Identification of Formaldehyde under Different Interfering Gas Conditions with Nanostructured Semiconductor Gas Sensors
Sensor array with pattern recognition method is often used for gas detection and classification. Processing time and accuracy have become matters of widespread concern in using data analysis with semiconductor gas sensor array for volatile organic compound gas mixture classification. In this paper,...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Hindawi - SAGE Publishing
2015-12-01
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| Series: | Nanomaterials and Nanotechnology |
| Subjects: | |
| Online Access: | http://www.intechopen.com/journals/nanomaterials_and_nanotechnology/identification-of-formaldehyde-under-different-interfering-gas-conditions-with-nanostructured-semico |
| _version_ | 1797430419598082048 |
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| author | Lin Zhao Jing Wang Xiaogan Li |
| author_facet | Lin Zhao Jing Wang Xiaogan Li |
| author_sort | Lin Zhao |
| collection | DOAJ |
| description | Sensor array with pattern recognition method is often used for gas detection and classification. Processing time and accuracy have become matters of widespread concern in using data analysis with semiconductor gas sensor array for volatile organic compound gas mixture classification. In this paper, a sensor array consisting of four nanostruc‐ tured semiconductor gas sensors was used to generate the response signal. Three main categories of gas mixtures, including single-component gas, binary-component gas mixtures, and four-component gas mixtures, are tested. To shorten the training time, extreme learning machine (ELM) is introduced to classify the category of gas mixtures and the concentration level (low, middle, and high) of formal‐ dehyde in the gas mixtures. Our results demonstrate that, compared to traditional neural networks and support vector machines (SVM), ELM networks can achieve 204 and 817 times faster training speed. As for classification accuracy, ELM networks can achieve comparable results with SVM. |
| first_indexed | 2024-03-09T09:27:20Z |
| format | Article |
| id | doaj.art-bd77f96fee904448bc13874314dbf46a |
| institution | Directory Open Access Journal |
| issn | 1847-9804 |
| language | English |
| last_indexed | 2024-03-09T09:27:20Z |
| publishDate | 2015-12-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | Nanomaterials and Nanotechnology |
| spelling | doaj.art-bd77f96fee904448bc13874314dbf46a2023-12-02T05:38:14ZengHindawi - SAGE PublishingNanomaterials and Nanotechnology1847-98042015-12-01538http://dx.doi.org/10.5772/6211549765Identification of Formaldehyde under Different Interfering Gas Conditions with Nanostructured Semiconductor Gas SensorsLin ZhaoJing WangXiaogan LiSensor array with pattern recognition method is often used for gas detection and classification. Processing time and accuracy have become matters of widespread concern in using data analysis with semiconductor gas sensor array for volatile organic compound gas mixture classification. In this paper, a sensor array consisting of four nanostruc‐ tured semiconductor gas sensors was used to generate the response signal. Three main categories of gas mixtures, including single-component gas, binary-component gas mixtures, and four-component gas mixtures, are tested. To shorten the training time, extreme learning machine (ELM) is introduced to classify the category of gas mixtures and the concentration level (low, middle, and high) of formal‐ dehyde in the gas mixtures. Our results demonstrate that, compared to traditional neural networks and support vector machines (SVM), ELM networks can achieve 204 and 817 times faster training speed. As for classification accuracy, ELM networks can achieve comparable results with SVM.http://www.intechopen.com/journals/nanomaterials_and_nanotechnology/identification-of-formaldehyde-under-different-interfering-gas-conditions-with-nanostructured-semicoGas ClassificationNanostructured Semicon‐ ductor Gas SensorsVolatile Organic CompoundsExtreme Learning Machine |
| spellingShingle | Lin Zhao Jing Wang Xiaogan Li Identification of Formaldehyde under Different Interfering Gas Conditions with Nanostructured Semiconductor Gas Sensors Nanomaterials and Nanotechnology Gas Classification Nanostructured Semicon‐ ductor Gas Sensors Volatile Organic Compounds Extreme Learning Machine |
| title | Identification of Formaldehyde under Different Interfering Gas Conditions with Nanostructured Semiconductor Gas Sensors |
| title_full | Identification of Formaldehyde under Different Interfering Gas Conditions with Nanostructured Semiconductor Gas Sensors |
| title_fullStr | Identification of Formaldehyde under Different Interfering Gas Conditions with Nanostructured Semiconductor Gas Sensors |
| title_full_unstemmed | Identification of Formaldehyde under Different Interfering Gas Conditions with Nanostructured Semiconductor Gas Sensors |
| title_short | Identification of Formaldehyde under Different Interfering Gas Conditions with Nanostructured Semiconductor Gas Sensors |
| title_sort | identification of formaldehyde under different interfering gas conditions with nanostructured semiconductor gas sensors |
| topic | Gas Classification Nanostructured Semicon‐ ductor Gas Sensors Volatile Organic Compounds Extreme Learning Machine |
| url | http://www.intechopen.com/journals/nanomaterials_and_nanotechnology/identification-of-formaldehyde-under-different-interfering-gas-conditions-with-nanostructured-semico |
| work_keys_str_mv | AT linzhao identificationofformaldehydeunderdifferentinterferinggasconditionswithnanostructuredsemiconductorgassensors AT jingwang identificationofformaldehydeunderdifferentinterferinggasconditionswithnanostructuredsemiconductorgassensors AT xiaoganli identificationofformaldehydeunderdifferentinterferinggasconditionswithnanostructuredsemiconductorgassensors |