[1] LIU C. Development, innovation and prospect of converter steelmaking technologies in China [J]. Special Steel Technology, 2013, 19(4):6-9.(刘超.中国转炉炼钢技术的发展、创新与展望[J].特钢技术,2013,19(4):6-9.) [2] WANG Z, REN K, LIU Z. Sublance technology application of 120 t converter at Laiwu steelmaking plant [J]. Journal of Materials Sciences and Technology, 2009, 19(11): 30-32.(王忠刚,任科社,刘忠建.副枪技术在莱钢120 t转炉上的应用[J].中国冶金,2009,19(11):30-32.) [3] SHI Z, WANG G, LI Y. Gas analysis in converter dynamic control technology and its application at Masteel [J]. Iron and Steel, 2007, 42(4): 24-26.(石知机,汪国才,李应江.炉气分析终点控制技术在马钢转炉的应用[J].钢铁,2007,42(4):24-26.) [4] WEN H, ZHAO Q, CHEN Y, et al. Basic oxygen furnace endpoint forecasting model based on radiation and modified neural network [J]. Acta Optica Sinica, 2008, 28(11):2131-2135.(温宏愿,赵琦,陈延如,等.基于炉口辐射和改进神经网络的转炉终点预测模型[J].光学学报,2008,28(11):2131-2135.) [5] LIU H, ZHANG Y, ZHANG Y, et al. State recognition of BOF based on flame image features and GRNN [J]. Computer Engineering and Applications, 2011, 47(26): 7-10.(刘辉,张云生,张印辉,等.基于火焰图像特征与GRNN的转炉吹炼状态识别[J].计算机工程与应用,2011,47(26):7-10.) [6] XU L, LI W, ZHANG M, et al. A model of Basic Oxygen Furnace (BOF) end-point prediction based on spectrum information of the furnace flame with Support Vector Machine (SVM) [J]. Optik-International Journal for Light and Electron Optics, 2011, 122(7): 594-598. [7] LIU H, WANG B, XIONG X. Basic oxygen furnace steelmaking end-point prediction based on computer vision and general regression neural network [J]. Optik-International Journal for Light and Electron Optics, 2014, 125(18): 5241-5248. [8] LIU H, ZHANG Y, ZHANG Y, et al. Texture feature extraction of flame image based on gray-scale difference statistics [J]. Control Engineering of China, 2013, 20(2): 213-218.(刘辉,张云生,张印辉,等.基于灰度差分统计的火焰图像纹理特征提取[J].控制工程,2013,20(2):213-218.) [9] GADELMAWLA E S. A vision system for surface roughness characterization using the gray level co-occurrence matrix [J]. NDT&E International, 2004, 37(7): 577-588. [10] YOU J, WANG S, LI X, et al. Estimate blowing final point by analysing texture features of top-blowing BOF vessel mouth flame [J].Journal of University of Science and Technology Beijing, 2000, 22(6): 524-528.(尤佳,王绍纯,李希胜,等.通过转炉炉口火焰纹理分析判断氧气顶吹转炉吹炼终点[J].北京科技大学学报,2000,22(6):524-528.) [11] MENDOZA F, VALOUS N A, ALLEN P, et al. Analysis and classification of commercial ham slice image using directional fractal dimension features [J]. Meat Science, 2009, 81(2): 313-320. [12] PALM C. Color texture classification by integrative co-occurrence matrices [J]. Pattern Recognition, 2004, 37(5): 965-976. [13] LIN T. A new adaptive center weighted median filter for suppressing impulsive noise in images [J]. Information Science, 2007, 177(4): 1073-1087. [14] ZHOU L, HUANG S. Image dimensionality reduction based on HSI color model [J]. Modern Electronics Technique, 2013, 36(14): 79-81.(周刘兵,黄硕.基于HSI彩色模型的图像降维技术[J].现代电子技术,2013,36(14):79-81.) [15] DING S, CHANG X, WU Q, et al. Comparative study of two dimensional vectors pattern classification based on GRNN and BPNN [J]. Foreign Electronic Measurement Technology, 2014, 33(5): 56-58.(丁硕,常晓恒,巫庆辉,等.基于GRNN与BPNN的二维向量模式分类对比研究[J].国外电子测量技术,2014,33(5):56-58.) [16] WANG J, GE Y. Texture feature recognition based on Contourlet transform and support vector machine [J]. Journal of Computer Applications, 2013, 33(3): 677-679.(王佳奕,葛玉荣.基于Contourlet变换和支持向量机的纹理识别方法[J].计算机应用,2013,33(3):677-679.) [17] YAN X. Weighted KNN classification algorithm based on mean distance of category [J]. Computer Systems and Applications, 2014, 23(2): 128-132.(严晓明.基于类别平均距离的加权KNN分类算法[J].计算机系统应用,2014,23(2):128-132.) [18] HUANG Y, WANG Y. Decision tree classification based on naive Bayesian and ID3 algorithm [J]. Computer Engineering, 2012, 38(14): 41-43.(黄宇达,王迤冉.基于朴素贝叶斯与ID3算法的决策树分类[J].计算机工程,2012,38(14):41-43.) [19] FAKHERI M, SEDGHI T, SHAYESTEH M G, et al. Framework for image retrieval using machine learning and statistical similarity matching techniques [J]. IET Image Processing, 2013, 7(1): 1-11. [20] LIU G, YANG J. Content-based image retrieval using color difference histogram [J]. Pattern Recognition, 2013, 46(1): 188-198. |