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

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. I...

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Autores principales: Ma, Zhiyuan, Luo, Guangchun, Qin, Ke, Wang, Nan, Niu, Weina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5876707/
https://www.ncbi.nlm.nih.gov/pubmed/29494543
http://dx.doi.org/10.3390/s18030742
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author Ma, Zhiyuan
Luo, Guangchun
Qin, Ke
Wang, Nan
Niu, Weina
author_facet Ma, Zhiyuan
Luo, Guangchun
Qin, Ke
Wang, Nan
Niu, Weina
author_sort Ma, Zhiyuan
collection PubMed
description 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.
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spelling pubmed-58767072018-04-09 Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine Ma, Zhiyuan Luo, Guangchun Qin, Ke Wang, Nan Niu, Weina Sensors (Basel) Article 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. MDPI 2018-03-01 /pmc/articles/PMC5876707/ /pubmed/29494543 http://dx.doi.org/10.3390/s18030742 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ma, Zhiyuan
Luo, Guangchun
Qin, Ke
Wang, Nan
Niu, Weina
Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine
title Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine
title_full Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine
title_fullStr Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine
title_full_unstemmed Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine
title_short Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine
title_sort online sensor drift compensation for e-nose systems using domain adaptation and extreme learning machine
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5876707/
https://www.ncbi.nlm.nih.gov/pubmed/29494543
http://dx.doi.org/10.3390/s18030742
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