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

Sensors
Zhiyuan MaWeina Niu

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.

References

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Citations

Jul 19, 2019·Frontiers in Neuroscience·Ayon Borthakur, Thomas A Cleland
Aug 17, 2021·Frontiers in Neuroscience·Ahana Gangopadhyay, Shantanu Chakrabartty

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Software Mentioned

OSELM
SVM
ODAELM
ODAELM - T )
Linux Workstation
T Domain Adaptation Extreme Learning Machine ( ODAELM - T )
Nose
Matlab
DAELM
T Adaptation Extreme Learning Machine (

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