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書名 IMPROVE - innovative modelling approaches for production systems to raise validatable efficiency : intelligent methods for the factory of the future / edited by Oliver Niggemann, Peter Schuller
出版項 Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer Vieweg, 2018
國際標準書號 9783662578056 (electronic bk.)
9783662578049 (paper)
國際標準號碼 10.1007/978-3-662-57805-6 doi
book jacket
說明 1 online resource (vii, 129 pages) : illustrations (some color), digital ; 24 cm
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
text file PDF rda
系列 Technologien fur die intelligente automation, technologies for intelligent automation, 2522-8579 ; band 8
Technologien fur die intelligente automation, technologies for intelligent automation ; band 8
附註 Concept and Implementation of a Software Architecture for Unifying Data Transfer in Automated Production Systems -- Social Science Contributions to Engineering Projects: Looking Beyond Explicit Knowledge Through the Lenses of Social Theory -- Enable learning of Hybrid Timed Automata in Absence of Discrete Events through Self-Organizing Maps -- Anomaly Detection and Localization for Cyber-Physical Production Systems with Self-Organizing Maps -- A Sampling-Based Method for Robust and Efficient Fault Detection in Industrial Automation Processes -- Validation of similarity measures for industrial alarm flood analysis -- Concept for Alarm Flood Reduction with Bayesian Networks by Identifying the Root Cause
Open access
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 Schuller is postdoctoral researcher at Technische Universitat 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
Host Item Springer eBooks
主題 Industrial efficiency -- Computer simulation
Production management
Engineering
Quality Control, Reliability, Safety and Risk
Robotics and Automation
Input/Output and Data Communications
Alt Author Niggemann, Oliver, editor
Schuller, Peter, editor
SpringerLink (Online service)
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