Full Description
The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS - Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, September 29th, 2016.
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
A Concept for the Application of Reinforcement Learning in the Optimization of CAM-Generated Tool Paths.- Semantic Stream Processing in Dynamic Environments Using Dynamic Stream Selection.- Dynamic Bayesian Network-Based Anomaly Detection for In-Process Visual Inspection of Laser Surface Heat Treatment.- A Modular Architecture for Smart Data Analysis using AutomationML, OPC-UA and Data-driven Algorithms.- Cloud-based event detection platform for water distribution networks using machine-learning algorithms.- A Generic Data Fusion and Analysis Platform for Cyber-Physical Systems.- Agent Swarm Optimization: Exploding the search space.- Anomaly Detection in Industrial Networks using Machine Learning.
