文献类型：期刊文献

英文题名：Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine

作者：Ma, Zhiyuan[1];Luo, Guangchun[1];Qin, Ke[1];Wang, Nan[2];Niu, Weina[1,3]

机构：[1]Univ Elect & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Sichuan, Peoples R China;[2]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[3]Chengdu Univ Informat Technol, Sch Cybersecur, Chengdu 610225, Sichuan, Peoples R China

年份：2018

卷号：18

期号：3

外文期刊名：SENSORS

收录：;EI(收录号：20181004862142);WOS:【SCI-EXPANDED(收录号:WOS:000428805300063)】；

基金：The work of this paper is supported by the Science and Technology Department of Sichuan Province under Grant No. 2017JY0027 and 2016FZ0108. The authors would like to thank the editor and anonymous reviewers for their suggestions and comments. The work of Nan Wang was supported by the National Natural Science Foundation of China (NSFC) under Grant 61604054. Zhiyuan Ma would like to thank Pei Yang from South China University of Technology for his suggestions on refining the ideas in the paper. Additionally, the authors would like to thank Lei Zhang from Chongqing University for his instructions on implementing DAELM.

语种：英文

外文关键词：gas sensor; drift compensation; domain adaptation; online learning; extreme learning machine

摘要：Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. In this paper, we focus on building online drift compensation model by transforming two domain adaptation based methods into their online learning versions, which allow the recognition models to adapt to the changes of sensor responses in a time-efficient manner without losing the high accuracy. Experimental results using three different settings confirm that the proposed methods save large processing time when compared with their offline versions, and outperform other drift compensation methods in recognition accuracy.