[{"article_number":"11506","user_id":"83781","date_updated":"2025-06-26T07:50:56Z","year":"2023","language":[{"iso":"eng"}],"external_id":{"isi":["001096019200001"]},"date_created":"2025-04-16T07:27:52Z","status":"public","author":[{"full_name":"Hinterleitner, Alexander","last_name":"Hinterleitner","first_name":"Alexander"},{"last_name":"Schulz","full_name":"Schulz, Richard","first_name":"Richard"},{"first_name":"Lukas","last_name":"Hans","full_name":"Hans, Lukas"},{"full_name":"Subbotin, Aleksandr","last_name":"Subbotin","first_name":"Aleksandr"},{"first_name":"Nils","full_name":"Barthel, Nils","last_name":"Barthel"},{"first_name":"Noah","last_name":"Pütz","full_name":"Pütz, Noah"},{"first_name":"Martin","last_name":"Rosellen","full_name":"Rosellen, Martin"},{"first_name":"Thomas","last_name":"Bartz-Beielstein","full_name":"Bartz-Beielstein, Thomas"},{"first_name":"Christoph","last_name":"Geng","full_name":"Geng, Christoph","id":"61408"},{"first_name":"Phillip","last_name":"Priss","full_name":"Priss, Phillip"}],"place":"Basel","abstract":[{"text":"Cyber-Physical Systems (CPS) play an essential role in today’s production processes, leveraging Artificial Intelligence (AI) to enhance operations such as optimization, anomaly detection, and predictive maintenance. This article reviews a cognitive architecture for Artificial Intelligence, which has been developed to establish a standard framework for integrating AI solutions into existing production processes. Given that machines in these processes continuously generate large streams of data, Online Machine Learning (OML) is identified as a crucial extension to the existing architecture. To substantiate this claim, real-world experiments using a slitting machine are conducted, to compare the performance of OML to traditional Batch Machine Learning. The assessment of contemporary OML algorithms using a real production system is a fundamental innovation in this research. The evaluations clearly indicate that OML adds significant value to CPS, and it is strongly recommended as an extension of related architectures, such as the cognitive architecture for AI discussed in this article. Additionally, surrogate-model-based optimization is employed, to determine the optimal hyperparameter settings for the corresponding OML algorithms, aiming to achieve peak performance in their respective tasks.","lang":"eng"}],"volume":13,"_id":"12806","type":"scientific_journal_article","keyword":["machine learning","online algorithms","cyber-physical production systems","surrogate-based optimization"],"issue":"20","doi":"10.3390/app132011506","department":[{"_id":"DEP5023"}],"title":"Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture","publication_identifier":{"issn":["2076-3417"]},"publication_status":"published","isi":"1","publication":"  Applied Sciences : open access journal","intvolume":"        13","publisher":"MDPI AG","citation":{"din1505-2-1":"<span style=\"font-variant:small-caps;\"><span style=\"font-variant:small-caps;\">Hinterleitner, Alexander</span> ; <span style=\"font-variant:small-caps;\">Schulz, Richard</span> ; <span style=\"font-variant:small-caps;\">Hans, Lukas</span> ; <span style=\"font-variant:small-caps;\">Subbotin, Aleksandr</span> ; <span style=\"font-variant:small-caps;\">Barthel, Nils</span> ; <span style=\"font-variant:small-caps;\">Pütz, Noah</span> ; <span style=\"font-variant:small-caps;\">Rosellen, Martin</span> ; <span style=\"font-variant:small-caps;\">Bartz-Beielstein, Thomas</span> ; u. a.