Progressive prediction algorithm by multi-interval data sampling in multi-task learning for real-time gas identification

Effective surveillance of harmful gases is paramount in the safeguard of human health and the protection of environmental air quality, thereby emphasizing the imperative for robust detection strategies, such as electronic noses. While the integration of intelligent algorithms has significantly improved the detection capabilities of electronic noses, most existing models focus on enhancing accuracy but usually ignore the crucial need for detection speed. To address this issue, the progressive prediction algorithm (PPA) is proposed to accomplish realtime gas recognition. The implementation of PPA incorporates time correction and multi-interval data sampling for gas analysis. Leveraging multi-task networks, this method proposes a novel network architecture that predicts gas type and concentration. The architecture synergizes the flexible receptive field size of time convolutional networks (TCN) with the long-term temporal dependency capture of gated recurrent units (GRU) to optimize overall model performance. The PPA achieves a classification accuracy of 99.3 % and a regression R 2 score of 0.927 within half of the response time. For practical applications, this work enables the prediction of gas type and concentration during the early stages of sensor response, facilitating rapid detection of hazardous gases.