A Signal Response Visualization Gas Recognition Algorithm Based on a Wavelet Transform Coefficient Map-Capsule Network for Artificial Olfaction

Conventional gas recognition algorithms used in artificial olfaction rely on manually selected steady-state features of the sensor array signal and ignore the dynamic process signal response features of the sensor array; hence, a large amount of dynamic feature information, which can improve the gas recognition accuracy, is lost. To address this problem, a wavelet transform coefficient map-capsule network (WTCM-CapsNet) model for gas recognition is presented in this paper. It directly uses a raw signal from a sensor array without any signal-processing steps and consists of a wavelet coefficient map and a capsule network (CapsNet) model with two submodels. The WTCM-CapsNet model directly performs a five-scale wavelet transform on the sensor array signal and extracts 4,096 wavelet coefficients to form a 2D ${64}^{ \boldsymbol {\ast }}{64}$ wavelet coefficient image as the CapsNet input. Compared with conventional gas recognition algorithms, such as the convolutional neural network (CNN) with 99.3% gas recognition accuracy, the K-nearest neighbor (KNN) with 95.74% gas recognition accuracy, support vector machine (SVM) with 96.45% gas recognition accuracy, random forest with 95.74% gas recognition accuracy, naive Bayes with 92.91% gas recognition accuracy and BP neural network (BPNN) algorithms with 97.87% gas recognition accuracy, which extract the steady-state values of the time-domain signal and the wavelet coefficient maps of the whole dynamic response signal in the time-frequency domain as input features, CapsNet uses an iterative routing-by-agreement mechanism that can effectively improve the gas recognition accuracy to approximately 100%, as demonstrated by the dynamic response signal of the gas sensor array collected by our fabricated artificial olfactory system.