetSoft)}, author={Zafeiropoulos, A. and Fotopoulou, E. and Peuster, Manuel and Schneider, Stefan Balthasar and Gouvas, P. and Behnke, D. and Müller, M. and Bök, P. and Trakadas, P. and Karkazis, P. and et al.}, year={2020} }","chicago":"Zafeiropoulos, A., E. Fotopoulou, Manuel Peuster, Stefan Balthasar Schneider, P. Gouvas, D. Behnke, M. Müller, et al. “Benchmarking and Profiling 5G Verticals’ Applications: An Industrial IoT Use Case.” In <i>IEEE Conference on Network Softwarization (NetSoft)</i>, 2020.","short":"A. Zafeiropoulos, E. Fotopoulou, M. Peuster, S.B. Schneider, P. Gouvas, D. Behnke, M. Müller, P. Bök, P. Trakadas, P. Karkazis, H. Karl, in: IEEE Conference on Network Softwarization (NetSoft), 2020.","ama":"Zafeiropoulos A, Fotopoulou E, Peuster M, et al. Benchmarking and Profiling 5G Verticals’ Applications: An Industrial IoT Use Case. In: <i>IEEE Conference on Network Softwarization (NetSoft)</i>. ; 2020."},"publication":"IEEE Conference on Network Softwarization (NetSoft)","user_id":"35343","language":[{"iso":"eng"}],"_id":"16222","date_updated":"2022-01-06T06:52:46Z","author":[{"full_name":"Zafeiropoulos, A.","first_name":"A.","last_name":"Zafeiropoulos"},{"full_name":"Fotopoulou, E.","last_name":"Fotopoulou","first_name":"E."},{"id":"13271","full_name":"Peuster, Manuel","first_name":"Manuel","last_name":"Peuster"},{"last_name":"Schneider","first_name":"Stefan Balthasar","orcid":"0000-0001-8210-4011","full_name":"Schneider, Stefan Balthasar","id":"35343"},{"first_name":"P.","last_name":"Gouvas","full_name":"Gouvas, P."},{"full_name":"Behnke, D.","first_name":"D.","last_name":"Behnke"},{"first_name":"M.","last_name":"Müller","full_name":"Müller, M."},{"full_name":"Bök, P.","first_name":"P.","last_name":"Bök"},{"full_name":"Trakadas, P.","last_name":"Trakadas","first_name":"P."},{"full_name":"Karkazis, P.","first_name":"P.","last_name":"Karkazis"},{"full_name":"Karl, Holger","first_name":"Holger","last_name":"Karl","id":"126"}],"year":"2020","title":"Benchmarking and Profiling 5G Verticals' Applications: An Industrial IoT Use Case","status":"public"},{"page":"31-40","publisher":"ACM","_id":"16274","language":[{"iso":"eng"}],"user_id":"8447","status":"public","year":"2020","title":"Validating Test Case Migration via Mutation Analysis ","conference":{"location":"Seoul","name":"1st IEEE/ACM International Conference on Automation of Software Test (AST 2020)"},"author":[{"full_name":"Jovanovikj, Ivan","first_name":"Ivan","last_name":"Jovanovikj","orcid":"https://orcid.org/0000-0002-1838-794X","id":"39187"},{"first_name":"Achyuth","last_name":"Nagaraj","full_name":"Nagaraj, Achyuth"},{"first_name":"Enes","last_name":"Yigitbas","orcid":"0000-0002-5967-833X","full_name":"Yigitbas, Enes","id":"8447"},{"full_name":"Anjorin, Anthony","last_name":"Anjorin","first_name":"Anthony"},{"id":"447","last_name":"Sauer","first_name":"Stefan","full_name":"Sauer, Stefan"},{"id":"107","full_name":"Engels, Gregor","last_name":"Engels","first_name":"Gregor"}],"date_updated":"2022-01-06T06:52:47Z","date_created":"2020-03-09T13:11:25Z","type":"conference","department":[{"_id":"66"},{"_id":"534"}],"publication":"Proceedings of the 1st IEEE/ACM International Conference on Automation of Software Test AST","citation":{"mla":"Jovanovikj, Ivan, et al. “Validating Test Case Migration via Mutation Analysis .” <i>Proceedings of the 1st IEEE/ACM International Conference on Automation of Software Test AST</i>, ACM, 2020, pp. 31–40.","ama":"Jovanovikj I, Nagaraj A, Yigitbas E, Anjorin A, Sauer S, Engels G. Validating Test Case Migration via Mutation Analysis . In: <i>Proceedings of the 1st IEEE/ACM International Conference on Automation of Software Test AST</i>. ACM; 2020:31-40.","bibtex":"@inproceedings{Jovanovikj_Nagaraj_Yigitbas_Anjorin_Sauer_Engels_2020, title={Validating Test Case Migration via Mutation Analysis }, booktitle={Proceedings of the 1st IEEE/ACM International Conference on Automation of Software Test AST}, publisher={ACM}, author={Jovanovikj, Ivan and Nagaraj, Achyuth and Yigitbas, Enes and Anjorin, Anthony and Sauer, Stefan and Engels, Gregor}, year={2020}, pages={31–40} }","apa":"Jovanovikj, I., Nagaraj, A., Yigitbas, E., Anjorin, A., Sauer, S., &#38; Engels, G. (2020). Validating Test Case Migration via Mutation Analysis . In <i>Proceedings of the 1st IEEE/ACM International Conference on Automation of Software Test AST</i> (pp. 31–40). Seoul: ACM.","ieee":"I. Jovanovikj, A. Nagaraj, E. Yigitbas, A. Anjorin, S. Sauer, and G. Engels, “Validating Test Case Migration via Mutation Analysis ,” in <i>Proceedings of the 1st IEEE/ACM International Conference on Automation of Software Test AST</i>, Seoul, 2020, pp. 31–40.","chicago":"Jovanovikj, Ivan, Achyuth Nagaraj, Enes Yigitbas, Anthony Anjorin, Stefan Sauer, and Gregor Engels. “Validating Test Case Migration via Mutation Analysis .” In <i>Proceedings of the 1st IEEE/ACM International Conference on Automation of Software Test AST</i>, 31–40. ACM, 2020.","short":"I. Jovanovikj, A. Nagaraj, E. Yigitbas, A. Anjorin, S. Sauer, G. Engels, in: Proceedings of the 1st IEEE/ACM International Conference on Automation of Software Test AST, ACM, 2020, pp. 31–40."}},{"doi":"10.1016/j.vehcom.2020.100250","user_id":"126","article_number":"100250","language":[{"iso":"eng"}],"_id":"16278","date_updated":"2022-01-06T06:52:48Z","publication_status":"published","title":"A UAV-based moving 5G RAN for massive connectivity of mobile users and IoT devices","status":"public","year":"2020","publication_identifier":{"issn":["2214-2096"]},"author":[{"full_name":"Nomikos, Nikolaos","last_name":"Nomikos","first_name":"Nikolaos"},{"full_name":"Michailidis, Emmanouel T.","first_name":"Emmanouel T.","last_name":"Michailidis"},{"full_name":"Trakadas, Panagiotis","last_name":"Trakadas","first_name":"Panagiotis"},{"full_name":"Vouyioukas, Demosthenes","last_name":"Vouyioukas","first_name":"Demosthenes"},{"id":"126","full_name":"Karl, Holger","last_name":"Karl","first_name":"Holger"},{"last_name":"Martrat","first_name":"Josep","full_name":"Martrat, Josep"},{"full_name":"Zahariadis, Theodore","last_name":"Zahariadis","first_name":"Theodore"},{"last_name":"Papadopoulos","first_name":"Konstantinos","full_name":"Papadopoulos, Konstantinos"},{"full_name":"Voliotis, Stamatis","last_name":"Voliotis","first_name":"Stamatis"}],"type":"journal_article","department":[{"_id":"75"}],"date_created":"2020-03-10T15:59:56Z","abstract":[{"text":"Currently, the coexistence of multiple users and devices challenges the network's ability to reliably connect them. This article proposes a novel communication architecture that satisfies the requirements of fifth-generation (5G) mobile network applications. In particular, this architecture extends and combines ultra-dense networking (UDN), multi-access edge computing (MEC), and virtual infrastructure manager (VIM) concepts to provide a flexible network of moving radio access (RA) nodes, flying or moving to areas where users and devices struggle for connectivity and data rate. Furthermore, advances in radio communications and non-orthogonal multiple access (NOMA), virtualization technologies and energy-awareness mechanisms are integrated towards a mobile UDN that not only allows RA nodes to follow the user but also enables the virtualized network functions (VNFs) to adapt to user mobility by migrating from one node to another. Performance evaluation shows that the underlying network improves connectivity of users and devices through the flexible deployment of moving RA nodes and the use of NOMA.","lang":"eng"}],"publication":"Vehicular Communications","citation":{"bibtex":"@article{Nomikos_Michailidis_Trakadas_Vouyioukas_Karl_Martrat_Zahariadis_Papadopoulos_Voliotis_2020, title={A UAV-based moving 5G RAN for massive connectivity of mobile users and IoT devices}, DOI={<a href=\"https://doi.org/10.1016/j.vehcom.2020.100250\">10.1016/j.vehcom.2020.100250</a>}, number={100250}, journal={Vehicular Communications}, author={Nomikos, Nikolaos and Michailidis, Emmanouel T. and Trakadas, Panagiotis and Vouyioukas, Demosthenes and Karl, Holger and Martrat, Josep and Zahariadis, Theodore and Papadopoulos, Konstantinos and Voliotis, Stamatis}, year={2020} }","ama":"Nomikos N, Michailidis ET, Trakadas P, et al. A UAV-based moving 5G RAN for massive connectivity of mobile users and IoT devices. <i>Vehicular Communications</i>. 