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题名:
On-line dynamic monitoring automotive exhausts: Using BP-ANN for distinguishing multi-components
作者: Zhao, Yudi1,2; Wei, Ruyi1,2; Liu, Xuebin1,2
出版日期: 2017
会议名称: Applied Optics and Photonics China: Optical Spectroscopy and Imaging, AOPC 2017
会议日期: 2017-06-04
会议地点: Beijing, China
DOI: 10.1117/12.2285325
英文摘要:

Remote sensing-Fourier Transform infrared spectroscopy (RS-FTIR) is one of the most important technologies in atmospheric pollutant monitoring. It is very appropriate for on-line dynamic remote sensing monitoring of air pollutants, especially for the automotive exhausts. However, their absorption spectra are often seriously overlapped in the atmospheric infrared window bands, i.e. MWIR (3∼5μm). Artificial Neural Network (ANN) is an algorithm based on the theory of the biological neural network, which simplifies the partial differential equation with complex construction. For its preferable performance in nonlinear mapping and fitting, in this paper we utilize Back Propagation-Artificial Neural Network (BP-ANN) to quantitatively analyze the concentrations of four typical industrial automotive exhausts, including CO, NO, NO2and SO2. We extracted the original data of these automotive exhausts from the HITRAN database, most of which virtually overlapped, and established a mixed multi-component simulation environment. Based on Beer-Lambert Law, concentrations can be retrieved from the absorbance of spectra. Parameters including learning rate, momentum factor, the number of hidden nodes and iterations were obtained when the BP network was trained with 80 groups of input data. By improving these parameters, the network can be optimized to produce necessarily higher precision for the retrieved concentrations. This BP-ANN method proves to be an effective and promising algorithm on dealing with multi-components analysis of automotive exhausts. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

收录类别: EI ; ISTP
会议录: AOPC 2017: Optical Spectroscopy and Imaging
会议录出版者: SPIE
语种: 英语
作者部门: 光谱成像技术实验室
卷号: 10461
产权排序: 1
ISBN号: 9781510614031
ISSN号: 0277786X
Citation statistics:
内容类型: 会议论文
URI标识: http://ir.opt.ac.cn/handle/181661/29925
Appears in Collections:光谱成像技术实验室_会议论文

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作者单位: 1.Xi'An Institute of Optics and Precision Mechanics of Chinese Academy of Sciences, Key Laboratory of Spectral Imaging Technology of Chinese Academy of Sciences, Xi'an, 710119, China
2.University of Chinese Academy of Sciences, Beijing, 100190, China

Recommended Citation:
Zhao, Yudi,Wei, Ruyi,Liu, Xuebin. On-line dynamic monitoring automotive exhausts: Using BP-ANN for distinguishing multi-components[C]. 见:Applied Optics and Photonics China: Optical Spectroscopy and Imaging, AOPC 2017. Beijing, China. 2017-06-04.
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文件名: On-line dynamic monitoring automotive exhausts Using BP-ANN for distinguishing multi-components.pdf
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