Off-Axis Integral Cavity Carbon Dioxide Gas Sensor Based on Machine-Learning-Based Optimization
Abstract
:1. Introduction
2. Principles and Methods
2.1. Absorption Principles
2.1.1. Laser Propagation Path
2.1.2. Beer–Lambert Law
2.1.3. Principle of Integrating Cavity Output Spectrum
2.1.4. Selection of Absorption Lines
2.2. The Principle of the Extreme Learning Machine
2.3. Sensor Configuration
3. Results
3.1. Gas Preparation
3.2. SNR Estimation
3.2.1. Traditional Locked-In Amplifier
3.2.2. Optimized CIC Filtering Scheme
3.2.3. Extreme Learning Machine
3.2.4. Improvement of SNR of the Detection System
3.3. Signal Processing Performance of ELM
3.4. Fluctuation and Residual Analyses
3.5. Allan Deviation Analysis and Detection Limit
3.6. Field Test Results of Atmospheric CO2
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Li, P.; Lin, G.; Chen, J.; Wang, J. Off-Axis Integral Cavity Carbon Dioxide Gas Sensor Based on Machine-Learning-Based Optimization. Sensors 2024, 24, 5226. https://doi.org/10.3390/s24165226
Li P, Lin G, Chen J, Wang J. Off-Axis Integral Cavity Carbon Dioxide Gas Sensor Based on Machine-Learning-Based Optimization. Sensors. 2024; 24(16):5226. https://doi.org/10.3390/s24165226
Chicago/Turabian StyleLi, Pengbo, Guanyu Lin, Jianbo Chen, and Jianing Wang. 2024. "Off-Axis Integral Cavity Carbon Dioxide Gas Sensor Based on Machine-Learning-Based Optimization" Sensors 24, no. 16: 5226. https://doi.org/10.3390/s24165226
APA StyleLi, P., Lin, G., Chen, J., & Wang, J. (2024). Off-Axis Integral Cavity Carbon Dioxide Gas Sensor Based on Machine-Learning-Based Optimization. Sensors, 24(16), 5226. https://doi.org/10.3390/s24165226