Machine Learning for Cyber Physical Systems

<div>Machine Learning for Enhanced Waste Quantity Reduction: Insights from the MONSOON Industry 4.0 Project.- Deduction of time-dependent machine tool characteristics by fuzzy-clustering.- Unsupervised Anomaly Detection in Production Lines.- A Random Forest Based Classifer for Error Prediction of Highly Individualized Products.- Web-based Machine Learning Platform for Condition-Monitoring.- Selection and Application of Machine Learning-Algorithms in Production Quality.- Which deep artifificial neural network architecture to use for anomaly detection in Mobile Robots kinematic data.- GPU GEMM-Kernel Autotuning for scalable machine learners.- Process Control in a Press Hardening Production Line with Numerous Process Variables and Quality Criteria.- A Process Model for Enhancing Digital Assistance in Knowledge-Based Maintenance.- Detection of Directed Connectivities in Dynamic Systems for Different Excitation Signals using Spectral Granger Causality.- Enabling Self-Diagnosis of AutomationDevices through Industrial Analytics.- Making Industrial Analytics work for Factory Automation Applications.- Application of Reinforcement Learning in Production Planning and Control of Cyber Physical Production Systems.- LoRaWan for Smarter Management of Water Network: From metering</div><div>to data analysis.</div>