A Novel 1D Deep Convolutional Neural Network Based Algorithm for Mixture Gases Recognition

In this paper, we present a novel one-dimensional deep convolutional neural network (1D-DCNN) based algorithm for classifying mixture gases. Compared with the previously reported electronic nose system that can only recognize pure gas, the proposed implementation is capable of distinguishing the individual component of binary mixture gases composed of Ethylene, CO and Methane. To the best of our knowledge, the proposed 1D-DCNN algorithm is firstly applied in the mixture gases recognition. Compared with the conventional pattern recognition algorithms including support vector machine (SVM) and artificial neural network (ANN), the proposed 1D-DCNN exhibits higher average accuracy (96.15%) based on extensive experimental results using 10-fold cross validation.