Active Learning on Dynamic Clustering for Drift Compensation in an Electronic Nose System

Drift correction is an important concern in Electronic noses (E-nose) for maintaining stable performance during continuous work. A large number of reports have been presented for dealing with E-nose drift through machine-learning approaches in the laboratory. In this study, we aim to counter the dri...

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Detalles Bibliográficos
Autores principales: Liu, Tao, Li, Dongqi, Chen, Jianjun, Chen, Yanbing, Yang, Tao, Cao, Jianhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6721181/
https://www.ncbi.nlm.nih.gov/pubmed/31430909
http://dx.doi.org/10.3390/s19163601

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6721181/
https://www.ncbi.nlm.nih.gov/pubmed/31430909
http://dx.doi.org/10.3390/s19163601

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