Machine Learning for Cyber-Physical Systems

lt;p>Prof. Dr. Oliver Niggemann did his doctorate in 2001 at the University of Paderborn (Germany) with the topic "Visual Data Mining of Graph-Based Data". He then worked for eight years in leading positions in industry. From 2008-2019 he had a professorship at the Institute for Industrial Information Technologies (inIT) in Lemgo (Germany). Until 2019 Prof. Niggemann was also deputy head of the Fraunhofer IOSB-INA, which works in industrial automation. In 2019 Prof. Niggemann took over the university professorship "Computer Science in Mechanical Engineering" at the Helmut-Schmidt-University in Hamburg (Germany). There he does research at the Institute for Automation Technology IfA in the field of artificial intelligence and machine learning for cyber-physical systems.

Prof. Dr.-Ing. Jürgen Beyerer has been a full professor for informatics at the Institute for Anthropomatics and Robotics at the Karlsruhe Institute of Technology KIT (Germany) since March 2004 and director of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB in Ettlingen, Karlsruhe, Ilmenau, Görlitz, Lemgo, Oberkochen and Rostock (Germany). His research interests include automated visual inspection, signal and image processing, variable image acquisition and processing, active vision, metrology, information theory, fusion of data and information from heterogeneous sources, system theory, autonomous systems and automation.

Dr. Maria Krantz is a Postdoc at the Helmut-Schmidt-University in Hamburg (Germany). Her main research interests are causality in cyber-physical systems and applications of diagnosis algorithms in production systems.

Dr. Christian Kühnert is senior scientist at the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. His research interests are in the field of machine-learning, data-fusion and data analytics for cyber-physical systems.

Causal Structure Learning using PCMCI+ and Path Constraints from Wavelet-based Soft Interventions.- Reinforcement Learning from Human Feedback for Cyber-Physical Systems: On the Potential of Self-Supervised Pretraining.- Using ML-based Models in Simulation of CPPSs: A Case Study of Smart Meter Production.- Deploying machine learning in high pressure resin transfer molding and part post processing: a case study.- Development of a Robotic Bin Picking Approach based on Reinforcement Learning.- Control Reconfiguration of CPS via Online Identification using Sparse Regression (SINDYc).- Using Forest Structures for Passive Automata Learning.- Domain Knowledge Injection Guidance for Predictive Maintenance.- Towards a systematic approach for Prescriptive Analytics use cases in smart factories.- Development of a standardized data acquisition prototype for heterogeneous sensor environments as a basis for ML applications in pultrusion.- A Digital Twin Design for conveyor belts predictive maintenance.- Augmenting explainable data-driven models in energy systems: A Python framework for feature engineering.