基于电阻成像技术的水平管气液两相流研究
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摘要
两相流系统广泛存在于动力、化工、石油、冶金等领域,研究两相流体动力学,探讨两相流动的机理,准确检测两相流的各种参数,具有非常重要的意义。但是由于两相流的流动状态复杂多变,实时有效地检测两相流流动参数一直是工程技术和科学研究领域中的难题。
电学层析成像(Electrical Tomography,简称ET)技术因其价格低廉、响应快速、不干扰流场、便携性,在多相流测量领域具有重要的应用价值。论文工作主要围绕应用ET技术,尤其是ERT (Electrical Resistance Tomography)技术,对水平管气液两相流流动参数测量进行,完成的主要工作和创新点:
1、基于ERT技术应用流型识别的软测量模型对水平管道气水两相流进行流型识别,分别提取了反应信号的低阶统计量、高阶统计量的特征值和时域、时频域特征量,实验结果表明多特征数据融合具有较高的流型识别率。
2、将独立成分分析和电阻层析成像技术结合,提取的独立分量能够有效地反应水平管气液两相流中物理上相互独立的分相的变化情况,具有明确的物理意义;弹状流中峭度最大的独立分量对应液弹变化信息;结合多尺度分析方法可以进一步提取气液两相流界面波动特征。
3、将基于电阻层析成像的ICA结果结合相关技术,将电阻成像系统用于水平管气液两相流中弹状流的特征参数估计,获得弹状流中液弹平均速度、液弹长度。
4、将基于电阻层析成像的ICA结果结合相关技术,获得了水平管气液两相流中管道中心区域附近液相的平均速度、靠近相间界面部分的气相的平均速度和管壁附近液相的的平均速度。
The two-phase flow system exists widely in industrial processes, such as power, chemical, petroleum and metallurgy industries. Research on hydrodynamics, mechanisms and accurate measurement of parameters of two-phase flow are very important for the development of modern industry. However, because of its complexity and variability, the real-time parameter measurement of two-phase flow becomes a difficult issue.
As a kind of non-destructive and visible measurement techniques, electrical tomography (ET) has found important applications in multi-phase flow measurement area, due to its low cost, fast response and portability. This dissertation focuses on the application of ET technique, especially electrical resistance tomography (ERT) technique, in gas/liquid two-phase flow measurement in a horizontal pipeline.
The main achievements are listed as follows:
1. The soft sensing model of gas/liquid two-phase flow in horizontal pipe has been established to identify flow regimes based on ERT technique. The features from second-order statistics, higher-order statistics, time domain and time-frequency domain are extracted. Experimental results indicate that the method of multi-feature information fusion which combined the feature extraction methods could better express the essential information of gas/liquid two-phase flow, and achieve a higher identification rate of flow regimes.
2. Having obtained data of a gas/liquid flow by ERT, Independent component analysis (ICA) method has been applied. Experimental results demonstrate extracted independent components, having explicit physical meaning, correspond to the gas phase and the liquid phase separately. Good agreement between the independent component with the maximal kurtosis and liquid slug fluctuation of slug flow was observed. The interface fluctuation can be obtained by applying the multi-resolution analysis to extracted independent components.
3. Based on the extracted liquid slug fluctuation, combined with cross-correlation technique, the ERT system was applied to estimate characteristic parameter of slug flow in a horizontal pipeline. The mean velocity and the length of liquid slug were obtained.
4. Based on the extracted indepentdent components, the cross-correlation technique was adopted to estimate the mean velocity of the liquid phase in centre area, the gas phase at the interface and the liquid phase rounding pipe wall.
电学层析成像(Electrical Tomography,简称ET)技术因其价格低廉、响应快速、不干扰流场、便携性,在多相流测量领域具有重要的应用价值。论文工作主要围绕应用ET技术,尤其是ERT (Electrical Resistance Tomography)技术,对水平管气液两相流流动参数测量进行,完成的主要工作和创新点:
1、基于ERT技术应用流型识别的软测量模型对水平管道气水两相流进行流型识别,分别提取了反应信号的低阶统计量、高阶统计量的特征值和时域、时频域特征量,实验结果表明多特征数据融合具有较高的流型识别率。
2、将独立成分分析和电阻层析成像技术结合,提取的独立分量能够有效地反应水平管气液两相流中物理上相互独立的分相的变化情况,具有明确的物理意义;弹状流中峭度最大的独立分量对应液弹变化信息;结合多尺度分析方法可以进一步提取气液两相流界面波动特征。
3、将基于电阻层析成像的ICA结果结合相关技术,将电阻成像系统用于水平管气液两相流中弹状流的特征参数估计,获得弹状流中液弹平均速度、液弹长度。
4、将基于电阻层析成像的ICA结果结合相关技术,获得了水平管气液两相流中管道中心区域附近液相的平均速度、靠近相间界面部分的气相的平均速度和管壁附近液相的的平均速度。
The two-phase flow system exists widely in industrial processes, such as power, chemical, petroleum and metallurgy industries. Research on hydrodynamics, mechanisms and accurate measurement of parameters of two-phase flow are very important for the development of modern industry. However, because of its complexity and variability, the real-time parameter measurement of two-phase flow becomes a difficult issue.
As a kind of non-destructive and visible measurement techniques, electrical tomography (ET) has found important applications in multi-phase flow measurement area, due to its low cost, fast response and portability. This dissertation focuses on the application of ET technique, especially electrical resistance tomography (ERT) technique, in gas/liquid two-phase flow measurement in a horizontal pipeline.
The main achievements are listed as follows:
1. The soft sensing model of gas/liquid two-phase flow in horizontal pipe has been established to identify flow regimes based on ERT technique. The features from second-order statistics, higher-order statistics, time domain and time-frequency domain are extracted. Experimental results indicate that the method of multi-feature information fusion which combined the feature extraction methods could better express the essential information of gas/liquid two-phase flow, and achieve a higher identification rate of flow regimes.
2. Having obtained data of a gas/liquid flow by ERT, Independent component analysis (ICA) method has been applied. Experimental results demonstrate extracted independent components, having explicit physical meaning, correspond to the gas phase and the liquid phase separately. Good agreement between the independent component with the maximal kurtosis and liquid slug fluctuation of slug flow was observed. The interface fluctuation can be obtained by applying the multi-resolution analysis to extracted independent components.
3. Based on the extracted liquid slug fluctuation, combined with cross-correlation technique, the ERT system was applied to estimate characteristic parameter of slug flow in a horizontal pipeline. The mean velocity and the length of liquid slug were obtained.
4. Based on the extracted indepentdent components, the cross-correlation technique was adopted to estimate the mean velocity of the liquid phase in centre area, the gas phase at the interface and the liquid phase rounding pipe wall.
