An Improved Algorithm of Drift Compensation for Olfactory Sensors
This research mainly studies the semi-supervised learning algorithm of different domain data in machine olfaction, also known as sensor drift compensation algorithm. Usually for this kind of problem, it is difficult to obtain better recognition results by directly using the semi-supervised learning...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/19/9529 |
| _version_ | 1797480807278837760 |
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| author | Siyu Lu Jialiang Guo Shan Liu Bo Yang Mingzhe Liu Lirong Yin Wenfeng Zheng |
| author_facet | Siyu Lu Jialiang Guo Shan Liu Bo Yang Mingzhe Liu Lirong Yin Wenfeng Zheng |
| author_sort | Siyu Lu |
| collection | DOAJ |
| description | This research mainly studies the semi-supervised learning algorithm of different domain data in machine olfaction, also known as sensor drift compensation algorithm. Usually for this kind of problem, it is difficult to obtain better recognition results by directly using the semi-supervised learning algorithm. For this reason, we propose a domain transformation semi-supervised weighted kernel extreme learning machine (DTSWKELM) algorithm, which converts the data through the domain and uses SWKELM algorithmic classification to transform the semi-supervised classification problem of different domain data into a semi-supervised classification problem of the same domain data. |
| first_indexed | 2024-03-09T22:05:28Z |
| format | Article |
| id | doaj.art-51ff8bc3ef6e44e0b702c6835903d222 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:05:28Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-51ff8bc3ef6e44e0b702c6835903d2222023-11-23T19:41:10ZengMDPI AGApplied Sciences2076-34172022-09-011219952910.3390/app12199529An Improved Algorithm of Drift Compensation for Olfactory SensorsSiyu Lu0Jialiang Guo1Shan Liu2Bo Yang3Mingzhe Liu4Lirong Yin5Wenfeng Zheng6School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, ChinaSchool of Automation, University of Electronic Science and Technology of China, Chengdu 610054, ChinaSchool of Automation, University of Electronic Science and Technology of China, Chengdu 610054, ChinaSchool of Automation, University of Electronic Science and Technology of China, Chengdu 610054, ChinaSchool of Automation, University of Electronic Science and Technology of China, Chengdu 610054, ChinaDepartment of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70803, USASchool of Automation, University of Electronic Science and Technology of China, Chengdu 610054, ChinaThis research mainly studies the semi-supervised learning algorithm of different domain data in machine olfaction, also known as sensor drift compensation algorithm. Usually for this kind of problem, it is difficult to obtain better recognition results by directly using the semi-supervised learning algorithm. For this reason, we propose a domain transformation semi-supervised weighted kernel extreme learning machine (DTSWKELM) algorithm, which converts the data through the domain and uses SWKELM algorithmic classification to transform the semi-supervised classification problem of different domain data into a semi-supervised classification problem of the same domain data.https://www.mdpi.com/2076-3417/12/19/9529semi-supervised learningextreme learning machinesensor drift compensation |
| spellingShingle | Siyu Lu Jialiang Guo Shan Liu Bo Yang Mingzhe Liu Lirong Yin Wenfeng Zheng An Improved Algorithm of Drift Compensation for Olfactory Sensors Applied Sciences semi-supervised learning extreme learning machine sensor drift compensation |
| title | An Improved Algorithm of Drift Compensation for Olfactory Sensors |
| title_full | An Improved Algorithm of Drift Compensation for Olfactory Sensors |
| title_fullStr | An Improved Algorithm of Drift Compensation for Olfactory Sensors |
| title_full_unstemmed | An Improved Algorithm of Drift Compensation for Olfactory Sensors |
| title_short | An Improved Algorithm of Drift Compensation for Olfactory Sensors |
| title_sort | improved algorithm of drift compensation for olfactory sensors |
| topic | semi-supervised learning extreme learning machine sensor drift compensation |
| url | https://www.mdpi.com/2076-3417/12/19/9529 |
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