Hybrid 3DCNN-RBM Network for Gas Mixture Concentration Estimation With Sensor Array

This work proposes a hybrid 3D Convolutional Neural Network and Restricted Boltzmann Machine (Hybrid 3DCNN-RBM) architecture tailored for gas concentration estimation. The immense success of deep learning in computer vision and natural language processing inspired us to design a deep-learning-based gas concentration estimation network. The proposed network is a joint 3DCNN and RBM network. It uses raw time-series gas sensor array data and provides the concentration of each gas in a mixture of gases. This raw time series data is considered similar to video signal comparing sensor array response as an image, which varies with time. We first time utilized 3DCNN for sensor array data processing and developed a joint network with RBM to design an end-to-end gas concentration estimation model. Although several pattern recognition-based methods have been applied to estimate the gas concentration of the gas mixtures, the effectiveness of these techniques enormously depends on the hand-created feature engineering. Nevertheless, the experiments show that the proposed method is an efficient method for estimating gas concentration in the gas mixture for sensor array data.

Automated summary

This study presents a hybrid 3DCNN-RBM architecture tailored for gas concentration estimation. The success of deep learning inspired the design of a deep-learning-based gas concentration estimation network. Experimental results demonstrate the effectiveness of the proposed method for estimating gas concentration in sensor array data of gas mixtures.