2020. doi:<a href=\"https://doi.org/10.1016/j.vehcom.2020.100250\">10.1016/j.vehcom.2020.100250</a>","mla":"Nomikos, Nikolaos, et al. “A UAV-Based Moving 5G RAN for Massive Connectivity of Mobile Users and IoT Devices.” <i>Vehicular Communications</i>, 100250, 2020, doi:<a href=\"https://doi.org/10.1016/j.vehcom.2020.100250\">10.1016/j.vehcom.2020.100250</a>.","short":"N. Nomikos, E.T. Michailidis, P. Trakadas, D. Vouyioukas, H. Karl, J. Martrat, T. Zahariadis, K. Papadopoulos, S. Voliotis, Vehicular Communications (2020).","chicago":"Nomikos, Nikolaos, Emmanouel T. Michailidis, Panagiotis Trakadas, Demosthenes Vouyioukas, Holger Karl, Josep Martrat, Theodore Zahariadis, Konstantinos Papadopoulos, and Stamatis Voliotis. “A UAV-Based Moving 5G RAN for Massive Connectivity of Mobile Users and IoT Devices.” <i>Vehicular Communications</i>, 2020. <a href=\"https://doi.org/10.1016/j.vehcom.2020.100250\">https://doi.org/10.1016/j.vehcom.2020.100250</a>.","ieee":"N. Nomikos <i>et al.</i>, “A UAV-based moving 5G RAN for massive connectivity of mobile users and IoT devices,” <i>Vehicular Communications</i>, 2020.","apa":"Nomikos, N., Michailidis, E. T., Trakadas, P., Vouyioukas, D., Karl, H., Martrat, J., … Voliotis, S. (2020). A UAV-based moving 5G RAN for massive connectivity of mobile users and IoT devices. <i>Vehicular Communications</i>. <a href=\"https://doi.org/10.1016/j.vehcom.2020.100250\">https://doi.org/10.1016/j.vehcom.2020.100250</a>"}},{"department":[{"_id":"75"}],"type":"journal_article","date_created":"2020-03-10T16:02:30Z","abstract":[{"lang":"eng","text":"Assigning bands of the wireless spectrum as resources to users is a common problem in wireless networks. Typically, frequency bands were assumed to be available in a stable manner. Nevertheless, in recent scenarios where wireless networks may be deployed in unknown environments, spectrum competition is considered, making it uncertain whether a frequency band is available at all or at what quality. To fully exploit such resources with uncertain availability, the multi-armed bandit (MAB) method, a representative online learning technique, has been applied to design spectrum scheduling algorithms. This article surveys such proposals. We describe the following three aspects: how to model spectrum scheduling problems within the MAB framework, what the main thread is following which prevalent algorithms are designed, and how to evaluate algorithm performance and complexity. We also give some promising directions for future research in related fields."}],"citation":{"short":"F. Li, D. Yu, H. Yang, J. Yu, H. Karl, X. Cheng, IEEE Wireless Communications (2020) 24–30.","ama":"Li F, Yu D, Yang H, Yu J, Karl H, Cheng X. Multi-Armed-Bandit-Based Spectrum Scheduling Algorithms in Wireless Networks: A Survey. <i>IEEE Wireless Communications</i>. 2020:24-30. doi:<a href=\"https://doi.org/10.1109/mwc.001.1900280\">10.1109/mwc.001.1900280</a>","chicago":"Li, Feng, Dongxiao Yu, Huan Yang, Jiguo Yu, Holger Karl, and Xiuzhen Cheng. “Multi-Armed-Bandit-Based Spectrum Scheduling Algorithms in Wireless Networks: A Survey.” <i>IEEE Wireless Communications</i>, 2020, 24–30. <a href=\"https://doi.org/10.1109/mwc.001.1900280\">https://doi.org/10.1109/mwc.001.1900280</a>.","bibtex":"@article{Li_Yu_Yang_Yu_Karl_Cheng_2020, title={Multi-Armed-Bandit-Based Spectrum Scheduling Algorithms in Wireless Networks: A Survey}, DOI={<a href=\"https://doi.org/10.1109/mwc.001.1900280\">10.1109/mwc.001.1900280</a>}, journal={IEEE Wireless Communications}, author={Li, Feng and Yu, Dongxiao and Yang, Huan and Yu, Jiguo and Karl, Holger and Cheng, Xiuzhen}, year={2020}, pages={24–30} }","apa":"Li, F., Yu, D., Yang, H., Yu, J., Karl, H., &#38; Cheng, X. (2020). Multi-Armed-Bandit-Based Spectrum Scheduling Algorithms in Wireless Networks: A Survey. <i>IEEE Wireless Communications</i>, 24–30. <a href=\"https://doi.org/10.1109/mwc.001.1900280\">https://doi.org/10.1109/mwc.001.1900280</a>","mla":"Li, Feng, et al. “Multi-Armed-Bandit-Based Spectrum Scheduling Algorithms in Wireless Networks: A Survey.” <i>IEEE Wireless Communications</i>, 2020, pp. 24–30, doi:<a href=\"https://doi.org/10.1109/mwc.001.1900280\">10.1109/mwc.001.1900280</a>.","ieee":"F. Li, D. Yu, H. Yang, J. Yu, H. Karl, and X. Cheng, “Multi-Armed-Bandit-Based Spectrum Scheduling Algorithms in Wireless Networks: A Survey,” <i>IEEE Wireless Communications</i>, pp. 24–30, 2020."},"publication":"IEEE Wireless Communications","user_id":"126","doi":"10.1109/mwc.001.1900280","_id":"16280","language":[{"iso":"eng"}],"page":"24-30","publication_status":"published","date_updated":"2022-01-06T06:52:48Z","publication_identifier":{"issn":["1536-1284","1558-0687"]},"author":[{"full_name":"Li, Feng","last_name":"Li","first_name":"Feng"},{"full_name":"Yu, Dongxiao","last_name":"Yu","first_name":"Dongxiao"},{"full_name":"Yang, Huan","last_name":"Yang","first_name":"Huan"},{"first_name":"Jiguo","last_name":"Yu","full_name":"Yu, Jiguo"},{"id":"126","full_name":"Karl, Holger","last_name":"Karl","first_name":"Holger"},{"full_name":"Cheng, Xiuzhen","first_name":"Xiuzhen","last_name":"Cheng"}],"year":"2020","title":"Multi-Armed-Bandit-Based Spectrum Scheduling Algorithms in Wireless Networks: A Survey","status":"public"},{"publication_identifier":{"issn":["0167-2789"]},"author":[{"first_name":"Stefan","last_name":"Klus","full_name":"Klus, Stefan"},{"full_name":"Nüske, Feliks","first_name":"Feliks","orcid":"0000-0003-2444-7889","last_name":"Nüske","id":"81513"},{"orcid":"https://orcid.org/0000-0002-3389-793X","first_name":"Sebastian","last_name":"Peitz","full_name":"Peitz, Sebastian","id":"47427"},{"full_name":"Niemann, Jan-Hendrik","first_name":"Jan-Hendrik","last_name":"Niemann"},{"full_name":"Clementi, Cecilia","last_name":"Clementi","first_name":"Cecilia"},{"full_name":"Schütte, Christof","last_name":"Schütte","first_name":"Christof"}],"year":"2020","status":"public","title":"Data-driven approximation of the Koopman generator: Model reduction, system identification, and control","intvolume":"       406","publication_status":"published","date_updated":"2022-01-06T06:52:48Z","_id":"16288","language":[{"iso":"eng"}],"article_number":"132416","volume":406,"user_id":"47427","doi":"10.1016/j.physd.2020.132416","citation":{"ieee":"S. Klus, F. Nüske, S. Peitz, J.-H. Niemann, C. Clementi, and C. Schütte, “Data-driven approximation of the Koopman generator: Model reduction, system identification, and control,” <i>Physica D: Nonlinear Phenomena</i>, vol. 406, 2020.","apa":"Klus, S., Nüske, F., Peitz, S., Niemann, J.-H., Clementi, C., &#38; Schütte, C. (2020). Data-driven approximation of the Koopman generator: Model reduction, system identification, and control. <i>Physica D: Nonlinear Phenomena</i>, <i>406</i>. <a href=\"https://doi.org/10.1016/j.physd.2020.132416\">https://doi.org/10.1016/j.physd.2020.132416</a>","short":"S. Klus, F. Nüske, S. Peitz, J.-H. Niemann, C. Clementi, C. Schütte, Physica D: Nonlinear Phenomena 406 (2020).","chicago":"Klus, Stefan, Feliks Nüske, Sebastian Peitz, Jan-Hendrik Niemann, Cecilia Clementi, and Christof Schütte. “Data-Driven Approximation of the Koopman Generator: Model Reduction, System Identification, and Control.” <i>Physica D: Nonlinear Phenomena</i> 406 (2020). <a href=\"https://doi.org/10.1016/j.physd.2020.132416\">https://doi.org/10.1016/j.physd.2020.132416</a>.","mla":"Klus, Stefan, et al. “Data-Driven Approximation of the Koopman Generator: Model Reduction, System Identification, and Control.” <i>Physica D: Nonlinear Phenomena</i>, vol. 406, 132416, 2020, doi:<a href=\"https://doi.org/10.1016/j.physd.2020.132416\">10.1016/j.physd.2020.132416</a>.","bibtex":"@article{Klus_Nüske_Peitz_Niemann_Clementi_Schütte_2020, title={Data-driven approximation of the Koopman generator: Model reduction, system identification, and control}, volume={406}, DOI={<a href=\"https://doi.org/10.1016/j.physd.2020.132416\">10.1016/j.physd.2020.132416</a>}, number={132416}, journal={Physica D: Nonlinear Phenomena}, author={Klus, Stefan and Nüske, Feliks and Peitz, Sebastian and Niemann, Jan-Hendrik and Clementi, Cecilia and Schütte, Christof}, year={2020} }","ama":"Klus S, Nüske F, Peitz S, Niemann J-H, Clementi C, Schütte C. Data-driven approximation of the Koopman generator: Model reduction, system identification, and control. <i>Physica D: Nonlinear Phenomena</i>. 