Enhancing accuracy of flame equivalence ratio measurements: an attention-based convolutional neural network approach for overcoming limitations in traditional color modeling

Zheng, L., Yang, T., Liu, W. et al. (2 more authors) (2024) Enhancing accuracy of flame equivalence ratio measurements: an attention-based convolutional neural network approach for overcoming limitations in traditional color modeling. Sensors, 24 (21). 6853. ISSN 1424-8220

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Item Type: Article
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Keywords: equivalence ratio; color modeling; convolutional neural network; spatial attention mechanism
Dates:
  • Published: 25 October 2024
  • Published (online): 25 October 2024
  • Accepted: 22 October 2024
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > School of Electrical and Electronic Engineering
Depositing User: Symplectic Sheffield
Date Deposited: 12 Nov 2024 16:35
Last Modified: 12 Nov 2024 16:35
Published Version: http://dx.doi.org/10.3390/s24216853
Status: Published
Publisher: MDPI AG
Refereed: Yes
Identification Number: 10.3390/s24216853
Open Archives Initiative ID (OAI ID):

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