Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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. The Editors 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. Dr. Peter Schüller is postdoctoral researcher at Technische Universität Wien. His research interests are hybrid reasoning systems that combine Knowledge Representation and Machine Learning and applications in the fields of Cyber-Physical systems and Natural Language Processing.

Engineering. --- Input-output equipment (Computers). --- Robotics. --- Automation. --- Quality control. --- Reliability. --- Industrial safety. --- Quality Control, Reliability, Safety and Risk. --- Robotics and Automation. --- Input/Output and Data Communications. --- Industrial accidents --- Industries --- Job safety --- Occupational hazards, Prevention of --- Occupational health and safety --- Occupational safety and health --- Prevention of industrial accidents --- Prevention of occupational hazards --- Safety, Industrial --- Safety engineering --- Safety measures --- Safety of workers --- Accidents --- System safety --- Dependability --- Trustworthiness --- Conduct of life --- Factory management --- Industrial engineering --- Reliability (Engineering) --- Sampling (Statistics) --- Standardization --- Quality assurance --- Quality of products --- Automatic factories --- Automatic production --- Computer control --- Engineering cybernetics --- Factories --- Mechanization --- Assembly-line methods --- Automatic control --- Automatic machinery --- CAD/CAM systems --- Robotics --- Automation --- Machine theory --- Computer hardware --- Computer I/O equipment --- Computers --- Electronic analog computers --- Electronic digital computers --- Hardware, Computer --- I/O equipment (Computers) --- Input equipment (Computers) --- Input-output equipment (Computers) --- Output equipment (Computers) --- Computer systems --- Construction --- Industrial arts --- Technology --- Prevention --- Input-output equipment --- System safety. --- Data transmission systems. --- Data communication systems --- Transmission of data --- Digital communications --- Electronic data processing --- Electronic systems --- Information theory --- Telecommunication systems --- Safety, System --- Safety of systems --- Systems safety --- Industrial safety --- Systems engineering --- Engineering --- Quality control --- Reliability

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This open access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains selected papers from the fifth international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Berlin, March 12-13, 2020. 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. 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. Oliver Niggemann got his doctorate in 2001 at the University of Paderborn with the topic "Visual Data Mining of Graph-Based Data". He then worked for almost 8 years in leading positions in the industry. From 2008-2019 he held a professorship at the Institute for Industrial Information Technologies (inIT) in Lemgo/Germany. Until 2019 Prof. Niggemann was also deputy head of the Fraunhofer IOSB-INA, which works in industrial automation. On April 1, 2019 Prof. Niggemann took over the university professorship "Computer Science in Mechanical Engineering" at the Helmut-Schmidt-University in Hamburg / Germany. There he does research at the Institute for Automation Technology IfA in the field of artificial intelligence and machine learning for cyber-physical systems.

Computer engineering. --- Internet of things. --- Embedded computer systems. --- Electrical engineering. --- Computer organization. --- Cyber-physical systems, IoT. --- Communications Engineering, Networks. --- Computer Systems Organization and Communication Networks. --- Organization, Computer --- Electronic digital computers --- Electric engineering --- Engineering --- Embedded systems (Computer systems) --- Computer systems --- Architecture Analysis and Design Language --- IoT (Computer networks) --- Things, Internet of --- Computer networks --- Embedded Internet devices --- Machine-to-machine communications --- Computers --- Design and construction --- Machine learning --- Cyber-physical systems, IoT --- Communications Engineering, Networks --- Computer Systems Organization and Communication Networks --- Cyber-Physical Systems --- Computer Engineering and Networks --- Machine Learning --- Artificial Intelligence --- Cognitive Robotics --- Internet of Things --- Computational intelligence --- Computer-based algorithms --- Smart grid --- Open Access --- Industry 4.0 --- Electrical engineering --- Cybernetics & systems theory --- Communications engineering / telecommunications --- Computer networking & communications