Li Pengbo, Lin Guanyu, Chen Jianbo, Wang Jianing
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
University of Chinese Academy of Sciences, Beijing 101408, China.
Sensors (Basel). 2024 Aug 13;24(16):5226. doi: 10.3390/s24165226.
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 (CO) concentration daily.
准确检测大气中的二氧化碳是应对全球温室效应的重要环节。传统的离轴积分腔检测系统计算量大且易受环境因素影响。本研究将一种结合了级联积分梳状（CIC）滤波器的极限学习机模型应用于离轴积分腔。结果表明，合适的参数能够在降低检测限、提高准确性和均方根偏差方面有效提升仪器性能。所提出的方法已成功应用于一个位于工业区附近的监测站，用于每日检测大气中二氧化碳（CO）的浓度。
