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  • Conference proceedings
  • Open Access
  • © 2019

Machine Learning for Cyber Physical Systems

Selected papers from the International Conference ML4CPS 2018

  • Includes the full proceedings of the 2018 ML4CPS – Machine Learning for Cyber Physical Systems Conference

  • Presents recent and new advances in automated machine learning methods

  • Provides an accessible and succinct overview on machine learning for cyber physical systems, industry 4.0 and IOT

Part of the book series: Technologien für die intelligente Automation (TIA, volume 9)

Buying options

Softcover Book USD 59.99
Price excludes VAT (USA)

Table of contents (15 papers)

  1. Front Matter

    Pages I-VII
  2. Machine Learning for Enhanced Waste Quantity Reduction: Insights from the MONSOON Industry 4.0 Project

    • Christian Beecks, Shreekantha Devasya, Ruben Schlutter
    Pages 1-6Open Access
  3. Deduction of time-dependent machine tool characteristics by fuzzy-clustering

    • Uwe Frieß, Martin Kolouch, Matthias Putz
    Pages 7-17Open Access
  4. Unsupervised Anomaly Detection in Production Lines

    • Alexander Graß, Christian Beecks, Jose Angel Carvajal Soto
    Pages 18-25Open Access
  5. Web-based Machine Learning Platform for Condition- Monitoring

    • Thomas Bernard, Christian Kühnert, Enrique Campbell
    Pages 36-45Open Access
  6. Selection and Application of Machine Learning- Algorithms in Production Quality

    • Jonathan Krauß, Maik Frye, Gustavo Teodoro Döhler Beck, Robert H. Schmitt
    Pages 46-57Open Access
  7. Which deep artifical neural network architecture to use for anomaly detection in Mobile Robots kinematic data?

    • Oliver Rettig, Silvan Müller, Marcus Strand, Darko Katic
    Pages 58-65Open Access
  8. GPU GEMM-Kernel Autotuning for scalable machine learners

    • Johannes Sailer, Christian Frey, Christian Kühnert
    Pages 66-76Open Access
  9. Process Control in a Press Hardening Production Line with Numerous Process Variables and Quality Criteria

    • Anke Stoll, Norbert Pierschel, Ken Wenzel, Tino Langer
    Pages 77-86Open Access
  10. A Process Model for Enhancing Digital Assistance in Knowledge-Based Maintenance

    • Klaudia Kovacs, Fazel Ansari, Claudio Geisert, Eckart Uhlmann, Robert Glawar, Wilfried Sihn
    Pages 87-96Open Access
  11. Enabling Self-Diagnosis of Automation Devices through Industrial Analytics

    • Carlos Paiz Gatica, Alexander Boschmann
    Pages 107-115Open Access
  12. LoRaWan for Smarter Management of Water Network: From metering to data analysis

    • Jorge Francés-Chust, Joaquín Izquierdo, Idel Montalvo
    Pages 133-136Open Access

About this book

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. 

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.  


Keywords

  • Machine Learning
  • Artificial Intelligence
  • Cognitive Robotics
  • Internet of Things
  • Computational intelligence
  • Cyber-Physical Systems
  • Computer-based algorithms
  • Smart grid
  • Open Access

Editors and Affiliations

  • Institut für Optronik, Systemtechnik und Bildauswertung, Fraunhofer, Karlsruhe, Germany

    Jürgen Beyerer

  • MRD, Fraunhofer Institute for Optronics, System Technologies and Image Exploitation IOSB, Karlsruhe, Germany

    Christian Kühnert

  • inIT - Institut für industrielle Informationstechnik, Hochschule Ostwestfalen-Lippe, Lemgo, Germany

    Oliver Niggemann

About the editors

Prof. Dr.-Ing. Jürgen Beyerer is Professor at the  Department for Interactive Real-Time Systems at the Karlsruhe Institute of Technology. In addition he manages the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB.

Dr. Christian Kühnert is a senior researcher at the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. His research interests are in the field of machine-learning, data-fusion and data-driven condition monitoring.   

Prof. Dr. Oliver Niggemann is Professor for Artificial Intelligence in Automation. His research interests are in the fields of machine learning and data analysis for Cyber-Physical Systems and in the fields of planning and diagnosis of distributed systems. He is a board member of the research institute inIT and deputy director at the Fraunhofer Application Center Industrial Automation INA located in Lemgo.


Bibliographic Information

Buying options

Softcover Book USD 59.99
Price excludes VAT (USA)