引文
[1]李海青,两相流参数检测及其应用,杭州:浙江大学出版社,1991
[2]陈学俊,多相流热物理研究进展,西安交通大学学报,1994,Vol.28:1~8
[3]林宗虎,能源和动力工程中的重要理论基础-多相流热物理学,中国科学基金,2000,6:359~361
[4]陈学俊,迅速发展中的一门新兴交叉学科一多相流热物理的进展,西安交通大学出版社,1996,30(4):9~17
[5]陈甘棠,前言,第一届全国多相流检测机技术学术讨论会论文集,1986
[6]林宗虎,王栋,王树众等,多相流的近期工程应用趋向,西安交通大学学报,2001,35(9): 886~890
[7]白博峰,郭烈锦,王忠勇等,油气水多相流压力和压差信号特征分析与流型在线识别,2002,23(3):357~360
[8] Baker R C, et al., Response of bulk flowmeters to multiphase flows, Proc.lnstrMech.Engrs, 1991, 205: 217~229
[9]仲朔平,佟允宪,王文然,利用孔板差压噪声测量汽水两相流,清华大学学报,1997,37(5):15~18
[10]罗毓珊,李爱华,陈听宽等,差压法测量两相流含率的研究,工程热物理学报,2004,25(5):789~792
[11] Meng Jianbo. The new approach to the vortex flowmeter. Proc. of In t. Symposium on Measuring Techniques for Multiphase Flow, Apr.10-13,1995, Nanjing , China, 348~355
[12] Anderson J.L. and Fincke J.R., Mass flow measurement in air/water mixtures using drag devices and gamma densitometer. ISA Trans, 1980, 19(1): 37~48
[13] Han K-F F-L, Banerjee S., Measurement of mass flux in two-phase flow using combinations of pitot tubes and gamma densitometer. AIChE J., 1981, 27:177~184
[14]李海青,黄志尧,软测量技术原理及应用,北京:化学工业出版社,2000,47~55
[15]权志英,陈德运,孙立镌,基于小波变换的气液两相流系统软测量,哈尔滨理工大学学报,2005,10(1):79~82
[16]吴浩江,周芳德,油气水多相流流型智能识别系统的设计与实现,西安交通大学学报,2000,34(3):31~35
[17] Y. Mi, M. Ishi, L. H. Tsoukalas, Vertical two-phase flow identification using advanced instrumentation and neural networks, Nuclear Engineering and Design, 1998, 84: 409~420
[18] C. L. Briens, C. Hudson, L. A. Briens, Rapid characterization of flow regimes in multiphase reactors through box-counting dimensions with an embedding dimension of two phase flow, The Chemical Engineering Journal, 1996, 64: 169~178
[19] Thomes J .M ..Contemplative stance for chemical process control.An IFAC Report, Automatic, 1992, 28(2):441
[20]李劲松,叶琛,梅德松,超声波检测数据高速采集和传输技术的研究,无损检测,2003,25(8):395~398
[21]阎春生,曾楠,赖淑蓉等,光学层析成像技术的研究动态,激光杂志,2001,22(5):5~7
[22]吴志军,孙志军,张建华等,粒子图像速度场仪(PIV)成像系统开发,吉林工业大学自然科学学报,1999,3(29):6~11
[23] G. F. Lynch, S. L. Segal, Direct measurement of the void fraction of two-phase fluid by nuclear magnetic resonance, Int. J. of mass transfer, 1977, 20(1):7~14
[24]王俊,电导式纵向多电极阵列油/水两相流测量方法研究:[硕士学位论文],天津;天津大学,2004
[25]徐苓安,相关流量测量技术,天津:天津大学出版社,1988
[26]宿成基,梯度相关法测量两相流流的精度与离散性研究,计量学报,1996,32(2):135~139
[27]高晋占,参数估计法测量测量两相流流速,清华大学学报,1992, 32(1):93~98
[28]陈之航,曹柏林,赵在三.气液双相流动和传热.北京:机械工业出版社,1983.
[29] Baker O.C.. Simultaneous flow of oil and gas. Oil and Gas, 1954, 53:185~195
[30] Scott D.S.. Properties of co-current gas-liquid flow. Advances in Chem. Eng, 1963, 4 :199~277
[31] Hewitt G.F., Roberts D.N. Studies of two-phase flow patterns by simul taneous X-ray and flush photography. Rept.AERE-M2159, 1969, UKAEA Harwell.
[32] Weisman J.. Effects of fluid properties and pipes diameter on two phase flow patterns in horizontal lines. Int.J.Multiphase Flow, 1979, 5:437~462.
[33] Weisman J., Kang S.Y.. Flow patern transitions in vertical and upwardly inclining line. Int. J. Multiphase Flow, 1981,7: 271~291.
[34] Taitel Y., Dukler A.E.. A model for predicting flow regime transitions in horizontal and near horizontal gas-liquid flow. AIChE J., 1976, 22(1):47~54
[35] ChoeW.G., Wemberg L., Weisman J.. Observation and collection of flow pattern transition in horizontal concurrent gas-liquid flow. Two Phase Transport and Reactor Safety, Washington, Hemisphere, 1978, 1357~1375.
[36] Taitel Y., Bomea D., Dukler A.E., Modeling flow pattern transitions for steady upward gas-liquid flow in vertical tubes. AIChE J .,1980, 26 (3):345~354
[37] Griffith P., Wallis G.B.. Two phase slug flow. Journal of heat transfer, 1961, 83: 307~318
[38] Vince M.A., Lahey R.T., JR.. On the development of an objective flow regime Indicator. Int.J. Multiphase Flow, 1982, 8: 93~12
[39] Hewitt G F, Measurement of two-phase flow parameters, Academic Press, London, 1978
[40] Barnea D., Shoham O., Taitel Y.. Flow pattern characterization in two phase flow by electrical conductance probe, Int. J. Multiphase Flow, 1980, 6 :387~397
[41] Paglianti A., Pintus S., Giona M.. Time-series analysis approach for the identification of flooding/loading transition in gas-liquid stirred tank reactors. Chem. Eng. Sci., 2000, 55: 5793~5802
[42] Waterfall R C, He R, and Beck C M. Visualizing combustion using electrical impedance tomography. Chemical Engineering Science, 1997, 52(13):2129~2138,
[43] Hawkins A R, Liu H, Oliphant T E, and Schultz S M. Noncontact scanning impedance imaging in an aqueous solution. Applied Physics Letters, 2004, 85(6): 1080~1082
[44] Tossavainen O, Vauhkonen M, Kolehmainen V, and Kim K. Tracking of moving interfaces in sedimentation processes using electrical impedance tomography. Chemical Engineering Science, 2006, 61:7717~7729
[45] Lehmann C and Schilcher K. Approximate 3-dimensional electrical impedance imaging. Physics Letters A, 2001, 292:188~194
[46]王妍,任超世. 3D-EIT图像重建的研究进展,国外医学:生物医学工程分册,2003,26(6):265~268
[47]薛健,张兆田,熊晓芸,李慧,田捷,工业过程断层图像的三维动态可视化,中国体视学与图像分析,2005,10(3):183~188
[48] Warsito W and Fan L S. Imaging the bubble behavior using the 3-D electric capacitance tomography. Chemical Engineering Science, 2005, 22:6073~6084
[49] Wajman R, Banasiak R, Mazurkiewicz L, Dyakowski T, and Sankowski D. Spatial imaging with 3D capacitance measurements. Measurement Science and Technology, 2006, 17:2113~2118,
[50] Warsito W, Marashdeh Q, and Fan L S. Electrical capacitance volume tomography. Sensors Journal, IEEE, 2007, 7(4):525~535
[51] Hubbard M G, Dukler A E, The characterization of flow regimes for horizontal two-phase flow, Proc. Heat Transfer &Fluid Mechanics Inst. 1966:100~121.
