[1]
|
Jiang Z W, Luo Z X, Zhou H C. A simple measurement method of temperature and emissivity of coal-fired flames from visible radiation image and its application in a CFB boiler furnace. Fuel, 2009, 88(6): 980-987[2] Luo Z X, Zhou H C. A combustion-monitoring system with 3-D temperature reconstruction based on flame-image processing technique. IEEE Transactions on Instrumentation and Measurement, 2007, 56(5): 1877-1882[3] Bae H, Kim S, Wang B H, Lee M H, Harashima F. Flame detection for the steam boiler using neural networks and image information in the Ulsan steam power generation plant. IEEE Transactions on Industrial Electronics, 2006, 53(1): 338-348[4] Liu He. Judging boiler combustion stability based on flame image and fuzzy neural network. Chinese Journal of Scientific Instrument, 2008, 29(6): 1280-1284 (刘禾. 基于火焰图像和模糊神经网络的锅炉燃烧稳定性判别. 仪器仪表学报, 2008, 29(6): 1280-1284)[5] Krabicka J, Lu G, Yan Y. Profiling and characterization of flame radicals by combining spectroscopic imaging and neural network techniques. IEEE Transactions on Instrumentation and Measurement, 2011, 60(5): 1854-1860[6] Smart J, Lu G, Yan Y, Riley G. Characterisation of an oxy-coal flame through digital imaging. Combustion and Flame, 2010, 157(6): 1132-1139[7] Zhang Xiao-Gang, Chen Hua, Zhang Jing, Liu Xiao-Yan. Intelligent predictive control strategy applied to sintering temperature in rotary kiln based on image feedback. Control Theory and Applications, 2007, 24(6): 995-998 (张小刚, 陈华, 章兢, 刘小燕. 基于图像反馈的回转窑烧结温度智能预测控制. 控制理论与应用, 2007, 24(6): 995-998)[8] Zhang Xiao-Gang, Chen Hua, Zhang Jing. Rotary kiln sintering temperature measurement and control based on fuzzy multisensor data fusion. Control and Decision, 2002, 17(6): 865-870 (张小刚, 陈华, 章兢. 基于多传感器数据融合的回转窑烧结温度检测和控制方法. 控制与决策, 2002, 17(6): 865-870)[9] Lin B, Jorgensen S B. Soft sensor design by multivariate fusion of image features and process measurements. Journal of Process Control, 2011, 21(4): 547-553[10] Jiang Hui-Yan, Wang Xiao-Dan, Zhou Xiao-Jie, Chai Tian-You. Study on soft sensor for temperature of burning zone based on SVR. Journal of System Simulation, 2008, 20(11): 2951-2955 (姜慧研, 王晓丹, 周晓杰, 柴天佑. 基于SVR的回转窑烧成带温度软测量方法的研究. 系统仿真学报, 2008, 20(11): 2951-2955)[11] Yi Zheng-Ming, Lv Zi-Jian, Liu Zhi-Ming. Flame image processing and its characteristic extraction for alumina rotary kiln. Chinese Journal of Scientific Instrument, 2006, 27(8): 969-972 (易正明, 吕子剑, 刘志明. 氧化铝回转窑火焰图像处理与特征提取. 仪器仪表学报, 2006, 27(8): 969-972)[12] Yi Zheng-Ming, Yan Ming, Chi Yun-Guang, Wang Li-You. Measurement technique of flame temperature in rotary kiln based on image processing. Acta Metrologica Sinica, 2008, 29(1): 43-46 (易正明, 鄢明, 迟云广, 王理猷. 基于图像处理的回转窑火焰温度测量技术研究. 计量学报, 2008, 29(1): 43-46)[13] Sun Peng, Zhou Xiao-Jie, Chai Tian-You. FCM segmentation for flame image of rotary kiln based on texture coarseness. Journal of System Simulation, 2008, 20(16): 4438-4445(孙鹏, 周晓杰, 柴天佑. 基于纹理粗糙度的回转窑火焰图像FCM分割方法. 系统仿真学报, 2008, 20(16): 4438-4445)[14] He Min, Zhang Jing, He Zhao-Hui, Jing Li-Wei. Measurement of filling percentage of clinker using rotary kiln image. Journal of Scientific Instrument, 2009, 30(12): 2586-2591 (何敏, 章兢, 何昭晖, 靖立伟. 基于回转窑图像的熟料填充率测量. 仪器仪表学报, 2009, 30(12): 2586-2591)[15] Li Shu-Tao, Wang Yao-Nan. The segmentation of kiln flame image based on neural networks. Journal of Scientific Instrument, 2001, 22(1): 10-13 (李树涛, 王耀南. 