| DOI | Trouver le DOI : https://doi.org/10.1016/j.firesaf.2022.103629 |
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| Auteur | Rechercher : Li, Yuchuan1; Rechercher : Ko, Yoon1Identifiant ORCID : https://orcid.org/0000-0001-9644-5108; Rechercher : Lee, Wonsook |
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| Affiliation | - Conseil national de recherches du Canada. Construction
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| Bailleur de fonds | Rechercher : National Research Council |
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| Format | Texte, Article |
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| Description physique | 13 p. |
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| Sujet | flashover; deep neural networks; dual-attention generative adversarial network; image processing; fire safety science |
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| Résumé | This paper proposes a novel hybrid model for flashover prediction in a compartment fire based on visual information from RGB images that are the same as those captured by regular vision cameras. The proposed model was developed as a research tool to study the feasibility of predicting flashover based on RGB vision data. This model consists of sub-modules with data-based methods using Deep Neural Networks and knowledge-based methods using fire safety science and mathematical model. One of the crucial features of the proposed model is enabled by a novel Dual-Attention Generative Adversarial Network that is developed in this study for the vision-to-infrared conversion process. The model and the overall procedure were validated against published test data from a compartment fire. Results show that the proposed model achieved promising performance, which also shows the potential to monitor the constant changes in a room fire through continuous processing images of flame and smoke. |
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| Date de publication | 2022-07-20 |
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| Maison d’édition | Elsevier BV |
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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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| Identificateur | S0379711222001072 |
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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 | 1f7a6170-f305-4225-9c33-9a3e11d0069c |
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| Enregistrement créé | 2022-08-03 |
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| Enregistrement modifié | 2022-08-04 |
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