</span>: Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture. In: <i>  Applied Sciences : open access journal</i> Bd. 13. Basel, MDPI AG (2023), Nr. 20","chicago":"Hinterleitner, Alexander, Richard Schulz, Lukas Hans, Aleksandr Subbotin, Nils Barthel, Noah Pütz, Martin Rosellen, Thomas Bartz-Beielstein, Christoph Geng, and Phillip Priss. “Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture.” <i>  Applied Sciences : Open Access Journal</i> 13, no. 20 (2023). <a href=\"https://doi.org/10.3390/app132011506\">https://doi.org/10.3390/app132011506</a>.","short":"A. Hinterleitner, R. Schulz, L. Hans, A. Subbotin, N. Barthel, N. Pütz, M. Rosellen, T. Bartz-Beielstein, C. Geng, P. Priss,   Applied Sciences : Open Access Journal 13 (2023).","mla":"Hinterleitner, Alexander, et al. “Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture.” <i>  Applied Sciences : Open Access Journal</i>, vol. 13, no. 20, 11506, 2023, <a href=\"https://doi.org/10.3390/app132011506\">https://doi.org/10.3390/app132011506</a>.","chicago-de":"Hinterleitner, Alexander, Richard Schulz, Lukas Hans, Aleksandr Subbotin, Nils Barthel, Noah Pütz, Martin Rosellen, Thomas Bartz-Beielstein, Christoph Geng und Phillip Priss. 2023. Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture. <i>  Applied Sciences : open access journal</i> 13, Nr. 20. doi:<a href=\"https://doi.org/10.3390/app132011506\">10.3390/app132011506</a>, .","bjps":"<b>Hinterleitner A <i>et al.</i></b> (2023) Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture. <i>  Applied Sciences : open access journal</i> <b>13</b>.","ufg":"<b>Hinterleitner, Alexander u. a.</b>: Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture, in: <i>  Applied Sciences : open access journal</i> 13 (2023), H. 20.","havard":"A. Hinterleitner, R. Schulz, L. Hans, A. Subbotin, N. Barthel, N. Pütz, M. Rosellen, T. Bartz-Beielstein, C. Geng, P. Priss, Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture,   Applied Sciences : Open Access Journal. 13 (2023).","apa":"Hinterleitner, A., Schulz, R., Hans, L., Subbotin, A., Barthel, N., Pütz, N., Rosellen, M., Bartz-Beielstein, T., Geng, C., &#38; Priss, P. (2023). Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture. <i>  Applied Sciences : Open Access Journal</i>, <i>13</i>(20), Article 11506. <a href=\"https://doi.org/10.3390/app132011506\">https://doi.org/10.3390/app132011506</a>","ama":"Hinterleitner A, Schulz R, Hans L, et al. Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture. <i>  Applied Sciences : open access journal</i>. 2023;13(20). doi:<a href=\"https://doi.org/10.3390/app132011506\">10.3390/app132011506</a>","van":"Hinterleitner A, Schulz R, Hans L, Subbotin A, Barthel N, Pütz N, et al. Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture.   Applied Sciences : open access journal. 2023;13(20).","ieee":"A. Hinterleitner <i>et al.</i>, “Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture,” <i>  Applied Sciences : open access journal</i>, vol. 13, no. 20, Art. no. 11506, 2023, doi: <a href=\"https://doi.org/10.3390/app132011506\">10.3390/app132011506</a>."}},{"user_id":"83781","date_updated":"2025-06-26T13:37:53Z","year":"2022","language":[{"iso":"eng"}],"date_created":"2025-04-16T06:58:04Z","corporate_editor":["Neural Information Processing Systems Foundation "],"status":"public","author":[{"first_name":"Andreas","full_name":"Besginow, Andreas","id":"61743","last_name":"Besginow"},{"first_name":"Markus","last_name":"Lange-Hegermann","id":"71761","full_name":"Lange-Hegermann, Markus"}],"place":"Red Hook, NY ","editor":[{"first_name":"S.","full_name":"Koyejo, S.","last_name":"Koyejo"},{"first_name":"S.","last_name":"Mohamed","full_name":"Mohamed, S."},{"last_name":"Agarwal","full_name":"Agarwal, A.","first_name":"A."},{"first_name":"D.","last_name":"Belgrave","full_name":"Belgrave, D."},{"first_name":"K.","full_name":"Cho, K.","last_name":"Cho"},{"last_name":"Oh","full_name":"Oh, A.","first_name":"A."