@misc{12806,
  abstract     = {{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.}},
  author       = {{Hinterleitner, Alexander and Schulz, Richard and Hans, Lukas and Subbotin, Aleksandr and Barthel, Nils and Pütz, Noah and Rosellen, Martin and Bartz-Beielstein, Thomas and Geng, Christoph and Priss, Phillip}},
  booktitle    = {{  Applied Sciences : open access journal}},
  issn         = {{2076-3417}},
  keywords     = {{machine learning, online algorithms, cyber-physical production systems, surrogate-based optimization}},
  number       = {{20}},
  publisher    = {{MDPI AG}},
  title        = {{{Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture}}},
  doi          = {{10.3390/app132011506}},
  volume       = {{13}},
  year         = {{2023}},
}

@misc{12804,
  abstract     = {{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.}},
  author       = {{Besginow, Andreas and Lange-Hegermann, Markus}},
  booktitle    = {{36th Conference on Neural Information Processing Systems (NeurIPS 2022) }},
  editor       = {{Koyejo, S. and Mohamed, S. and Agarwal, A. and Belgrave, D. and Cho, K. and Oh, A.}},
  isbn         = {{978-1-7138-7108-8 }},
  issn         = {{1049-5258}},
  keywords     = {{SMITH NORMAL-FORM, ALGORITHMS, REDUCTION}},
  location     = {{New Orleans, La.; Online}},
  pages        = {{29386 -- 29399}},
  publisher    = {{Curran Associates, Inc.}},
  title        = {{{Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations}}},
  volume       = {{35}},
  year         = {{2022}},
}

@misc{12786,
  abstract     = {{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.}},
  author       = {{Lange-Hegermann, Markus}},
  booktitle    = {{24th International Conference on Artificial Intelligence and Statistics (AISTATS)}},
  editor       = {{Banerjee, A. and Fukumizu, K.}},
  issn         = {{2640-3498}},
  keywords     = {{FUNCTIONAL REGRESSION, PREDICTION, ALGORITHMS, COMPLEXITY, MODELS}},
  location     = {{Virtual}},
  publisher    = {{MLResearchPress }},
  title        = {{{Linearly Constrained Gaussian Processes with Boundary Conditions}}},
  volume       = {{130}},
  year         = {{2021}},
}

@inproceedings{2007,
  abstract     = {{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.}},
  author       = {{Holst, Christoph-Alexander and Lohweg, Volker}},
  booktitle    = {{23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  keywords     = {{Multisensor systems, Temperature measurement, Current measurement, Redundancy, Pollution measurement, Detection algorithms}},
  location     = {{Torino, Italy}},
  title        = {{{A Conflict-Based Drift Detection And Adaptation Approach for Multisensor Information Fusion}}},
  doi          = {{10.1109/ETFA.2018.8502571}},
  year         = {{2018}},
}