Chemical gas sensor array dataset

被引：25

作者：

Fonollosa, Jordi ^{[1
]}

Rodriguez-Lujan, Irene ^{[1
]}

Huerta, Ramon ^{[1
]}

机构：

[1] Univ Calif San Diego, BioCircuits Inst, La Jolla, CA 92093 USA

来源：

DATA IN BRIEF | 2015年 / 3卷

关键词：

Chemometrics; Machine olfaction; Electronic nose; Chemical sensing; Machine learning;

D O I：

10.1016/j.dib.2015.01.003

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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 corn pounds 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 damset can be accessed publicly at Lhe UCl repository upon citation of: http://archive.ics.uci.edu/ml/datasets/Gas+ Sensor + Array + Drift + Dataset + at + Differenl + Concentrations (C) 2015 The Authors. Published by Elsevier Inc.

引用

页码：85 / 89

页数：5

共 5 条

[1]

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