Titlebook: Machine Learning for Cyber-Physical Systems; Selected papers from Oliver Niggemann,Jürgen Beyerer,Christian Kühnert Conference proceedings‘

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,Using ML-Based Models in?Simulation of?CPPSs: A Case Study of?Smart Meter Production, costly process. This paper describes an approach which uses: 1) recorded data to automatically learn timed automata models of system components; and 2) manual logic based on prior knowledge that extends and enables the utilization of the learned models for simulation. Experiments in a smart meter p

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Felicitous

,Development of?a?Robotic Bin Picking Approach Based on Reinforcement Learning,r decades, there is still a gap between research and industrial application. The presented work intends to improve the utilization of bin picking for the industrial manufacturing of electrotechnical components. In this context, the development process of a system approach based on machine learning i

Enthralling

,Control Reconfiguration of?CPS via?Online Identification Using Sparse Regression (SINDYc),ation of the system are crucial. This paper proposes a method for controlling reconfiguration by identifying faults in cyber-physical systems online. The approach utilizes sparse regression (SINDYc) to identify the system dynamics, including faults, and adjusts the control law accordingly by leverag

intention

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,Domain Knowledge Injection Guidance for?Predictive Maintenance, Unique challenges that often occur in real-time manufacturing environments require the use of domain knowledge from different experts. However, there is hardly any guidance that suggests data scientists how to inject knowledge from predictive maintenance use cases in machine learning models. This p

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