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

Autor: Kai Zhang, Yujun Zhang, Kun You, Ying He, Qiankun Gao, Guohua Liu, Chungui He, Yibing Lu, Boqiang Fan, Qixing Tang, Wenqing Liu
Jazyk: angličtina
Rok vydání: 2017
Předmět:
Zdroj: Journal of Spectroscopy, Vol 2017 (2017)
Druh dokumentu: article
ISSN: 2314-4920
2314-4939
DOI: 10.1155/2017/3290420
Popis: 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.
Databáze: Directory of Open Access Journals
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