[52] Matuszkiwicz A, Flamand J C, Bour J A, The bubble-slug flow pattern transition and instabilities of void fraction waves, Int. J. Multiphase Flow, 1987, 13(2): 199~217.
[53]陈珙,黄志尧,王保良,李海青,小波变换辨识流型的一种新方法研究,仪器仪表学报,1999, 20(2):117~120.
[54]陈珙,王保良,杨江,李海青,基于小波分析的气液两相流流型模糊辨识,高校化工学报,1999, 13(4): 303~308.
[55] Ji Haifeng, Huang Zhiyao, Wang Baoliang, et al.. Wavelet-based flow regime identification of gas-liquid two-phase flow. Proc.of Int. Conf.on Power Engineering, Oct.8-12, 2001,Xi’an, China, 492~496.
[56]冀海峰,小波分析技术在两相流检测中的应用研究:[博士学位论文],浙江;浙江大学,2002.
[57] Bakshi B R, Zhong H, Jiang P, Fan L S, Analysis of flow in gas-liquid bubble columns using multi-resolution methods, Trans IChemE, 1995, Vol.75: 608~614.
[58]劳力云,基于动态压差信号分析的两相流参数辨识方法研究:[博士学位论文],浙江;浙江大学,1998.
[59] Huang Norden E, Shen Zheng, Long Steven R et .al, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. R. Soc. Lond. A, 1998, 454: 903~995.
[60]黄海,黄轶伦,气固流化床压力脉动信号的Hilbert-Huang谱分析,化工学报,2004, 55(9):1441~1447.
[61]孙斌,张宏建,岳为挺,HHT与神经网络在油气两相流流型识别中的应用,化工学报,2004,55(10):1723~1727.
[62]王晓萍,基于现代非线性信息处理技术的气固流化床流型识别方法与试验研究,博士论文,浙江大学,2004.
[63]丁浩,新型信息处理技术在气液两相流流型辨识中的应用:[博士学位论文],浙江;浙江大学,2005
[64] Jones Jr, Zuber N, The interrelation between void fraction fluctuation and flow pattern in two-phase flow, Int. J. Multiphase Flow, 1975, 2(2): 273~306.
[65] Chul Hwa Song, Hee Cheon No, Moon Ki Chung, Investigation of bubbly flow developments and its transition based on the instability of void fraction waves, Int.J.Multiphase Flow,1995, 21: 381~404.
[66] Tutu N.K., Pressure fluctuations and flow pattern recognition in vertical two phase gas-liquid flows. lnt. J. Multiphase Flow, 1982, 8:443~447.
[67] Matsui G. Identification of flow regimes in vertical gas-liquid two-phase flow using differential pressure fluctuations. Int. J .Multiphase Flow, 1984,10: 711~720.
[68]刘明言,胡宗定,气液固三相流化床流区及其过渡的混沌分析,化学反应工程与工艺,2000, 16(4):363~367.
[69]白博峰,郭烈锦,空气水两相流压力波动现象非线性分析,工程热物理学报,2001,22(3):359~362.
[70]吴浩江,吴浩扬,分形理论在油气水多相流流型识别中的应用,西安交通大学学报,1999,33(9): 50~52,57.
[71]陈伯川,黄春燕,甄玲,王晓萍,气固流化床压力信号时间序列复杂性特征研究,仪器仪表学报,2003,24(3):236~240.
[72]王晓萍,黄轶伦,气固流化床多传感器信息传输的复杂性研究,高校化学工程学报,2003,17(5):585~590.
[73] Fan, LT, Neogi D, Stochastic analysis of a three-phase fluided bed: fractal approach, AICHEJ, 1990, 36(10):1529~1534.
[74] Franca F., Acikgoz M., Lahey Jr R.T., et al.. The use of fractal techniques for flow regime identification. Int.J. Multiphase Flow, 1991, 17: 545~552
[75]钟兴福,混沌动力学方法在石油两相管流参数检测中的应用:[博士学位论文],浙江;浙江大学,2000
[76]金宁德,李伟波,赵鑫,利用符号时间序列分析方法表征垂直上升管中油水两相流流型,化工学报,2005, 56(1):116~120.
[77] Zhao S, Jin N D , Hai M, Liu X B, Characterization of oil/water two phase flow based on complexity theory, IEEE-The Third International Conference on MachineLearning and Cybernetics , August 26-29, Shanghai, China, 2004, 1:1530~1534,.
[78]肖楠,金宁德,基于混沌吸引子形态特性的两相流流型分类方法研究,物理学报,2007,56(9):5149~5157
[79]董芳,金宁德,宗艳波,王振亚,两相流流型动力学特征多尺度递归定量分析,物理学报,2008,57(10):6145~6154
[80] Matsumoto S,Suzuki M.. Statistical analysis of fluctuations of froth pressure on perforated plates without downcomers. Int. J. Multiphase Flow, 1984, 10:217~228.
[81] King C.H., Ouyang M.S., Pei B.S. et al. Identification of two-phase flow regimes by an optimum modeling method. Nuclear Technology, 1988, 82: 211~226.
[82]厉燕新,ARMA模型在两相流检测中的应用研究,[浙江大学硕士研究论文],浙江;浙江大学,2004.
[83]任杰,朱衡君,时间序列分析和参数识别方法确定两相流流态,北方交通大学学报,1998,22(4):83~86
[84] Wu Haojiang, Zhou Fangde, Wu Yuyuan, Intelligent identification system of flow regime of oil/gas/water, International Journal of Multiphase Flow, 2001 Vol.27: 459~475.
[85]周云龙,孙斌,陆军,改进BP神经网络在气液两相流流型识别中的应用,化工学报,2005,56(1):110~115
[86]董峰,姜之旭,乔旭彤等,基于ERT技术的垂直管道两相流流型辨识,仪器仪表学报,2004,5(4): 457~461.
[87] Wu Mengmeng, Dong Feng, Qi Guohua, Feature extraction method for gas/liquid two-phase flow based on wavelets transform, IEEE-International Conference on Machine Learning and Cybernetics, Dalian, China, 13-15 August 2006, 3:1422~1427.
[88]贾志海,牛刚,王经,基于动态聚类算法的两相流流型识别方法研究,热能动力工程,2004, 19(2): 182~185.