基于神经网络的回转窑火焰图像分割. 仪器仪表学报, 2001, 22(1): 10-13)[16] Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: theory and applications. Neurocomputing, 2006, 70(1-3): 489-501[17] Li Han-Zhou, Pan Quan, Zhang Hong-Cai, Zhao Chun-Hui, Feng Min. A study of algorithms of temperature measurement based on digital image processing. Proceedings of the Chinese Society for Electrical Engineering, 2003, 23(6): 195-200(李汉舟, 潘泉, 张洪才, 赵春晖, 冯旻. 基于数字图像处理的温度检测算法研究. 中国电机工程学报, 2003, 23(6): 195-200)[18] Lou Chun, Zhou Huai-Chun, Zhu Guo-Liang, Shao Yong. Analysis and measurement of radiative properties of particulate medium in a large-scale coal-fired boiler of power plant. Journal of Engineering Thermophysics, 2007, 28(S2): 217-220 (娄春, 周怀春, 朱国良, 邵勇. 煤粉炉内颗粒辐射特性的检测与分析. 工程热物理学报, 2007, 28(S2): 217-220)[19] Huang Ben-Yuan, Luo Zi-Xue, Zhou Huai-Chun. Diagnosis of combustion stability in furnace by using flame image method. Thermal Power Generation, 2007, 36(12): 19-22 (黄本元, 罗自学, 周怀春. 炉膛燃烧稳定性的火焰图像诊断方法. 热 力发电, 2007, 36(12): 19-22)[20] Lamel L, Rabiner L, Rosenberg A, Wilpon J. An improved endpoint detector for isolated word recognition. IEEE Acoustics, Speech and Signal Processing Magazine, 1981, 29(4): 777-785[21] Huang G B, Chen L, Siew C K. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks, 2006, 17(4): 879-892[22] Huang G B, Wang D H, Lan Y. Extreme learning machines: a survey. International Journal of Machine Learning and Cybernetics, 2011, 2(2): 107-122[23] Suresh S, Babu R V, Kim H J. No-reference image quality assessment using modified extreme learning machine classifier. Applied Soft Computing, 2009, 9(2) 541-552[24] Saraswathi S, Sundaram S, Sundararajan N, Zimmermann M, Nilsen-Hamilton M. ICGA-PSO-ELM approach for accurate multiclass cancer classification resulting in reduced gene sets in which genes encoding secreted proteins are highly represented. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2011, 8(2): 452-463[25] Tian H X, Mao Z Z. An ensemble ELM based on modified AdaBoost.RT algorithm for predicting the temperature of molten steel in ladle furnace. IEEE Transactions on Automation Science and Engineering, 2010, 7(1): 73-80[26] Heeswijk M, Miche Y, Lindh-Knuutila T, Hilbers P A, Honkela T, Oja E, Lendasse A. Adaptive ensemble models of extreme learning machines for time series prediction. In: Proceedings of the 19th International Conference on Artificial Neural Networks: Part II. Berlin, Heidelberg: Springer-Verlag, 2009. 305-314[27] Soria-Olivas E, Gómez-Sanchis J, Martin J D, Vila-Francés J, Martínez M, Magdalena J R, Serrano A J. BELM: Bayesian extreme learning machine. IEEE Transactions on Neural Networks, 2011, 22(3): 505-509[28] Deng Wan-Yu, Zheng Qing-Hua, Chen Lin, Xu Xue-Bing. Research on extreme learning of neural networks. Chinese Journal of Computers, 2010, 33(2): 279-287 (邓万宇, 郑庆华, 陈琳, 许学斌. 神经网络极速学习方法研究. 计算机学报, 2010, 33(2): 279-287)[29] Bartlett P L. The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Transactions on Information Theory, 1998, 44(2): 525-536[30] Jiang Rui, Luo Gui-Ming. Optimal adaptive controller for stochastic systems based on weighted least-squares algorithm. Acta Automatica Sinica, 2006, 32(1): 140-147 (姜睿, 罗贵明. 基于加权最小二乘法的最优适应控制器. 自动化学报, 2006, 32(1): 140-147)[31] Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62-66
|