Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different Concentrations
Loading...
Files
Size: 1.73 MB, File format: Adobe PDF
Date
Authors
Sun, Yunlong
Luo, Dehan
Li, Hui
Zhu, Chuchu
Xu, Ou
GholamHosseini, Hamid
Supervisor
Item type
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
Hindawi Ltd.
Abstract
Gas sensors have been widely reported for industrial gas detection and monitoring. However, the rapid detection and identification of industrial gases are still a challenge. In this work, we measure four typical industrial gases including CO2, CH4, NH3, and volatile organic compounds (VOCs) based on electronic nose (EN) at different concentrations. To solve the problem of effective classification and identification of different industrial gases, we propose an algorithm based on the selective local linear embedding (SLLE) to reduce the dimensionality and extract the features of high-dimensional data. Combining the Euclidean distance (ED) formula with the proposed algorithm, we can achieve better classification and identification of four kinds of gases. We compared the classification and recognition results of classical principal component analysis (PCA), linear discriminate analysis (LDA), and PCA + LDA algorithms with the proposed SLLE algorithm after selecting the original data and performing feature extraction. The experimental results show that the recognition accuracy rate of the SLLE reaches 91.36%, which is better than the other three algorithms. In addition, the SLLE algorithm provides more efficient and accurate responses to high-dimensional industrial gas data. It can be used in real-time industrial gas detection and monitoring combined with gas sensor networks.
Description
Keywords
Science & Technology, Technology, Computer Science, Information Systems, Computer Science, e-Nose, Electronic noses, Quality, Sensors, 40 Engineering, 4009 Electronics, Sensors and Digital Hardware, 0802 Computation Theory and Mathematics, 1006 Computer Hardware, 4009 Electronics, sensors and digital hardware
Source
Journal of Electrical and Computer Engineering, ISSN: 2090-0147 (Print); 2090-0155 (Online), HINDAWI LTD, 2018(1). doi: 10.1155/2018/1092718
Publisher's version
Rights statement
© 2018 Yunlong Sun et al. Open access.
Permanent link
Endorsement
Review
Supplemented By
Referenced By
Creative Commons license
Except where otherwise noted, this item's license is described as Creative Commons Attribution License