Improve - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency: Intelligent Methods for the Factory of the Future
| PUBLISHER | Springer Vieweg (08/31/2018) |
| PRODUCT TYPE | Paperback (Paperback) |
Description
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.
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Product Format
Product Details
ISBN-13:
9783662578049
ISBN-10:
3662578042
Binding:
Paperback or Softback (Trade Paperback (Us))
Content Language:
English
More Product Details
Page Count:
129
Carton Quantity:
30
Product Dimensions:
6.69 x 0.30 x 9.61 inches
Weight:
0.52 pound(s)
Feature Codes:
Maps,
Illustrated
Country of Origin:
NL
Subject Information
BISAC Categories
Technology & Engineering | Quality Control
Technology & Engineering | Robotics
Technology & Engineering | Data Transmission Systems - General
Dewey Decimal:
004.6
Descriptions, Reviews, Etc.
jacket back
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 Schller is postdoctoral researcher at Technische Universitt 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.
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 Schller is postdoctoral researcher at Technische Universitt 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.
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publisher marketing
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.
Show More
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