Off-Axis Integral Cavity Carbon Dioxide Gas Sensor Based on Machine-Learning-Based Optimization-Reference-Cited by-同舟云学术

Off-Axis Integral Cavity Carbon Dioxide Gas Sensor Based on Machine-Learning-Based Optimization

Published:2024-08-13 Issue:16 Volume:24 Page:5226
ISSN:1424-8220
Container-title:Sensors
language:en
Short-container-title:Sensors

Author:

Li Pengbo¹²,Lin Guanyu¹,Chen Jianbo³,Wang Jianing¹

Affiliation:

1. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China

2. University of Chinese Academy of Sciences, Beijing 101408, China

3. School of optoelectronic engineering, Changchun University of Science and Technology, Changchun 130012, China

Abstract

Accurately detecting atmospheric carbon dioxide is a vital part of responding to the global greenhouse effect. Conventional off-axis integral cavity detection systems are computationally intensive and susceptible to environmental factors. This study deploys an Extreme Learning Machine model incorporating a cascaded integrator comb (CIC) filter into the off-axis integrating cavity. It is shown that appropriate parameters can effectively improve the performance of the instrument in terms of lower detection limit, accuracy, and root mean square deviation. The proposed method is incorporated successfully into a monitoring station situated near an industrial area for detecting atmospheric carbon dioxide (CO2) concentration daily.

Funder

National Natural Science Foundation of China

Key Research and Development; Program of Jilin Province

Youth Innovation Promotion Association CAS

Open bidding for selecting the best candidates of Changchun City

Publisher

MDPI AG

Link

https://www.mdpi.com/1424-8220/24/16/5226/pdf

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