2020;406. doi:<a href=\"https://doi.org/10.1016/j.physd.2020.132416\">10.1016/j.physd.2020.132416</a>"},"publication":"Physica D: Nonlinear Phenomena","abstract":[{"text":"We derive a data-driven method for the approximation of the Koopman generator called gEDMD, which can be regarded as a straightforward extension of EDMD (extended dynamic mode decomposition). This approach is applicable to deterministic and stochastic dynamical systems. It can be used for computing eigenvalues, eigenfunctions, and modes of the generator and for system identification. In addition to learning the governing equations of deterministic systems, which then reduces to SINDy (sparse identification of nonlinear dynamics), it is possible to identify the drift and diffusion terms of stochastic differential equations from data. Moreover, we apply gEDMD to derive coarse-grained models of high-dimensional systems, and also to determine efficient model predictive control strategies. We highlight relationships with other methods and demonstrate the efficacy of the proposed methods using several guiding examples and prototypical molecular dynamics problems.","lang":"eng"}],"date_created":"2020-03-13T12:35:40Z","department":[{"_id":"101"}],"type":"journal_article"},{"status":"public","page":"257-282","_id":"16289","publisher":"Springer","user_id":"47427","volume":484,"citation":{"short":"S. Peitz, S. Klus, in: Lecture Notes in Control and Information Sciences, Springer, Cham, 2020, pp. 257–282.","chicago":"Peitz, Sebastian, and Stefan Klus. “Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator.” In <i>Lecture Notes in Control and Information Sciences</i>, 484:257–82. Lecture Notes in Control and Information Sciences. Cham: Springer, 2020. <a href=\"https://doi.org/10.1007/978-3-030-35713-9_10\">https://doi.org/10.1007/978-3-030-35713-9_10</a>.","apa":"Peitz, S., &#38; Klus, S. (2020). Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator. In <i>Lecture Notes in Control and Information Sciences</i> (Vol. 484, pp. 257–282). Cham: Springer. <a href=\"https://doi.org/10.1007/978-3-030-35713-9_10\">https://doi.org/10.1007/978-3-030-35713-9_10</a>","ieee":"S. Peitz and S. Klus, “Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator,” in <i>Lecture Notes in Control and Information Sciences</i>, vol. 484, Cham: Springer, 2020, pp. 257–282.","ama":"Peitz S, Klus S. Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator. In: <i>Lecture Notes in Control and Information Sciences</i>. Vol 484. Lecture Notes in Control and Information Sciences. Cham: Springer; 2020:257-282. doi:<a href=\"https://doi.org/10.1007/978-3-030-35713-9_10\">10.1007/978-3-030-35713-9_10</a>","bibtex":"@inbook{Peitz_Klus_2020, place={Cham}, series={Lecture Notes in Control and Information Sciences}, title={Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator}, volume={484}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-35713-9_10\">10.1007/978-3-030-35713-9_10</a>}, booktitle={Lecture Notes in Control and Information Sciences}, publisher={Springer}, author={Peitz, Sebastian and Klus, Stefan}, year={2020}, pages={257–282}, collection={Lecture Notes in Control and Information Sciences} }","mla":"Peitz, Sebastian, and Stefan Klus. “Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator.” <i>Lecture Notes in Control and Information Sciences</i>, vol. 484, Springer, 2020, pp. 257–82, doi:<a href=\"https://doi.org/10.1007/978-3-030-35713-9_10\">10.1007/978-3-030-35713-9_10</a>."},"place":"Cham","year":"2020","title":"Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator","publication_identifier":{"issn":["0170-8643","1610-7411"],"isbn":["9783030357122","9783030357139"]},"author":[{"id":"47427","first_name":"Sebastian","last_name":"Peitz","orcid":"https://orcid.org/0000-0002-3389-793X","full_name":"Peitz, Sebastian"},{"last_name":"Klus","first_name":"Stefan","full_name":"Klus, Stefan"}],"publication_status":"published","date_updated":"2022-01-06T06:52:48Z","intvolume":"       484","language":[{"iso":"eng"}],"series_title":"Lecture Notes in Control and Information Sciences","doi":"10.1007/978-3-030-35713-9_10","publication":"Lecture Notes in Control and Information Sciences","abstract":[{"lang":"eng","text":"In the development of model predictive controllers for PDE-constrained problems, the use of reduced order models is essential to enable real-time applicability. Besides local linearization approaches, proper orthogonal decomposition (POD) has been most widely used in the past in order to derive such models. Due to the huge advances concerning both theory as well as the numerical approximation, a very promising alternative based on the Koopman operator has recently emerged. In this chapter, we present two control strategies for model predictive control of nonlinear PDEs using data-efficient approximations of the Koopman operator. In the first one, the dynamic control system is replaced by a small number of autonomous systems with different yet constant inputs. The control problem is consequently transformed into a switching problem. In the second approach, a bilinear surrogate model is obtained via a convex combination of these autonomous systems. Using a recent convergence result for extended dynamic mode decomposition (EDMD), convergence of the reduced objective function can be shown. We study the properties of these two strategies with respect to solution quality, data requirements, and complexity of the resulting optimization problem using the 1-dimensional Burgers equation and the 2-dimensional Navier–Stokes equations as examples. Finally, an extension for online adaptivity is presented."}],"date_created":"2020-03-13T12:38:52Z","type":"book_chapter","department":[{"_id":"101"}]},{"status":"public","volume":34,"user_id":"47427","_id":"16290","page":"577–591","project":[{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"citation":{"chicago":"Bieker, Katharina, Sebastian Peitz, Steven L. Brunton, J. Nathan Kutz, and Michael Dellnitz. “Deep Model Predictive Flow Control with Limited Sensor Data and Online Learning.” <i>Theoretical and Computational Fluid Dynamics</i> 34 (2020): 577–591. <a href=\"https://doi.org/10.1007/s00162-020-00520-4\">https://doi.org/10.1007/s00162-020-00520-4</a>.","short":"K. Bieker, S. Peitz, S.L. Brunton, J.N. Kutz, M. Dellnitz, Theoretical and Computational Fluid Dynamics 34 (2020) 577–591.","ieee":"K. Bieker, S. Peitz, S. L. Brunton, J. N. Kutz, and M. Dellnitz, “Deep model predictive flow control with limited sensor data and online learning,” <i>Theoretical and Computational Fluid Dynamics</i>, vol. 34, pp. 577–591, 2020.","apa":"Bieker, K., Peitz, S., Brunton, S. L., Kutz, J. N., &#38; Dellnitz, M. (2020). Deep model predictive flow control with limited sensor data and online learning. <i>Theoretical and Computational Fluid Dynamics</i>, <i>34</i>, 577–591. <a href=\"https://doi.org/10.1007/s00162-020-00520-4\">https://doi.org/10.1007/s00162-020-00520-4</a>","bibtex":"@article{Bieker_Peitz_Brunton_Kutz_Dellnitz_2020, title={Deep model predictive flow control with limited sensor data and online learning}, volume={34}, DOI={<a href=\"https://doi.org/10.1007/s00162-020-00520-4\">10.1007/s00162-020-00520-4</a>}, journal={Theoretical and Computational Fluid Dynamics}, author={Bieker, Katharina and Peitz, Sebastian and Brunton, Steven L. and Kutz, J. Nathan and Dellnitz, Michael}, year={2020}, pages={577–591} }","ama":"Bieker K, Peitz S, Brunton SL, Kutz JN, Dellnitz M. Deep model predictive flow control with limited sensor data and online learning. <i>Theoretical and Computational Fluid Dynamics</i>. 2020;34:577–591. doi:<a href=\"https://doi.org/10.1007/s00162-020-00520-4\">10.1007/s00162-020-00520-4</a>","mla":"Bieker, Katharina, et al. “Deep Model Predictive Flow Control with Limited Sensor Data and Online Learning.” <i>Theoretical and Computational Fluid Dynamics</i>, vol. 34, 2020, pp. 577–591, doi:<a href=\"https://doi.org/10.1007/s00162-020-00520-4\">10.1007/s00162-020-00520-4</a>."