文献类型：期刊文献

英文题名：Gas Sensor Drift Compensation Using Semi-Supervised Ensemble Classifiers with Multi-Level Features and Center Loss

作者：Jiang, Kai[1,2];Zeng, Min[1];Wang, Tao[3];Wu, Yu[4];Ni, Wangze[1,2];Chen, Lechen[1,2];Yang, Jianhua[1,2];Hu, Nantao[1,2];Zhang, Bowei[3];Xuan, Fuzhen[3];Li, Siying[5];Shi, Anwei[6];Yang, Zhi[1]

机构：[1]Shanghai Jiao Tong Univ, Natl Key Lab Adv Micro & Nano Manufacture Technol, Shanghai 200240, Peoples R China;[2]Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Dept Micro Nano Elect, Shanghai 200240, Peoples R China;[3]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai Key Lab Intelligent Sensing & Detect Tech, Shanghai 200237, Peoples R China;[4]Shanghai Marine Diesel Engine Res Inst, Natl Key Lab Marine Engine Sci & Technol, Shanghai 201108, Peoples R China;[5]Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Dept Elect Engn, Shanghai 200240, Peoples R China;[6]Ningbo Xinrui Zhice Technol Co Ltd, Ningbo 315800, Peoples R China

年份：2025

卷号：10

期号：4

起止页码：2906

外文期刊名：ACS SENSORS

收录：;EI(收录号：20251618257896);WOS:【SCI-EXPANDED(收录号:WOS:001461922600001)】；

基金：This work was supported by the National Key Research and Development Program of China (2022YFC3104700), the National Natural Science Foundation of China (62104143, 62301314, 62371299, and 62471298), the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (52321002), the Natural Science Foundation of Shanghai (23ZR1430100). We also acknowledge analysis support from the Instrumental Analysis Center of Shanghai Jiao Tong University and the Center for Advanced Electronic Materials and Devices of Shanghai Jiao Tong University. The computations in this paper were run on the pi 2.0 cluster supported by the Center for High-Performance Computing at Shanghai Jiao Tong University.

语种：英文

外文关键词：electronic nose; drift compensation; domainadaptation; semisupervised learning; ensemble classifier; center loss

摘要：The drift compensation of gas sensors is a significant and challenging issue in the field of electronic noses (E-nose). Compensating sensor drift has a great benefit in improving the performance of E-nose systems. However, conventional methods often perform poorly due to complex data relationships before and after drifting, or require label information for both nondrift (source data) and drift data (target data) to enhance performance, which is hard to achieve and even unrealistic. In this study, we propose a semisupervised domain adaptive convolutional neural network (CNN) based on ensemble classifiers of multilevel features, pretraining, and center loss to tackle the drift problem. The main idea is to make full use of multilevel features extracted from the network and apply Hilbert space's maximum mean discrepancy (MMD) to evaluate the domain similarity of the features at different levels. Then the corresponding MMD is used as a weight to achieve the weighted fusion of predictions in the classifier ensemble module, so as to obtain a more reliable result. Furthermore, to optimize training, MMD is used as a loss for pretraining to help feature extractors learn more robust and common features in two domains. Center loss is also applied to achieve more focused learning for features of the same class. The results on two data sets demonstrate the effectiveness of our method. The average classification accuracies under different settings reach 76.06% (long-drift) and 82.07% (short-drift), respectively, and the average R 2 score reaches 0.804 in the regression task, which has significant improvements compared with several conventional methods. Our work provides an effective and reliable method at the algorithm level to solve the drift compensation problem of gas sensors.