[89]吴新杰,王师,王凤翔,基于粗糙集理论两相流流型辨识方法研究,仪器仪表学报,2003,24(3): 221~225.
[90]邵晓寅,黄志尧,冀海峰等,基于电容层析成像和模糊模式识别的油气两相流流型辨识新方法的研究,高校化学工程学报,2003,17(6): 616~621.
[91]彭黎辉,张宝芬,基于模糊神经元网络的两相流流型辨识方法,模式识别与人智能,1997,10(4):332~337.
[92] Mi Y, Tsoukalas L.H., Ishii M., et al.. Hybrid fuzzy-neural flow identification methodology. Proc. of the IEEE Int. Conf, on Fuzzy Systems, September8-11, 1996, New Orleans, USA, 2: 1332~1338
[93]王强,周云龙,崔玉峰,孙斌,EMD与神经网络在气液两相流流型识别中的应用,工程热物理学报,2007,28(3):442~444
[94] Ali Mahvash, Annie Ross. Two-phase flow pattern identification using continuous hidden Markov model. International Journal of Multiphase Flow, 2008, 34: 303~311
[95]高忠科,金宁德,两相流流型复杂网络社团结构及其统计特性,物理学报,2008,57(11): 6909~6920
[96]郭烈锦,两相与多相流动力学,西安:西安交通大学出版社,2002
[97]李广军,郭烈锦,陈学俊,陈永利,气液两相流界面波的双平行电导探针测量方法研究,计量学报,18(3):167~172
[98]何利民,水平油气混输管道中段塞流流动特性研究:[博士学位论文],西安;西安交通大学,2002
[99]曹章,电学层析成像系统模型、算法研究,[博士学位论文],天津;天津大学,2007
[100] Plaskowsky A. ,Beck M. S., Thorn R., Dyakowsky T., Imaging Industrial Flows-Applications of Electrical Process Tomography, Institute of Physics Publishing
[101] Loh W. W., Waterfall R.C., Cory J., Lucas G.P., Using ERT for Multi-Phase Flow Monitioring,Processing of 1st World Congress on Industrial Tomography,UK,1999,47~53
[102] G. P. Lucas, J. Cory, R. C. Waterfall, W. W. Loh, F. J. Dickin, Measurement of the Solids Volume Fraction and Velocity Distribution in Solids-Liquid Flows using Dual-Plane Electrical Resistance Tomography. Flow Measurement and Instrumentation, 1999,10: 249~258
[103] Artur J Jaworski, Tomasz Dyakowski, Application of Electrical Capacitance Tomography For Measurement of Gas-solid Flow Characteristic in a Pneumatic Conveying System, Measurement Science And Technology. 2001, 12, 1109~1111
[104] Deng X, Dong F, Xu L J, et al. The design of a dual-plane ERT system for cross correlation measurement of bubbly gas/liquid pipe flow. Measurement Science and Technology, 2001, 12: 1024~1031.
[105] V Mosorov, D Sankowski, ?Mazurkiewicz and T Dyakowski, The‘best-correlated pixels’method for solid mass flow measurements using electrical capacitance tomography, Meas. Sci. Technol. 13 (2002) 1810-1814
[106] Dai Y, Wang M, Panayotopoulos N, et al. 3-D visualisation of a swirling flow using electrical resistance tomography. 4th World Congress on Industrial Process Tomography, Aizu, Japan, 2005: 362~367.
[107] Shirhatti V S, Wang Mi, Williams R A. Visualisation of dispersion, dissolution and settling of powders in a stirred mixing vessel by electrical resistance tomography. 4th World Congress on Industrial Process Tomography, Aizu, Japan, 2005: 474-479.
[108] Zhang Yan, Yao Jun, Wang Chi Hwa, Electrical capacitance tomography measurements on inclined conveying pipes. 4th World Congress on Industrial Process Tomography, Aizu, Japan, 2005: 404~409
[109] LI Hua, WANG Mi , WU Ying-xiang, MA Yi-xin , WILLIAMS Richard, Measurement of oil volume fraction and velocity distributions in vertical oil-in-water flows using ERT and a local probe, Journal of Zhejiang University SCIENCE, 2005 6A(12):1412~1415
[110] Wang M, Lucas G, Dai Y, et al. Visualisation of bubbly velocity distribution in a swirling flow using electrical resistance tomography. Particle & Particle Systems Characterization, 2006, 23: 321~329.
[111] Urmila Datta, Tomasz Dyakowski, Saba Mylvaganam, Estimation of particulate velocity components in pneumatic transport using pixel based correlation with dual plane ECT, Chemical Engineering Journal, 2007, 130: 87~99
[112] S.A. Razzak, S. Barghi, J.-X. Zhu, Electrical resistance tomography for flow characterization of a gas–liquid–solid three-phase circulating fluidized bed, Chemical Engineering Science, 2007, 62: 7253~7263
[113] G. Vilar, R.A. Williams, M. Wang, R.J. Tweedie, On line analysis of structure of dispersions in an oscillatory baffled reactor using electrical impedance tomography, Chemical Engineering Journal, 2008, 141: 58~66
[114] Dong F, Xu Y B, Xu L J, et al. Application of dual-plane ERT system and cross-correlation technique to measure gas-liquid flows in vertical upward pipe. Flow Measurement and Instrumentation, 2005, 16(2-3): 191~197.
[115]张贤达,时间序列分析——高阶统计量方法,北京:清华大学出版社,1996
[116] D.B.Percival, A.T.Walden, Wavelet Methods for Time Series Analysis, Cambridge University Press, Cambridge, England, 2000, 2~8
[117] Roger Baker, Multi-phase flow moves on, C&I, 1989, 2:35~36
[118] Vapnik V N著,张学工译,统计学习理论的本质,北京:清华大学出版社,2000,20~50
[119] Cherkassky V, Mulier F, Guest editoral Vapnik-Cherkassky(VC) learning theory and its application, Transaction on Neural Networks, 1999, 10(5):985~987
[120]邓乃扬,田英杰,数据挖掘中的新方法——支持向量机,北京:科学出版社,2004,174-180
[121] A.E.E.Roger, R.P.Ingalls. Venus: mapping the surface reflectivity by radar interferometry, ln science, 165(1969):797-799.
[122] J.Hérault and C.Jutten. Space or time adaptive signal processing by neural network models. In DenkerJ.S.(ed), editor, Neural networks for computing: AIP conference processings 151, New York.1986.
[123] P.Comon, C.Jutten, J.Hérault. Blind separation of sources, PartⅡ: Problems statement. Signal Proccssing, 1991,24:11-20
[124] P.Comon. Independent component analysis—a new concept? In Signal Processing, 1994(36):287-314
[125] C. Jutten, J. Hérault. Blind separation of sources, PartⅠ: An adaptive algorithm based on neuromimetic architecture. In signal Processing, 1991,24:1-10
[126] C. Jutten, J. Hérault. Independent component analysis versus principle component analysis. In Proceeding of European symposium on Signal Processing,2(1988):643-646.
