Machine Learning for Cyber Physical Systems
Springer Berlin (Verlag)
978-3-662-59083-6 (ISBN)
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.
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 Lemgo, October 25th-26th, 2017.
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.
Prof. Dr.-Ing. Jurgen 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. Alexander Maier is head of group Machine Learning at Fraunhofer IOSB-INA. His focus is on the development of algorithms for big data applications in Cyber-Physical Systems (diagnostics, optimization, predictive maintenance) and the transfer of research results to industry. 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.
Prescriptive Maintenance of CPPS by Integrating Multi-modal Data with Dynamic Bayesian Networks.- Evaluation of Deep Autoencoders for Prediction of Adjustment Points in the Mass Production of Sensors.- Differential Evolution in Production Process Optimization of Cyber Physical Systems.- Machine Learning for Process-X: A Taxonomy.- Intelligent edge processing.- Learned Abstraction: Knowledge Based Concept Learning for Cyber Physical Systems.- Semi-supervised Case-based Reasoning Approach to Alarm Flood Analysis.- Verstehen von Maschinenverhalten mit Hilfe von Machine Learning.- Adaptable Realization of Industrial Analytics Functions on Edge-Devices using Recongurable Architectures.- The Acoustic Test System for Transmissions in the VW Group.
| Erscheinungsdatum | 30.04.2019 |
|---|---|
| Reihe/Serie | Technologien für die intelligente Automation |
| Zusatzinfo | VII, 87 p. 1 illus. |
| Verlagsort | Berlin |
| Sprache | englisch |
| Maße | 168 x 240 mm |
| Gewicht | 176 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Netzwerke |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Technik ► Nachrichtentechnik | |
| Schlagworte | Artificial Intelligence • Big Data • Cognitive Robotics • Computational Intelligence • Computer-based algorithms • Cyber-Physical Systems • Data Mining • internet of things • machine learning • Smart Grid |
| ISBN-10 | 3-662-59083-2 / 3662590832 |
| ISBN-13 | 978-3-662-59083-6 / 9783662590836 |
| Zustand | Neuware |
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