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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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| Foilsithe in: | Sensors (Basel) |
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| Main Authors: | , , , , |
| Formáid: | Artigo |
| Teanga: | Inglês |
| Foilsithe: |
MDPI
2018
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| Ábhair: | |
| Rochtain Ar Líne: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6210950/ https://ncbi.nlm.nih.gov/pubmed/30249024 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/s18103209 |
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