单光照条件变化的镨/钕元素组分含量软测量
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摘要
针对镨/钕稀土萃取工业生产现场光照条件变化导致具有颜色特征的镨/钕组分含量难以准确检测的问题,提出了一种基于单光照条件变化的组分含量软测量方法。首先,采用参数优化的Grey Edge算法,将不同光照条件下的稀土溶液图像校正到标准光照下;然后,以镨/钕溶液图像HSI颜色空间中的H、S、I分量一阶矩为模型的输入变量,利用加权最小二乘支持向量机(WLSSVM)建立组分含量软测量模型;最后采用工业数据对所提方法进行仿真实验,结果表明所提方法在不同光照条件下均能满足稀土萃取过程组分含量检测的准确度和快速性要求。
It is known that due to the change of light condition on rare earth extraction industry, makes soft-sensing model for rare earth element content based on the color characteristics of the rare earth solution have larger error.First, the Grey Edge algorithm with parameter optimization is used to correct the image of rare earth solution underdifferent illumination conditions to the standard illumination. Then, the first moment of H, S and I components inthe HSI color space of the Pr/Nd solution image is used as a model. The weighted least squares support vectormachine(WLSSVM) is used to model the component content. Finally, the simulation experiments of rare earthsolution images under different illumination conditions are carried out. The simulation results show that the imagesof rare earth solutions under different illumination conditions can meet the high-accuracy and rapid requirements of element component content detection in rare earth extraction process.
It is known that due to the change of light condition on rare earth extraction industry, makes soft-sensing model for rare earth element content based on the color characteristics of the rare earth solution have larger error.First, the Grey Edge algorithm with parameter optimization is used to correct the image of rare earth solution underdifferent illumination conditions to the standard illumination. Then, the first moment of H, S and I components inthe HSI color space of the Pr/Nd solution image is used as a model. The weighted least squares support vectormachine(WLSSVM) is used to model the component content. Finally, the simulation experiments of rare earthsolution images under different illumination conditions are carried out. The simulation results show that the imagesof rare earth solutions under different illumination conditions can meet the high-accuracy and rapid requirements of element component content detection in rare earth extraction process.
引文
[1]陆荣秀,何丽娟,杨辉,等.稀土萃取分离过程组分含量区间控制方法[J].化工学报, 2017, 68(3):1058-1064.Lu R X, He L J, Yang H, et al. Component content control withzone control for rare earth extraction process[J]. CIESC Journal,2017, 68(3):1058-1064.
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[13] Zhou Z, Xu R, Wu D, et al. Illumination correction of dyeingproducts based on Grey-Edge and kernel extreme learningmachine[J]. Optik, 2016, 127(19):7978-7985.
[14]方帅,赵育坤,李心科,等.基于光照估计的夜间图像去雾[J].电子学报, 2016, 44(11):2569-2575.Fang S, Zhao Y K, Li X K, et al. Nighttime haze removal based onillumination estimation[J]. Acta Electronica Sinica, 2016, 44(11):2569-2575.
[15] Weijer J V D, Gevers T, Gijsenij A. Edge-based color constancy[J]. IEEE Transactions on Image Processing, 2007, 16(9):2207-2214.
[16] Land E H. The Retinex theory of color vision[J]. ScientificAmerican, 1977, 237(6):108.
[17] Buchsbaum G. A spatial processor model for object colourperception[J]. Journal of the Franklin Institute, 1980, 310(1):1-26.
[18] Finlayson G D, Trezzi E. Shades of gray and colour constancy[C]//The Twelfth Color Imaging Conference:Color Science andEngineering Systems, Technologies, Applications, CIC 2004,Scottsdale, Arizona, USA. 2004:37-41.
[19] Kries J. Chromatic adaptation[C]//Festschrift der Albrecht-Ludwigs-Universit?t in Freiburg zum FünfzigjahrigenRegierungs-Jubil?um seiner K?niglichen Hoheit des Gro?herzogs Friedrich, 1902:145-158.
[20] Gijsenij A, Gevers T, Weijer J V D. Computational colorconstancy:survey and experiments[J]. IEEE Transactions onIMAGE Processing, 2011, 20(9):2475-2490.
[21] Gao S B, Yang K F, Li C Y, et al. Color constancy using double-opponency[J]. IEEE Transactions on Pattern Analysis&MachineIntelligence, 2015, 37(10):1973-1985.
[22] Zhang X S, Gao S B, Li R X, et al. A retinal mechanism inspiredcolor constancy model[J]. IEEE Transactions on Image Processing,2016, 25(3):1219-1232.
[23]赵新超,刘国莅,刘虎球,等.基于非均匀变异和多阶段扰动的粒子群优化算法[J].计算机学报, 2014, 37(9):2058-2070.Zhao X C, Liu G L, Liu H Q, et al. Particle swarm optimizationalgorithm based on non-uniform mutation and multiple stagesperturbation[J]. Chinese Journal of Computers, 2014, 37(9):2058-2070.
