Domain Correction Based on Kernel Transformation for Drift Compensation in the E-Nose System
This paper proposes a way for drift compensation in electronic noses (e-nose) that often suffers from uncertain and unpredictable sensor drift. Traditional machine learning methods for odor recognition require consistent data distribution, which makes the model trained with previous data less genera...
Main Authors: | Yang Tao, Juan Xu, Zhifang Liang, Lian Xiong, Haocheng Yang |
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Format: | Article |
Language: | English |
Published: |
MDPI AG
2018-09-01
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Series: | Sensors |
Subjects: | |
Online Access: | http://www.mdpi.com/1424-8220/18/10/3209 |
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