A Novel Sparse Representation Classification Method for Gas Identification Using Self-Adapted Temperature Modulated Gas Sensors
A novel sparse representation classification method (SRC), namly SRC based on Method of Optimal Directions (SRC_MOD), is proposed for electronic nose system in this paper. By finding both a synthesis dictionary and a corresponding coefficient vector, the <i>i</i>-th class training sample...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/9/2173 |
| _version_ | 1798003445531148288 |
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| author | Aixiang He Guangfen Wei Jun Yu Meihua Li Zhongzhou Li Zhenan Tang |
| author_facet | Aixiang He Guangfen Wei Jun Yu Meihua Li Zhongzhou Li Zhenan Tang |
| author_sort | Aixiang He |
| collection | DOAJ |
| description | A novel sparse representation classification method (SRC), namly SRC based on Method of Optimal Directions (SRC_MOD), is proposed for electronic nose system in this paper. By finding both a synthesis dictionary and a corresponding coefficient vector, the <i>i</i>-th class training samples are approximated as a linear combination of a few of the dictionary atoms. The optimal solutions of the synthesis dictionary and coefficient vector are found by MOD. Finally, testing samples are identified by evaluating which class causes the least reconstruction error. The proposed algorithm is evaluated on the analysis of hydrogen, methane, carbon monoxide, and benzene at self-adapted modulated operating temperature. Experimental results show that the proposed method is quite efficient and computationally inexpensive to obtain excellent identification for the target gases. |
| first_indexed | 2024-04-11T12:08:53Z |
| format | Article |
| id | doaj.art-cc729ddb52c0481d8613c7b022710a06 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:08:53Z |
| publishDate | 2019-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-cc729ddb52c0481d8613c7b022710a062022-12-22T04:24:40ZengMDPI AGSensors1424-82202019-05-01199217310.3390/s19092173s19092173A Novel Sparse Representation Classification Method for Gas Identification Using Self-Adapted Temperature Modulated Gas SensorsAixiang He0Guangfen Wei1Jun Yu2Meihua Li3Zhongzhou Li4Zhenan Tang5School of Information & Electronic Engineering, Shandong Technology and Business University, Yantai 264005, ChinaSchool of Information & Electronic Engineering, Shandong Technology and Business University, Yantai 264005, ChinaThe Key Laboratory of Liaoning for Integrated Circuits Technology, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116023, ChinaSchool of Information & Electronic Engineering, Shandong Technology and Business University, Yantai 264005, ChinaThe Key Laboratory of Liaoning for Integrated Circuits Technology, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116023, ChinaThe Key Laboratory of Liaoning for Integrated Circuits Technology, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116023, ChinaA novel sparse representation classification method (SRC), namly SRC based on Method of Optimal Directions (SRC_MOD), is proposed for electronic nose system in this paper. By finding both a synthesis dictionary and a corresponding coefficient vector, the <i>i</i>-th class training samples are approximated as a linear combination of a few of the dictionary atoms. The optimal solutions of the synthesis dictionary and coefficient vector are found by MOD. Finally, testing samples are identified by evaluating which class causes the least reconstruction error. The proposed algorithm is evaluated on the analysis of hydrogen, methane, carbon monoxide, and benzene at self-adapted modulated operating temperature. Experimental results show that the proposed method is quite efficient and computationally inexpensive to obtain excellent identification for the target gases.https://www.mdpi.com/1424-8220/19/9/2173electronic nosegas identificationsparse representation classification (SRC)method of optimal directions (MOD)temperature modulation |
| spellingShingle | Aixiang He Guangfen Wei Jun Yu Meihua Li Zhongzhou Li Zhenan Tang A Novel Sparse Representation Classification Method for Gas Identification Using Self-Adapted Temperature Modulated Gas Sensors Sensors electronic nose gas identification sparse representation classification (SRC) method of optimal directions (MOD) temperature modulation |
| title | A Novel Sparse Representation Classification Method for Gas Identification Using Self-Adapted Temperature Modulated Gas Sensors |
| title_full | A Novel Sparse Representation Classification Method for Gas Identification Using Self-Adapted Temperature Modulated Gas Sensors |
| title_fullStr | A Novel Sparse Representation Classification Method for Gas Identification Using Self-Adapted Temperature Modulated Gas Sensors |
| title_full_unstemmed | A Novel Sparse Representation Classification Method for Gas Identification Using Self-Adapted Temperature Modulated Gas Sensors |
| title_short | A Novel Sparse Representation Classification Method for Gas Identification Using Self-Adapted Temperature Modulated Gas Sensors |
| title_sort | novel sparse representation classification method for gas identification using self adapted temperature modulated gas sensors |
| topic | electronic nose gas identification sparse representation classification (SRC) method of optimal directions (MOD) temperature modulation |
| url | https://www.mdpi.com/1424-8220/19/9/2173 |
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