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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Detalles Bibliográficos
Autores principales: Tao, Yang, Xu, Juan, Liang, Zhifang, Xiong, Lian, Yang, Haocheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210950/
https://www.ncbi.nlm.nih.gov/pubmed/30249024
http://dx.doi.org/10.3390/s18103209

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210950/
https://www.ncbi.nlm.nih.gov/pubmed/30249024
http://dx.doi.org/10.3390/s18103209

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