}],"abstract":[{"text":"Data in many applications follows systems of Ordinary Differential Equations (ODEs). This paper presents a novel algorithmic and symbolic construction for covariance functions of Gaussian Processes (GPs) with realizations strictly following a system of linear homogeneous ODEs with constant coefficients, which we call LODE-GPs. Introducing this strong inductive bias into a GP improves modelling of such data. Using smith normal form algorithms, a symbolic technique, we overcome two current restrictions in the state of the art: (1) the need for certain uniqueness conditions in the set of solutions, typically assumed in classical ODE solvers and their probabilistic counterparts, and (2) the restriction to controllable systems, typically assumed when encoding differential equations in covariance functions. We show the effectiveness of LODE-GPs in a number of experiments, for example learning physically interpretable parameters by maximizing the likelihood.","lang":"eng"}],"volume":35,"_id":"12804","page":"29386 - 29399","type":"conference_editor_article","keyword":["SMITH NORMAL-FORM","ALGORITHMS","REDUCTION"],"title":"Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations","department":[{"_id":"DEP5000"}],"publication_identifier":{"isbn":["978-1-7138-7108-8 "],"issn":["1049-5258"],"eisbn":["978-1-7138-7312-9"]},"publication_status":"published","publication":"36th Conference on Neural Information Processing Systems (NeurIPS 2022) ","conference":{"name":"36th Conference on Neural Information Processing Systems (NeurIPS)","end_date":"2022-12-09","location":"New Orleans, La.; Online","start_date":"2022-11-28"},"intvolume":"        35","series_title":"Advances in Neural Information Processing Systems","citation":{"havard":"A. Besginow, M. Lange-Hegermann, Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations, Curran Associates, Inc., Red Hook, NY , 2022.","ufg":"<b>Besginow, Andreas/Lange-Hegermann, Markus</b>: Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations, Bd. 35, hg. von Koyejo, S. u. a., Red Hook, NY  2022 (Advances in Neural Information Processing Systems).","mla":"Besginow, Andreas, and Markus Lange-Hegermann. “Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations.” <i>36th Conference on Neural Information Processing Systems (NeurIPS 2022) </i>, edited by S. Koyejo et al., vol. 35, Curran Associates, Inc., 2022, pp. 29386–99.","short":"A. Besginow, M. Lange-Hegermann, Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations, Curran Associates, Inc., Red Hook, NY , 2022.","ama":"Besginow A, Lange-Hegermann M. <i>Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations</i>. Vol 35. (Koyejo S, Mohamed S, Agarwal A, et al., eds.). Curran Associates, Inc.; 2022:29386-29399.","chicago":"Besginow, Andreas, and Markus Lange-Hegermann. <i>Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations</i>. Edited by S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh, and Neural Information Processing Systems Foundation . <i>36th Conference on Neural Information Processing Systems (NeurIPS 2022) </i>. Vol. 35. Advances in Neural Information Processing Systems. Red Hook, NY : Curran Associates, Inc., 2022.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Besginow, Andreas</span> ; <span style=\"font-variant:small-caps;\">Lange-Hegermann, Markus</span> ; <span style=\"font-variant:small-caps;\">Koyejo, S.</span> ; <span style=\"font-variant:small-caps;\">Mohamed, S.</span> ; <span style=\"font-variant:small-caps;\">Agarwal, A.</span> ; <span style=\"font-variant:small-caps;\">Belgrave, D.</span> ; <span style=\"font-variant:small-caps;\">Cho, K.</span> ; <span style=\"font-variant:small-caps;\">Oh, A.</span> ; <span style=\"font-variant:small-caps;\">Neural Information Processing Systems Foundation </span> (Hrsg.): <i>Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations</i>, <i>Advances in Neural Information Processing Systems</i>. Bd. 35. Red Hook, NY  : Curran Associates, Inc., 2022","apa":"Besginow, A., &#38; Lange-Hegermann, M. (2022). Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh, &#38; Neural Information Processing Systems Foundation  (Eds.), <i>36th Conference on Neural Information Processing Systems (NeurIPS 2022) </i> (Vol. 35, pp. 29386–29399). Curran Associates, Inc.","ieee":"A. Besginow and M. Lange-Hegermann, <i>Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations</i>, vol. 35. Red Hook, NY : Curran Associates, Inc., 2022, pp. 29386–29399.","van":"Besginow A, Lange-Hegermann M. Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations. Koyejo S, Mohamed S, Agarwal A, Belgrave D, Cho K, Oh A, et al., editors. 36th Conference on Neural Information Processing Systems (NeurIPS 2022) . Red Hook, NY : Curran Associates, Inc.; 2022. (Advances in Neural Information Processing Systems; vol. 35).","bjps":"<b>Besginow A and Lange-Hegermann M</b> (2022) <i>Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations</i>, Koyejo S et al. (eds). Red Hook, NY : Curran Associates, Inc.","chicago-de":"Besginow, Andreas und Markus Lange-Hegermann. 2022. <i>Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations</i>. Hg. von S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh, und Neural Information Processing Systems Foundation . <i>36th Conference on Neural Information Processing Systems (NeurIPS 2022) </i>. Bd. 35. Advances in Neural Information Processing Systems. Red Hook, NY : Curran Associates, Inc."},"publisher":"Curran Associates, Inc."},{"quality_controlled":"1","publisher":"MLResearchPress ","citation":{"havard":"M. Lange-Hegermann, Linearly Constrained Gaussian Processes with Boundary Conditions, MLResearchPress , 2021.","ufg":"<b>Lange-Hegermann, Markus</b>: Linearly Constrained Gaussian Processes with Boundary Conditions, Bd. 130, hg. von Banerjee, A./Fukumizu, K., o. O. 2021 (Proceedings of machine learning research : PMLR ).","mla":"Lange-Hegermann, Markus. “Linearly Constrained Gaussian Processes with Boundary Conditions.” <i>24th International Conference on Artificial Intelligence and Statistics (AISTATS)</i>, edited by A. Banerjee and K. Fukumizu, vol. 130, MLResearchPress , 2021.","ama":"Lange-Hegermann M. <i>Linearly Constrained Gaussian Processes with Boundary Conditions</i>. Vol 130. (Banerjee A, Fukumizu K, eds.). MLResearchPress ; 2021.","short":"M. Lange-Hegermann, Linearly Constrained Gaussian Processes with Boundary Conditions, MLResearchPress , 2021.","chicago":"Lange-Hegermann, Markus. <i>Linearly Constrained Gaussian Processes with Boundary Conditions</i>. Edited by A. Banerjee and K. Fukumizu. <i>24th International Conference on Artificial Intelligence and Statistics (AISTATS)</i>. Vol. 130. Proceedings of Machine Learning Research : PMLR . MLResearchPress , 2021.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Lange-Hegermann, Markus</span> ; <span style=\"font-variant:small-caps;\">Banerjee, A.</span> ; <span style=\"font-variant:small-caps;\">Fukumizu, K.</span> (Hrsg.): <i>Linearly Constrained Gaussian Processes with Boundary Conditions</i>, <i>Proceedings of machine learning research : PMLR </i>. Bd. 130 : MLResearchPress , 2021","apa":"Lange-Hegermann, M. (2021). Linearly Constrained Gaussian Processes with Boundary Conditions. In A. Banerjee &#38; K. Fukumizu (Eds.), <i>24th International Conference on Artificial Intelligence and Statistics (AISTATS)</i> (Vol. 130). MLResearchPress .","ieee":"M. Lange-Hegermann, <i>Linearly Constrained Gaussian Processes with Boundary Conditions</i>, vol. 130. MLResearchPress , 2021.","van":"Lange-Hegermann M. Linearly Constrained Gaussian Processes with Boundary Conditions. Banerjee A, Fukumizu K, editors. 24th International Conference on Artificial Intelligence and Statistics (AISTATS). MLResearchPress ; 2021. (Proceedings of machine learning research : PMLR ; vol. 130).","chicago-de":"Lange-Hegermann, Markus. 