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Towards AI-assisted...
Towards AI-assisted digital twins for smart railways : preliminary guideline and reference architecture
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- De Donato, Lorenzo (författare)
- University of Naples Federico II, Italy
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- Dirnfeld, Ruth (författare)
- Linnéuniversitetet,Institutionen för datavetenskap och medieteknik (DM)
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- Somma, Alessandra (författare)
- University of Naples Federico II, Italy
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- De Benedictis, Alessandra (författare)
- University of Naples Federico II, Italy
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- Flammini, Francesco, Senior Lecturer, 1978- (författare)
- Linnéuniversitetet,Mälardalens universitet,Innovation och produktrealisering,Department of Computer Science and Media Technology, Linnaeus University, Växjö, Sweden,Institutionen för datavetenskap och medieteknik (DM),Mälardalen University, Sweden
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- Marrone, Stefano (författare)
- University of Naples Federico II, Italy
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- Saman Azari, Mehdi (författare)
- Linnéuniversitetet,Institutionen för datavetenskap och medieteknik (DM)
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- Vittorini, Valeria (författare)
- University of Naples Federico II, Italy
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(creator_code:org_t)
- Springer Science and Business Media Deutschland GmbH, 2023
- 2023
- Engelska.
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Ingår i: Journal of Reliable Intelligent Environments. - : Springer Science and Business Media Deutschland GmbH. - 2199-4668 .- 2199-4676.
- Relaterad länk:
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Abstract
Ämnesord
Stäng
- In the last years, there has been a growing interest in the emerging concept of digital twins (DTs) among software engineers and researchers. DTs not only represent a promising paradigm to improve product quality and optimize production processes, but they also may help enhance the predictability and resilience of cyber-physical systems operating in critical contexts. In this work, we investigate the adoption of DTs in the railway sector, focusing in particular on the role of artificial intelligence (AI) technologies as key enablers for building added-value services and applications related to smart decision-making. In this paper, in particular, we address predictive maintenance which represents one of the most promising services benefiting from the combination of DT and AI. To cope with the lack of mature DT development methodologies and standardized frameworks, we detail a workflow for DT design and development specifically tailored to a predictive maintenance scenario and propose a high-level architecture for AI-enabled DTs supporting such workflow.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Transportteknik och logistik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Transport Systems and Logistics (hsv//eng)
Nyckelord
- Artificial intelligence
- Cyber-physical system
- Digital twin
- Internet of things
- Machine learning
- Railway
- Decision making
- E-learning
- Embedded systems
- Railroad transportation
- Railroads
- Artificial intelligence technologies
- Cybe-physical systems
- Cyber-physical systems
- Machine-learning
- Predictive maintenance
- Production process
- Products quality
- Reference architecture
- Work-flows
- Cyber Physical System
- Computer Science
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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