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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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| Pubblicato in: | Sensors (Basel) |
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| Autori principali: | , , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
MDPI
2019
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6721181/ https://ncbi.nlm.nih.gov/pubmed/31430909 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/s19163601 |
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