},"oa":"1","article_type":"original","intvolume":"        34","publication_status":"published","date_updated":"2022-01-06T06:52:48Z","author":[{"id":"32829","last_name":"Bieker","first_name":"Katharina","full_name":"Bieker, Katharina"},{"first_name":"Sebastian","last_name":"Peitz","orcid":"https://orcid.org/0000-0002-3389-793X","full_name":"Peitz, Sebastian","id":"47427"},{"full_name":"Brunton, Steven L.","first_name":"Steven L.","last_name":"Brunton"},{"first_name":"J. Nathan","last_name":"Kutz","full_name":"Kutz, J. Nathan"},{"full_name":"Dellnitz, Michael","last_name":"Dellnitz","first_name":"Michael"}],"publication_identifier":{"issn":["0935-4964","1432-2250"]},"year":"2020","title":"Deep model predictive flow control with limited sensor data and online learning","doi":"10.1007/s00162-020-00520-4","language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"https://link.springer.com/content/pdf/10.1007/s00162-020-00520-4.pdf"}],"abstract":[{"lang":"eng","text":"The control of complex systems is of critical importance in many branches of science, engineering, and industry, many of which are governed by nonlinear partial differential equations. Controlling an unsteady fluid flow is particularly important, as flow control is a key enabler for technologies in energy (e.g., wind, tidal, and combustion), transportation (e.g., planes, trains, and automobiles), security (e.g., tracking airborne contamination), and health (e.g., artificial hearts and artificial respiration). However, the high-dimensional, nonlinear, and multi-scale dynamics make real-time feedback control infeasible. Fortunately, these high- dimensional systems exhibit dominant, low-dimensional patterns of activity that can be exploited for effective control in the sense that knowledge of the entire state of a system is not required. Advances in machine learning have the potential to revolutionize flow control given its ability to extract principled, low-rank feature spaces characterizing such complex systems.We present a novel deep learning modelpredictive control framework that exploits low-rank features of the flow in order to achieve considerable improvements to control performance. Instead of predicting the entire fluid state, we use a recurrent neural network (RNN) to accurately predict the control relevant quantities of the system, which are then embedded into an MPC framework to construct a feedback loop. In order to lower the data requirements and to improve the prediction accuracy and thus the control performance, incoming sensor data are used to update the RNN online. The results are validated using varying fluid flow examples of increasing complexity."}],"publication":"Theoretical and Computational Fluid Dynamics","department":[{"_id":"101"}],"type":"journal_article","date_created":"2020-03-13T12:40:09Z"},{"date_created":"2020-03-13T12:55:53Z","department":[{"_id":"63"}],"type":"journal_article","citation":{"ieee":"J. Castenow, M. Fischer, J. Harbig, D. Jung, and F. Meyer auf der Heide, “Gathering Anonymous, Oblivious Robots on a Grid,” <i>Theoretical Computer Science</i>, vol. 815, pp. 289–309, 2020.","apa":"Castenow, J., Fischer, M., Harbig, J., Jung, D., &#38; Meyer auf der Heide, F. (2020). Gathering Anonymous, Oblivious Robots on a Grid. <i>Theoretical Computer Science</i>, <i>815</i>, 289–309. <a href=\"https://doi.org/10.1016/j.tcs.2020.02.018\">https://doi.org/10.1016/j.tcs.2020.02.018</a>","short":"J. Castenow, M. Fischer, J. Harbig, D. Jung, F. Meyer auf der Heide, Theoretical Computer Science 815 (2020) 289–309.","chicago":"Castenow, Jannik, Matthias Fischer, Jonas Harbig, Daniel Jung, and Friedhelm Meyer auf der Heide. “Gathering Anonymous, Oblivious Robots on a Grid.” <i>Theoretical Computer Science</i> 815 (2020): 289–309. <a href=\"https://doi.org/10.1016/j.tcs.2020.02.018\">https://doi.org/10.1016/j.tcs.2020.02.018</a>.","mla":"Castenow, Jannik, et al. “Gathering Anonymous, Oblivious Robots on a Grid.” <i>Theoretical Computer Science</i>, vol. 815, 2020, pp. 289–309, doi:<a href=\"https://doi.org/10.1016/j.tcs.2020.02.018\">10.1016/j.tcs.2020.02.018</a>.","bibtex":"@article{Castenow_Fischer_Harbig_Jung_Meyer auf der Heide_2020, title={Gathering Anonymous, Oblivious Robots on a Grid}, volume={815}, DOI={<a href=\"https://doi.org/10.1016/j.tcs.2020.02.018\">10.1016/j.tcs.2020.02.018</a>}, journal={Theoretical Computer Science}, author={Castenow, Jannik and Fischer, Matthias and Harbig, Jonas and Jung, Daniel and Meyer auf der Heide, Friedhelm}, year={2020}, pages={289–309} }","ama":"Castenow J, Fischer M, Harbig J, Jung D, Meyer auf der Heide F. Gathering Anonymous, Oblivious Robots on a Grid. <i>Theoretical Computer Science</i>. 2020;815:289-309. doi:<a href=\"https://doi.org/10.1016/j.tcs.2020.02.018\">10.1016/j.tcs.2020.02.018</a>"},"publication":"Theoretical Computer Science","language":[{"iso":"eng"}],"_id":"16299","page":"289-309","volume":815,"user_id":"38705","doi":"10.1016/j.tcs.2020.02.018","author":[{"id":"38705","full_name":"Castenow, Jannik","last_name":"Castenow","first_name":"Jannik"},{"id":"146","first_name":"Matthias","last_name":"Fischer","full_name":"Fischer, Matthias"},{"first_name":"Jonas","last_name":"Harbig","full_name":"Harbig, Jonas"},{"last_name":"Jung","first_name":"Daniel","full_name":"Jung, Daniel","id":"37827"},{"full_name":"Meyer auf der Heide, Friedhelm","last_name":"Meyer auf der Heide","first_name":"Friedhelm","id":"15523"}],"publication_identifier":{"issn":["0304-3975"]},"title":"Gathering Anonymous, Oblivious Robots on a Grid","status":"public","year":"2020","intvolume":"       815","publication_status":"published","date_updated":"2022-01-06T06:52:48Z"},{"author":[{"id":"47427","full_name":"Peitz, Sebastian","last_name":"Peitz","first_name":"Sebastian","orcid":"0000-0002-3389-793X"},{"first_name":"Samuel E.","last_name":"Otto","full_name":"Otto, Samuel E."},{"first_name":"Clarence W.","last_name":"Rowley","full_name":"Rowley, Clarence W."}],"year":"2020","title":"Data-Driven Model Predictive Control using Interpolated Koopman  Generators","status":"public","intvolume":"        19","date_updated":"2022-01-06T06:52:48Z","_id":"16309","language":[{"iso":"eng"}],"main_file_link":[{"url":"https://epubs.siam.org/doi/pdf/10.1137/20M1325678"}],"page":"2162-2193","volume":19,"doi":"10.1137/20M1325678","user_id":"47427","citation":{"mla":"Peitz, Sebastian, et al. “Data-Driven Model Predictive Control Using Interpolated Koopman  Generators.” <i>SIAM Journal on Applied Dynamical Systems</i>, vol. 19, no. 3, 2020, pp. 2162–93, doi:<a href=\"https://doi.org/10.1137/20M1325678\">10.1137/20M1325678</a>.","bibtex":"@article{Peitz_Otto_Rowley_2020, title={Data-Driven Model Predictive Control using Interpolated Koopman  Generators}, volume={19}, DOI={<a href=\"https://doi.org/10.1137/20M1325678\">10.1137/20M1325678</a>}, number={3}, journal={SIAM Journal on Applied Dynamical Systems}, author={Peitz, Sebastian and Otto, Samuel E. and Rowley, Clarence W.}, year={2020}, pages={2162–2193} }","ama":"Peitz S, Otto SE, Rowley CW. Data-Driven Model Predictive Control using Interpolated Koopman  Generators. <i>SIAM Journal on Applied Dynamical Systems</i>. 2020;19(3):2162-2193. doi:<a href=\"https://doi.org/10.1137/20M1325678\">10.1137/20M1325678</a>","ieee":"S. Peitz, S. E. Otto, and C. W. Rowley, “Data-Driven Model Predictive Control using Interpolated Koopman  Generators,” <i>SIAM Journal on Applied Dynamical Systems</i>, vol. 19, no. 3, pp. 2162–2193, 2020.","apa":"Peitz, S., Otto, S. E., &#38; Rowley, C. W. (2020). Data-Driven Model Predictive Control using Interpolated Koopman  Generators. <i>SIAM Journal on Applied Dynamical Systems</i>, <i>19</i>(3), 2162–2193. <a href=\"https://doi.org/10.1137/20M1325678\">https://doi.org/10.1137/20M1325678</a>","chicago":"Peitz, Sebastian, Samuel E. Otto, and Clarence W. Rowley. “Data-Driven Model Predictive Control Using Interpolated Koopman  Generators.” <i>SIAM Journal on Applied Dynamical Systems</i> 19, no. 3 (2020): 2162–93. <a href=\"https://doi.org/10.1137/20M1325678\">https://doi.org/10.1137/20M1325678</a>.","short":"S. Peitz, S.E. Otto, C.W. Rowley, SIAM Journal on Applied Dynamical Systems 19 (2020) 2162–2193."},"publication":"SIAM Journal on Applied Dynamical Systems","issue":"3","abstract":[{"lang":"eng","text":"In recent years, the success of the Koopman operator in dynamical systems\r\nanalysis has also fueled the development of Koopman operator-based control\r\nframeworks. In order to preserve the relatively low data requirements for an\r\napproximation via Dynamic Mode Decomposition, a quantization approach was\r\nrecently proposed in [Peitz & Klus, Automatica 106, 2019]. This way, control\r\nof nonlinear dynamical systems can be realized by means of switched systems\r\ntechniques, using only a finite set of autonomous Koopman operator-based\r\nreduced models. These individual systems can be approximated very efficiently\r\nfrom data. The main idea is to transform a control system into a set of\r\nautonomous systems for which the optimal switching sequence has to be computed.\r\nIn this article, we extend these results to continuous control inputs using\r\nrelaxation. This way, we combine the advantages of the data efficiency of\r\napproximating a finite set of autonomous systems with continuous controls. We\r\nshow that when using the Koopman generator, this relaxation --- realized by\r\nlinear interpolation between two operators --- does not introduce any error for\r\ncontrol affine systems. This allows us to control high-dimensional nonlinear\r\nsystems using bilinear, low-dimensional surrogate models. The efficiency of the\r\nproposed approach is demonstrated using several examples with increasing\r\ncomplexity, from the Duffing oscillator to the chaotic fluidic pinball."