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Weber, S. Heid, H. Bode, J. Lange, E. Hüllermeier, and O. Wallscheid, “Safe Bayesian Optimization for Data-Driven Power Electronics Control Design in Microgrids: From Simulations to Real-World Experiments,” <i>IEEE Access</i>, vol. 9, pp. 35654–35669, 2021, doi: <a href=\"https://doi.org/10.1109/ACCESS.2021.3062144\">10.1109/ACCESS.2021.3062144</a>.","apa":"Weber, D., Heid, S., Bode, H., Lange, J., Hüllermeier, E., &#38; Wallscheid, O. (2021). Safe Bayesian Optimization for Data-Driven Power Electronics Control Design in Microgrids: From Simulations to Real-World Experiments. <i>IEEE Access</i>, <i>9</i>, 35654–35669. <a href=\"https://doi.org/10.1109/ACCESS.2021.3062144\">https://doi.org/10.1109/ACCESS.2021.3062144</a>","chicago":"Weber, Daniel, Stefan Heid, Henrik Bode, Jarren Lange, Eyke Hüllermeier, and Oliver Wallscheid. “Safe Bayesian Optimization for Data-Driven Power Electronics Control Design in Microgrids: From Simulations to Real-World Experiments.” <i>IEEE Access</i> 9 (2021): 35654–35669. <a href=\"https://doi.org/10.1109/ACCESS.2021.3062144\">https://doi.org/10.1109/ACCESS.2021.3062144</a>.","short":"D. Weber, S. Heid, H. Bode, J. Lange, E. Hüllermeier, O. Wallscheid, IEEE Access 9 (2021) 35654–35669.","mla":"Weber, Daniel, et al. “Safe Bayesian Optimization for Data-Driven Power Electronics Control Design in Microgrids: From Simulations to Real-World Experiments.” <i>IEEE Access</i>, vol. 9, IEEE, 2021, pp. 35654–35669, doi:<a href=\"https://doi.org/10.1109/ACCESS.2021.3062144\">10.1109/ACCESS.2021.3062144</a>.","bibtex":"@article{Weber_Heid_Bode_Lange_Hüllermeier_Wallscheid_2021, title={Safe Bayesian Optimization for Data-Driven Power Electronics Control Design in Microgrids: From Simulations to Real-World Experiments}, volume={9}, DOI={<a href=\"https://doi.org/10.1109/ACCESS.2021.3062144\">10.1109/ACCESS.2021.3062144</a>}, journal={IEEE Access}, publisher={IEEE}, author={Weber, Daniel and Heid, Stefan and Bode, Henrik and Lange, Jarren and Hüllermeier, Eyke and Wallscheid, Oliver}, year={2021}, pages={35654–35669} }","ama":"Weber D, Heid S, Bode H, Lange J, Hüllermeier E, Wallscheid O. Safe Bayesian Optimization for Data-Driven Power Electronics Control Design in Microgrids: From Simulations to Real-World Experiments. <i>IEEE Access</i>. 2021;9:35654–35669. doi:<a href=\"https://doi.org/10.1109/ACCESS.2021.3062144\">10.1109/ACCESS.2021.3062144</a>"},"publication":"IEEE Access","department":[{"_id":"52"},{"_id":"57"}],"type":"journal_article","date_created":"2022-01-28T14:11:05Z","intvolume":"         9","date_updated":"2022-02-23T08:34:42Z","author":[{"id":"24041","first_name":"Daniel","orcid":"0000-0003-3367-5998","last_name":"Weber","full_name":"Weber, Daniel"},{"full_name":"Heid, Stefan","last_name":"Heid","orcid":"0000-0002-9461-7372","first_name":"Stefan","id":"39640"},{"last_name":"Bode","first_name":"Henrik","full_name":"Bode, Henrik","id":"40880"},{"last_name":"Lange","first_name":"Jarren","full_name":"Lange, Jarren","id":"78801"},{"last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke"},{"full_name":"Wallscheid, Oliver","last_name":"Wallscheid","first_name":"Oliver","orcid":"https://orcid.org/0000-0001-9362-8777","id":"11291"}],"year":"2021","title":"Safe Bayesian Optimization for Data-Driven Power Electronics Control Design in Microgrids: From Simulations to Real-World Experiments","status":"public","volume":9,"doi":"10.1109/ACCESS.2021.3062144","user_id":"66","language":[{"iso":"eng"}],"_id":"29653","publisher":"IEEE","page":"35654–35669"},{"date_created":"2022-01-28T14:11:08Z","department":[{"_id":"52"},{"_id":"57"}],"type":"journal_article","citation":{"apa":"Wallscheid, O. (2021). Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges. <i>IEEE Open Journal of Industry Applications</i>.","ieee":"O. Wallscheid, “Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges,” <i>IEEE Open Journal of Industry Applications</i>, 2021.","short":"O. Wallscheid, IEEE Open Journal of Industry Applications (2021).","chicago":"Wallscheid, Oliver. “Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges.” <i>IEEE Open Journal of Industry Applications</i>, 2021.","mla":"Wallscheid, Oliver. “Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges.” <i>IEEE Open Journal of Industry Applications</i>, IEEE, 2021.","ama":"Wallscheid O. Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges. <i>IEEE Open Journal of Industry Applications</i>. Published online 2021.","bibtex":"@article{Wallscheid_2021, title={Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges}, journal={IEEE Open Journal of Industry Applications}, publisher={IEEE}, author={Wallscheid, Oliver}, year={2021} }"},"publication":"IEEE Open Journal of Industry Applications","publisher":"IEEE","_id":"29664","language":[{"iso":"eng"}],"user_id":"11291","author":[{"first_name":"Oliver","orcid":"https://orcid.org/0000-0001-9362-8777","last_name":"Wallscheid","full_name":"Wallscheid, Oliver","id":"11291"}],"title":"Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges","status":"public","year":"2021","date_updated":"2022-02-25T20:30:50Z"},{"status":"public","year":"2021","title":"Improved Exploring Starts by Kernel Density Estimation-Based State-Space Coverage Acceleration in Reinforcement Learning","author":[{"full_name":"Schenke, Maximilian","orcid":"0000-0001-5427-9527","last_name":"Schenke","first_name":"Maximilian","id":"52638"},{"full_name":"Wallscheid, Oliver","last_name":"Wallscheid","orcid":"https://orcid.org/0000-0001-9362-8777","first_name":"Oliver","id":"11291"}],"date_updated":"2022-02-25T20:31:17Z","_id":"29662","language":[{"iso":"eng"}],"user_id":"11291","publication":"arXiv preprint arXiv:2105.08990","citation":{"short":"M. Schenke, O. Wallscheid, ArXiv Preprint ArXiv:2105.08990 (2021).","chicago":"Schenke, Maximilian, and Oliver Wallscheid. “Improved Exploring Starts by Kernel Density Estimation-Based State-Space Coverage Acceleration in Reinforcement Learning.” <i>ArXiv Preprint ArXiv:2105.08990</i>, 2021.","apa":"Schenke, M., &#38; Wallscheid, O. (2021). Improved Exploring Starts by Kernel Density Estimation-Based State-Space Coverage Acceleration in Reinforcement Learning. <i>ArXiv Preprint ArXiv:2105.08990</i>.","ieee":"M. Schenke and O. Wallscheid, “Improved Exploring Starts by Kernel Density Estimation-Based State-Space Coverage Acceleration in Reinforcement Learning,” <i>arXiv preprint arXiv:2105.08990</i>, 2021.","ama":"Schenke M, Wallscheid O. Improved Exploring Starts by Kernel Density Estimation-Based State-Space Coverage Acceleration in Reinforcement Learning. <i>arXiv preprint arXiv:210508990</i>. Published online 2021.","bibtex":"@article{Schenke_Wallscheid_2021, title={Improved Exploring Starts by Kernel Density Estimation-Based State-Space Coverage Acceleration in Reinforcement Learning}, journal={arXiv preprint arXiv:2105.08990}, author={Schenke, Maximilian and Wallscheid, Oliver}, year={2021} }","mla":"Schenke, Maximilian, and Oliver Wallscheid. “Improved Exploring Starts by Kernel Density Estimation-Based State-Space Coverage Acceleration in Reinforcement Learning.” <i>ArXiv Preprint ArXiv:2105.08990</i>, 2021."},"date_created":"2022-01-28T14:11:08Z","type":"journal_article","department":[{"_id":"52"},{"_id":"57"}]},{"publication":"IEEE Transactions on Energy Conversion","issue":"2","date_created":"2023-01-09T16:49:08Z","keyword":["Electrical and Electronic Engineering","Energy Engineering and Power Technology"],"type":"journal_article","department":[{"_id":"57"}],"year":"2021","title":"Guest Editorial Model Predictive Control in Energy Conversion Systems","publication_identifier":{"issn":["0885-8969","1558-0059"]},"author":[{"last_name":"Dragicevic","first_name":"Tomislav","full_name":"Dragicevic, Tomislav"},{"full_name":"Parisio, Alessandra","last_name":"Parisio","first_name":"Alessandra"},{"last_name":"Rodriguez","first_name":"Jose","full_name":"Rodriguez, Jose"},{"last_name":"Jones","first_name":"Colin","full_name":"Jones, Colin"},{"full_name":"Quevedo, Daniel","first_name":"Daniel","last_name":"Quevedo"},{"full_name":"Ferrarini, Luca","first_name":"Luca","last_name":"Ferrarini"},{"first_name":"Matthias","last_name":"Preindl","full_name":"Preindl, Matthias"},{"full_name":"Shafiee, Qobad","first_name":"Qobad","last_name":"Shafiee"},{"full_name":"Morstyn, Thomas","first_name":"Thomas","last_name":"Morstyn"}],"date_updated":"2023-01-09T16:49:23Z","publication_status":"published","intvolume":"        36","language":[{"iso":"eng"}],"doi":"10.1109/tec.2021.3076279","citation":{"apa":"Dragicevic, T., Parisio, A., Rodriguez, J., Jones, C., Quevedo, D., Ferrarini, L., Preindl, M., Shafiee, Q., &#38; Morstyn, T. (2021). Guest Editorial Model Predictive Control in Energy Conversion Systems. <i>IEEE Transactions on Energy Conversion</i>, <i>36</i>(2), 1311–1312. <a href=\"https://doi.org/10.1109/tec.2021.3076279\">https://doi.org/10.1109/tec.2021.3076279</a>","ieee":"T. Dragicevic <i>et al.