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Machine Learning for Cyber Physical Systems: Selected papers from the International Conference ML4CPS 2018
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.
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.
This book is included in DOAB.
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Rights Information
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- 65 - pdf (CC BY) at OAPEN Library.
- 283 - pdf (CC BY) at Unglue.it.
Keywords
- artificial intelligence
- Cognitive Robotics
- Communications engineering / telecommunications
- Computational intelligence
- Computer networking & communications
- Computer organization
- Computer science
- Computer-based algorithms
- Computing & information technology
- cyber-physical systems
- Data mining
- Databases
- Electrical engineering
- Electronics & communications engineering
- Engineering
- Internet of Things
- Machine learning
- Smart grid
- Technology, engineering, agriculture
Links
DOI: 10.1007/978-3-662-58485-9web: https://link.springer.com/book/10.1007/978-3-662-58485-9
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