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...

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Main Authors: Yang Tao, Juan Xu, Zhifang Liang, Lian Xiong, Haocheng Yang
Format: Artigo
Jezik:Inglês
Izdano: MDPI AG 2018-09-01
Serija:Sensors
Teme:
drift compensation
transfer learning
domain correction
electronic nose
Online dostop:http://www.mdpi.com/1424-8220/18/10/3209
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