IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency | Intelligent Methods for the Factory of the Future

Oliver Niggemann (u. a.)

ISBN 10: 3662578042 / ISBN 13: 9783662578049

Published by Springer, 2018

About this Item

IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency | Intelligent Methods for the Factory of the Future | Oliver Niggemann (u. a.) | Taschenbuch | Technologien für die intelligente Automation | vii | Englisch | 2018 | Springer | EAN 9783662578049 | Verantwortliche Person für die EU: Springer Vieweg in Springer Science + Business Media, Abraham-Lincoln-Str. 46, 65189 Wiesbaden, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Seller Inventory # 113967286

Bibliographic Details

• Title: IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency | Intelligent Methods for the Factory of the Future
• Publisher: Springer
• Publication Date: 2018
• Language: English
• ISBN 10: 3662578042
• ISBN 13: 9783662578049
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 240W x 168H x 8D millimeters 240 millimeters width by 168 millimeters height by 8 millimeters depth
• Weight: 248 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.

About the Author

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.