Drift compensation of the gas sensor based on self-training and semisupervised learning

Conference: CAIBDA 2022 - 2nd International Conference on Artificial Intelligence, Big Data and Algorithms
06/17/2022 - 06/19/2022 at Nanjing, China

Proceedings: CAIBDA 2022

Pages: 5Language: englishTyp: PDF

Authors:
Lu, Fanghua (Shanghai University, Baoshan District, Shanghai City, China)
Zhang, Junxiu (Shanghai Normal University, Fengxian District, Shanghai City, China)

Abstract:
The electronic nose is composed of a group of gas sensor arrays. It is often used to detect the composition and concentration of gas. Sensor drift is the main reason affecting its accuracy. In this article, a self-training and semisupervised learning method is proposed. We use the strategy of transductive learning to train the model, which effectively suppresses the drift of the sensor and improves the accuracy of gas classification. It has a certain reference significance in the actual application of gas identification and detection.