New approach for gas identification using supervised learning methods (SVM and LVQ)

This article proposes a new approach for gas identification, this approach relies on applying supervised learning methods to identify a single gas as well as a mixture of two gases. The gas is trapped in a gas discharge tube, it is then ionized at a relatively low pressure using an HV transformer. The images captured after the ionization of each single gas is then captured and transformed into a database after being treated in order to be classified. The obtained results were very satisfying for SVM as well as for LVQ. For the case of identification of a single gas, the learning rate as well as the validation rate for both methods were 100%. However, for the case of mixture of two gases, a Multi-Layer Perceptron neural network was used to identify the gases, the learning rate as well as the validation rate were 98.59% and 98.77% respectively. The program developed on MATLAB takes the captured image as an input and outputs the identified gases for the user. The gases used in the experiments are Argon (Ar), oxygen (O2), Helium (He) and carbon dioxide (CO2).

Abstract licence: CC BY

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