Research on Rapid Gas Classification and Incremental Learning Based on Sensor Array

Abstract

In the field of machine olfaction, odor recognition whose focus is to accurately classify odor information and improve efficiency, is one of the important research directions. In recent years, deep learning for gas identification has been used in some researches, which significantly improves the identification accuracy. As a variant of deep learning, incremental learning is widely used in the field of image classification and can effectively solve the problem of catastrophic forgetting in image classification. Incremental learning has not been practically used in gas identification research, this paper will try to use an incremental learning method to identify and classify multiple types of gases. The main objectives of the research are as follows: 1) The experimental data set was divided into two equal parts, a data preprocessing method was used to convert the sensor array data into gas images which would be the input of incremental learning. And the pre-part was trained and classified by the incremental learning network. 2) The latter part of the dataset was taked as a newly emerging gas dataset, which would be incrementally learned and classified by incremental learning model. This study used an open source gas dataset, applied machine learning and deep learning architecture to analyze and compare with the algorithm. The proposed supervised contrastive replay (SCR) was used for data training and classification in this study, achieving a 94.63% classification accuracy, and the training time is only 69.7s.