Machine Learning for Cyber Physical Systems

Chapter 1 Energy Profile Prediction of Milling Processes Using Machine Learning Techniques

Chapter 2 Improvement of the prediction quality of electrical load profiles with artificial neural networks

Chapter 3 Detection and localization of an underwater docking station in acoustic images using machine learning and generalized fuzzy hough transform

Chapter 4 Deployment architecture for the local delivery of ML-Models to the industrial shop floor

Chapter 5 Deep Learning in Resource and Data Constrained Edge Computing Systems

Chapter 6 Prediction of Batch Processes Runtime Applying Dynamic Time Warping and Survival Analysis

Chapter 7 Proposal for requirements on industrial AI solutions

Chapter 8 Information modeling and knowledge extraction for machine learning applications in industrial production systems

Chapter 9 Explanation Framework for Intrusion Detection

Chapter 10 Automatic Generation of Improvement Suggestions for Legacy, PLC Controlled Manufacturing Equipment Utilizing Machine Learning

Chapter 11 Hardening Deep Neural Networks in Condition Monitoring Systems against Adversarial Example Attacks

Chapter 12 First Approaches to Automatically Diagnose and Reconfigure Hybrid Cyber-Physical Systems

Chapter 13 Machine learning for reconstruction of highly porous structures from FIB-SEM nano-tomographic data