A high precise E-nose for daily indoor air quality monitoring in living environment

被引：44
|
作者：
He, Jie [1 ,2 ]
Xu, Liyuan [2 ]
Wang, Peng [2 ]
Wang, Qin [2 ]
机构：
[1] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China
[2] Beijing Key Lab Knowledge Engn Mat Sci, Beijing 100083, Peoples R China
来源：
INTEGRATION-THE VLSI JOURNAL | 2017年 / 58卷
基金：
中国国家自然科学基金; 北京市自然科学基金;
关键词：
Indoor air quality; E-nose; Neural network; BP; NEURAL-NETWORK ANALYSIS; ELECTRONIC NOSE; SENSOR ARRAY; VOCS; OPTIMIZATION; CONTAMINANTS; SPACE;
D O I：
10.1016/j.vlsi.2016.12.010
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
E-nose, whose major components include a sensor array and a pattern recognition algorithm, is considered to be a potential way to balance the trade-off between cost and accuracy for daily indoor air quality monitoring in living environment. In this paper, we presented a high precise E-nose for such application. QS-01 from FIS, TGS2600 and TGS2602 from FIGARO, temperature and humidity sensor SHT10 are selected to compose the sensor array. Back Propagation (BP) nueral network, the typical machine learning algorithm is used to be the pattern recognition algorithm of the E-nose. The performance comparison between the proposed E-nose and other E-nose solutions shows the improvement.
引用
收藏
页码：286 / 294
页数：9
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