Research on gas drift compensation based on multi-branch convolutional neural network

Zheng Wang; Sihao Xiang

doi:10.1117/12.2680929

8 June 2023 Research on gas drift compensation based on multi-branch convolutional neural network

Zheng Wang, Sihao Xiang

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127071X (2023) https://doi.org/10.1117/12.2680929
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

Research on gas sensor drift compensation for electronic noses can be traced back as far as the 1990s, but to this day, the gas sensor drift problem is still a thorny issue. Research on sensor drift compensation algorithms is still indispensable. Most of the traditional methods of electronic nose drift compensation operate from the aspect of signal processing, but this method has limited application environment, while nowadays more compensation is performed from the perspective of pattern recognition. Convolutional neural network, as a classical deep learning model, can extract not only samplespecific features but also domain-invariant features between different domains. In this paper, based on the above theory, we propose a novel drift compensation model based on convolutional neural network by combining the characteristics of real-world mammalian olfactory system, which is designed with multi-branch multi-classifier convolutional network structure to do specificity feature extraction on gas data and make the network model have better generalization and flexibility. Experiments on a short-term drift dataset show that the multi-branch convolutional neural network proposed in this paper has better performance.

Citation Download Citation

Zheng Wang and Sihao Xiang "Research on gas drift compensation based on multi-branch convolutional neural network", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127071X (8 June 2023); https://doi.org/10.1117/12.2680929

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