Machine Learning for Cyber Physical Systems
 |
| Der Artikel wird am Ende des Bestellprozesses zum Download zur Verfügung gestellt. |
Beschreibung:
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.
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.