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A Generic DigitalTwin Model for Artificial Intelligence Applications

 
: Niggemann, Oliver; Diedrich, Alexander; Kühnert, Christian; Pfannstiel, Erik; Schraven, Joshua

:
Postprint urn:nbn:de:0011-n-6391823 (472 KByte PDF)
MD5 Fingerprint: ed7db4b0bed5d755703cb973deafdaa4
© IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Erstellt am: 3.9.2021


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Industrial Electronics Society -IES-:
4th IEEE International Conference on Industrial Cyber-Physical Systems, ICPS 2021. Proceedings : Online, 10 - 13 May 2021, Victoria, BC, Canada
Piscataway, NJ: IEEE, 2021
ISBN: 978-1-6654-3045-6
ISBN: 978-1-7281-6207-2
S.55-62
International Conference on Industrial Cyber-Physical Systems (ICPS) <4, 2021, Online>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
artificial intelligence applications; cyber-physical system; machine learning; generic DigitalTwin model; AI reference model; AITwin

Abstract
Services for Cyber-Physical Systems based on Artificial Intelligence and Machine Learning require a virtual representation of the physical. To reduce modeling efforts and to synchronize results, for each system, a common and unique virtual representation used by all services during the whole system life-cycle is needed-i.e. a DigitalTwin. In this paper such a DigitalTwin, namely the AI reference model AITwin, is defined. This reference model is verified by using a running example from process industry and by analyzing the work done in recent projects.

: http://publica.fraunhofer.de/dokumente/N-639182.html