Study of Neural Network Architectures for Determining Gas Concentrations by Spectra
https://doi.org/10.25205/1818-7900-2024-22-2-68-78
Abstract
A study of a number of neural networks of different architectures for determining gas concentrations from spectra obtained using an optical emission gas analyzer, which measures the spectrum of electromagnetic radiation emitted by gases when excited by an electric discharge, is presented. The neural network is trained on data from the optical spectroscopy laboratory and is able to predict gas concentrations from spectra at high speed. The research concerned the deep neural network architectures with convolutional and recurrent layers. Convolutional layers highlight the features of the spectra, while recurrent layers take into account the consistent structure of the data. The quality of the neural network is evaluated by the R2 coefficient of determination, and the comparison between networks by the RMSE indicator between the predicted and real gas concentrations.
About the Authors
A. E. Shchelokov
Novosibirsk State University; Institute of Automation and Electrometry SB RAS
Russian Federation
Russian Federation
Alexander E. Shchelokov, Master’s Degree Student, Department of Computer Technologies, Faculty of Information Technology; Software Engineer
Novosibirsk
K. I. Budnikov
Novosibirsk State University; Institute of Automation and Electrometry SB RAS
Russian Federation
Russian Federation
Konstantin I. Budnikov, PhD in Computer Science, Associate Professor, Department of Computer Technologies, Faculty of Information Technologies; Senior Researcher, Head of the Thematic Group
Novosibirsk
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Review
For citations:
Shchelokov A.E., Budnikov K.I. Study of Neural Network Architectures for Determining Gas Concentrations by Spectra. Vestnik NSU. Series: Information Technologies. 2024;22(2):68-78. (In Russ.) https://doi.org/10.25205/1818-7900-2024-22-2-68-78
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