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Full Description
This open access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains selected papers from the fifth international Conference ML4CPS - Machine Learning for Cyber Physical Systems, which was held in Berlin, March 12-13, 2020.
Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.
Contents
Preface.- Energy Profile Prediction of Milling Processes Using Machine Learning Techniques.- Improvement of the prediction quality of electrical load profiles with artficial neural networks.- Detection and localization of an underwater docking station.- Deployment architecture for the local delivery of ML-Models to the industrial shop floor.- Deep Learning in Resource and Data Constrained Edge Computing Systems.- Prediction of Batch Processes Runtime Applying Dynamic Time Warping and Survival Analysis.- Proposal for requirements on industrial AI solutions.- Information modeling and knowledge extraction for machine learning applications in industrial production systems.- Explanation Framework for Intrusion Detection.- Automatic Generation of Improvement Suggestions for Legacy, PLC Controlled Manufacturing Equipment Utilizing Machine Learning.- Hardening Deep Neural Networks in Condition Monitoring Systems against Adversarial ExampleAttacks.- First Approaches to Automatically Diagnose and Reconfigure Hybrid Cyber-Physical Systems.- Machine learning for reconstruction of highly porous structures from FIB-SEM nano-tomographic data.
