A system for detecting sulfur dioxide (SO2) based on differential optical absorption spectrometry theory was studied. The detection system can eliminate the noise from light source and light path by using the double optical path. Background noise was generated by the photoelectric device. It also effects the quantitative analysis. The Support Vector Machine (SVM) is proposed to process the SO2 ultraviolet absorption spectrum. The SO2 ultraviolet absorption spectra at 220nm-340nm were obtained by using the SO2 detection system in this article. Then the spectral was denoised by the SVM. The experimental results showed that the absorption line was more smoothness after denoising by the SVM, and the SNR and mean square error were 48.9398 and 1×10-7, respectively. The de-noising data was applied to the SO2 detection system, the linearity of the measurement was good with the coefficients of more than 0.9971. Compare the result with the wavelet and Empirical Mode Decomposition (EMD) denoising methods, which illustrates that SVM has better effects. It shows that the SVM method applied to noise reduction of SO2 detection system is superior.
| Published in | Journal of Energy, Environmental & Chemical Engineering (Volume 3, Issue 4) |
| DOI | 10.11648/j.jeece.20180304.11 |
| Page(s) | 54-60 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2019. Published by Science Publishing Group |
Sulfur Dioxide, Denoising, Support Vector Machine, Wavelet, Empirical Mode Decomposition
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APA Style
Zhifang Wang, Shutao Wang. (2019). Sulfur Dioxide Detection Signal Denoising Based on Support Vector Machine. Journal of Energy, Environmental & Chemical Engineering, 3(4), 54-60. https://doi.org/10.11648/j.jeece.20180304.11
ACS Style
Zhifang Wang; Shutao Wang. Sulfur Dioxide Detection Signal Denoising Based on Support Vector Machine. J. Energy Environ. Chem. Eng. 2019, 3(4), 54-60. doi: 10.11648/j.jeece.20180304.11
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Download@article{10.11648/j.jeece.20180304.11,
author = {Zhifang Wang and Shutao Wang},
title = {Sulfur Dioxide Detection Signal Denoising Based on Support Vector Machine},
journal = {Journal of Energy, Environmental & Chemical Engineering},
volume = {3},
number = {4},
pages = {54-60},
doi = {10.11648/j.jeece.20180304.11},
url = {https://doi.org/10.11648/j.jeece.20180304.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeece.20180304.11},
abstract = {A system for detecting sulfur dioxide (SO2) based on differential optical absorption spectrometry theory was studied. The detection system can eliminate the noise from light source and light path by using the double optical path. Background noise was generated by the photoelectric device. It also effects the quantitative analysis. The Support Vector Machine (SVM) is proposed to process the SO2 ultraviolet absorption spectrum. The SO2 ultraviolet absorption spectra at 220nm-340nm were obtained by using the SO2 detection system in this article. Then the spectral was denoised by the SVM. The experimental results showed that the absorption line was more smoothness after denoising by the SVM, and the SNR and mean square error were 48.9398 and 1×10-7, respectively. The de-noising data was applied to the SO2 detection system, the linearity of the measurement was good with the coefficients of more than 0.9971. Compare the result with the wavelet and Empirical Mode Decomposition (EMD) denoising methods, which illustrates that SVM has better effects. It shows that the SVM method applied to noise reduction of SO2 detection system is superior.},
year = {2019}
}
TY - JOUR T1 - Sulfur Dioxide Detection Signal Denoising Based on Support Vector Machine AU - Zhifang Wang AU - Shutao Wang Y1 - 2019/03/19 PY - 2019 N1 - https://doi.org/10.11648/j.jeece.20180304.11 DO - 10.11648/j.jeece.20180304.11 T2 - Journal of Energy, Environmental & Chemical Engineering JF - Journal of Energy, Environmental & Chemical Engineering JO - Journal of Energy, Environmental & Chemical Engineering SP - 54 EP - 60 PB - Science Publishing Group SN - 2637-434X UR - https://doi.org/10.11648/j.jeece.20180304.11 AB - A system for detecting sulfur dioxide (SO2) based on differential optical absorption spectrometry theory was studied. The detection system can eliminate the noise from light source and light path by using the double optical path. Background noise was generated by the photoelectric device. It also effects the quantitative analysis. The Support Vector Machine (SVM) is proposed to process the SO2 ultraviolet absorption spectrum. The SO2 ultraviolet absorption spectra at 220nm-340nm were obtained by using the SO2 detection system in this article. Then the spectral was denoised by the SVM. The experimental results showed that the absorption line was more smoothness after denoising by the SVM, and the SNR and mean square error were 48.9398 and 1×10-7, respectively. The de-noising data was applied to the SO2 detection system, the linearity of the measurement was good with the coefficients of more than 0.9971. Compare the result with the wavelet and Empirical Mode Decomposition (EMD) denoising methods, which illustrates that SVM has better effects. It shows that the SVM method applied to noise reduction of SO2 detection system is superior. VL - 3 IS - 4 ER -
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