[127] E. Sorouchyari. Blind separation of sources, PartⅢ:Stability analysis. Signal Processing, 1991,24:21-29
[128] G.Burel. Blind separation of sources: a nonlinear neural algorithm. Neural Networks, l992, 5(6):937-947
[129] J.P.Nadal and N.Parga. Non-linear neurons in the low noise limit: a factorial code maximizes information transfer. Network, 1994, 5:565-581.
[130] A.Cichocki and L.Moszczynski. A new learning algorithm for blind separation of sources. Electronics letter, 1992, 28(21):1386-1387.
[131] A.Cichocki, R.Unbehauen, E.Rummert. Robust learning algorithm for blind separation of signals. Electronics letters, 1994, 30(17):1986-1987.
[132] A.Cichocki, R.Unbehauen. Robust neural networks with on-line learning for blind identification and blind separation of sources. IEEE Transactions on Circuits and Systems, 1996, 43(11):894-906.
[133] J.Karhunen, J.Loutsensalo. Representation and separation of signals using nonlinear PCA type learning. Neural Networks, 1994,7(1):113-127
[134] E.oja, H.Ogawa, J.Wangviwattana. Learning in nonlinear constrained Hebbian networks. In Proc. Int. Conf.on Artificial Neural Netwofks(ICANN’91), Espoo, Finland, 1991:385-390
[135] A.Bell, T.Sejnowski. An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 1995, 7(6):1004-1034
[136] S.I. Amari, A.Cichocki, H.Yang. A new learning algorithm for b1ind source separation. Advances in Neural Information Processing System, MIT Press, Cambridge, MA, 1996, 8:757-763
[137] A.Hyv?rinen, E.Oja. A fast fixed-point algorithm for independent component analysis. Neural Computation, 1997, 9(7): 483~1492.
[138] A.Hyv?rinen. Fast and robust fixed-point algorithm for independent component analysis. IEEE Transactions on Neural Networks, 1999, 10(3):626~634
[139] A.Hyv?rinen, E.Oja. Independent component analysis: algorithms and applications. Neural Networks, 2000, 13: 411~430.
[140] J.F.Cardoso. Source separation using higher order moments. In Proc of Lcassp[C]. Glasgow.UK, 1989, 2109-2112
[141] J.F.Cardoso. The invariant approach to source separation, In International Symposium on Nonlinear Theory and ites Application(NoLTA), LasVegas, NV, Dec 10-14, 1995, 55-60.
[142] R.Linsker. Local synaptic learning rules suffice to maximize information in a linear network. In Neural Computation, 1992, 4(30):491~702
[143] J.P.Nadal, N.Brunel, N.Parga, Nonlinear Feedforward Networks with Stochastic lnputs: Infomax lmplies Redundancy Reduction. InNetwork: Computation in Neural systems, 1998,9(2).
[144] A.J.Bell, T.Sejnowski. An Information maximization approach to blind separation and blind deconvolution, In Neural Computation, 1996, 7(6):1129~1159
[145] Z.Roth, Y.Baram. Multidimensional density shaping by sigmoids, ln IEEE Transactions on Neural Networks, 1996, 7(5):1291~1298
[146] J.F.Cardoso. Informax and maximum likelihood for source separation, In IEEE Signal Processing Letters, Vol.4, No.4, Appril 1997, 112-114
[147] D.Obradovic, G.Deco. Information maximization and independent component analysis: Is there a difference? Neural Computation, 1998, 10(8):2085~2101
[148] M.Girolami. An alternative perspective on adaptive independent component analysis algorithms. Neural Computation, 1998, 10:2103~2114.
[149] M.Girolami. Self-organising neural networks-independent component analysis and blind source separation. Springer-Verlag.1999
[150] T.W. Lee, M.Girolami. Independent component analysis using an extended informax algorithm for mixed subgaussian and supergaussian sources. In Neural Computation, 1999, 11(2):417-441
[151] K.Waheed, F.M.Salem. Algebraic Independent Component Analysis. Proceedings of the 2003 IEEE, International Conference on Robotics , Intelligent Systems and Signal Procecsing,4724~477
[152] J.Porill, J.V.Stone(1998). Undercomplete independent component analysis for signal separation and dimension reduction. Technical report, University of Shefield, Department of Psychology
[153] S.Amari. Natural Gradient Learning for Over-and Under-Complete Bases in ICA. Neural Computalion.1999;11:1875~1883
[154] F.J.Theis, E.W.Lang, C.G.Puntonet. A geometric algorithm for overcomplete linear ICA. NeurocomPuting, 2004, 56:381~398
[155] H.Attias. Independent factor analysis. Neural Computation, 1999, 11(5):803~852
[156] L.B. Almeida. Linear and nonlinear ICA based on mutual information- the MISEP method. Signal Processing, 2004, 84:231~245
[157] J.Karhunen, A.Hyv?rinen, et al. Application of neural blind separation to signal and image processing. In: Proceedings of the IEEE 1997 International Conference on Acoustics, Speech, and Signal Processing,Munich,Germany, 1997, to appear:53
[158] M.B.Zadeh, C.Jutten. A general approach for mutual information minimization and its application to blind source separation. Signal Processing, 2005, 85:975~995
[159] S.Dodel, J.M. Herrmann, T.Geisel. Localization of brain activity-blind separation for fMRI data. Neurocomputing, 2000, 32-33:701~708
[160] V.D.Calhoun, T.A.Dali, etal. Semi-blind ICA of fMRI-A method for utilizing hypothesis-derived time courses in a spatial ICA analysis. NeuroImage, 2005, 25:527~53
[161] T.P.Jung, et al. Removing eletroncephalographic artifacts:Comparison between ICA and PCA. Neural Network for signal Processing, 1998, VⅢ,63~72
[162] C.J. Jame, et al. Temporally constrained ICA: An application to artifacts rejection in electromagnetic brain signal analysis. IEEE Trans.on BME, 2003, 50(9):1008-1116
[163] C.J.James, et al. Independent component analysis for biomedical signals (topical Review). Physiological Measurement, 2005, 26(1):R15~R39
[164] S.Akaho. Conditionally independent component analysis for supervised feature extraction. Neurocomputing, 2002, 49:139~150
[165] J.Lin, A.Zhang. Fault feature separation using wavelet-ICA filter. NDT&E lnternational, 2005, 38: 421~427
[166] N Kwak. C.HChoi, J.Choi. Feature Extraction Using ICA.LNCS, 2001, 2130:568~573
[167] K.Torkko la. Blind separation for audio signals-are we there yet? In:Proc lnt Workshop on Independent Component Analysis and Signal Separation(ICA’99). Aussiois, France, 1999, 239~244
