Identification of Formaldehyde under Different Interfering Gas Conditions with Nanostructured Semiconductor Gas Sensors
发表时间：2019-10-22
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- 论文类型：
- 期刊论文
- 第一作者：
- Zhao, Lin
- 通讯作者：
- Wang, J (reprint author), Dalian Univ Technol, Sch Elect Sci & Technol, Dalian, Peoples R China.
- 合写作者：
- Wang, Jing,Li, Xiaogan
- 发表时间：
- 2015-12-22
- 发表刊物：
- NANOMATERIALS AND NANOTECHNOLOGY
- 收录刊物：
- SCIE
- 文献类型：
- J
- 卷号：
- 5
- ISSN号：
- 1847-9804
- 关键字：
- Gas Classification; Nanostructured Semiconductor Gas Sensors; Volatile Organic Compounds; Extreme Learning Machine
- 摘要：
- 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 nanostructured 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 formaldehyde 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.
- 是否译文：
- 否