2021. <i>Linearly Constrained Gaussian Processes with Boundary Conditions</i>. Hg. von A. Banerjee und K. Fukumizu. <i>24th International Conference on Artificial Intelligence and Statistics (AISTATS)</i>. Bd. 130. Proceedings of machine learning research : PMLR . MLResearchPress .","bjps":"<b>Lange-Hegermann M</b> (2021) <i>Linearly Constrained Gaussian Processes with Boundary Conditions</i>, Banerjee A and Fukumizu K (eds). MLResearchPress ."},"series_title":"Proceedings of machine learning research : PMLR ","intvolume":"       130","conference":{"location":"Virtual","start_date":"2021-04-13","end_date":"2021-04-15","name":"24th International Conference on Artificial Intelligence and Statistics (AISTATS)"},"publication":"24th International Conference on Artificial Intelligence and Statistics (AISTATS)","publication_status":"published","publication_identifier":{"issn":["2640-3498"]},"title":"Linearly Constrained Gaussian Processes with Boundary Conditions","department":[{"_id":"DEP5000"},{"_id":"DEP5023"}],"keyword":["FUNCTIONAL REGRESSION","PREDICTION","ALGORITHMS","COMPLEXITY","MODELS"],"type":"conference_editor_article","_id":"12786","volume":130,"abstract":[{"lang":"eng","text":"One goal in Bayesian machine learning is to encode prior knowledge into prior distributions, to model data efficiently. We consider prior knowledge from systems of linear partial differential equations together with their boundary conditions. We construct multi-output Gaussian process priors with realizations in the solution set of such systems, in particular only such solutions can be represented by Gaussian process regression. The construction is fully algorithmic via Grobner bases and it does not employ any approximation. It builds these priors combining two parametrizations via a pullback: the first parametrizes the solutions for the system of differential equations and the second parametrizes all functions adhering to the boundary conditions."}],"editor":[{"first_name":"A.","full_name":"Banerjee, A.","last_name":"Banerjee"},{"first_name":"K.","full_name":"Fukumizu, K.","last_name":"Fukumizu"}],"author":[{"first_name":"Markus","last_name":"Lange-Hegermann","full_name":"Lange-Hegermann, Markus","id":"71761"}],"status":"public","date_created":"2025-04-14T13:58:16Z","language":[{"iso":"eng"}],"year":"2021","date_updated":"2025-06-26T13:42:36Z","user_id":"83781"},{"year":2018,"date_updated":"2023-03-15T13:49:38Z","keyword":["Multisensor systems","Temperature measurement","Current measurement","Redundancy","Pollution measurement","Detection algorithms"],"user_id":"15514","date_created":"2019-11-25T08:35:47Z","conference":{"location":"Torino, Italy","start_date":"2018-09-04","end_date":"2018-09-07","name":"IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA) 2018"},"publication":"23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)","main_file_link":[{"url":"https://ieeexplore.ieee.org/abstract/document/8502571"}],"doi":"10.1109/ETFA.2018.8502571","language":[{"iso":"eng"}],"department":[{"_id":"DEP5023"}],"title":"A Conflict-Based Drift Detection And Adaptation Approach for Multisensor Information Fusion","publication_status":"published","place":"Torino, Italy","status":"public","author":[{"first_name":"Christoph-Alexander","full_name":"Holst, Christoph-Alexander","id":"64782","last_name":"Holst"},{"id":"1804","full_name":"Lohweg, Volker","last_name":"Lohweg","first_name":"Volker","orcid":"0000-0002-3325-7887"}],"citation":{"bjps":"<b>Holst C-A and Lohweg V</b> (2018) A Conflict-Based Drift Detection And Adaptation Approach for Multisensor Information Fusion. <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>. Torino, Italy.","chicago-de":"Holst, Christoph-Alexander und Volker Lohweg. 2018. A Conflict-Based Drift Detection And Adaptation Approach for Multisensor Information Fusion. In: <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>. Torino, Italy. doi:<a href=\"https://doi.org/10.1109/ETFA.2018.8502571,\">10.1109/ETFA.2018.8502571,</a> .","short":"C.