Classification and Identification of Industrial Gases Based on Electronic Nose Technology
Rapid detection and identification of industrial gases is a challenging problem. They have a complex composition and different specifications. This paper presents a method based on the kernel discriminant analysis (KDA) algorithm to identify industrial gases. The smell prints of four typical industr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/22/5033 |
| _version_ | 1811187910470270976 |
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| author | Hui Li Dehan Luo Yunlong Sun Hamid GholamHosseini |
| author_facet | Hui Li Dehan Luo Yunlong Sun Hamid GholamHosseini |
| author_sort | Hui Li |
| collection | DOAJ |
| description | Rapid detection and identification of industrial gases is a challenging problem. They have a complex composition and different specifications. This paper presents a method based on the kernel discriminant analysis (KDA) algorithm to identify industrial gases. The smell prints of four typical industrial gases were collected by an electronic nose. The extracted features of the collected gases were employed for gas identification using different classification algorithms, including principal component analysis (PCA), linear discriminant analysis (LDA), PCA + LDA, and KDA. In order to obtain better classification results, we reduced the dimensions of the original high-dimensional data, and chose a good classifier. The KDA algorithm provided a high classification accuracy of 100% by selecting the offset of the kernel function <i>c</i> = 10 and the degree of freedom <i>d</i> = 5. It was found that this accuracy was 4.17% higher than the one obtained using PCA. In the case of standard deviation, the KDA algorithm has the highest recognition rate and the least time consumption. |
| first_indexed | 2024-04-11T14:10:27Z |
| format | Article |
| id | doaj.art-92fd4583be484213b28aa4f850084960 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T14:10:27Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-92fd4583be484213b28aa4f8500849602022-12-22T04:19:44ZengMDPI AGSensors1424-82202019-11-011922503310.3390/s19225033s19225033Classification and Identification of Industrial Gases Based on Electronic Nose TechnologyHui Li0Dehan Luo1Yunlong Sun2Hamid GholamHosseini3School of Information and Engineering, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Information and Engineering, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Electric and Automatic Engineering, Changshu Institute of Technology, Changshu 215500, ChinaSchool of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Private Bag 92006, Auckland 1142, New ZealandRapid detection and identification of industrial gases is a challenging problem. They have a complex composition and different specifications. This paper presents a method based on the kernel discriminant analysis (KDA) algorithm to identify industrial gases. The smell prints of four typical industrial gases were collected by an electronic nose. The extracted features of the collected gases were employed for gas identification using different classification algorithms, including principal component analysis (PCA), linear discriminant analysis (LDA), PCA + LDA, and KDA. In order to obtain better classification results, we reduced the dimensions of the original high-dimensional data, and chose a good classifier. The KDA algorithm provided a high classification accuracy of 100% by selecting the offset of the kernel function <i>c</i> = 10 and the degree of freedom <i>d</i> = 5. It was found that this accuracy was 4.17% higher than the one obtained using PCA. In the case of standard deviation, the KDA algorithm has the highest recognition rate and the least time consumption.https://www.mdpi.com/1424-8220/19/22/5033electronic noseindustrial gasclassification and identificationkernel discriminant analysis |
| spellingShingle | Hui Li Dehan Luo Yunlong Sun Hamid GholamHosseini Classification and Identification of Industrial Gases Based on Electronic Nose Technology Sensors electronic nose industrial gas classification and identification kernel discriminant analysis |
| title | Classification and Identification of Industrial Gases Based on Electronic Nose Technology |
| title_full | Classification and Identification of Industrial Gases Based on Electronic Nose Technology |
| title_fullStr | Classification and Identification of Industrial Gases Based on Electronic Nose Technology |
| title_full_unstemmed | Classification and Identification of Industrial Gases Based on Electronic Nose Technology |
| title_short | Classification and Identification of Industrial Gases Based on Electronic Nose Technology |
| title_sort | classification and identification of industrial gases based on electronic nose technology |
| topic | electronic nose industrial gas classification and identification kernel discriminant analysis |
| url | https://www.mdpi.com/1424-8220/19/22/5033 |
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