Machine Learning for Cyber Physical Systems

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.