How to Be Certain: Using Known Relations and Trust Discount to Determine Confidence About the Degree of Uncertainty
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Le résumé fourni par la source
Ensuring certainty in the rapidly evolving digital twin environments amidst inherent uncertainty is paramount in decision-making for critical engineering systems. This paper presents a novel approach for quantifying and tracking confidence levels within a network of sensors and structural models, specifically addressing uncertainty arising from faulty sensors. By leveraging known relations and implementing a trust discount mechanism, our methodology offers a fundamental framework for navigating in the presence of uncertainties. A quasi-real-time case study on a cantilever beam model is simulated to demonstrate the efficacy of the proposed approach. We showcase the ability of our method to accurately assess and adapt to varying levels of uncertainty introduced by faulty sensors. Our findings highlight the importance of incorporating trust dynamics and established relationships within digital twin environments to achieve improved certainty despite imperfect data. This research contributes to the theoretical underpinnings of digital twin technology and offers practical insights for its application across diverse domains.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- How to Be Certain: Using Known Relations and Trust Discount to Determine Confidence About the Degree of Uncertainty
- Date Crossref
- 01/07/2024
- Éditeur
- NDT.net GmbH & Co. KG
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Leibniz University Hannover Institute for Risk and Reliability pays non établi dans la noticeUniversité ou école supérieure
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Tongji University International Joint Research Center for Resilient Infrastructure & International Joint Research Center for Engineering Reliability and Stochastic Mechanics pays non établi dans la noticeUniversité ou école supérieure
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University of Liverpool Institute for Risk and Uncertainty pays non établi dans la noticeUniversité ou école supérieure
Institute for Risk and Reliability — Leibniz University Hannover, International Joint Research Center for Resilient Infrastructure & International Joint Research Center for Engineering Reliability and Stochastic Mechanics — Tongji University et Institute for Risk and Uncertainty — University of Liverpool.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.