[24] Wang J, Fan X, Zhang C, et al. A graph-based ant colonyoptimization approach for integrated process planning andscheduling[J]. Chinese Journal of Chemical Engineering, 2014,22:748-753.
[25]刘波,王凌,金以慧.差分进化算法研究进展[J].控制与决策,2007, 22(7):721-729.Liu B, Wang L, Jin Y H. Advances in differential evolution[J].Control and Decision, 2007, 22(7):721-729.
[26] Xu Y, Wang Z Y, Zhu Q X. An improved hybrid genetic algorithmfor chemical plant layout optimization with novel non-overlappingand toxic gas dispersion constraints[J]. Chinese Journal ofChemical Engineering, 2013, 21(4):412-419.
[27]席裕庚,柴天佑.遗传算法综述[J].控制理论与应用, 1996, 13(6):697-708.Xi Y G, Chai T Y. Survey on genetic algorithm[J]. Control Theoryand Applications, 1996, 13(6):697-708.
[28]戴晓晖,李敏强,寇纪淞.遗传算法理论研究综述[J].控制与决策, 2000, 15(3):263-268.Dai X H, Li M Q, Kou J S. Survey on theory of genetic algorithms[J]. Control and Decision, 2000, 15(3):263-268.
[29] Suykens J A K, Brabanter J D, Lukas L, et al. Weighted leastsquares support vector machines:robustness and sparseapproximation[J]. Neurocomputing, 2002, 48:85-105.
[30] Ding J L, Chai T Y, Wang H. Offline modeling for product qualityprediction of mineral processing using modeling error PDFshaping and entropy minimization[J]. IEEE Transactions onNeural Network. 2011, 22(3):408-419.
[31] Feng X Y, Yu T, Wang J L. Nonlinear GPC with in-place trainedRLS-SVM model for DOC control in a fed-batch bioreactor[J].Chinese Journal of Chemical Engineering, 2012, 20(5):988-994.
[2]贾文君,柴天佑.稀土串级萃取分离过程元素组分含量的多模型软测量[J].控制理论与应用, 2007, 24(4):569-573.Jia W J, Chai T Y. Soft-sensor of element component contentbased on multiple models for the rare cascade extraction process[J]. Control Theory&Application, 2007, 24(4):569-573.
[3]向峥嵘,刘松青.基于LS-SVM的稀土萃取组分含量软测量[J].中国稀土学报, 2009, 27(1):132-136.Xiang Z R, Liu S Q. Component content soft-sensor in rare earthextraction based on LS-SVM[J]. Journal of the Chinese RareEarth Society, 2009, 27(1):132-136.
[4]田海,郭智恒,李兰云.稀土萃取分离过程软测量方法的研究[J].中国稀土学报, 2015, 33(2):201-205.Tian H, Guo Z H, Li L Y. Soft-sensing in rare earth extraction[J].Journal of the Chinese Rare Earth Society, 2015, 33(2):201-205.
[5]杨辉,高子洁,陆荣秀.基于稀土离子颜色特征识别的组分含量检测方法[J].中国稀土学报, 2012, 30(1):108-112.Yang H, Gao Z J, Lu R X. Detection method of component contentbased on rare earth ions color characteristics identification[J].Journal of the Chinese Rare Earth Society, 2012, 30(1):108-112.
[6]陆荣秀,叶兆斌,杨辉,等.镨/钕萃取过程组分含量多RBF模型预测[J].化工学报, 2016, 67(3):974-981.Lu R X, Ye Z B, Yang H, et al. Multi-RBF models basedprediction of component content for Pr/Nd extraction process[J].CIESC Journal, 2016, 67(3):974-981.
[7]陆荣秀,杨辉,欧阳超明,等.基于PCA-LS_SVM的镨/钕萃取过程元素组分含量预测[J].南昌大学学报(理科版), 2013, 37(6):589-593.Lu R X, Yang H, Ouyang C M, et al. Forecast of elementcomponent content in Pr/Nd extraction process based on PCA-LS_SVM[J]. Journal of Nanchang University(Natural Science),2013, 37(6):589-593.
[8] Barnard K, Cardei V, Funt B. A comparison of computationalcolor constancy algorithms(I):Methodology and experiments withsynthesized data[J]. IEEE Transactions on Image Processing,2002, 11(9):972-983.
[9] Barnard K, Martin L, Coath A. A comparison of computationalcolor constancy algorithms(II):Experiments with image data[J].IEEE Transactions on Image Processing, 2002, 11(9):985-996.
[10]李兵.颜色恒常性计算研究[D].北京:北京交通大学, 2009.Li B. Research on color constancy computation[D]. Beijing:Beijing Jiaotong University, 2009.
[11]鹿瑞.自然图像的颜色恒常性计算研究[D].北京:北京交通大学, 2010.Lu R. Research on computational color constancy for naturalimages[D]. Beijing:Beijing Jiaotong University, 2010.