}],"date_created":"2020-03-17T09:53:01Z","department":[{"_id":"101"}],"type":"journal_article"},{"publication":"GECCO '20: Proceedings of the Genetic and Evolutionary Computation Conference Companion","department":[{"_id":"78"}],"type":"conference","date_created":"2020-04-02T10:07:10Z","publication_status":"published","date_updated":"2022-01-06T06:52:49Z","publication_identifier":{"isbn":["978-1-4503-7127-8"]},"author":[{"id":"49992","first_name":"Tim","last_name":"Hansmeier","orcid":"0000-0003-1377-3339","full_name":"Hansmeier, Tim"},{"first_name":"Paul","last_name":"Kaufmann","full_name":"Kaufmann, Paul"},{"id":"398","full_name":"Platzner, Marco","last_name":"Platzner","first_name":"Marco"}],"year":"2020","title":"Enabling XCSF to Cope with Dynamic Environments via an Adaptive Error Threshold","doi":"10.1145/3377929.3389968","language":[{"iso":"eng"}],"project":[{"_id":"4","name":"SFB 901 - Project Area C"},{"_id":"1","name":"SFB 901"},{"_id":"14","name":"SFB 901 - Subproject C2"}],"citation":{"bibtex":"@inproceedings{Hansmeier_Kaufmann_Platzner_2020, place={New York, NY, United States}, title={Enabling XCSF to Cope with Dynamic Environments via an Adaptive Error Threshold}, DOI={<a href=\"https://doi.org/10.1145/3377929.3389968\">10.1145/3377929.3389968</a>}, booktitle={GECCO ’20: Proceedings of the Genetic and Evolutionary Computation Conference Companion}, publisher={Association for Computing Machinery (ACM)}, author={Hansmeier, Tim and Kaufmann, Paul and Platzner, Marco}, year={2020}, pages={125–126} }","ama":"Hansmeier T, Kaufmann P, Platzner M. Enabling XCSF to Cope with Dynamic Environments via an Adaptive Error Threshold. In: <i>GECCO ’20: Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>. New York, NY, United States: Association for Computing Machinery (ACM); 2020:125-126. doi:<a href=\"https://doi.org/10.1145/3377929.3389968\">10.1145/3377929.3389968</a>","mla":"Hansmeier, Tim, et al. “Enabling XCSF to Cope with Dynamic Environments via an Adaptive Error Threshold.” <i>GECCO ’20: Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, Association for Computing Machinery (ACM), 2020, pp. 125–26, doi:<a href=\"https://doi.org/10.1145/3377929.3389968\">10.1145/3377929.3389968</a>.","short":"T. Hansmeier, P. Kaufmann, M. Platzner, in: GECCO ’20: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery (ACM), New York, NY, United States, 2020, pp. 125–126.","chicago":"Hansmeier, Tim, Paul Kaufmann, and Marco Platzner. “Enabling XCSF to Cope with Dynamic Environments via an Adaptive Error Threshold.” In <i>GECCO ’20: Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 125–26. New York, NY, United States: Association for Computing Machinery (ACM), 2020. <a href=\"https://doi.org/10.1145/3377929.3389968\">https://doi.org/10.1145/3377929.3389968</a>.","ieee":"T. Hansmeier, P. Kaufmann, and M. Platzner, “Enabling XCSF to Cope with Dynamic Environments via an Adaptive Error Threshold,” in <i>GECCO ’20: Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, Cancún, Mexico, 2020, pp. 125–126.","apa":"Hansmeier, T., Kaufmann, P., &#38; Platzner, M. (2020). Enabling XCSF to Cope with Dynamic Environments via an Adaptive Error Threshold. In <i>GECCO ’20: Proceedings of the Genetic and Evolutionary Computation Conference Companion</i> (pp. 125–126). New York, NY, United States: Association for Computing Machinery (ACM). <a href=\"https://doi.org/10.1145/3377929.3389968\">https://doi.org/10.1145/3377929.3389968</a>"},"place":"New York, NY, United States","conference":{"end_date":"2020-07-12","start_date":"2020-07-08","name":"The Genetic and Evolutionary Computation Conference (GECCO 2020)","location":"Cancún, Mexico"},"status":"public","user_id":"477","publisher":"Association for Computing Machinery (ACM)","_id":"16363","page":"125-126"},{"_id":"16400","language":[{"iso":"eng"}],"publisher":"IEEE","user_id":"35343","year":"2020","title":"Cloud-Native Threat Detection and Containment for Smart Manufacturing","status":"public","author":[{"full_name":"Müller, Marcel","first_name":"Marcel","last_name":"Müller"},{"first_name":"Daniel","last_name":"Behnke","full_name":"Behnke, Daniel"},{"last_name":"Bök","first_name":"Patrick-Benjamin","full_name":"Bök, Patrick-Benjamin"},{"id":"35343","full_name":"Schneider, Stefan Balthasar","last_name":"Schneider","first_name":"Stefan Balthasar","orcid":"0000-0001-8210-4011"},{"id":"13271","full_name":"Peuster, Manuel","last_name":"Peuster","first_name":"Manuel"},{"full_name":"Karl, Holger","last_name":"Karl","first_name":"Holger","id":"126"}],"conference":{"name":"IEEE Conference on Network Softwarization (NetSoft) Demo Track","location":"Ghent, Belgium"},"date_updated":"2022-01-06T06:52:50Z","date_created":"2020-04-03T11:53:00Z","place":"Ghent, Belgium","type":"conference","department":[{"_id":"75"}],"publication":"IEEE Conference on Network Softwarization (NetSoft) Demo Track","citation":{"chicago":"Müller, Marcel, Daniel Behnke, Patrick-Benjamin Bök, Stefan Balthasar Schneider, Manuel Peuster, and Holger Karl. “Cloud-Native Threat Detection and Containment for Smart Manufacturing.” In <i>IEEE Conference on Network Softwarization (NetSoft) Demo Track</i>. Ghent, Belgium: IEEE, 2020.","short":"M. Müller, D. Behnke, P.-B. Bök, S.B. Schneider, M. Peuster, H. Karl, in: IEEE Conference on Network Softwarization (NetSoft) Demo Track, IEEE, Ghent, Belgium, 2020.","ieee":"M. Müller, D. Behnke, P.-B. Bök, S. B. Schneider, M. Peuster, and H. Karl, “Cloud-Native Threat Detection and Containment for Smart Manufacturing,” in <i>IEEE Conference on Network Softwarization (NetSoft) Demo Track</i>, Ghent, Belgium, 2020.","apa":"Müller, M., Behnke, D., Bök, P.-B., Schneider, S. B., Peuster, M., &#38; Karl, H. (2020). Cloud-Native Threat Detection and Containment for Smart Manufacturing. In <i>IEEE Conference on Network Softwarization (NetSoft) Demo Track</i>. Ghent, Belgium: IEEE.","bibtex":"@inproceedings{Müller_Behnke_Bök_Schneider_Peuster_Karl_2020, place={Ghent, Belgium}, title={Cloud-Native Threat Detection and Containment for Smart Manufacturing}, booktitle={IEEE Conference on Network Softwarization (NetSoft) Demo Track}, publisher={IEEE}, author={Müller, Marcel and Behnke, Daniel and Bök, Patrick-Benjamin and Schneider, Stefan Balthasar and Peuster, Manuel and Karl, Holger}, year={2020} }","ama":"Müller M, Behnke D, Bök P-B, Schneider SB, Peuster M, Karl H. Cloud-Native Threat Detection and Containment for Smart Manufacturing. In: <i>IEEE Conference on Network Softwarization (NetSoft) Demo Track</i>. Ghent, Belgium: IEEE; 2020.","mla":"Müller, Marcel, et al. “Cloud-Native Threat Detection and Containment for Smart Manufacturing.” <i>IEEE Conference on Network Softwarization (NetSoft) Demo Track</i>, IEEE, 2020."},"abstract":[{"lang":"eng","text":"Softwarization facilitates the introduction of smart\r\nmanufacturing applications in the industry. Manifold devices\r\nsuch as machine computers, Industrial IoT devices, tablets,\r\nsmartphones and smart glasses are integrated into factory networks\r\nto enable shop floor digitalization and big data analysis. To\r\nhandle the increasing number of devices and the resulting traffic,\r\na flexible and scalable factory network is necessary which can be\r\nrealized using softwarization technologies like Network Function\r\nVirtualization (NFV). However, the security risks increase with\r\nthe increasing number of new devices, so that cyber security must\r\nalso be considered in NFV-based networks.\r\n\r\nTherefore, extending our previous work, we showcase threat\r\ndetection using a cloud-native NFV-driven intrusion detection\r\nsystem (IDS) that is integrated in our industrial-specific network\r\nservices. As a result of the threat detection, the affected network\r\nservice is put into quarantine via automatic network reconfiguration.\r\nWe use the 5GTANGO service platform to deploy our\r\ndeveloped network services on Kubernetes and to initiate the\r\nnetwork reconfiguration."