Machine Learning for Cyber Physical Systems

Selected papers from the International Conference ML4CPS 2018

by Jürgen Beyerer, Christian Kühnert, Oliver Niggemann

DescriptionDetailsHashtagsReport an issue

Book Description

This book 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 EditorsProf. 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 open book is licensed under a Creative Commons License (CC BY). You can download Machine Learning for Cyber Physical Systems ebook for free in PDF format (8.9 MB).

Book Details

Subject
Engineering and Technology
Publisher
Springer
Published
2019
Pages
144
Edition
1
Language
English
ISBN13
9783662584842
ISBN10
3662584840
ISBN13 Digital
9783662584859
ISBN10 Digital
3662584859
PDF Size
8.9 MB
License
CC BY

Related Books

IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency
This open access work presents selected results from the European research and innovation project IMPROVE which yielded novel data-based solutions to enhance machine reliability and efficiency in the fields of simulation and optimization, condition monitoring, alarm management, and quality prediction. Prof. Dr. Oliver Niggemann is Professor for Ar...
Cyber-Physical Systems of Systems
Technical Systems-of-Systems (SoS) – in the form of networked, independent constituent computing systems temporarily collaborating to achieve a well-defined objective – form the backbone of most of today's infrastructure. The energy grid, most transportation systems, the global banking industry, the water-supply system, the military equipment, ...
Automated Machine Learning
This book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created ...
Machine Learning Yearning
AI is transforming numerous industries. Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects. This book is focused not on teaching you ML algorithms, but on how to make ML algorithms work. After reading Machine Learning Yearning, you will be able to: - Prioritize the most promising direc...
Multiple-Aspect Analysis of Semantic Trajectories
This free book constitutes the refereed post-conference proceedings of the First International Workshop on Multiple-Aspect Analysis of Semantic Trajectories, MASTER 2019, held in conjunction with the 19th European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, in Würzburg, Germany, in September 2019. The 8 ful...
Python Machine Learning Projects
As machine learning is increasingly leveraged to find patterns, conduct analysis, and make decisions - sometimes without final input from humans who may be impacted by these findings - it is crucial to invest in bringing more stakeholders into the fold. This book of Python projects in machine learning tries to do just that: to equip the developers ...