A Novel Channel-attention-based Dense Network for Gas Recognition in Complex Airflow Environments

Abstract

An electronic nose is a commonly used technology for gas detection. However, due to the complex diffusion mechanism of gaseous chemical analytes in the natural environment, the gas often exhibits irregular turbulent flow patterns. This variability results in different responses to the same gas in the e-nose, which presents significant challenges in the gas identification tasks. In this study, we propose an efficient method for gas recognition by combining a Dense Convolutional Network (DenseNet) with an Efficient Channel Attention Network (ECANet), which uses one-dimensional convolutional neural networks to improve the capability of extracting sequence signals. We evaluate the proposed method using an open-source dataset and observe that it outperforms the best current methods available, including the ResNet, Long Short-term Memory Network (LSTM) networks, and Gate Recurrent Unit (GRU) networks, with a classification accuracy of 99.8%.