A Novel Feature Extraction Approach Using Window Function Capturing and QPSO-SVM for Enhancing Electronic Nose Performance
In this paper, a novel feature extraction approach which can be referred to as moving window function capturing (MWFC) has been proposed to analyze signals of an electronic nose (E-nose) used for detecting types of infectious pathogens in rat wounds. Meanwhile, a quantum-behaved particle swarm optim...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2015-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/15/7/15198 |
| _version_ | 1798005193438134272 |
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| author | Xiuzhen Guo Chao Peng Songlin Zhang Jia Yan Shukai Duan Lidan Wang Pengfei Jia Fengchun Tian |
| author_facet | Xiuzhen Guo Chao Peng Songlin Zhang Jia Yan Shukai Duan Lidan Wang Pengfei Jia Fengchun Tian |
| author_sort | Xiuzhen Guo |
| collection | DOAJ |
| description | In this paper, a novel feature extraction approach which can be referred to as moving window function capturing (MWFC) has been proposed to analyze signals of an electronic nose (E-nose) used for detecting types of infectious pathogens in rat wounds. Meanwhile, a quantum-behaved particle swarm optimization (QPSO) algorithm is implemented in conjunction with support vector machine (SVM) for realizing a synchronization optimization of the sensor array and SVM model parameters. The results prove the efficacy of the proposed method for E-nose feature extraction, which can lead to a higher classification accuracy rate compared to other established techniques. Meanwhile it is interesting to note that different classification results can be obtained by changing the types, widths or positions of windows. By selecting the optimum window function for the sensor response, the performance of an E-nose can be enhanced. |
| first_indexed | 2024-04-11T12:36:24Z |
| format | Article |
| id | doaj.art-20ccfe3000714a41b690ca7bb7d86071 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:36:24Z |
| publishDate | 2015-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-20ccfe3000714a41b690ca7bb7d860712022-12-22T04:23:37ZengMDPI AGSensors1424-82202015-06-01157151981521710.3390/s150715198s150715198A Novel Feature Extraction Approach Using Window Function Capturing and QPSO-SVM for Enhancing Electronic Nose PerformanceXiuzhen Guo0Chao Peng1Songlin Zhang2Jia Yan3Shukai Duan4Lidan Wang5Pengfei Jia6Fengchun Tian7College of Electronics and Information Engineering, Southwest University, Chongqing 400715, ChinaCollege of Electronics and Information Engineering, Southwest University, Chongqing 400715, ChinaCollege of Electronics and Information Engineering, Southwest University, Chongqing 400715, ChinaCollege of Electronics and Information Engineering, Southwest University, Chongqing 400715, ChinaCollege of Electronics and Information Engineering, Southwest University, Chongqing 400715, ChinaCollege of Electronics and Information Engineering, Southwest University, Chongqing 400715, ChinaCollege of Electronics and Information Engineering, Southwest University, Chongqing 400715, ChinaCollege of Communication Engineering, Chongqing University, Chongqing 400044, ChinaIn this paper, a novel feature extraction approach which can be referred to as moving window function capturing (MWFC) has been proposed to analyze signals of an electronic nose (E-nose) used for detecting types of infectious pathogens in rat wounds. Meanwhile, a quantum-behaved particle swarm optimization (QPSO) algorithm is implemented in conjunction with support vector machine (SVM) for realizing a synchronization optimization of the sensor array and SVM model parameters. The results prove the efficacy of the proposed method for E-nose feature extraction, which can lead to a higher classification accuracy rate compared to other established techniques. Meanwhile it is interesting to note that different classification results can be obtained by changing the types, widths or positions of windows. By selecting the optimum window function for the sensor response, the performance of an E-nose can be enhanced.http://www.mdpi.com/1424-8220/15/7/15198feature extractionelectronic noseMWFCQPSOSVM |
| spellingShingle | Xiuzhen Guo Chao Peng Songlin Zhang Jia Yan Shukai Duan Lidan Wang Pengfei Jia Fengchun Tian A Novel Feature Extraction Approach Using Window Function Capturing and QPSO-SVM for Enhancing Electronic Nose Performance Sensors feature extraction electronic nose MWFC QPSO SVM |
| title | A Novel Feature Extraction Approach Using Window Function Capturing and QPSO-SVM for Enhancing Electronic Nose Performance |
| title_full | A Novel Feature Extraction Approach Using Window Function Capturing and QPSO-SVM for Enhancing Electronic Nose Performance |
| title_fullStr | A Novel Feature Extraction Approach Using Window Function Capturing and QPSO-SVM for Enhancing Electronic Nose Performance |
| title_full_unstemmed | A Novel Feature Extraction Approach Using Window Function Capturing and QPSO-SVM for Enhancing Electronic Nose Performance |
| title_short | A Novel Feature Extraction Approach Using Window Function Capturing and QPSO-SVM for Enhancing Electronic Nose Performance |
| title_sort | novel feature extraction approach using window function capturing and qpso svm for enhancing electronic nose performance |
| topic | feature extraction electronic nose MWFC QPSO SVM |
| url | http://www.mdpi.com/1424-8220/15/7/15198 |
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