An electronic nose for harmful gas early detection based on a hybrid deep learning method H-CRNN

Abstract

Among harmful gases, carbon monoxide (CO) has posed a significant threat to human safety due to its concealment and harmfulness. Therefore, it’s imperative for the preservation of human safety to implement early detection for CO concentration. However, traditional methods for gas detection are either plagued by the limitations of insufficient accuracy like electronic nose (E-nose) equipped with relatively simple machine learning algorithms such as vanilla SVM or RNN, or limited by the incapacity to make early prediction such as sensory analysis, which requires a large amount of manpower resources and time. To address this issue, a novel technique, namely H-CRNN, is presented for E-nose to perform early prediction of low concentration CO, leveraging the strengths of convolutional and recurrent neural network to efficiently capture long-term dependencies within data. Also, shortcut connection and a novel gated attention mechanism is applied to enhance the capacity to capture crucial features. Furthermore, a linear process unit has been incorporated to handle the scale insensitivity issue. The experimental results show that the proposed H-CRNN outperforms RNN with a notable average reduction of 50.51% in the root relative squared error and 53.8% in the relative absolute error, and exhibits varying degrees of improvement when compared to state-of-the-art algorithms (TCN, TPA-LSTM, STCN, LSTNet, LSTM). Additionally, the H-CRNN achieved an average accuracy rate of 96.42%, surpassing all other algorithms and demonstrating the highest level of performance. Therefore, our work substantiates the proposed H-CRNN as an applicable method for early prediction of CO concentration.