Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different Concentrations
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...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2018/1092718 |
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| _version_ | 1850236768625885184 |
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| author | Yunlong Sun Dehan Luo Hui Li Chuchu Zhu Ou Xu Hamid Gholam Hosseini |
| author_facet | Yunlong Sun Dehan Luo Hui Li Chuchu Zhu Ou Xu Hamid Gholam Hosseini |
| author_sort | Yunlong Sun |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1229b5fd0d4248fd8bca688abe84cdb9 |
| institution | OA Journals |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-1229b5fd0d4248fd8bca688abe84cdb92025-08-20T02:01:54ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552018-01-01201810.1155/2018/10927181092718Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different ConcentrationsYunlong Sun0Dehan Luo1Hui Li2Chuchu Zhu3Ou Xu4Hamid Gholam Hosseini5School of Information and Engineering, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Information and Engineering, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Information and Engineering, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Information and Engineering, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Information and Engineering, Guangdong University of Technology, Guangzhou 510006, ChinaDepartment of Electrical and Electronic Engineering, Auckland University of Technology, Auckland, New ZealandGas 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.http://dx.doi.org/10.1155/2018/1092718 |
| spellingShingle | Yunlong Sun Dehan Luo Hui Li Chuchu Zhu Ou Xu Hamid Gholam Hosseini Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different Concentrations Journal of Electrical and Computer Engineering |
| title | Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different Concentrations |
| title_full | Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different Concentrations |
| title_fullStr | Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different Concentrations |
| title_full_unstemmed | Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different Concentrations |
| title_short | Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different Concentrations |
| title_sort | detecting and identifying industrial gases by a method based on olfactory machine at different concentrations |
| url | http://dx.doi.org/10.1155/2018/1092718 |
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