[168] T.Kim, S.Y.Lee. Learning self-organized topology-preserving complex speech features at primary auditory cortex. Neurocomputing, 2005, 65:793~800
[169] D.E.Callan, A.M.Callan, et al. Multimodal contribution to speech perception revealed by independent component analysis: a single-sweep EEG case study.Cognitive Brain Research, 2001, 10:349~353
[170] D.R.Tailor, L.H.Finkel, G.Buchsbaum. Color-opponent receptive fields derived from independent component analysis of natural images. Vision Research, 2005, 40:2671-2676
[171] Z.Wang, C.S.Leung, et al. Data compression on the illumination adjustable images by PCA and ICA. Signal Processing: Image Communication, 2004, 19:939~954
[172] N.Katsumata, Y.Matsuyama. Database retrieval for similar images using ICA and PCA bascs. Engineering Applications of Artificial Intelligence, 2005, 18:705~717
[173] P.C.Yuen, J.H.Lai. Face representation using independent component analysis. Pattern Recognition, 2002, 35:1247~1257
[174] O.Déniz, M.Castrillón, M.Hernández. Face recognition using independent component analysis and support vector machines. Pattern Recognition Letters, 2003, 24:2153~2157
[175] H.K.Ekenel, B.Sankur. Feature selection in the independent component subspace for face recognition. Pattern Recognition Letters, 2004, 25:1377~1388
[176] B.A.Drapter, K.Baek, et al. Recognizing faces with PCA and ICA. Computer vision and Image Understanding, 2003, 91:115~137
[177]彭佩星,王保良,李海青,基于ICA和ES-SVM的油气两相流空隙率测量,化工自动化及仪表,2007,34(4):60~62
[178]吴新杰,石玉珠,陈跃宁,藏树良,独立成分分析在两相流速度测量中的应用,仪器仪表学报,27(2): 115~117
[179] Aapo Hyvarinen, Juha Karhunen, Erkki Oja, Independent Component Analysis, New YorK: John, Wiley&Sons, 2001
[180] K. J. Hay, Z. C. Liu, T. J. Hanratty, A backlighted imaging technique for particle size measurements in two-phase flows. Experiments in Fluids, 1998, 25: 226-232
[181] W. A. Gardner. Introduction to random processes with application to signals and systems. New York: Macmillan, 1985
[182] W. A. Gardner. The spectral correlation theory of cyclostationary time-series. Signal Processing [EURASIP], 1986,11(1): 13~36
[183] W. A. Gardner. Measurement of spectral correlation. IEEE Trans. ASSP, 1986, ASSP -34(10):1111~1123
[184] M.S.Beck,A.Plaskowski,徐苓安,相关流量计的设计与应用,天津:天津大学出版社,1992
[185]邓湘,基于层析成像技术的智能化两相流测量系统研究:博士学位论文,天津,天津大学,2001
[186] F sanchez Silva, M ToledoV, P Quinto D, J Cruz Maya, Experimental Slug Flow Characterization in a Horizontal pipe, Experimental Heat Transfer, Fluid Mechanics and Thermodynamics, pp 893~900, 1997.
[187]蔡鸿宇等,波形法与相关法在气、固两相流速度测量中的比较,多相流检测技术进展,李海青、乔贺堂主编,石油工业出版社,1996,70~74,
[188] Lin P.Y. et al, Detection of slug flow from pressure measurements, Int. J. Multiphase Flow, 1987, 13(1):13~21
[189]赵国文,向阳,彭勇,王利,卢艳辉,互相关和高阶谱时延估计在混凝土板冲击反射法测量中的应用,无损检测,25(10),2003,524~527
[190]李英伟,孔令富,刘兴斌,胡金海,基于互相关理论的油井流量测量系统,电子器件,2007,30(4), 1458~1461
[191] Nydal O.J., Pintos S. and Andreussi P, Statistical characterization of slug flow in horizontal pipes, Int. J. Multiphase Flow, 1992, Vol.18, No.3, pp .439~453
[192] Barnea D. and Taitel Y. A model for slug length distribution in gas-liquid slug flow. Int. J. Multiphase Flow, 1993, Vol.19, No.5, pp.829-838
[193] Dukler A.E. and Hubbard, M.D. A model for gas-liquid slug flow in horizontal and near horizontal tubes, Ind. Eng.Chem Fundamentals, 1975, Vol.14, No.4, pp.337-347
[194] Nicholson M.K., Aziz K. and Gregory G.A. Intermittent two phase flow in horizontal pipes: predictive models. Can. J. Chem Eng, 1978, Vol.56, N o.12, pp.653-663
[195] Barnea, D. and Brauner, N. Holdup of the liquid slug in two-phase intermittent flow. Int.J. Multiphase Flow, 1985,Vol.11, No.1, pp.43~49
[196] Cook, M. and Behnia, M. Slug length prediction in near horizontal g as-liquid intermittent flow, Chem.Eng.Sci., 2000, Vo1.55, pp.2009~2018
[2]陈学俊,多相流热物理研究进展,西安交通大学学报,1994,Vol.28:1~8
[3]林宗虎,能源和动力工程中的重要理论基础-多相流热物理学,中国科学基金,2000,6:359~361
[4]陈学俊,迅速发展中的一门新兴交叉学科一多相流热物理的进展,西安交通大学出版社,1996,30(4):9~17
[5]陈甘棠,前言,第一届全国多相流检测机技术学术讨论会论文集,1986
[6]林宗虎,王栋,王树众等,多相流的近期工程应用趋向,西安交通大学学报,2001,35(9): 886~890
[7]白博峰,郭烈锦,王忠勇等,油气水多相流压力和压差信号特征分析与流型在线识别,2002,23(3):357~360
[8] Baker R C, et al., Response of bulk flowmeters to multiphase flows, Proc.lnstrMech.Engrs, 1991, 205: 217~229
[9]仲朔平,佟允宪,王文然,利用孔板差压噪声测量汽水两相流,清华大学学报,1997,37(5):15~18
[10]罗毓珊,李爱华,陈听宽等,差压法测量两相流含率的研究,工程热物理学报,2004,25(5):789~792
[11] Meng Jianbo. The new approach to the vortex flowmeter. Proc. of In t. Symposium on Measuring Techniques for Multiphase Flow, Apr.10-13,1995, Nanjing , China, 348~355
[12] Anderson J.L. and Fincke J.R., Mass flow measurement in air/water mixtures using drag devices and gamma densitometer. ISA Trans, 1980, 19(1): 37~48
[13] Han K-F F-L, Banerjee S., Measurement of mass flux in two-phase flow using combinations of pitot tubes and gamma densitometer. AIChE J., 1981, 27:177~184
[14]李海青,黄志尧,软测量技术原理及应用,北京:化学工业出版社,2000,47~55
[15]权志英,陈德运,孙立镌,基于小波变换的气液两相流系统软测量,哈尔滨理工大学学报,2005,10(1):79~82
[16]吴浩江,周芳德,油气水多相流流型智能识别系统的设计与实现,西安交通大学学报,2000,34(3):31~35
[17] Y. Mi, M. Ishi, L. H. Tsoukalas, Vertical two-phase flow identification using advanced instrumentation and neural networks, Nuclear Engineering and Design, 1998, 84: 409~420
[18] C. L. Briens, C. Hudson, L. A. Briens, Rapid characterization of flow regimes in multiphase reactors through box-counting dimensions with an embedding dimension of two phase flow, The Chemical Engineering Journal, 1996, 64: 169~178
[19] Thomes J .M ..Contemplative stance for chemical process control.An IFAC Report, Automatic, 1992, 28(2):441
[20]李劲松,叶琛,梅德松,超声波检测数据高速采集和传输技术的研究,无损检测,2003,25(8):395~398
[21]阎春生,曾楠,赖淑蓉等,光学层析成像技术的研究动态,激光杂志,2001,22(5):5~7
[22]吴志军,孙志军,张建华等,粒子图像速度场仪(PIV)成像系统开发,吉林工业大学自然科学学报,1999,3(29):6~11
[23] G. F. Lynch, S. L. Segal, Direct measurement of the void fraction of two-phase fluid by nuclear magnetic resonance, Int. J. of mass transfer, 1977, 20(1):7~14
[24]王俊,电导式纵向多电极阵列油/水两相流测量方法研究:[硕士学位论文],天津;天津大学,2004
[25]徐苓安,相关流量测量技术,天津:天津大学出版社,1988
[26]宿成基,梯度相关法测量两相流流的精度与离散性研究,计量学报,1996,32(2):135~139
[27]高晋占,参数估计法测量测量两相流流速,清华大学学报,1992, 32(1):93~98
[28]陈之航,曹柏林,赵在三.气液双相流动和传热.北京:机械工业出版社,1983.
