Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

ML4CPS – Machine Learning for Cyber Physical Systems and Industry 4.0

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.

Nowadays machines can learn und develop self maintaining procedures, meaning that those systems can provide a broad range of substantial improvements if introduced in production. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis.

The 5th Conference on Machine Learning for Cyber Physical Systems and Industry 4.0 - ML4CPS - addresses these topics.

 

 We are looking forward to welcome you in Lemgo
the 23. - 24. October 2019!

 

Main topics of the conference are:

Machine Learning Methods – Deep Learning

  • Learning of automata and state-based systems
  • Time series prediction
  • Dimensionality reduction
  • Clustering, classification

Smart Data – Semantics and Meta Data

  • Description of Data for automatic model learning.
  • Usage of technologies like, OPCUA, AML, ontology learning,
    knowledge representation, information extraction

Machine Learning for Security

  • Intrusion Detection
  • Network Data Analysis
  • Log Analysis
  • Malware Detection
  • Cyber Attack Classification
  • Zero-Day Detection
  • Adversarial ML

Ehtics of Machine Learning

  • Legal usage of AI-based cyber physical systems
  • Planning of staff
  • Ethical questions on decisions for employees
  • Safe collaboration off humans and cyber physical systems
  • Legal developments in Germany, Europe and Worldwide.

Machine Learning in Robotics

  • Image Processing
  • Learning of new tasks
  • Collaboration, navigation and machine to robot interaction

Business Models for Machine Learning

  • Maintenance Services
  • Optimization assistance
  • New structures in development, platform services

Machine Learning on the Edge

  • Scalable Deep Learning services
  • Distributed modelling
  • Security through decentralized analysis
  • Decentralized deep learning
  • Machine learning for resource-constrained devices
  • Distributed optimization



 

The conference offers a forum to present new approaches to Machine Learning for Cyber Physical Systems, to discuss experiences and to develop visions. Therefore, the conference addresses researchers and users from different industry sectors such as production technology, automation, automotive and telecommunication.

 

Chairmen

Professor Dr.-Ing. habil. Jürgen Beyerer, Fraunhofer Institut IOSB
Professor Dr. rer. nat. Oliver Niggemann, Fraunhofer IOSB-INA

 

We are looking forward to welcome you in Lemgo

For more information and registration to the conference please contact

christian.kuehnert@iosb.fraunhofer.de

 

Fraunhofer IOSB

Vernissage der Ausstellung »Fotografie: Ukraine / Usbekistan«

Karlsruhe, 18.1.2019 - Fotos, die erzählen, in denen man gerne verweilt – so beschreibt der Fotograf Markus Breig sein Ideal. In der Ausstellung »Fotografie: Ukraine / Usbekistan« im Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB in Karlsruhe sind entsprechende Werke bis zum 11. April zu sehen. Zur feierlichen Eröffnung am Donnerstag, 24. Januar 2019, um 17.30 Uhr ist das interessierte Publikum herzlich eingeladen.

mehr lesen

W-Net 4.0: Wasserversorgern Potenziale der Digitalisierung erschließen

Karlsruhe, 15.1.2019 - Industrie 4.0 ist in aller Munde. Eine entsprechende Digitalisierungsstrategie könnte auch Wasserversorgungsunternehmen großen Mehrwert bieten – scheitert aber in der Regel an Softwareausstattung, unvollständiger Datengrundlage und/oder fehlendem In-house-Expertenwissen. Abhilfe verspricht das Verbundprojekt W-Net 4.0. Es hat zum Ziel, Geoinformations-, Simulations- und Datenanalyse-Tools in einer sicheren, einfach zu handhabenden Web-Plattform zu vereinen. Das Bundesministerium für Bildung und Forschung (BMBF) fördert das Projekt mit rund 1,6 Mio. Euro.

Read more