5th ML4CPS – Machine Learning ­for ­Cyber Physical ­Systems

03/12 until 03/13/2020 in Berlin

  • Überblick

    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.

    An event of:

    Advisory board
    Prof. Michael Heizmann


    Advisory board
    Dr. Markus Köster


    Advisory board
    Prof. Markus Lange-Hegermann

    Hochschule Ostwestfalen-Lippe

    Advisory board
    Prof. Volker Lohweg


    Advisory board
    Dr. Alexander Maier

    Fraunhofer IOSB-INA

    Advisory board
    Dr. Mark Mattingley-Scott


    Advisory board
    Dr. Idel Montalvo


    Advisory board
    Dr. Thilo Steckel


    Advisory board
    Dr. Ljiljana Stojanovic

    Fraunhofer IOSB

    Advisory board
    Dr. Martin Wagner

    Technologiezentrum Wasser

    Advisory board
    Prof. Stefan Wrobel

    Fraunhofer IAIS

    Your contact persons:

    Prof. Dr. Jürgen Beyerer

    Institutsleiter Fraunhofer IOSB

    Prof. Dr. Oliver Niggemann

    Institut für Automatisierungstechnik, Helmut-Schmidt-Universität / Universität der Bundeswehr Hamburg

    Dr. Christian Kühnert

    Fraunhofer IOSB

    Dr. Alexander Maier

    Fraunhofer IOSB-INA

    Dr. Olaf Sauer

    Fraunhofer IOSB

    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
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