[29] Baker O.C.. Simultaneous flow of oil and gas. Oil and Gas, 1954, 53:185~195
[30] Scott D.S.. Properties of co-current gas-liquid flow. Advances in Chem. Eng, 1963, 4 :199~277
[31] Hewitt G.F., Roberts D.N. Studies of two-phase flow patterns by simul taneous X-ray and flush photography. Rept.AERE-M2159, 1969, UKAEA Harwell.
[32] Weisman J.. Effects of fluid properties and pipes diameter on two phase flow patterns in horizontal lines. Int.J.Multiphase Flow, 1979, 5:437~462.
[33] Weisman J., Kang S.Y.. Flow patern transitions in vertical and upwardly inclining line. Int. J. Multiphase Flow, 1981,7: 271~291.
[34] Taitel Y., Dukler A.E.. A model for predicting flow regime transitions in horizontal and near horizontal gas-liquid flow. AIChE J., 1976, 22(1):47~54
[35] ChoeW.G., Wemberg L., Weisman J.. Observation and collection of flow pattern transition in horizontal concurrent gas-liquid flow. Two Phase Transport and Reactor Safety, Washington, Hemisphere, 1978, 1357~1375.
[36] Taitel Y., Bomea D., Dukler A.E., Modeling flow pattern transitions for steady upward gas-liquid flow in vertical tubes. AIChE J .,1980, 26 (3):345~354
[37] Griffith P., Wallis G.B.. Two phase slug flow. Journal of heat transfer, 1961, 83: 307~318
[38] Vince M.A., Lahey R.T., JR.. On the development of an objective flow regime Indicator. Int.J. Multiphase Flow, 1982, 8: 93~12
[39] Hewitt G F, Measurement of two-phase flow parameters, Academic Press, London, 1978
[40] Barnea D., Shoham O., Taitel Y.. Flow pattern characterization in two phase flow by electrical conductance probe, Int. J. Multiphase Flow, 1980, 6 :387~397
[41] Paglianti A., Pintus S., Giona M.. Time-series analysis approach for the identification of flooding/loading transition in gas-liquid stirred tank reactors. Chem. Eng. Sci., 2000, 55: 5793~5802
[42] Waterfall R C, He R, and Beck C M. Visualizing combustion using electrical impedance tomography. Chemical Engineering Science, 1997, 52(13):2129~2138,
[43] Hawkins A R, Liu H, Oliphant T E, and Schultz S M. Noncontact scanning impedance imaging in an aqueous solution. Applied Physics Letters, 2004, 85(6): 1080~1082
[44] Tossavainen O, Vauhkonen M, Kolehmainen V, and Kim K. Tracking of moving interfaces in sedimentation processes using electrical impedance tomography. Chemical Engineering Science, 2006, 61:7717~7729
[45] Lehmann C and Schilcher K. Approximate 3-dimensional electrical impedance imaging. Physics Letters A, 2001, 292:188~194
[46]王妍,任超世. 3D-EIT图像重建的研究进展,国外医学:生物医学工程分册,2003,26(6):265~268
[47]薛健,张兆田,熊晓芸,李慧,田捷,工业过程断层图像的三维动态可视化,中国体视学与图像分析,2005,10(3):183~188
[48] Warsito W and Fan L S. Imaging the bubble behavior using the 3-D electric capacitance tomography. Chemical Engineering Science, 2005, 22:6073~6084
[49] Wajman R, Banasiak R, Mazurkiewicz L, Dyakowski T, and Sankowski D. Spatial imaging with 3D capacitance measurements. Measurement Science and Technology, 2006, 17:2113~2118,
[50] Warsito W, Marashdeh Q, and Fan L S. Electrical capacitance volume tomography. Sensors Journal, IEEE, 2007, 7(4):525~535
[51] Hubbard M G, Dukler A E, The characterization of flow regimes for horizontal two-phase flow, Proc. Heat Transfer &Fluid Mechanics Inst. 1966:100~121.
[52] Matuszkiwicz A, Flamand J C, Bour J A, The bubble-slug flow pattern transition and instabilities of void fraction waves, Int. J. Multiphase Flow, 1987, 13(2): 199~217.
[53]陈珙,黄志尧,王保良,李海青,小波变换辨识流型的一种新方法研究,仪器仪表学报,1999, 20(2):117~120.
[54]陈珙,王保良,杨江,李海青,基于小波分析的气液两相流流型模糊辨识,高校化工学报,1999, 13(4): 303~308.
[55] Ji Haifeng, Huang Zhiyao, Wang Baoliang, et al.. Wavelet-based flow regime identification of gas-liquid two-phase flow. Proc.of Int. Conf.on Power Engineering, Oct.8-12, 2001,Xi’an, China, 492~496.
[56]冀海峰,小波分析技术在两相流检测中的应用研究:[博士学位论文],浙江;浙江大学,2002.
[57] Bakshi B R, Zhong H, Jiang P, Fan L S, Analysis of flow in gas-liquid bubble columns using multi-resolution methods, Trans IChemE, 1995, Vol.75: 608~614.
[58]劳力云,基于动态压差信号分析的两相流参数辨识方法研究:[博士学位论文],浙江;浙江大学,1998.