}],"project":[{"grant_number":"761493","_id":"28","name":"5G Development and validation platform for global industry-specific network services and Apps"},{"_id":"1","name":"SFB 901"},{"_id":"4","name":"SFB 901 - Project Area C"},{"_id":"16","name":"SFB 901 - Subproject C4"}]},{"publication":"Proceedings of the 46th International Conference on Current Trends in Theory and Practice of Computer Science (SOFSEM)","citation":{"short":"S. Pukrop, A. Mäcker, F. Meyer auf der Heide, in: Proceedings of the 46th International Conference on Current Trends in Theory and Practice of Computer Science (SOFSEM), 2020.","ama":"Pukrop S, Mäcker A, Meyer auf der Heide F. Approximating Weighted Completion Time for Order Scheduling with Setup Times. In: <i>Proceedings of the 46th International Conference on Current Trends in Theory and Practice of Computer Science (SOFSEM)</i>. ; 2020.","chicago":"Pukrop, Simon, Alexander Mäcker, and Friedhelm Meyer auf der Heide. “Approximating Weighted Completion Time for Order Scheduling with Setup Times.” In <i>Proceedings of the 46th International Conference on Current Trends in Theory and Practice of Computer Science (SOFSEM)</i>, 2020.","bibtex":"@inproceedings{Pukrop_Mäcker_Meyer auf der Heide_2020, title={Approximating Weighted Completion Time for Order Scheduling with Setup Times}, booktitle={Proceedings of the 46th International Conference on Current Trends in Theory and Practice of Computer Science (SOFSEM)}, author={Pukrop, Simon and Mäcker, Alexander and Meyer auf der Heide, Friedhelm}, year={2020} }","mla":"Pukrop, Simon, et al. “Approximating Weighted Completion Time for Order Scheduling with Setup Times.” <i>Proceedings of the 46th International Conference on Current Trends in Theory and Practice of Computer Science (SOFSEM)</i>, 2020.","apa":"Pukrop, S., Mäcker, A., &#38; Meyer auf der Heide, F. (2020). Approximating Weighted Completion Time for Order Scheduling with Setup Times. In <i>Proceedings of the 46th International Conference on Current Trends in Theory and Practice of Computer Science (SOFSEM)</i>.","ieee":"S. Pukrop, A. Mäcker, and F. Meyer auf der Heide, “Approximating Weighted Completion Time for Order Scheduling with Setup Times,” in <i>Proceedings of the 46th International Conference on Current Trends in Theory and Practice of Computer Science (SOFSEM)</i>, 2020."},"project":[{"name":"SFB 901 - Project Area C","_id":"4"},{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Subproject C4","_id":"16"}],"date_created":"2019-10-15T12:19:49Z","type":"conference","department":[{"_id":"63"}],"status":"public","title":"Approximating Weighted Completion Time for Order Scheduling with Setup Times","year":"2020","author":[{"id":"44428","full_name":"Pukrop, Simon","first_name":"Simon","last_name":"Pukrop"},{"id":"13536","last_name":"Mäcker","first_name":"Alexander","full_name":"Mäcker, Alexander"},{"full_name":"Meyer auf der Heide, Friedhelm","first_name":"Friedhelm","last_name":"Meyer auf der Heide","id":"15523"}],"date_updated":"2022-01-06T06:51:45Z","_id":"13868","language":[{"iso":"eng"}],"user_id":"44428"},{"department":[{"_id":"360"},{"_id":"98"}],"type":"book_chapter","date_created":"2019-08-30T12:00:51Z","abstract":[{"lang":"ger","text":"Diagrammatisches Schlie{\\ss}en wird im Zusammenhang mit dem Lernen von Mathmematik und ihrer Symbolsprache als wesentliche Theorie der Wissenskonstruktion diskutiert. Dabei wird h{\\\"{a}}ufig davon ausgegangen, dass die Wissenskonstruktion im Sinne diagrammatischen Schlie{\\ss}ens erfolgt. Deskriptive Rekonstruktionen diagrammatischen Schlie{\\ss}ens bei Lernenden stellen jedoch ein Desiderat der mathematikdidaktischen Forschung dar. Der vorliegende Beitrag befasst sich mit der Fragestellung, wie sich diagrammatisches Schlie{\\ss}en bei Lernenden rekonstruieren l{\\\"{a}}sst. Als m{\\\"{o}}gliche Werkzeuge f{\\\"{u}}r eine solche Rekonstruktion werden Toulmins Argumentationsschema und Vergnauds Schema-Begriff exemplarisch angewandt, um das diagrammatische Schlie{\\ss}en eines Sch{\\\"{u}}lerpaars beim Einstieg in die Subtraktion negativer Zahlen zu rekonstruieren. Abschlie{\\ss}end wird die tats{\\\"{a}}chliche Eignung der beiden Ans{\\\"{a}}tze zur Rekonstruktion diagrammatischen Schlie{\\ss}ens diskutiert."}],"quality_controlled":"1","citation":{"apa":"Schumacher, J., &#38; Rezat, S. (2020). Rekonstruktion diagrammatischen Schließens beim Erlernen der Subtraktion negativer Zahlen. Vergleich zweier methodischer Zugänge. In G. Kadunz (Ed.), <i>Zeichen und Sprache im Mathematikunterricht</i>. Springer. <a href=\"https://doi.org/10.1007/978-3-662-61194-4_5\">https://doi.org/10.1007/978-3-662-61194-4_5</a>","ieee":"J. Schumacher and S. Rezat, “Rekonstruktion diagrammatischen Schließens beim Erlernen der Subtraktion negativer Zahlen. Vergleich zweier methodischer Zugänge,” in <i>Zeichen und Sprache im Mathematikunterricht</i>, G. Kadunz, Ed. Springer, 2020.","short":"J. Schumacher, S. Rezat, in: G. Kadunz (Ed.), Zeichen und Sprache im Mathematikunterricht, Springer, 2020.","chicago":"Schumacher, Jan, and Sebastian Rezat. “Rekonstruktion diagrammatischen Schließens beim Erlernen der Subtraktion negativer Zahlen. Vergleich zweier methodischer Zugänge.” In <i>Zeichen und Sprache im Mathematikunterricht</i>, edited by Gert Kadunz. Springer, 2020. <a href=\"https://doi.org/10.1007/978-3-662-61194-4_5\">https://doi.org/10.1007/978-3-662-61194-4_5</a>.","mla":"Schumacher, Jan, and Sebastian Rezat. “Rekonstruktion diagrammatischen Schließens beim Erlernen der Subtraktion negativer Zahlen. Vergleich zweier methodischer Zugänge.” <i>Zeichen und Sprache im Mathematikunterricht</i>, edited by Gert Kadunz, Springer, 2020, doi:<a href=\"https://doi.org/10.1007/978-3-662-61194-4_5\">10.1007/978-3-662-61194-4_5</a>.","ama":"Schumacher J, Rezat S. Rekonstruktion diagrammatischen Schließens beim Erlernen der Subtraktion negativer Zahlen. Vergleich zweier methodischer Zugänge. In: Kadunz G, ed. <i>Zeichen und Sprache im Mathematikunterricht</i>. Springer; 2020. doi:<a href=\"https://doi.org/10.1007/978-3-662-61194-4_5\">10.1007/978-3-662-61194-4_5</a>","bibtex":"@inbook{Schumacher_Rezat_2020, title={Rekonstruktion diagrammatischen Schließens beim Erlernen der Subtraktion negativer Zahlen. Vergleich zweier methodischer Zugänge}, DOI={<a href=\"https://doi.org/10.1007/978-3-662-61194-4_5\">10.1007/978-3-662-61194-4_5</a>}, booktitle={Zeichen und Sprache im Mathematikunterricht}, publisher={Springer}, author={Schumacher, Jan and Rezat, Sebastian}, editor={Kadunz, GertEditor}, year={2020} }"},"publication":"Zeichen und Sprache im Mathematikunterricht","editor":[{"full_name":"Kadunz, Gert","last_name":"Kadunz","first_name":"Gert"}],"doi":"10.1007/978-3-662-61194-4_5","user_id":"26809","publisher":"Springer","_id":"13108","language":[{"iso":"ger"}],"date_updated":"2022-01-06T06:51:28Z","publication_status":"published","author":[{"last_name":"Schumacher","orcid":"0000-0002-6676-9774","first_name":"Jan","full_name":"Schumacher, Jan","id":"26809"},{"full_name":"Rezat, Sebastian","first_name":"Sebastian","last_name":"Rezat"}],"status":"public","year":"2020","title":"Rekonstruktion diagrammatischen Schließens beim Erlernen der Subtraktion negativer Zahlen. Vergleich zweier methodischer Zugänge"},{"citation":{"bibtex":"@article{Kiesel_Riehmann_Wachsmuth_Stein_Fröhlich, title={Visual Analysis of Argumentation in Essays}, volume={27}, number={2}, journal={IEEE Transactions of Visualization &#38; Computer Graphics}, author={Kiesel, Dora and Riehmann, Patrick and Wachsmuth, Henning and Stein, Benno and Fröhlich, Bernd}, pages={1139–1148} }","ama":"Kiesel D, Riehmann P, Wachsmuth H, Stein B, Fröhlich B. Visual Analysis of Argumentation in Essays. <i>IEEE Transactions of Visualization &#38; Computer Graphics</i>. 27(2):1139-1148.","short":"D. Kiesel, P. Riehmann, H. Wachsmuth, B. Stein, B. Fröhlich, IEEE Transactions of Visualization &#38; Computer Graphics 27 (n.d.) 1139–1148.","chicago":"Kiesel, Dora, Patrick Riehmann, Henning Wachsmuth, Benno Stein, and Bernd Fröhlich. “Visual Analysis of Argumentation in Essays.” <i>IEEE Transactions of Visualization &#38; Computer Graphics</i> 27, no. 2 (n.d.): 1139–48.","ieee":"D. Kiesel, P. Riehmann, H. Wachsmuth, B. Stein, and B. Fröhlich, “Visual Analysis of Argumentation in Essays,” <i>IEEE Transactions of Visualization &#38; Computer Graphics</i>, vol. 27, no. 2, pp. 1139–1148.","apa":"Kiesel, D., Riehmann, P., Wachsmuth, H., Stein, B., &#38; Fröhlich, B. (n.d.). Visual Analysis of Argumentation in Essays. <i>IEEE Transactions of Visualization &#38; Computer Graphics</i>, <i>27</i>(2), 1139–1148.","mla":"Kiesel, Dora, et al. “Visual Analysis of Argumentation in Essays.” <i>IEEE Transactions of Visualization &#38; Computer Graphics</i>, vol. 27, no. 2, pp. 1139–48."