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Online Pattern Recognition of Time-series Gas Sensor Data with Adaptive 2D-CNN Ensemble

Intelligent sensor system design demands the efficient pattern recognition method that uses raw time-series sensor data to identify the target gas with fast and accurate results. The presented work uses gas sensor array response (open sampling setting) for 10 bio-marker gases. The responses are obtained at different concentrations. We propose a 2D convolution neural network (2D-CNN) based adaptive ensemble network for gas identification. The Spatio-temporal correlation of sensor array responses inspired us to design deep-learning-based gas identification networks. The network uses raw time-series gas sensor array data and identifies the target gas mixtures with improved accuracy despite sensor drift. Experimental results show that the proposed methods are an effective technique with identification accuracy approximately 91% for identifying gas mixture for smart sensor system application. The proposed method outperforms and provides higher identification accuracy than comparable various machine learning and deep learning methods.