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Sensors 2018, 18(3), 742; https://doi.org/10.3390/s18030742

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

1
School of Computer Science and Engineering, University of Electronics and Technology of China, Chengdu 611731, China
2
School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
3
School of Cybersecurity, Chengdu University of Information Technology, Chengdu 610225, China
*
Author to whom correspondence should be addressed.
Received: 31 January 2018 / Revised: 25 February 2018 / Accepted: 26 February 2018 / Published: 1 March 2018
(This article belongs to the Special Issue Artificial Olfaction and Taste)
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Abstract

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. View Full-Text
Keywords: gas sensor; drift compensation; domain adaptation; online learning; extreme learning machine gas sensor; drift compensation; domain adaptation; online learning; extreme learning machine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Ma, Z.; Luo, G.; Qin, K.; Wang, N.; Niu, W. Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine. Sensors 2018, 18, 742.

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