},"publication":"IEEE Transactions of Visualization & Computer Graphics","issue":"2","department":[{"_id":"600"}],"type":"journal_article","date_created":"2019-06-27T12:58:13Z","intvolume":"        27","publication_status":"accepted","date_updated":"2022-01-06T06:50:37Z","author":[{"full_name":"Kiesel, Dora","first_name":"Dora","last_name":"Kiesel"},{"first_name":"Patrick","last_name":"Riehmann","full_name":"Riehmann, Patrick"},{"full_name":"Wachsmuth, Henning","first_name":"Henning","last_name":"Wachsmuth","id":"3900"},{"last_name":"Stein","first_name":"Benno","full_name":"Stein, Benno"},{"full_name":"Fröhlich, Bernd","last_name":"Fröhlich","first_name":"Bernd"}],"title":"Visual Analysis of Argumentation in Essays","year":"2020","status":"public","volume":27,"user_id":"82920","language":[{"iso":"eng"}],"_id":"10330","main_file_link":[{"url":"https://www.uni-weimar.de/fileadmin/user/fak/medien/professuren/Virtual_Reality/documents/publications/Visual_Analysis_of_Argumentation_in_Essays.pdf"}],"page":"1139-1148"},{"type":"journal_article","department":[{"_id":"52"}],"date_created":"2021-02-16T21:23:53Z","publication":"IEEE Transactions on Neural Networks and Learning Systems","citation":{"mla":"Traue, Arne, et al. “Toward a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control.” <i>IEEE Transactions on Neural Networks and Learning Systems</i>, 2020, pp. 1–10, doi:<a href=\"https://doi.org/10.1109/tnnls.2020.3029573\">10.1109/tnnls.2020.3029573</a>.","bibtex":"@article{Traue_Book_Kirchgässner_Wallscheid_2020, title={Toward a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control}, DOI={<a href=\"https://doi.org/10.1109/tnnls.2020.3029573\">10.1109/tnnls.2020.3029573</a>}, journal={IEEE Transactions on Neural Networks and Learning Systems}, author={Traue, Arne and Book, Gerrit and Kirchgässner, Wilhelm and Wallscheid, Oliver}, year={2020}, pages={1–10} }","ama":"Traue A, Book G, Kirchgässner W, Wallscheid O. Toward a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control. <i>IEEE Transactions on Neural Networks and Learning Systems</i>. Published online 2020:1-10. doi:<a href=\"https://doi.org/10.1109/tnnls.2020.3029573\">10.1109/tnnls.2020.3029573</a>","ieee":"A. Traue, G. Book, W. Kirchgässner, and O. Wallscheid, “Toward a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control,” <i>IEEE Transactions on Neural Networks and Learning Systems</i>, pp. 1–10, 2020, doi: <a href=\"https://doi.org/10.1109/tnnls.2020.3029573\">10.1109/tnnls.2020.3029573</a>.","apa":"Traue, A., Book, G., Kirchgässner, W., &#38; Wallscheid, O. (2020). Toward a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control. <i>IEEE Transactions on Neural Networks and Learning Systems</i>, 1–10. <a href=\"https://doi.org/10.1109/tnnls.2020.3029573\">https://doi.org/10.1109/tnnls.2020.3029573</a>","short":"A. Traue, G. Book, W. Kirchgässner, O. Wallscheid, IEEE Transactions on Neural Networks and Learning Systems (2020) 1–10.","chicago":"Traue, Arne, Gerrit Book, Wilhelm Kirchgässner, and Oliver Wallscheid. “Toward a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control.” <i>IEEE Transactions on Neural Networks and Learning Systems</i>, 2020, 1–10. <a href=\"https://doi.org/10.1109/tnnls.2020.3029573\">https://doi.org/10.1109/tnnls.2020.3029573</a>."},"user_id":"49265","doi":"10.1109/tnnls.2020.3029573","page":"1-10","language":[{"iso":"eng"}],"_id":"21252","publication_status":"published","date_updated":"2022-02-18T13:26:18Z","year":"2020","status":"public","title":"Toward a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control","publication_identifier":{"issn":["2162-237X","2162-2388"]},"author":[{"full_name":"Traue, Arne","first_name":"Arne","last_name":"Traue"},{"first_name":"Gerrit","last_name":"Book","full_name":"Book, Gerrit"},{"last_name":"Kirchgässner","first_name":"Wilhelm","orcid":"0000-0001-9490-1843","full_name":"Kirchgässner, Wilhelm","id":"49265"},{"id":"11291","full_name":"Wallscheid, Oliver","orcid":"https://orcid.org/0000-0001-9362-8777","first_name":"Oliver","last_name":"Wallscheid"}]},{"conference":{"name":"SPEEDAM","start_date":"2020"},"author":[{"first_name":"Joachim","orcid":"0000-0002-8480-7295","last_name":"Böcker","full_name":"Böcker, Joachim","id":"66"}],"year":"2020","title":"Analysis of the Magnetic Skin Effekt in Motors and Inductors","status":"public","date_updated":"2022-02-19T10:09:10Z","publication_status":"published","publisher":"IEEE","_id":"29884","language":[{"iso":"eng"}],"doi":"10.1109/speedam48782.2020.9161895","user_id":"66","citation":{"mla":"Böcker, Joachim. “Analysis of the Magnetic Skin Effekt in Motors and Inductors.” <i>2020 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM)</i>, IEEE, 2020, doi:<a href=\"https://doi.org/10.1109/speedam48782.2020.9161895\">10.1109/speedam48782.2020.9161895</a>.","ama":"Böcker J. Analysis of the Magnetic Skin Effekt in Motors and Inductors. In: <i>2020 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM)</i>. IEEE; 2020. doi:<a href=\"https://doi.org/10.1109/speedam48782.2020.9161895\">10.1109/speedam48782.2020.9161895</a>","bibtex":"@inproceedings{Böcker_2020, title={Analysis of the Magnetic Skin Effekt in Motors and Inductors}, DOI={<a href=\"https://doi.org/10.1109/speedam48782.2020.9161895\">10.1109/speedam48782.2020.9161895</a>}, booktitle={2020 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM)}, publisher={IEEE}, author={Böcker, Joachim}, year={2020} }","apa":"Böcker, J. (2020). Analysis of the Magnetic Skin Effekt in Motors and Inductors. <i>2020 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM)</i>. 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IEEE, 2020. <a href=\"https://doi.org/10.1109/speedam48782.2020.9161895\">https://doi.org/10.1109/speedam48782.2020.9161895</a>."},"publication":"2020 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM)","date_created":"2022-02-19T09:45:52Z","department":[{"_id":"34"},{"_id":"52"}],"type":"conference"},{"date_updated":"2022-02-19T14:16:58Z","publication_status":"published","status":"public","year":"2020","title":"Variation-Aware Test for Logic Interconnects using Neural Networks - A Case Study","conference":{"end_date":"2020-10-21","start_date":"2020-10-19"},"author":[{"id":"22707","first_name":"Alexander","last_name":"Sprenger","full_name":"Sprenger, Alexander"},{"id":"78614","last_name":"Sadeghi-Kohan","first_name":"Somayeh","full_name":"Sadeghi-Kohan, Somayeh"},{"id":"36703","first_name":"Jan Dennis","last_name":"Reimer","full_name":"Reimer, Jan Dennis"},{"id":"209","full_name":"Hellebrand, Sybille","orcid":"0000-0002-3717-3939","first_name":"Sybille","last_name":"Hellebrand"}],"user_id":"209","language":[{"iso":"eng"}],"_id":"19422","publication":"IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT’20), October 2020","citation":{"ama":"Sprenger A, Sadeghi-Kohan S, Reimer JD, Hellebrand S. Variation-Aware Test for Logic Interconnects using Neural Networks - A Case Study. In: <i>IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT’20), October 2020</i>. ; 2020.","bibtex":"@inproceedings{Sprenger_Sadeghi-Kohan_Reimer_Hellebrand_2020, place={Virtual Conference - Originally Frascati (Rome), Italy}, title={Variation-Aware Test for Logic Interconnects using Neural Networks - A Case Study}, booktitle={IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT’20), October 2020}, author={Sprenger, Alexander and Sadeghi-Kohan, Somayeh and Reimer, Jan Dennis and Hellebrand, Sybille}, year={2020} }","mla":"Sprenger, Alexander, et al. “Variation-Aware Test for Logic Interconnects Using Neural Networks - A Case Study.” <i>IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT’20), October 2020</i>, 2020.","short":"A. Sprenger, S. Sadeghi-Kohan, J.D. Reimer, S. Hellebrand, in: IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT’20), October 2020, Virtual Conference - Originally Frascati (Rome), Italy, 2020.","chicago":"Sprenger, Alexander, Somayeh Sadeghi-Kohan, Jan Dennis Reimer, and Sybille Hellebrand. “Variation-Aware Test for Logic Interconnects Using Neural Networks - A Case Study.” In <i>IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT’20), October 2020</i>. Virtual Conference - Originally Frascati (Rome), Italy, 2020.","apa":"Sprenger, A., Sadeghi-Kohan, S., Reimer, J. D., &#38; Hellebrand, S. (2020). Variation-Aware Test for Logic Interconnects using Neural Networks - A Case Study. <i>IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT’20), October 2020</i>.","ieee":"A. Sprenger, S. Sadeghi-Kohan, J. D. Reimer, and S. Hellebrand, “Variation-Aware Test for Logic Interconnects using Neural Networks - A Case Study,” 2020."