Machine Learning for Cyber Physical Systems Selected papers from the International Conference ML4CPS 2018 /

Machine Learning for Cyber Physical Systems Selected papers from the International Conference ML4CPS 2018 /

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This Open Access proceedings 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, October 23-24, 2018. Cyb...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Beyerer, Jürgen (Editor), Kühnert, Christian (Editor), Niggemann, Oliver (Editor)
Format: Electronic eBook
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer Vieweg, 2019.
Edition:1st ed. 2019.
Series:Technologien für die intelligente Automation, Technologies for Intelligent Automation, 9
Subjects:
Computational intelligence.
Computer engineering.
Computer networks .
Telecommunication.
Data mining.
Computational Intelligence.
Computer Engineering and Networks.
Communications Engineering, Networks.
Data Mining and Knowledge Discovery.
Online Access:Link to Metadata
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Table of Contents:
  • 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 to data analysis.

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