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Mixed Gas Concentration Inversion Based on the Ultraviolet Absorption Spectrum by a Hierarchical Convolutional Neural Network
Abstract
A hierarchical convolutional neural network (CNN) model for mixed gas concentration inversion is proposed. In our experiment, mixtures of SO2, NO2, and NH3 were analyzed. SO2 and NO2 were the detected gases, while NH3 was the interfering gas. For the simulation samples, the average absolute errors were 0.5 and 0.9 ppm for SO2 and NO2, respectively. For the experimental samples, the model performed well when the absorption intensities of components differed by no more than one order of magnitude. Compared with the single-module CNN model without a hierarchical structure, the results demonstrate that the hierarchical structure reduces cross-interference and improves the prediction accuracy to a great extent. We believe that our model will have a promising application in the field of gas detection.
Supporting agencies: This research was funded by the National Natural Science Foundation of China (NSFC) (No. 11874107).
About the Authors
C. Lu
Advanced Photonics Center, School of Electronic Science & Engineering at Southeast University
China
China
Nanjing, Jiangsu
Y. Bian
Advanced Photonics Center, School of Electronic Science & Engineering at Southeast University
China
China
Nanjing, Jiangsu
X. Hu
Advanced Photonics Center, School of Electronic Science & Engineering at Southeast University
China
China
Nanjing, Jiangsu
S. Jin
Advanced Photonics Center, School of Electronic Science & Engineering at Southeast University
China
China
Nanjing, Jiangsu
Y. Huang
Advanced Photonics Center, School of Electronic Science & Engineering at Southeast University
China
China
Nanjing, Jiangsu
Y. Cui
Advanced Photonics Center, School of Electronic Science & Engineering at Southeast University
China
China
Nanjing, Jiangsu
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Review
For citations:
Lu C., Bian Y., Hu X., Jin S., Huang Y., Cui Y. Mixed Gas Concentration Inversion Based on the Ultraviolet Absorption Spectrum by a Hierarchical Convolutional Neural Network. Zhurnal Prikladnoii Spektroskopii. 2022;89(4):591.
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