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Trust model experimental validation to improve the digital twin recommendation system

2022Conference paper Restricted access
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dc.contributor.authorPires, Flávia
dc.contributor.authorAhmad, Bilal
dc.contributor.authorMoreira, António Paulo G. M.
dc.contributor.authorLeitão, Paulo
dc.date.accessioned2023-02-16T12:20:17Z
dc.date.available2023-02-16T12:20:17Z
dc.date.issued2022
dc.description.abstractIn the manufacturing domain, the digital twin has become an emerging concept for decision-making through the integration of what-if simulation capabilities. In such systems, the processing of the entire space of alternative solutions is very time-consuming; recommendation systems are used to solve this; however, these suffer from several problems, namely data sparsity and cold-start. The application of trust-based models can mitigate these problems, particularly the cold-start problems, by providing valuable background for the recommendation system. This paper presents the implementation and experimental validation of a trust-based model for improving the digital twin based what-if simulation recommendation system, addressing the cold-start problems. The proposed trust model was applied in an assembly line case study to recommend the best configurations for the optimal number of AGVs (Autonomous Guided Vehicles). The results show that applying the trust-based model with similarity metrics improved the mitigation of the cold-start problem.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationPires, Flavia; Ahmad, Bilal; Moreira, Antonio Paulo; Leitão, Paulo (2022). Trust model experimental validation to improve the digital twin recommendation system. In 5th International Conference on Industrial Cyber-Physical Systems (ICPS). Coventry, United Kingdom. p. 01 - 06pt_PT
dc.identifier.doi10.1109/ICPS51978.2022.9816922pt_PT
dc.identifier.urihttp://hdl.handle.net/10198/26994
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationAn Intelligent Decision Support Approach for Industrial Digital Twin
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectTrust modelpt_PT
dc.subjectDigital twinpt_PT
dc.subjectRecommendation systempt_PT
dc.titleTrust model experimental validation to improve the digital twin recommendation systempt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleAn Intelligent Decision Support Approach for Industrial Digital Twin
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/POR_NORTE/SFRH%2FBD%2F143243%2F2019/PT
oaire.citation.conferencePlaceCoventry, United Kingdompt_PT
oaire.citation.endPage06pt_PT
oaire.citation.startPage01pt_PT
oaire.citation.title2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS)pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStreamPOR_NORTE
person.familyNamePires
person.familyNameLeitão
person.givenNameFlávia
person.givenNamePaulo
person.identifierhttps://scholar.google.pt/citations?user=an9quSsAAAAJ&hl=pt-PT
person.identifierA-8390-2011
person.identifier.ciencia-idA119-72AB-6255
person.identifier.ciencia-id8316-8F13-DA71
person.identifier.orcid0000-0001-7899-3020
person.identifier.orcid0000-0002-2151-7944
person.identifier.scopus-author-id57200412919
person.identifier.scopus-author-id35584388900
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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relation.isAuthorOfPublication68d9eb25-ad4f-439b-aeb2-35e8708644cc
relation.isAuthorOfPublication.latestForDiscovery68d9eb25-ad4f-439b-aeb2-35e8708644cc
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