</i>, “Guest Editorial Model Predictive Control in Energy Conversion Systems,” <i>IEEE Transactions on Energy Conversion</i>, vol. 36, no. 2, pp. 1311–1312, 2021, doi: <a href=\"https://doi.org/10.1109/tec.2021.3076279\">10.1109/tec.2021.3076279</a>.","short":"T. Dragicevic, A. Parisio, J. Rodriguez, C. Jones, D. Quevedo, L. Ferrarini, M. Preindl, Q. Shafiee, T. Morstyn, IEEE Transactions on Energy Conversion 36 (2021) 1311–1312.","chicago":"Dragicevic, Tomislav, Alessandra Parisio, Jose Rodriguez, Colin Jones, Daniel Quevedo, Luca Ferrarini, Matthias Preindl, Qobad Shafiee, and Thomas Morstyn. “Guest Editorial Model Predictive Control in Energy Conversion Systems.” <i>IEEE Transactions on Energy Conversion</i> 36, no. 2 (2021): 1311–12. <a href=\"https://doi.org/10.1109/tec.2021.3076279\">https://doi.org/10.1109/tec.2021.3076279</a>.","mla":"Dragicevic, Tomislav, et al. “Guest Editorial Model Predictive Control in Energy Conversion Systems.” <i>IEEE Transactions on Energy Conversion</i>, vol. 36, no. 2, Institute of Electrical and Electronics Engineers (IEEE), 2021, pp. 1311–12, doi:<a href=\"https://doi.org/10.1109/tec.2021.3076279\">10.1109/tec.2021.3076279</a>.","ama":"Dragicevic T, Parisio A, Rodriguez J, et al. Guest Editorial Model Predictive Control in Energy Conversion Systems. <i>IEEE Transactions on Energy Conversion</i>. 2021;36(2):1311-1312. doi:<a href=\"https://doi.org/10.1109/tec.2021.3076279\">10.1109/tec.2021.3076279</a>","bibtex":"@article{Dragicevic_Parisio_Rodriguez_Jones_Quevedo_Ferrarini_Preindl_Shafiee_Morstyn_2021, title={Guest Editorial Model Predictive Control in Energy Conversion Systems}, volume={36}, DOI={<a href=\"https://doi.org/10.1109/tec.2021.3076279\">10.1109/tec.2021.3076279</a>}, number={2}, journal={IEEE Transactions on Energy Conversion}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Dragicevic, Tomislav and Parisio, Alessandra and Rodriguez, Jose and Jones, Colin and Quevedo, Daniel and Ferrarini, Luca and Preindl, Matthias and Shafiee, Qobad and Morstyn, Thomas}, year={2021}, pages={1311–1312} }"},"status":"public","page":"1311-1312","_id":"35589","publisher":"Institute of Electrical and Electronics Engineers (IEEE)","user_id":"158","volume":36},{"abstract":[{"lang":"eng","text":"We consider the joint design of control and scheduling under stochastic\r\nDenial-of-Service (DoS) attacks in the context of networked control systems. A\r\nsensor takes measurements of the system output and forwards its dynamic state\r\nestimates to a remote controller over a packet-dropping link. The controller\r\ndetermines the optimal control law for the process using the estimates it\r\nreceives. An attacker aims at degrading the control performance by increasing\r\nthe packet-dropout rate with a DoS attack towards the sensor-controller\r\nchannel. We assume both the controller and the attacker are rational in a\r\ngame-theoretic sense and establish a partially observable stochastic game to\r\nderive the optimal joint design of scheduling and control. Using dynamic\r\nprogramming we prove that the control and scheduling policies can be designed\r\nseparately without sacrificing optimality, making the problem equivalent to a\r\ncomplete information game. We employ Nash Q-learning to solve the problem and\r\nprove that the solution is guaranteed to constitute an $\\epsilon$-Nash\r\nequilibrium. Numerical examples are provided to illustrate the tradeoffs\r\nbetween control performance and communication cost."}],"citation":{"mla":"Lu, Jingyi, and Daniel E. Quevedo. “A Jointly Optimal Design of Control and Scheduling in Networked Systems  under Denial-of-Service Attacks.” <i>ArXiv:2103.05893</i>, 2021.","ama":"Lu J, Quevedo DE. A Jointly Optimal Design of Control and Scheduling in Networked Systems  under Denial-of-Service Attacks. <i>arXiv:210305893</i>. Published online 2021.","bibtex":"@article{Lu_Quevedo_2021, title={A Jointly Optimal Design of Control and Scheduling in Networked Systems  under Denial-of-Service Attacks}, journal={arXiv:2103.05893}, author={Lu, Jingyi and Quevedo, Daniel E.}, year={2021} }","apa":"Lu, J., &#38; Quevedo, D. E. (2021). A Jointly Optimal Design of Control and Scheduling in Networked Systems  under Denial-of-Service Attacks. In <i>arXiv:2103.05893</i>.","ieee":"J. Lu and D. E. Quevedo, “A Jointly Optimal Design of Control and Scheduling in Networked Systems  under Denial-of-Service Attacks,” <i>arXiv:2103.05893</i>. 2021.","chicago":"Lu, Jingyi, and Daniel E. Quevedo. “A Jointly Optimal Design of Control and Scheduling in Networked Systems  under Denial-of-Service Attacks.” <i>ArXiv:2103.05893</i>, 2021.","short":"J. Lu, D.E. Quevedo, ArXiv:2103.05893 (2021)."},"publication":"arXiv:2103.05893","department":[{"_id":"57"}],"type":"preprint","date_created":"2023-01-09T16:48:44Z","external_id":{"arxiv":["2103.05893"]},"date_updated":"2023-01-09T18:04:57Z","author":[{"full_name":"Lu, Jingyi","first_name":"Jingyi","last_name":"Lu"},{"full_name":"Quevedo, Daniel E.","first_name":"Daniel E.","last_name":"Quevedo"}],"status":"public","title":"A Jointly Optimal Design of Control and Scheduling in Networked Systems  under Denial-of-Service Attacks","year":"2021","user_id":"158","_id":"35588","language":[{"iso":"eng"}]},{"citation":{"bibtex":"@article{Heid_Weber_Bode_Hüllermeier_Wallscheid_2020, title={OMG: A scalable and flexible simulation and testing environment toolbox for intelligent microgrid control}, volume={5}, number={54}, journal={Journal of Open Source Software}, author={Heid, Stefan and Weber, Daniel and Bode, Henrik and Hüllermeier, Eyke and Wallscheid, Oliver}, year={2020}, pages={2435} }","ama":"Heid S, Weber D, Bode H, Hüllermeier E, Wallscheid O. OMG: A scalable and flexible simulation and testing environment toolbox for intelligent microgrid control. <i>Journal of Open Source Software</i>. 