},"type":"conference","department":[{"_id":"48"}],"place":"Virtual Conference - Originally Frascati (Rome), Italy","date_created":"2020-09-15T14:03:02Z"},{"citation":{"ieee":"W. Kirchgässner, O. Wallscheid, and J. Böcker, “Estimating Electric Motor Temperatures with Deep Residual Machine Learning,” <i>IEEE Transactions on Power Electronics</i>, vol. 36, no. 7, pp. 7480–7488, 2020, doi: <a href=\"https://doi.org/10.1109/tpel.2020.3045596\">10.1109/tpel.2020.3045596</a>.","apa":"Kirchgässner, W., Wallscheid, O., &#38; Böcker, J. (2020). Estimating Electric Motor Temperatures with Deep Residual Machine Learning. <i>IEEE Transactions on Power Electronics</i>, <i>36</i>(7), 7480–7488. <a href=\"https://doi.org/10.1109/tpel.2020.3045596\">https://doi.org/10.1109/tpel.2020.3045596</a>","short":"W. Kirchgässner, O. Wallscheid, J. Böcker, IEEE Transactions on Power Electronics 36 (2020) 7480–7488.","chicago":"Kirchgässner, Wilhelm, Oliver Wallscheid, and Joachim Böcker. “Estimating Electric Motor Temperatures with Deep Residual Machine Learning.” <i>IEEE Transactions on Power Electronics</i> 36, no. 7 (2020): 7480–88. <a href=\"https://doi.org/10.1109/tpel.2020.3045596\">https://doi.org/10.1109/tpel.2020.3045596</a>.","mla":"Kirchgässner, Wilhelm, et al. “Estimating Electric Motor Temperatures with Deep Residual Machine Learning.” <i>IEEE Transactions on Power Electronics</i>, vol. 36, no. 7, 2020, pp. 7480–88, doi:<a href=\"https://doi.org/10.1109/tpel.2020.3045596\">10.1109/tpel.2020.3045596</a>.","bibtex":"@article{Kirchgässner_Wallscheid_Böcker_2020, title={Estimating Electric Motor Temperatures with Deep Residual Machine Learning}, volume={36}, DOI={<a href=\"https://doi.org/10.1109/tpel.2020.3045596\">10.1109/tpel.2020.3045596</a>}, number={7}, journal={IEEE Transactions on Power Electronics}, author={Kirchgässner, Wilhelm and Wallscheid, Oliver and Böcker, Joachim}, year={2020}, pages={7480–7488} }","ama":"Kirchgässner W, Wallscheid O, Böcker J. Estimating Electric Motor Temperatures with Deep Residual Machine Learning. <i>IEEE Transactions on Power Electronics</i>. 2020;36(7):7480-7488. doi:<a href=\"https://doi.org/10.1109/tpel.2020.3045596\">10.1109/tpel.2020.3045596</a>"},"issue":"7","publication":"IEEE Transactions on Power Electronics","date_created":"2021-02-16T21:22:32Z","department":[{"_id":"52"}],"type":"journal_article","author":[{"first_name":"Wilhelm","orcid":"0000-0001-9490-1843","last_name":"Kirchgässner","full_name":"Kirchgässner, Wilhelm","id":"49265"},{"full_name":"Wallscheid, Oliver","first_name":"Oliver","orcid":"https://orcid.org/0000-0001-9362-8777","last_name":"Wallscheid","id":"11291"},{"first_name":"Joachim","orcid":"0000-0002-8480-7295","last_name":"Böcker","full_name":"Böcker, Joachim","id":"66"}],"publication_identifier":{"issn":["0885-8993","1941-0107"]},"status":"public","title":"Estimating Electric Motor Temperatures with Deep Residual Machine Learning","year":"2020","intvolume":"        36","publication_status":"published","date_updated":"2022-02-19T16:51:20Z","_id":"21250","language":[{"iso":"eng"}],"page":"7480-7488","volume":36,"user_id":"49265","doi":"10.1109/tpel.2020.3045596"},{"doi":"10.23919/epe20ecceeurope43536.2020.9215687","user_id":"34289","language":[{"iso":"eng"}],"_id":"29939","publisher":"IEEE","main_file_link":[{"url":"https://ieeexplore.ieee.org/abstract/document/9215687"}],"date_updated":"2022-02-21T17:00:16Z","publication_status":"published","conference":{"end_date":"2020-09-11","name":"22nd European Conference on Power Electronics and Applications (EPE'20 ECCE Europe)","start_date":"2020-09-07","location":"Lyon, France"},"author":[{"full_name":"Unruh, Roland","last_name":"Unruh","first_name":"Roland","id":"34289"},{"first_name":"Frank","last_name":"Schafmeister","full_name":"Schafmeister, Frank","id":"71291"},{"full_name":"Böcker, Joachim","last_name":"Böcker","first_name":"Joachim","orcid":"0000-0002-8480-7295","id":"66"}],"title":"Evaluation of MMCs for High-Power Low-Voltage DC-Applications in Combination with the Module LLC-Design","status":"public","year":"2020","department":[{"_id":"52"}],"type":"conference","keyword":["Multilevel converters","Resonant converter","High voltage power converters","ZVS Converters","Combination MMC LLC"],"date_created":"2022-02-21T16:33:14Z","abstract":[{"text":"In this paper, a full-bridge modular multilevel converter (MMC) and two half-bridge-based MMCs are evaluated for high-current low-voltage e.g. 100 - 400V DC-applications such as electrolysis, arc welding or datacenters with DC-power distribution. Usually, modular multilevel converters are used in high-voltage DC-applications (HVDC) in the multiple kV-range, but to meet the needs of a high-current demand at low output voltage levels, the modular converter concept requires adaptations. In the proposed concept, the MMC is used to step-down the three-phase medium-voltage of 10kV, and provide up to 1 MW to the load. Therefore, each module is extended by an LLC resonant converter to adapt to the specific electrolyzers DC-voltage range of 142 - 220V and to provide galvanic isolation. The six-arm MMC converter with half-bridge modules can be simplified and optimized by removing three arms, and thus halving the number of modules. In addition, the module voltage ripple and capacitor losses are decreased by 22% and 30% respectively. By rearranging the components of the half-bridge MMC to build a MMC consisting of grid-side full-bridge modules, the voltage ripple is further reduced by 78% and capacitor losses by 64%, while ensuring identical costs and volume for all MMCs. Finally, the LLC resonant converter is designed for the most efficient full-bridge MMC. The LLC can not operate at resonance with a fixed nominal module voltage of 770V because the output voltage is varying between 142 - 220V. By decreasing the module voltage down to 600V, additional points of operation can be operated in resonance, and the remaining are closer to resonance. The option to decrease the module voltage down to 600V, increases the number of required modules per arm from 12 to 15, which requires to balance the losses of the LLCs and the grid-side stages.","lang":"eng"}],"citation":{"mla":"Unruh, Roland, et al. “Evaluation of MMCs for High-Power Low-Voltage DC-Applications in Combination with the Module LLC-Design.” <i>2020 22nd European Conference on Power Electronics and Applications (EPE’20 ECCE Europe)</i>, IEEE, 2020, doi:<a href=\"https://doi.org/10.23919/epe20ecceeurope43536.2020.9215687\">10.23919/epe20ecceeurope43536.2020.9215687</a>.","ama":"Unruh R, Schafmeister F, Böcker J. Evaluation of MMCs for High-Power Low-Voltage DC-Applications in Combination with the Module LLC-Design. In: <i>2020 22nd European Conference on Power Electronics and Applications (EPE’20 ECCE Europe)</i>. IEEE; 2020. doi:<a href=\"https://doi.org/10.23919/epe20ecceeurope43536.2020.9215687\">10.23919/epe20ecceeurope43536.2020.9215687</a>","bibtex":"@inproceedings{Unruh_Schafmeister_Böcker_2020, title={Evaluation of MMCs for High-Power Low-Voltage DC-Applications in Combination with the Module LLC-Design}, DOI={<a href=\"https://doi.org/10.23919/epe20ecceeurope43536.2020.9215687\">10.23919/epe20ecceeurope43536.2020.9215687</a>}, booktitle={2020 22nd European Conference on Power Electronics and Applications (EPE’20 ECCE Europe)}, publisher={IEEE}, author={Unruh, Roland and Schafmeister, Frank and Böcker, Joachim}, year={2020} }","apa":"Unruh, R., Schafmeister, F., &#38; Böcker, J. (2020). Evaluation of MMCs for High-Power Low-Voltage DC-Applications in Combination with the Module LLC-Design. <i>2020 22nd European Conference on Power Electronics and Applications (EPE’20 ECCE Europe)</i>. 22nd European Conference on Power Electronics and Applications (EPE’20 ECCE Europe), Lyon, France. <a href=\"https://doi.org/10.23919/epe20ecceeurope43536.2020.9215687\">https://doi.org/10.23919/epe20ecceeurope43536.2020.9215687</a>","ieee":"R. Unruh, F. Schafmeister, and J. Böcker, “Evaluation of MMCs for High-Power Low-Voltage DC-Applications in Combination with the Module LLC-Design,” presented at the 22nd European Conference on Power Electronics and Applications (EPE’20 ECCE Europe), Lyon, France, 2020, doi: <a href=\"https://doi.org/10.23919/epe20ecceeurope43536.2020.9215687\">10.23919/epe20ecceeurope43536.2020.9215687</a>.","chicago":"Unruh, Roland, Frank Schafmeister, and Joachim Böcker. “Evaluation of MMCs for High-Power Low-Voltage DC-Applications in Combination with the Module LLC-Design.” In <i>2020 22nd European Conference on Power Electronics and Applications (EPE’20 ECCE Europe)</i>. IEEE, 2020. <a href=\"https://doi.org/10.23919/epe20ecceeurope43536.2020.9215687\">https://doi.org/10.23919/epe20ecceeurope43536.2020.9215687</a>.","short":"R. Unruh, F. Schafmeister, J. Böcker, in: 2020 22nd European Conference on Power Electronics and Applications (EPE’20 ECCE Europe), IEEE, 2020."},"publication":"2020 22nd European Conference on Power Electronics and Applications (EPE'20 ECCE Europe)"}]