[12]陈宁,黄璐,桂卫华,等.泡沫图像统计建模与恒常颜色校正算法研究[J].控制理论与应用, 2016, 33(5):613–620.Chen N, Huang L, Gui W H, et al. The statistical modeling andconstant color correction algorithms for froth image[J]. ControlTheory&Applications, 2016, 33(5):613–620.
[13] Zhou Z, Xu R, Wu D, et al. Illumination correction of dyeingproducts based on Grey-Edge and kernel extreme learningmachine[J]. Optik, 2016, 127(19):7978-7985.
[14]方帅,赵育坤,李心科,等.基于光照估计的夜间图像去雾[J].电子学报, 2016, 44(11):2569-2575.Fang S, Zhao Y K, Li X K, et al. Nighttime haze removal based onillumination estimation[J]. Acta Electronica Sinica, 2016, 44(11):2569-2575.
[15] Weijer J V D, Gevers T, Gijsenij A. Edge-based color constancy[J]. IEEE Transactions on Image Processing, 2007, 16(9):2207-2214.
[16] Land E H. The Retinex theory of color vision[J]. ScientificAmerican, 1977, 237(6):108.
[17] Buchsbaum G. A spatial processor model for object colourperception[J]. Journal of the Franklin Institute, 1980, 310(1):1-26.
[18] Finlayson G D, Trezzi E. Shades of gray and colour constancy[C]//The Twelfth Color Imaging Conference:Color Science andEngineering Systems, Technologies, Applications, CIC 2004,Scottsdale, Arizona, USA. 2004:37-41.
[19] Kries J. Chromatic adaptation[C]//Festschrift der Albrecht-Ludwigs-Universit?t in Freiburg zum FünfzigjahrigenRegierungs-Jubil?um seiner K?niglichen Hoheit des Gro?herzogs Friedrich, 1902:145-158.
[20] Gijsenij A, Gevers T, Weijer J V D. Computational colorconstancy:survey and experiments[J]. IEEE Transactions onIMAGE Processing, 2011, 20(9):2475-2490.
[21] Gao S B, Yang K F, Li C Y, et al. Color constancy using double-opponency[J]. IEEE Transactions on Pattern Analysis&MachineIntelligence, 2015, 37(10):1973-1985.
[22] Zhang X S, Gao S B, Li R X, et al. A retinal mechanism inspiredcolor constancy model[J]. IEEE Transactions on Image Processing,2016, 25(3):1219-1232.
[23]赵新超,刘国莅,刘虎球,等.基于非均匀变异和多阶段扰动的粒子群优化算法[J].计算机学报, 2014, 37(9):2058-2070.Zhao X C, Liu G L, Liu H Q, et al. Particle swarm optimizationalgorithm based on non-uniform mutation and multiple stagesperturbation[J]. Chinese Journal of Computers, 2014, 37(9):2058-2070.
[24] Wang J, Fan X, Zhang C, et al. A graph-based ant colonyoptimization approach for integrated process planning andscheduling[J]. Chinese Journal of Chemical Engineering, 2014,22:748-753.
[25]刘波,王凌,金以慧.差分进化算法研究进展[J].控制与决策,2007, 22(7):721-729.Liu B, Wang L, Jin Y H. Advances in differential evolution[J].Control and Decision, 2007, 22(7):721-729.
[26] Xu Y, Wang Z Y, Zhu Q X. An improved hybrid genetic algorithmfor chemical plant layout optimization with novel non-overlappingand toxic gas dispersion constraints[J]. Chinese Journal ofChemical Engineering, 2013, 21(4):412-419.
[27]席裕庚,柴天佑.遗传算法综述[J].控制理论与应用, 1996, 13(6):697-708.Xi Y G, Chai T Y. Survey on genetic algorithm[J]. Control Theoryand Applications, 1996, 13(6):697-708.
[28]戴晓晖,李敏强,寇纪淞.遗传算法理论研究综述[J].控制与决策, 2000, 15(3):263-268.Dai X H, Li M Q, Kou J S. Survey on theory of genetic algorithms[J]. Control and Decision, 2000, 15(3):263-268.
[29] Suykens J A K, Brabanter J D, Lukas L, et al. Weighted leastsquares support vector machines:robustness and sparseapproximation[J]. Neurocomputing, 2002, 48:85-105.
[30] Ding J L, Chai T Y, Wang H. Offline modeling for product qualityprediction of mineral processing using modeling error PDFshaping and entropy minimization[J]. IEEE Transactions onNeural Network. 2011, 22(3):408-419.
[31] Feng X Y, Yu T, Wang J L. Nonlinear GPC with in-place trainedRLS-SVM model for DOC control in a fed-batch bioreactor[J].Chinese Journal of Chemical Engineering, 2012, 20(5):988-994.
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