Cross-Domain Active Learning for Electronic Nose Drift Compensation

The problem of drift in the electronic nose (E-nose) is an important factor in the distortion of data. The existing active learning methods do not take into account the misalignment of the data feature distribution between different domains due to drift when selecting samples. For this, we proposed...

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Detalles Bibliográficos
Autores principales: Sun, Fangyu, Sun, Ruihong, Yan, Jia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9413090/
https://www.ncbi.nlm.nih.gov/pubmed/36014182
http://dx.doi.org/10.3390/mi13081260

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9413090/
https://www.ncbi.nlm.nih.gov/pubmed/36014182
http://dx.doi.org/10.3390/mi13081260

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