Machine Learning for Cyber Physical Systems

Zusatztext

<p>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. </p><p>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. </p><p> </p>

Autorenportrait

<p><b>Prof. Dr.-Ing. Jürgen Beyerer</b> 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. </p><p><b>Prof. Dr. Oliver Niggemann</b> is Professor for Embedded Software Engineering. His research interests are in the field of Distributed Real-time Software and in the fields of analysis and diagnosis of distributed systems. He is a board member of the inIT and a senior researcher at the Fraunhofer Application Center Industrial Automation INA located in Lemgo. </p><p><b>Dr. Christian Kühnert</b> 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. </p>

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