TDLAS gas concentration retrieval application via SPFE-WV with deep learning

Haixu Liu; Zhengzhuo Li; Yawen Li; Chenxi Wang; Cunguang Zhu

doi:10.1117/12.3055365

12 December 2024 TDLAS gas concentration retrieval application via SPFE-WV with deep learning

Haixu Liu, Zhengzhuo Li, Yawen Li, Chenxi Wang, Cunguang Zhu

Proceedings Volume 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024); 134390A (2024) https://doi.org/10.1117/12.3055365
Event: Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 2024, Xiamen, China

Abstract

In this study, a deep multilayer perceptron machine network (DMLP) framework that integrates feature engineering and weighted voting integration methods, called spectral profile feature engineering and weighted voting method (SPFE-WV), is proposed. In order to accurately predict the gas concentration in an experimental environment where various noises and background baseline interferences are present, through the preprocessing of the second-harmonic data time series, a feature engineering and weighted voting integration method to successfully extract key features such as the height and area of the main peak and the height difference between different concentrations.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Haixu Liu, Zhengzhuo Li, Yawen Li, Chenxi Wang, and Cunguang Zhu "TDLAS gas concentration retrieval application via SPFE-WV with deep learning", Proc. SPIE 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 134390A (12 December 2024); https://doi.org/10.1117/12.3055365

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