-A. Holst, V. Lohweg, in: 23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Torino, Italy, 2018.","chicago":"Holst, Christoph-Alexander, and Volker Lohweg. “A Conflict-Based Drift Detection And Adaptation Approach for Multisensor Information Fusion.” In <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>. Torino, Italy, 2018. <a href=\"https://doi.org/10.1109/ETFA.2018.8502571\">https://doi.org/10.1109/ETFA.2018.8502571</a>.","mla":"Holst, Christoph-Alexander, and Volker Lohweg. “A Conflict-Based Drift Detection And Adaptation Approach for Multisensor Information Fusion.” <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>, 2018, doi:<a href=\"https://doi.org/10.1109/ETFA.2018.8502571\">10.1109/ETFA.2018.8502571</a>.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Holst, Christoph-Alexander</span> ; <span style=\"font-variant:small-caps;\">Lohweg, Volker</span>: A Conflict-Based Drift Detection And Adaptation Approach for Multisensor Information Fusion. In: <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>. Torino, Italy, 2018","van":"Holst C-A, Lohweg V. A Conflict-Based Drift Detection And Adaptation Approach for Multisensor Information Fusion. In: 23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). Torino, Italy; 2018.","ieee":"C.-A. Holst and V. Lohweg, “A Conflict-Based Drift Detection And Adaptation Approach for Multisensor Information Fusion,” in <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>, Torino, Italy, 2018.","havard":"C.-A. Holst, V. Lohweg, A Conflict-Based Drift Detection And Adaptation Approach for Multisensor Information Fusion, in: 23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Torino, Italy, 2018.","ufg":"<b>Holst, Christoph-Alexander/Lohweg, Volker (2018)</b>: A Conflict-Based Drift Detection And Adaptation Approach for Multisensor Information Fusion, in: <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>, Torino, Italy.","ama":"Holst C-A, Lohweg V. A Conflict-Based Drift Detection And Adaptation Approach for Multisensor Information Fusion. In: <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>. Torino, Italy; 2018. doi:<a href=\"https://doi.org/10.1109/ETFA.2018.8502571\">10.1109/ETFA.2018.8502571</a>","apa":"Holst, C.-A., &#38; Lohweg, V. (2018). A Conflict-Based Drift Detection And Adaptation Approach for Multisensor Information Fusion. In <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>. Torino, Italy. <a href=\"https://doi.org/10.1109/ETFA.2018.8502571\">https://doi.org/10.1109/ETFA.2018.8502571</a>"},"_id":"2007","type":"conference","abstract":[{"lang":"eng","text":"Multisensor systems are susceptible to sensor ageing effects as well as to environmental changes. Due to these effects, the distribution of sensor measurements may change over time, which is referred to as sensor drift. A multisensor system which adapts to drift by self-monitoring is more durable, requires less manual maintenance, and provides information of higher quality. This contribution proposes an approach for detecting and adapting to sensor drift. The proposed detection algorithm determines the reliability of a sensor based on fuzzy pattern classifiers and a consistency measure. By this means, the inherent redundancy in multisensor systems is exploited to detect drift. Detected drift leads then to a retraining of the classifier on batched data guided by information fusion. The retraining incorporates the estimated magnitude of the drift. The proposed algorithms are evaluated in comparison with state-of-the-art methods in the scope of a publicly available dataset. It is shown that the drift detection algorithm yields results similar to the benchmark algorithm but is less computationally complex. Relearning with the drift-adapted approach results in more robust classifiers with regard to potential future drift."}]}]