Optical temperature field reconstruction based on joint algorithm of ART and neural network

Jie Chen; Shi Liu

doi:10.1117/12.2640346

15 July 2022 Optical temperature field reconstruction based on joint algorithm of ART and neural network

Jie Chen, Shi Liu

Proceedings Volume 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022); 122580O (2022) https://doi.org/10.1117/12.2640346
Event: International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 2022, Qingdao, China

Abstract

In order to obtain more accurate online information of boiler temperature field and achieve the purpose of real-time measurement and monitoring of flame temperature field distribution in the furnace, an algebraic reconstruction-neural network algorithm (ART-NN) based on optical tomography measurement was proposed. The algorithm combines the advantages of Algebraic Reconstruction Algorithm (ART) and BP neural network. Using this algorithm in the case of adding random errors, a variety of classical temperature fields are numerically simulated. The results show that the stability and reconstruction results of the ART-NN algorithm are better than those of traditional algorithms such as ART and TSVD under the same error level. Optical tomography temperature field measurement provides an efficient method.

Citation Download Citation

Jie Chen and Shi Liu "Optical temperature field reconstruction based on joint algorithm of ART and neural network", Proc. SPIE 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 122580O (15 July 2022); https://doi.org/10.1117/12.2640346

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