Chemical gas sensor array dataset
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
Elsevier Inc.Date
2015-06-01Citation
10.1016/j.dib.2015.01.003
Data in Brief 3 (2015): 85-89
ISSN
2352-3409DOI
10.1016/j.dib.2015.01.003Funded by
This work has been supported by the California Institute for Telecommunications and Information Technology (CALIT2) under Grant number 2014 CSRO 136.Editor's Version
http://dx.doi.org/10.1016/j.dib.2015.01.003Subjects
Chemical sensing; Chemometrics; Electronic nose; Machine learning; Machine olfaction; InformáticaRights
© 2015 The AuthorsAbstract
To address drift in chemical sensing, an extensive dataset was collected over a period of three years. An array of 16 metal-oxide gas sensors was exposed to six different volatile organic compounds at different concentration levels under tightly-controlled operating conditions. Moreover, the generated dataset is suitable to tackle a variety of challenges in chemical sensing such as sensor drift, sensor failure or system calibration. The data is related to "Chemical gas sensor drift compensation using classifier ensembles", by Vergara et al. [1], and "On the calibration of sensor arrays for pattern recognition using the minimal number of experiments", by Rodriguez-Lujan et al. [2] The dataset can be accessed publicly at the UCI repository upon citation of: http://archive.ics.uci.edu/ml/datasets/Gas+Sensor+Array+Drift+Dataset+at+Different+Concentrations.
Files in this item
Google Scholar:Fonollosa, Jordi
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Rodríguez-Luján, Irene
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Huerta, Ramón
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