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Study on the Noise Reduction of Vehicle Exhaust NOX Spectra Based on Adaptive EEMD Algorithm

文献类型：期刊论文

作者Zhang, Kai1,2,3; Zhang, Yujun1; You, Kun1; He, Ying1,2; Gao, Qiankun1,2; Liu, Guohua1,2; He, Chungui1,2; Lu, Yibing1,2; Fan, Boqiang1,2; Tang, Qixing1,2
刊名JOURNAL OF SPECTROSCOPY
出版日期2017
期号无页码:1-7
DOI10.1155/2017/3290420
文献子类Article
英文摘要It becomes a key technology to measure the concentration of the vehicle exhaust components with the transmission spectra. But in the conventional methods for noise reduction and baseline correction, such as wavelet transform, derivative, interpolation, polynomial fitting, and so forth, the basic functions of these algorithms, the number of decomposition layers, and the way to reconstruct the signal have to be adjusted according to the characteristics of different components in the transmission spectra. The parameter settings of the algorithms above are not transcendental, so with them, it is difficult to achieve the best noise reduction effect for the vehicle exhaust spectra which are sharp and drastic in the waveform. In this paper, an adaptive ensemble empirical mode decomposition (EEMD) denoising model based on a special normalized index optimization is proposed and used in the spectral noise reduction of vehicle exhaust NOX. It is shown with the experimental results that the method can effectively improve the accuracy of the spectral noise reduction and simplify the denoising process and its operation difficulty.
WOS研究方向Biochemistry & Molecular Biology ; Spectroscopy
语种英语
WOS记录号WOS:000404413900001
资助机构National Key Research and Development Program of China(2016YFC0201003) ; National Key Research and Development Program of China(2016YFC0201003) ; National Key Research and Development Program of China(2016YFC0201003) ; National Key Research and Development Program of China(2016YFC0201003) ; National Key Research and Development Program of China(2016YFC0201003) ; National Key Research and Development Program of China(2016YFC0201003) ; National Key Research and Development Program of China(2016YFC0201003) ; National Key Research and Development Program of China(2016YFC0201003) ; 863 National High Technology Research and Development Program of China(2014AA06A503) ; 863 National High Technology Research and Development Program of China(2014AA06A503) ; 863 National High Technology Research and Development Program of China(2014AA06A503) ; 863 National High Technology Research and Development Program of China(2014AA06A503) ; 863 National High Technology Research and Development Program of China(2014AA06A503) ; 863 National High Technology Research and Development Program of China(2014AA06A503) ; 863 National High Technology Research and Development Program of China(2014AA06A503) ; 863 National High Technology Research and Development Program of China(2014AA06A503) ; National Key Research and Development Program of China(2016YFC0201003) ; National Key Research and Development Program of China(2016YFC0201003) ; National Key Research and Development Program of China(2016YFC0201003) ; National Key Research and Development Program of China(2016YFC0201003) ; National Key Research and Development Program of China(2016YFC0201003) ; National Key Research and Development Program of China(2016YFC0201003) ; National Key Research and Development Program of China(2016YFC0201003) ; National Key Research and Development Program of China(2016YFC0201003) ; 863 National High Technology Research and Development Program of China(2014AA06A503) ; 863 National High Technology Research and Development Program of China(2014AA06A503) ; 863 National High Technology Research and Development Program of China(2014AA06A503) ; 863 National High Technology Research and Development Program of China(2014AA06A503) ; 863 National High Technology Research and Development Program of China(2014AA06A503) ; 863 National High Technology Research and Development Program of China(2014AA06A503) ; 863 National High Technology Research and Development Program of China(2014AA06A503) ; 863 National High Technology Research and Development Program of China(2014AA06A503)
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/31999]  
专题合肥物质科学研究院_中科院安徽光学精密机械研究所
作者单位1.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Key Lab Environm Opt & Technol, Hefei 230031, Anhui, Peoples R China
2.Univ Sci & Technol China, Hefei 230026, Anhui, Peoples R China
3.Key Lab Opt Monitoring Technol Environm, Hefei 230031, Anhui, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Kai,Zhang, Yujun,You, Kun,et al. Study on the Noise Reduction of Vehicle Exhaust NOX Spectra Based on Adaptive EEMD Algorithm[J]. JOURNAL OF SPECTROSCOPY,2017(无):1-7.
APA Zhang, Kai.,Zhang, Yujun.,You, Kun.,He, Ying.,Gao, Qiankun.,...&Liu, Wenqing.(2017).Study on the Noise Reduction of Vehicle Exhaust NOX Spectra Based on Adaptive EEMD Algorithm.JOURNAL OF SPECTROSCOPY(无),1-7.
MLA Zhang, Kai,et al."Study on the Noise Reduction of Vehicle Exhaust NOX Spectra Based on Adaptive EEMD Algorithm".JOURNAL OF SPECTROSCOPY .无(2017):1-7.

入库方式： OAI收割

来源：合肥物质科学研究院

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