2020;5(54):2435.","mla":"Heid, Stefan, et al. “OMG: A Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid Control.” <i>Journal of Open Source Software</i>, vol. 5, no. 54, 2020, p. 2435.","short":"S. Heid, D. Weber, H. Bode, E. Hüllermeier, O. Wallscheid, Journal of Open Source Software 5 (2020) 2435.","chicago":"Heid, Stefan, Daniel Weber, Henrik Bode, Eyke Hüllermeier, and Oliver Wallscheid. “OMG: A Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid Control.” <i>Journal of Open Source Software</i> 5, no. 54 (2020): 2435.","ieee":"S. Heid, D. Weber, H. Bode, E. Hüllermeier, and O. Wallscheid, “OMG: A scalable and flexible simulation and testing environment toolbox for intelligent microgrid control,” <i>Journal of Open Source Software</i>, vol. 5, no. 54, p. 2435, 2020.","apa":"Heid, S., Weber, D., Bode, H., Hüllermeier, E., &#38; Wallscheid, O. (2020). OMG: A scalable and flexible simulation and testing environment toolbox for intelligent microgrid control. <i>Journal of Open Source Software</i>, <i>5</i>(54), 2435."},"issue":"54","publication":"Journal of Open Source Software","department":[{"_id":"52"},{"_id":"57"}],"type":"journal_article","date_created":"2022-01-28T14:11:04Z","intvolume":"         5","date_updated":"2022-03-02T07:16:15Z","author":[{"full_name":"Heid, Stefan","last_name":"Heid","orcid":"0000-0002-9461-7372","first_name":"Stefan","id":"39640"},{"id":"24041","full_name":"Weber, Daniel","last_name":"Weber","first_name":"Daniel","orcid":"0000-0003-3367-5998"},{"id":"40880","last_name":"Bode","first_name":"Henrik","full_name":"Bode, Henrik"},{"last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke"},{"full_name":"Wallscheid, Oliver","first_name":"Oliver","last_name":"Wallscheid","orcid":"https://orcid.org/0000-0001-9362-8777","id":"11291"}],"title":"OMG: A scalable and flexible simulation and testing environment toolbox for intelligent microgrid control","status":"public","year":"2020","volume":5,"user_id":"11291","_id":"29649","language":[{"iso":"eng"}],"page":"2435"},{"citation":{"bibtex":"@article{Lu_Leong_Quevedo_2020, title={Optimal event‐triggered transmission scheduling for privacy‐preserving wireless state estimation}, volume={30}, DOI={<a href=\"https://doi.org/10.1002/rnc.4910\">10.1002/rnc.4910</a>}, number={11}, journal={International Journal of Robust and Nonlinear Control}, publisher={Wiley}, author={Lu, Jingyi and Leong, Alex S. and Quevedo, Daniel E.}, year={2020}, pages={4205–4224} }","ama":"Lu J, Leong AS, Quevedo DE. Optimal event‐triggered transmission scheduling for privacy‐preserving wireless state estimation. <i>International Journal of Robust and Nonlinear Control</i>. 2020;30(11):4205-4224. doi:<a href=\"https://doi.org/10.1002/rnc.4910\">10.1002/rnc.4910</a>","mla":"Lu, Jingyi, et al. “Optimal Event‐triggered Transmission Scheduling for Privacy‐preserving Wireless State Estimation.” <i>International Journal of Robust and Nonlinear Control</i>, vol. 30, no. 11, Wiley, 2020, pp. 4205–24, doi:<a href=\"https://doi.org/10.1002/rnc.4910\">10.1002/rnc.4910</a>.","short":"J. Lu, A.S. Leong, D.E. Quevedo, International Journal of Robust and Nonlinear Control 30 (2020) 4205–4224.","chicago":"Lu, Jingyi, Alex S. Leong, and Daniel E. Quevedo. “Optimal Event‐triggered Transmission Scheduling for Privacy‐preserving Wireless State Estimation.” <i>International Journal of Robust and Nonlinear Control</i> 30, no. 11 (2020): 4205–24. <a href=\"https://doi.org/10.1002/rnc.4910\">https://doi.org/10.1002/rnc.4910</a>.","ieee":"J. Lu, A. S. Leong, and D. E. Quevedo, “Optimal event‐triggered transmission scheduling for privacy‐preserving wireless state estimation,” <i>International Journal of Robust and Nonlinear Control</i>, vol. 30, no. 11, pp. 4205–4224, 2020, doi: <a href=\"https://doi.org/10.1002/rnc.4910\">10.1002/rnc.4910</a>.","apa":"Lu, J., Leong, A. S., &#38; Quevedo, D. E. (2020). Optimal event‐triggered transmission scheduling for privacy‐preserving wireless state estimation. <i>International Journal of Robust and Nonlinear Control</i>, <i>30</i>(11), 4205–4224. <a href=\"https://doi.org/10.1002/rnc.4910\">https://doi.org/10.1002/rnc.4910</a>"},"publisher":"Wiley","_id":"35585","page":"4205-4224","volume":30,"user_id":"158","status":"public","date_created":"2023-01-09T16:46:15Z","department":[{"_id":"57"}],"keyword":["Electrical