A novel DenseNet with warm restarts for gas recognition in complex airflow environments

Abstract

The diffusion mechanisms in natural environments often result in irregular and turbulent flow patterns in electronic nose field. This leads to random responses of electronic nose when exposed to the same gas ambiance, and thus brings serious challenges in gas identification. Recent studies have shown that the introducing of skip connections can enhance the capability of neural networks to extract features from gas data. In response to this finding, a novel One-dimensional DenseNet with Warm Restarts (1D-DNR) comprising skip connections is presented in this study. The network combines shallow and deep features using DenseNet and extracts the signal relevant to the unique characteristics of gas data through a 1D convolution. Furthermore, the classification accuracy and the stability of the proposed network are enhanced via the cosine annealing with warm restarts method. Further, zero-offset baseline compensation and Discrete Wavelet Transform (DWT) were employed to correct long-term drift and short-term drift effects. The results revealed that it is superior to the other models when dealing with the data of irregular and turbulent flow patterns of gases, with a classification accuracy of 99.83%.