| Téléchargement | - Voir la version finale : Multi-label classification algorithms for composite materials under infrared thermography testing (PDF, 17.7 Mio)
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| DOI | Trouver le DOI : https://doi.org/10.1080/17686733.2022.2126638 |
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| Auteur | Rechercher : Alhammad, Muflih1; Rechercher : Avdelidis, Nicolas Peter1Identifiant ORCID : https://orcid.org/0000-0003-1314-0603; Rechercher : Ibarra Castanedo, Clemente2; Rechercher : Maldague, Xavier2; Rechercher : Zolotas, Argyrios1; Rechercher : Torbali, Ebubekir1; Rechercher : Genest, Marc3 |
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| Affiliation | - Cranfield University
- Université Laval
- Conseil national de recherches du Canada. Aérospatiale
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| Format | Texte, Article |
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| Sujet | composite materials; infrared thermography; thermal datasets; machine learning; multi-label classification |
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| Résumé | The key idea in this paper is to propose multi-labels classification algorithms to handle benchmark thermal datasets that are practically associated with different data characteristics and have only one health condition (damaged composite materials). A suggested alternative approach for extracting the statistical contents from the thermal images, is also employed. This approach offers comparable advantages for classifying multi-labelled datasets over more complex methods. Overall scored accuracy of different methods utilised in this approach showed that Random Forest algorithm has a clear higher performance over the others. This investigation is very unique as there has been no similar work published so far. Finally, the results demonstrated in this work provide a new perspective on the inspection of composite materials using Infrared Pulsed Thermography. |
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| Date de publication | 2022-10-14 |
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| Maison d’édition | Taylor & Francis |
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| Licence | |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 62ed138d-71fc-45ac-9361-c3daa5dfc1bb |
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| Enregistrement créé | 2023-08-30 |
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| Enregistrement modifié | 2023-09-18 |
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