and Electronic Engineering","Industrial and Manufacturing Engineering","Mechanical Engineering","Aerospace Engineering","Biomedical Engineering","General Chemical Engineering","Control and Systems Engineering"],"type":"journal_article","issue":"11","publication":"International Journal of Robust and Nonlinear Control","language":[{"iso":"eng"}],"doi":"10.1002/rnc.4910","publication_identifier":{"issn":["1049-8923","1099-1239"]},"author":[{"last_name":"Lu","first_name":"Jingyi","full_name":"Lu, Jingyi"},{"full_name":"Leong, Alex S.","first_name":"Alex S.","last_name":"Leong"},{"full_name":"Quevedo, Daniel E.","last_name":"Quevedo","first_name":"Daniel E."}],"title":"Optimal event‐triggered transmission scheduling for privacy‐preserving wireless state estimation","year":"2020","intvolume":"        30","date_updated":"2023-01-09T16:46:29Z","publication_status":"published"},{"status":"public","title":"On noise-to-state stability of stochastic discrete-time systems via finite-step Lyapunov functions","year":"2020","author":[{"full_name":"Noroozi, Navid","first_name":"Navid","last_name":"Noroozi"},{"full_name":"Jackson, Roxanne","first_name":"Roxanne","last_name":"Jackson"},{"full_name":"Quevedo, Daniel E.","last_name":"Quevedo","first_name":"Daniel E."},{"full_name":"Wirth, Fabian R.","first_name":"Fabian R.","last_name":"Wirth"},{"full_name":"Findeisen, Rolf","first_name":"Rolf","last_name":"Findeisen"}],"publication_status":"published","date_updated":"2023-02-14T11:08:09Z","_id":"42073","language":[{"iso":"eng"}],"publisher":"IEEE","user_id":"238","doi":"10.1109/cdc40024.2019.9030178","publication":"2019 IEEE 58th Conference on Decision and Control (CDC)","citation":{"mla":"Noroozi, Navid, et al. “On Noise-to-State Stability of Stochastic Discrete-Time Systems via Finite-Step Lyapunov Functions.” <i>2019 IEEE 58th Conference on Decision and Control (CDC)</i>, IEEE, 2020, doi:<a href=\"https://doi.org/10.1109/cdc40024.2019.9030178\">10.1109/cdc40024.2019.9030178</a>.","ama":"Noroozi N, Jackson R, Quevedo DE, Wirth FR, Findeisen R. On noise-to-state stability of stochastic discrete-time systems via finite-step Lyapunov functions. In: <i>2019 IEEE 58th Conference on Decision and Control (CDC)</i>. IEEE; 2020. doi:<a href=\"https://doi.org/10.1109/cdc40024.2019.9030178\">10.1109/cdc40024.2019.9030178</a>","bibtex":"@inproceedings{Noroozi_Jackson_Quevedo_Wirth_Findeisen_2020, title={On noise-to-state stability of stochastic discrete-time systems via finite-step Lyapunov functions}, DOI={<a href=\"https://doi.org/10.1109/cdc40024.2019.9030178\">10.1109/cdc40024.2019.9030178</a>}, booktitle={2019 IEEE 58th Conference on Decision and Control (CDC)}, publisher={IEEE}, author={Noroozi, Navid and Jackson, Roxanne and Quevedo, Daniel E. and Wirth, Fabian R. and Findeisen, Rolf}, year={2020} }","apa":"Noroozi, N., Jackson, R., Quevedo, D. E., Wirth, F. R., &#38; Findeisen, R. (2020). On noise-to-state stability of stochastic discrete-time systems via finite-step Lyapunov functions. <i>2019 IEEE 58th Conference on Decision and Control (CDC)</i>. <a href=\"https://doi.org/10.1109/cdc40024.2019.9030178\">https://doi.org/10.1109/cdc40024.2019.9030178</a>","ieee":"N. Noroozi, R. Jackson, D. E. Quevedo, F. R. Wirth, and R. Findeisen, “On noise-to-state stability of stochastic discrete-time systems via finite-step Lyapunov functions,” 2020, doi: <a href=\"https://doi.org/10.1109/cdc40024.2019.9030178\">10.1109/cdc40024.2019.9030178</a>.","short":"N. Noroozi, R. Jackson, D.E. Quevedo, F.R. Wirth, R. Findeisen, in: 2019 IEEE 58th Conference on Decision and Control (CDC), IEEE, 2020.","chicago":"Noroozi, Navid, Roxanne Jackson, Daniel E. Quevedo, Fabian R. Wirth, and Rolf Findeisen. “On Noise-to-State Stability of Stochastic Discrete-Time Systems via Finite-Step Lyapunov Functions.” In <i>2019 IEEE 58th Conference on Decision and Control (CDC)</i>. IEEE, 2020. <a href=\"https://doi.org/10.1109/cdc40024.2019.9030178\">https://doi.org/10.1109/cdc40024.2019.9030178</a>."},"date_created":"2023-02-14T11:02:49Z","type":"conference","department":[{"_id":"57"}]},{"citation":{"ama":"Ding K, Ren X, Quevedo DE, Dey S, Shi L. Defensive deception against reactive jamming attacks in remote state estimation. <i>Automatica</i>. 2020;113.","bibtex":"@article{Ding_Ren_Quevedo_Dey_Shi_2020, title={Defensive deception against reactive jamming attacks in remote state estimation}, volume={113}, journal={Automatica}, author={Ding, K. and Ren, X. and Quevedo, D. E. and Dey, S. and Shi, L.