Sulfur Dioxide Detection Signal Denoising Based on Support Vector Machine
Journal of Energy, Environmental & Chemical Engineering
Volume 3, Issue 4, December 2018, Pages: 54-60
Received: Jan. 22, 2019; Accepted: Feb. 26, 2019; Published: Mar. 19, 2019
Views 796      Downloads 120
Zhifang Wang, Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China
Shutao Wang, Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China
Article Tools
Follow on us
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.
Sulfur Dioxide, Denoising, Support Vector Machine, Wavelet, Empirical Mode Decomposition
To cite this article
Zhifang Wang, Shutao Wang, Sulfur Dioxide Detection Signal Denoising Based on Support Vector Machine, Journal of Energy, Environmental & Chemical Engineering. Vol. 3, No. 4, 2018, pp. 54-60. doi: 10.11648/j.jeece.20180304.11
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
GUO H B, HUANG S J, Air pollutants and asthma patient visits: indication of source influence. SCI TOTAL ENVIRON, 2018, 625: 355-362.
YU Y J, YU Z L, SUN P, et al. Effects of ambient air pollution from municipal solid waste landfill on children's non-specific immunity and respiratory health. Environmental Pollution, 2018, 236: 382-390.
ZHAO Y, DUAN L, XING J, et al. Soil acidification in China: Is controlling SO2 emissions enough?. ENVIRON SCI TECH LET, 2009, 43(21): 8021-8026.
LIU Y Y, XU X Y, HEN Y, et al. An integrated micro-chip with Ru/Al2O3 /ZnO as sensing material for SO2 detection. SENSOR ACTUAT B-CHEM, 2018, 262: 26-34.
LIU W Q, CUI Z C, LIU J G, et al. Measurement of atmospheric trace gases by spectroscopic and chemical techniques. Chinese Journal of Quantum Electronics, 2004, 21(2): 202-210.
QI J, DONG X P, ZHENG J D, et al. An algorithm of filtering background noise of optical fiber gas senor. Chinese Journal of Lasers, 2011, 38(11): 195-200.
WANG Y T, WANG Y T. Research on de-noising of fluorescence detecting signal based on wavelet transform. Metrology& Measurement Technique, 2009, 36(01): 1-2, 5.
BIAN H L, CHEN G F. Anti-aliasing algorithm of nonstationary harmonic signal measurement based on interpolation in frequency domain using short time Fourier transform. Chinese Journal of Scientific Instrument, 2008, 29(2): 284-288.
WANG S T, LI M M, LI P, et al. Signal processing method based on empirical mode decomposition in the SO2 concentration monitoring. Acta Optica Sinica, 2014, 43(02): 14-19.
ZHAO X M, Patel T H, ZUO M J. Multivariate EMD and full spectrum based condition monitoring for rotating machinery. Mechanical Systems and Signal Processing, 2012, 27(1): 712-728.
ZHU B Z, HAN D, WANG P, et al. Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression. Applied Energy, 2017, 191:521-530.
HAO Z, ZHAO H L, ZHANG C, et al. Estimating winter wheat area based on an SVM and the variable fuzzy set method. REMOTE SENSING LETTERS, 2019, 10(4): 343-352.
PEND H B, CHEN G H, CHEN X X, et al. Hybrid classification of coal and biomass by laser-induced breakdown spectroscopy combined with K-means and SVM. PLASMA SCIENCE & TECHNOLOGY, 2019, 21(3): UNSP 034008.
RAPUR J. S., TIWARI, R.. On-line Time Domain Vibration and Current Signals Based Multi-fault Diagnosis of Centrifugal Pumps Using Support Vector Machines. JOURNAL OF NONDESTRUCTIVE EVALUATION, 2019, 38(1): 6.
BAHRAM C. B., Moradi Ehsan, Golshan Mohammad, et al. An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. SCI TOTAL ENVIRON, 2019, 651: 2087-2096.
ZHOU Y. L., CHANG F. J., CHANG L. C, et al. Multi-output support vector machine for regional multi-step-ahead PM2.5 forecasting. SCI TOTAL ENVIRON, 2019, 651: 230-240.
LI C B, LIN S S, XU F Q, et al. Short-term wind power prediction based on data mining technology and improved support vector machine method: A case study in Northwest China. JOURNAL OF CLEANER PRODUCTION, 2018, 205: 909-922.
Fabelo Himar, Ortega Samuel, Casselden Elizabeth, et al. SVM Optimization for Brain Tumor Identification Using Infrared Spectroscopic Samples. Sensors (Basel, Switzerland), 2018, 18(12).
ROUTRAY S, Ray A K, Mishra C, et al.. Efficient hybrid image denoising scheme based on SVM classification [J]. Optik-International Journal for Light and Electron Optics, 2018, 157: 503-511.
SHAHDOOSTI H R, HAZAVEI S M. Combined rippled and total variation image denoising methods using twin support vector machines [J]. Multimedia Tools & Applications, 2017, 12: 1-19.
CHEN C Y, LIN M L, ZHANG J. Study on signal filtering based on support vector machine [J]. Journal of Xi’an Jiaotong University, 2006, 40(4): 427-431.
JI Q C, LUO X H. Filter design and hardware implementation based on SVM [J]. Transducer and Microsystem Technologies, 2018, 37(03): 95-98, 102.
VAPNIK V. The Nature of Statistical Learning Theory [M]. New York: Springer-Verlag, 1995.
SMOLA A J, SCHOLKOPF B. A tutorial on support vector regression [J]. Statistics and Computing, 2004, 14(3): 199-222.
KEERTHI S S, LIN C J. Asymptotic behaviors of support vector machines with Gaussian kernel [J]. Neural Computation, 2003, 15(7): 1667-1689.
LI H, LIN Q Z, WANG Q J, et al. Research on spectrum denoising methods based on the combination of wavelet package transformation and mathematical morphology [J]. Spectroscopy and Spectral Analysis, 2010, 30(3): 644-648.
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
Tel: (001)347-983-5186