Chemical gas sensor drift compensation using classifier ensembles
Yazarlar (6)
Alexander Vergara
Shankar Vembu
Margaret A Ryan
Margie L Homer
Ramón Huerta
| Makale Türü | Özgün Makale (Uluslararası alan indekslerindeki dergilerde yayınlanan tam makale) | ||
| Dergi Adı | Sensors and Actuators B: Chemical | ||
| Makale Dili | – | Basım Tarihi | 05-2012 |
| Cilt / Sayı / Sayfa | 166 / 0 / 320–329 | DOI | – |
| Makale Linki | https://www.sciencedirect.com/science/article/pii/S0925400512002018 | ||
| UAK Araştırma Alanları |
Halı-Kilim ve Dokuma
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| Özet |
| Sensor drift remains to be the most challenging problem in chemical sensing. To address this problem we have collected an extensive dataset for six different volatile organic compounds over a period of three years under tightly controlled operating conditions using an array of 16 metal-oxide gas sensors. The recordings were made using the same sensor array and a robust gas delivery system. To the best of our knowledge, this is one of the most comprehensive datasets available for the design and development of drift compensation methods, which is freely reachable on-line. We introduced a machine learning approach, namely an ensemble of classifiers, to solve a gas discrimination problem over extended periods of time with high accuracy rates. Experiments clearly indicate the presence of drift in the sensors during the period of three years and that it degrades the performance of the classifiers. Our proposed … |
| Anahtar Kelimeler |
