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| 基于Fast ICA及神经网络的机动车尾气NO和NO2定量分析研究 | |
| 引用本文： | 张恺 张玉钧 何莹 尤坤 刘国华 陈晨 高彦伟 贺春贵 鲁一冰 刘文清. 基于Fast ICA及神经网络的机动车尾气NO和NO2定量分析研究[J]. 大气与环境光学学报, 2016, 11(6): 435-441 |
| 作者姓名： | 张恺 张玉钧 何莹 尤坤 刘国华 陈晨 高彦伟 贺春贵 鲁一冰 刘文清 |
| 作者单位： | (1中国科学院安徽光学精密机械研究所中国科学院环境光学与技术重点实验室,安徽 合肥, 230031;2.中国科学技术大学, 安徽 合肥, 230026) |
| 基金项目： | Supported by National High Technology Research and Development Program of China(国家863计划, 2014AA06A503）, National Major Scientific Instruments and Equipment Development Project(国家重大科学仪器设备开发专项, 2012YQ22011902） |
| 摘 要： | 机动车尾气对环境的危害日益加重，机动车尾气排放浓度的检测对大气污染治理具有重要意义。设计了基于非分散紫外的机动车尾气NO、NO2浓度检测系统，搭建了实验装置，获得NO、NO2混合气体的吸收光强后，利用快速不动点(Fast ICA)算法和人工神经网络模式识别算法对机动车尾气排放NO、NO2组分进行定量分析。实验结果表明，利用所设计的算法对600 ppm以内的NO气体和200 ppm以内的NO2气体浓度进行测量，其相对误差最大为1.54%，最小为0.25%。 |
| 关 键 词： | 机动车尾气 NO NO2 定量分析 快速固定点 神经网络 |
| 收稿时间： | 2016-07-11 |
| 修稿时间： | 2016-09-30 |
Quantitative Analysis of NO and NO2 from Vehicle Exhaust Emission Based on Fast ICA and ANN |
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| ZHANG Kai,ZHANG Yu-Jun,HE Ying,YOU Kun,LIU Guo-Hua,CHEN Chen,GAO Yan-Wei,HE Chun-Gui,LU Yi-Bing,LIU Wen-Qing. Quantitative Analysis of NO and NO2 from Vehicle Exhaust Emission Based on Fast ICA and ANN[J]. Journal of Atmospheric and Environmental Optics, 2016, 11(6): 435-441 | |
| Authors: | ZHANG Kai ZHANG Yu-Jun HE Ying YOU Kun LIU Guo-Hua CHEN Chen GAO Yan-Wei HE Chun-Gui LU Yi-Bing LIU Wen-Qing |
| Affiliation: | (1 Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China; 2. University of Science and Technology of China, Hefei 230026, China) |
| Abstract: | With the increasing number of vehicles, the harm from vehicle exhaust to the environment becomes more and more serious. So the monitoring of the concentration of vehicle exhaust emissions is very important to assess the emission levels. The NO and NO2 quantitative detection system based on nondispersion ultraviolet (NDUV) for vehicle exhaust emissions is built, and the original data of the mixed tail gas is obtained. And then, the identification and quantitative analysis of NO & NO2 gas is carried out with fast independent component analysis(Fast ICA) and artificial neural network(ANN) recognition algorithms. It can be drawn from the results that using the two algorithms, the NO concentration (under 600 ppm) and NO2 concentration (under 200 ppm) can be detected accurately and the maximum relative error is 1.54%, and the minimum is 0.25%. |
| Keywords: | vehicle exhaust emission NO NO2 quantitative analysis fast independent component analysis artificial neural network |
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