}, year={2020} }","mla":"Ding, K., et al. “Defensive Deception against Reactive Jamming Attacks in Remote State Estimation.” <i>Automatica</i>, vol. 113, 2020.","short":"K. Ding, X. Ren, D.E. Quevedo, S. Dey, L. Shi, Automatica 113 (2020).","chicago":"Ding, K., X. Ren, D. E. Quevedo, S. Dey, and L. Shi. “Defensive Deception against Reactive Jamming Attacks in Remote State Estimation.” <i>Automatica</i> 113 (2020).","apa":"Ding, K., Ren, X., Quevedo, D. E., Dey, S., &#38; Shi, L. (2020). Defensive deception against reactive jamming attacks in remote state estimation. <i>Automatica</i>, <i>113</i>.","ieee":"K. Ding, X. Ren, D. E. Quevedo, S. Dey, and L. Shi, “Defensive deception against reactive jamming attacks in remote state estimation,” <i>Automatica</i>, vol. 113, 2020."},"publication":"Automatica","department":[{"_id":"57"}],"type":"journal_article","date_created":"2023-02-14T11:10:42Z","intvolume":"       113","date_updated":"2023-02-14T11:29:59Z","author":[{"first_name":"K.","last_name":"Ding","full_name":"Ding, K."},{"last_name":"Ren","first_name":"X.","full_name":"Ren, X."},{"full_name":"Quevedo, D. E.","last_name":"Quevedo","first_name":"D. E."},{"full_name":"Dey, S.","last_name":"Dey","first_name":"S."},{"full_name":"Shi, L.","first_name":"L.","last_name":"Shi"}],"status":"public","year":"2020","title":"Defensive deception against reactive jamming attacks in remote state estimation","volume":113,"user_id":"238","_id":"42074","language":[{"iso":"eng"}]},{"publication":"Automatica","abstract":[{"text":"\r\nIn many cyber–physical systems, we encounter the problem of remote state estimation of geo- graphically distributed and remote physical processes. This paper studies the scheduling of sensor transmissions to estimate the states of multiple remote, dynamic processes. Information from the different sensors has to be transmitted to a central gateway over a wireless network for monitoring purposes, where typically fewer wireless channels are available than there are processes to be monitored. For effective estimation at the gateway, the sensors need to be scheduled appropriately, i.e., at each time instant one needs to decide which sensors have network access and which ones do not. To address this scheduling problem, we formulate an associated Markov decision process (MDP). This MDP is then solved using a Deep Q-Network, a recent deep reinforcement learning algorithm that is at once scalable and model-free. We compare our scheduling algorithm to popular scheduling algorithms such as round-robin and reduced-waiting-time, among others. Our algorithm is shown to significantly outperform these algorithms for many example scenario","lang":"eng"}],"date_created":"2020-01-31T15:55:27Z","file":[{"creator":"hkarl","date_created":"2020-01-31T15:57:50Z","date_updated":"2020-01-31T15:57:50Z","relation":"main_file","access_level":"closed","file_size":"675382","file_name":"leoram20a.pdf","content_type":"application/pdf","success":1,"file_id":"15743"}],"department":[{"_id":"7"},{"_id":"34"},{"_id":"3"},{"_id":"75"},{"_id":"57"}],"type":"journal_article","author":[{"full_name":"Leong, Alex S.","last_name":"Leong","first_name":"Alex S."},{"full_name":"Ramaswamy, Arunselvan","orcid":"https://orcid.org/ 0000-0001-7547-8111","first_name":"Arunselvan","last_name":"Ramaswamy","id":"66937"},{"full_name":"Quevedo, Daniel E.","first_name":"Daniel E.","last_name":"Quevedo"},{"full_name":"Karl, Holger","first_name":"Holger","last_name":"Karl","id":"126"},{"full_name":"Shi, Ling","first_name":"Ling","last_name":"Shi"}],"publication_identifier":{"issn":["0005-1098"]},"year":"2019","title":"Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems","date_updated":"2022-01-06T06:52:32Z","publication_status":"published","language":[{"iso":"eng"}],"article_number":"108759","doi":"10.1016/j.automatica.2019.108759","citation":{"mla":"Leong, Alex S., et al. “Deep Reinforcement Learning for Wireless Sensor Scheduling in Cyber–Physical Systems.” <i>Automatica</i>, 108759, 2019, doi:<a href=\"https://doi.org/10.1016/j.automatica.2019.108759\">10.1016/j.automatica.2019.108759</a>.","bibtex":"@article{Leong_Ramaswamy_Quevedo_Karl_Shi_2019, title={Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems}, DOI={<a href=\"https://doi.org/10.1016/j.automatica.2019.108759\">10.1016/j.automatica.2019.108759</a>}, number={108759}, journal={Automatica}, author={Leong, Alex S. and Ramaswamy, Arunselvan and Quevedo, Daniel E. and Karl, Holger and Shi, Ling}, year={2019} }","ama":"Leong AS, Ramaswamy A, Quevedo DE, Karl H, Shi L. Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems. <i>Automatica</i>. 2019. doi:<a href=\"https://doi.org/10.1016/j.automatica.2019.108759\">10.1016/j.automatica.2019.108759</a>","ieee":"A. S. Leong, A. Ramaswamy, D. E. Quevedo, H. Karl, and L. Shi, “Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems,” <i>Automatica</i>, 2019.","apa":"Leong, A. S., Ramaswamy, A., Quevedo, D. E., Karl, H., &#38; Shi, L. (2019). Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems. <i>Automatica</i>. <a href=\"https://doi.org/10.1016/j.automatica.2019.108759\">https://doi.org/10.1016/j.automatica.2019.108759</a>","short":"A.S. Leong, A. Ramaswamy, D.E. Quevedo, H. Karl, L. Shi, Automatica (2019).","chicago":"Leong, Alex S., Arunselvan Ramaswamy, Daniel E. Quevedo, Holger Karl, and Ling Shi. “Deep Reinforcement Learning for Wireless Sensor Scheduling in Cyber–Physical Systems.” <i>Automatica</i>, 2019. <a href=\"https://doi.org/10.1016/j.automatica.2019.108759\">https://doi.org/10.1016/j.automatica.2019.108759</a>."},"file_date_updated":"2020-01-31T15:57:50Z","project":[{"_id":"24","name":"Netzgewahre Regelung & regelungsgewahre Netze"}],"quality_controlled":"1","status":"public","has_accepted_license":"1","_id":"15741","ddc":["000"],"user_id":"126"},{"publication":"Automatica","department":[{"_id":"57"}],"type":"journal_article","keyword":["Electrical and Electronic Engineering","Control and Systems Engineering"],"date_created":"2023-01-09T16:44:58Z","intvolume":"       113","publication_status":"published","date_updated":"2023-01-09T16:45:15Z","author":[{"full_name":"Leong, Alex S.","first_name":"Alex S.","last_name":"Leong"},{"full_name":"Ramaswamy, Arunselvan","first_name":"Arunselvan","last_name":"Ramaswamy"},{"last_name":"Quevedo","first_name":"Daniel E.","full_name":"Quevedo, Daniel E."},{"first_name":"Holger","last_name":"Karl","full_name":"Karl, Holger"},{"first_name":"Ling","last_name":"Shi","full_name":"Shi, Ling"}],"publication_identifier":{"issn":["0005-1098"]},"year":"2019","title":"Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems","doi":"10.1016/j.automatica.2019.108759","language":[{"iso":"eng"}],"article_number":"108759","citation":{"chicago":"Leong, Alex S., Arunselvan Ramaswamy, Daniel E. Quevedo, Holger Karl, and Ling Shi. “Deep Reinforcement Learning for Wireless Sensor Scheduling in Cyber–Physical Systems.” <i>Automatica</i> 113 (2019). <a href=\"https://doi.org/10.1016/j.automatica.2019.108759\">https://doi.org/10.1016/j.automatica.2019.108759</a>.","short":"A.S. Leong, A. Ramaswamy, D.E. Quevedo, H. Karl, L. Shi, Automatica 113 (2019).","apa":"Leong, A. S., Ramaswamy, A., Quevedo, D. E., Karl, H., &#38; Shi, L. (2019). Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems. <i>Automatica</i>, <i>113</i>, Article 108759. <a href=\"https://doi.org/10.1016/j.automatica.2019.108759\">https://doi.org/10.1016/j.automatica.2019.108759</a>","ieee":"A. S. Leong, A. Ramaswamy, D. E. Quevedo, H. Karl, and L. Shi, “Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems,” <i>Automatica</i>, vol. 113, Art. no. 108759, 2019, doi: <a href=\"https://doi.org/10.1016/j.automatica.2019.108759\">10.1016/j.automatica.2019.108759</a>.","ama":"Leong AS, Ramaswamy A, Quevedo DE, Karl H, Shi L. Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems. <i>Automatica</i>. 2019;113. doi:<a href=\"https://doi.org/10.1016/j.automatica.2019.108759\">10.1016/j.automatica.2019.108759</a>","bibtex":"@article{Leong_Ramaswamy_Quevedo_Karl_Shi_2019, title={Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems}, volume={113}, DOI={<a href=\"https://doi.org/10.1016/j.automatica.2019.108759\">10.1016/j.automatica.2019.108759</a>}, number={108759}, journal={Automatica}, publisher={Elsevier BV}, author={Leong, Alex S. and Ramaswamy, Arunselvan and Quevedo, Daniel E. and Karl, Holger and Shi, Ling}, year={2019} }","mla":"Leong, Alex S., et al. “Deep Reinforcement Learning for Wireless Sensor Scheduling in Cyber–Physical Systems.” <i>Automatica</i>, vol. 113, 108759, Elsevier BV, 2019, doi:<a href=\"https://doi.org/10.1016/j.automatica.2019.108759\">10.1016/j.automatica.2019.108759</a>."},"status":"public","volume":113,"user_id":"158","_id":"35583","publisher":"Elsevier BV"},{"citation":{"short":"K. Ding, X. Ren, D.E. Quevedo, S. Dey, L. Shi, Automatica 113 (2019).","chicago":"Ding, Kemi, Xiaoqiang Ren, Daniel E. Quevedo, Subhrakanti Dey, and Ling Shi. “Defensive Deception against Reactive Jamming Attacks in Remote State Estimation.” <i>Automatica</i> 113 (2019). <a href=\"https://doi.org/10.1016/j.automatica.2019.108680\">https://doi.org/10.1016/j.automatica.2019.108680</a>.","apa":"Ding, K., Ren, X., Quevedo, D. E., Dey, S., &#38; Shi, L. (2019). Defensive deception against reactive jamming attacks in remote state estimation. <i>Automatica</i>, <i>113</i>, Article 108680. <a href=\"https://doi.org/10.1016/j.automatica.2019.108680\">https://doi.org/10.1016/j.automatica.2019.108680</a>","ieee":"K. Ding, X. Ren, D. E. Quevedo, S. Dey, and L. Shi, “Defensive deception against reactive jamming attacks in remote state estimation,” <i>Automatica</i>, vol. 113, Art. no. 108680, 2019, doi: <a href=\"https://doi.org/10.1016/j.automatica.2019.108680\">10.1016/j.automatica.2019.108680</a>.","ama":"Ding K, Ren X, Quevedo DE, Dey S, Shi L. Defensive deception against reactive jamming attacks in remote state estimation. <i>Automatica</i>. 