Drift compensation for electronic nose by semi-supervised domain adaption

Liu, Qihe, Li, Xue, Ye, Mao, Ge, Shuzhi Sam and Du,Xiaosong (2014) Drift compensation for electronic nose by semi-supervised domain adaption. IEEE Sensors Journal, 14 3: 657-665. doi:10.1109/JSEN.2013.2285919


Author Liu, Qihe
Li, Xue
Ye, Mao
Ge, Shuzhi Sam
Du,Xiaosong
Title Drift compensation for electronic nose by semi-supervised domain adaption
Journal name IEEE Sensors Journal   Check publisher's open access policy
ISSN 1530-437X
1558-1748
Publication date 2014
Year available 2014
Sub-type Article (original research)
DOI 10.1109/JSEN.2013.2285919
Open Access Status
Volume 14
Issue 3
Start page 657
End page 665
Total pages 9
Place of publication Piscataway, NJ United States
Publisher Institute of Electrical and Electronics Engineers
Collection year 2015
Language eng
Subject 2208 Electrical and Electronic Engineering
3105 Instrumentation
Abstract Drift compensation is an important issue for electronic nose systems. Traditional methods are costly and laborious because they need to frequently recalibrate referred gases or continually provide data labeling. In this paper, a new drift compensation method is proposed. The inspiration of our method is originated from semi-supervised domain adaption that can effectively tackle the mismatches between source domain and target domain. In our approach, a weighted geodesic flow kernel is initially constructed, then the combination of such kind of kernels is proposed considering that there are intermediate unlabeled data between the source and target domains. We will discuss how unlabeled data is selected from the target domain. The selected unlabeled data is used to provide incremental knowledge in order to dynamically adapt classifier to the target domain. Based on the kernel combination and selected unlabeled data, manifold regularization is used to train the classifier. To the best of our knowledge, we are the first to apply domain adaption to deal with the sensor drift problem. The advantages of our method include degrading recalibration rate, requiring few labeled data, and the robustness in handling the drift. Our experiments show that the proposed method significantly outperforms the baseline methods.
Keyword Domain adaption
drift compensation
Electronic nose
Geodesic flow
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Information Technology and Electrical Engineering Publications
 
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