2019;113. doi:<a href=\"https://doi.org/10.1016/j.automatica.2019.108680\">10.1016/j.automatica.2019.108680</a>","bibtex":"@article{Ding_Ren_Quevedo_Dey_Shi_2019, title={Defensive deception against reactive jamming attacks in remote state estimation}, volume={113}, DOI={<a href=\"https://doi.org/10.1016/j.automatica.2019.108680\">10.1016/j.automatica.2019.108680</a>}, number={108680}, journal={Automatica}, publisher={Elsevier BV}, author={Ding, Kemi and Ren, Xiaoqiang and Quevedo, Daniel E. and Dey, Subhrakanti and Shi, Ling}, year={2019} }","mla":"Ding, Kemi, et al. “Defensive Deception against Reactive Jamming Attacks in Remote State Estimation.” <i>Automatica</i>, vol. 113, 108680, Elsevier BV, 2019, doi:<a href=\"https://doi.org/10.1016/j.automatica.2019.108680\">10.1016/j.automatica.2019.108680</a>."},"user_id":"158","volume":113,"_id":"35584","publisher":"Elsevier BV","status":"public","type":"journal_article","keyword":["Electrical and Electronic Engineering","Control and Systems Engineering"],"department":[{"_id":"57"}],"date_created":"2023-01-09T16:45:46Z","publication":"Automatica","doi":"10.1016/j.automatica.2019.108680","article_number":"108680","language":[{"iso":"eng"}],"date_updated":"2023-01-09T16:45:59Z","publication_status":"published","intvolume":"       113","title":"Defensive deception against reactive jamming attacks in remote state estimation","year":"2019","publication_identifier":{"issn":["0005-1098"]},"author":[{"first_name":"Kemi","last_name":"Ding","full_name":"Ding, Kemi"},{"last_name":"Ren","first_name":"Xiaoqiang","full_name":"Ren, Xiaoqiang"},{"full_name":"Quevedo, Daniel E.","first_name":"Daniel E.","last_name":"Quevedo"},{"last_name":"Dey","first_name":"Subhrakanti","full_name":"Dey, Subhrakanti"},{"last_name":"Shi","first_name":"Ling","full_name":"Shi, Ling"}]},{"issue":"1","publication":"Trans. Signal Processing","citation":{"short":"Z. Guo, D. Shi, D.E. Quevedo, L. Shi, Trans. Signal Processing 67 (2019) 194–207.","chicago":"Guo, Z., D. Shi, D. E. Quevedo, and L. Shi. “Secure State Estimation Against Integrity Attacks: A Gaussian Mixture Model Approach Secure State Estimation Against Integrity Attacks: A Gaussian Mixture Model Approach.” <i>Trans. Signal Processing</i> 67, no. 1 (2019): 194–207.","apa":"Guo, Z., Shi, D., Quevedo, D. E., &#38; Shi, L. (2019). Secure State Estimation Against Integrity Attacks: A Gaussian Mixture Model Approach Secure State Estimation Against Integrity Attacks: A Gaussian Mixture Model Approach. <i>Trans. Signal Processing</i>, <i>67</i>(1), 194–207.","ieee":"Z. Guo, D. Shi, D. E. Quevedo, and L. Shi, “Secure State Estimation Against Integrity Attacks: A Gaussian Mixture Model Approach Secure State Estimation Against Integrity Attacks: A Gaussian Mixture Model Approach,” <i>Trans. Signal Processing</i>, vol. 67, no. 1, pp. 194–207, 2019.","ama":"Guo Z, Shi D, Quevedo DE, Shi L. Secure State Estimation Against Integrity Attacks: A Gaussian Mixture Model Approach Secure State Estimation Against Integrity Attacks: A Gaussian Mixture Model Approach. <i>Trans Signal Processing</i>. 2019;67(1):194–207.","bibtex":"@article{Guo_Shi_Quevedo_Shi_2019, title={Secure State Estimation Against Integrity Attacks: A Gaussian Mixture Model Approach Secure State Estimation Against Integrity Attacks: A Gaussian Mixture Model Approach}, volume={67}, number={1}, journal={Trans. Signal Processing}, author={Guo, Z. and Shi, D. and Quevedo, D. E. and Shi, L.}, year={2019}, pages={194–207} }","mla":"Guo, Z., et al. “Secure State Estimation Against Integrity Attacks: A Gaussian Mixture Model Approach Secure State Estimation Against Integrity Attacks: A Gaussian Mixture Model Approach.” <i>Trans. Signal Processing</i>, vol. 67, no. 1, 2019, pp. 194–207."},"date_created":"2023-02-17T14:34:53Z","type":"journal_article","department":[{"_id":"57"}],"year":"2019","title":"Secure State Estimation Against Integrity Attacks: A Gaussian Mixture Model Approach Secure State Estimation Against Integrity Attacks: A Gaussian Mixture Model Approach","status":"public","author":[{"full_name":"Guo, Z.","first_name":"Z.","last_name":"Guo"},{"last_name":"Shi","first_name":"D.","full_name":"Shi, D."},{"first_name":"D. E.","last_name":"Quevedo","full_name":"Quevedo, D. E."},{"full_name":"Shi, L.","first_name":"L.","last_name":"Shi"}],"date_updated":"2023-02-17T14:38:38Z","intvolume":"        67","page":"194–207","language":[{"iso":"eng"}],"_id":"42220","user_id":"238","volume":67}]