[59] Huang Norden E, Shen Zheng, Long Steven R et .al, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. R. Soc. Lond. A, 1998, 454: 903~995.
[60]黄海,黄轶伦,气固流化床压力脉动信号的Hilbert-Huang谱分析,化工学报,2004, 55(9):1441~1447.
[61]孙斌,张宏建,岳为挺,HHT与神经网络在油气两相流流型识别中的应用,化工学报,2004,55(10):1723~1727.
[62]王晓萍,基于现代非线性信息处理技术的气固流化床流型识别方法与试验研究,博士论文,浙江大学,2004.
[63]丁浩,新型信息处理技术在气液两相流流型辨识中的应用:[博士学位论文],浙江;浙江大学,2005
[64] Jones Jr, Zuber N, The interrelation between void fraction fluctuation and flow pattern in two-phase flow, Int. J. Multiphase Flow, 1975, 2(2): 273~306.
[65] Chul Hwa Song, Hee Cheon No, Moon Ki Chung, Investigation of bubbly flow developments and its transition based on the instability of void fraction waves, Int.J.Multiphase Flow,1995, 21: 381~404.
[66] Tutu N.K., Pressure fluctuations and flow pattern recognition in vertical two phase gas-liquid flows. lnt. J. Multiphase Flow, 1982, 8:443~447.
[67] Matsui G. Identification of flow regimes in vertical gas-liquid two-phase flow using differential pressure fluctuations. Int. J .Multiphase Flow, 1984,10: 711~720.
[68]刘明言,胡宗定,气液固三相流化床流区及其过渡的混沌分析,化学反应工程与工艺,2000, 16(4):363~367.
[69]白博峰,郭烈锦,空气水两相流压力波动现象非线性分析,工程热物理学报,2001,22(3):359~362.
[70]吴浩江,吴浩扬,分形理论在油气水多相流流型识别中的应用,西安交通大学学报,1999,33(9): 50~52,57.
[71]陈伯川,黄春燕,甄玲,王晓萍,气固流化床压力信号时间序列复杂性特征研究,仪器仪表学报,2003,24(3):236~240.
[72]王晓萍,黄轶伦,气固流化床多传感器信息传输的复杂性研究,高校化学工程学报,2003,17(5):585~590.
[73] Fan, LT, Neogi D, Stochastic analysis of a three-phase fluided bed: fractal approach, AICHEJ, 1990, 36(10):1529~1534.
[74] Franca F., Acikgoz M., Lahey Jr R.T., et al.. The use of fractal techniques for flow regime identification. Int.J. Multiphase Flow, 1991, 17: 545~552
[75]钟兴福,混沌动力学方法在石油两相管流参数检测中的应用:[博士学位论文],浙江;浙江大学,2000
[76]金宁德,李伟波,赵鑫,利用符号时间序列分析方法表征垂直上升管中油水两相流流型,化工学报,2005, 56(1):116~120.
[77] Zhao S, Jin N D , Hai M, Liu X B, Characterization of oil/water two phase flow based on complexity theory, IEEE-The Third International Conference on MachineLearning and Cybernetics , August 26-29, Shanghai, China, 2004, 1:1530~1534,.
[78]肖楠,金宁德,基于混沌吸引子形态特性的两相流流型分类方法研究,物理学报,2007,56(9):5149~5157
[79]董芳,金宁德,宗艳波,王振亚,两相流流型动力学特征多尺度递归定量分析,物理学报,2008,57(10):6145~6154
[80] Matsumoto S,Suzuki M.. Statistical analysis of fluctuations of froth pressure on perforated plates without downcomers. Int. J. Multiphase Flow, 1984, 10:217~228.
[81] King C.H., Ouyang M.S., Pei B.S. et al. Identification of two-phase flow regimes by an optimum modeling method. Nuclear Technology, 1988, 82: 211~226.
[82]厉燕新,ARMA模型在两相流检测中的应用研究,[浙江大学硕士研究论文],浙江;浙江大学,2004.
[83]任杰,朱衡君,时间序列分析和参数识别方法确定两相流流态,北方交通大学学报,1998,22(4):83~86
[84] Wu Haojiang, Zhou Fangde, Wu Yuyuan, Intelligent identification system of flow regime of oil/gas/water, International Journal of Multiphase Flow, 2001 Vol.27: 459~475.
[85]周云龙,孙斌,陆军,改进BP神经网络在气液两相流流型识别中的应用,化工学报,2005,56(1):110~115
[86]董峰,姜之旭,乔旭彤等,基于ERT技术的垂直管道两相流流型辨识,仪器仪表学报,2004,5(4): 457~461.
[87] Wu Mengmeng, Dong Feng, Qi Guohua, Feature extraction method for gas/liquid two-phase flow based on wavelets transform, IEEE-International Conference on Machine Learning and Cybernetics, Dalian, China, 13-15 August 2006, 3:1422~1427.
[88]贾志海,牛刚,王经,基于动态聚类算法的两相流流型识别方法研究,热能动力工程,2004, 19(2): 182~185.
[89]吴新杰,王师,王凤翔,基于粗糙集理论两相流流型辨识方法研究,仪器仪表学报,2003,24(3): 221~225.
[90]邵晓寅,黄志尧,冀海峰等,基于电容层析成像和模糊模式识别的油气两相流流型辨识新方法的研究,高校化学工程学报,2003,17(6): 616~621.
[91]彭黎辉,张宝芬,基于模糊神经元网络的两相流流型辨识方法,模式识别与人智能,1997,10(4):332~337.
[92] Mi Y, Tsoukalas L.H., Ishii M., et al.. Hybrid fuzzy-neural flow identification methodology. Proc. of the IEEE Int. Conf, on Fuzzy Systems, September8-11, 1996, New Orleans, USA, 2: 1332~1338
[93]王强,周云龙,崔玉峰,孙斌,EMD与神经网络在气液两相流流型识别中的应用,工程热物理学报,2007,28(3):442~444
[94] Ali Mahvash, Annie Ross. Two-phase flow pattern identification using continuous hidden Markov model. International Journal of Multiphase Flow, 2008, 34: 303~311
[95]高忠科,金宁德,两相流流型复杂网络社团结构及其统计特性,物理学报,2008,57(11): 6909~6920
[96]郭烈锦,两相与多相流动力学,西安:西安交通大学出版社,2002
[97]李广军,郭烈锦,陈学俊,陈永利,气液两相流界面波的双平行电导探针测量方法研究,计量学报,18(3):167~172
[98]何利民,水平油气混输管道中段塞流流动特性研究:[博士学位论文],西安;西安交通大学,2002
[99]曹章,电学层析成像系统模型、算法研究,[博士学位论文],天津;天津大学,2007
[100] Plaskowsky A. ,Beck M. S., Thorn R., Dyakowsky T., Imaging Industrial Flows-Applications of Electrical Process Tomography, Institute of Physics Publishing
[101] Loh W. W., Waterfall R.C., Cory J., Lucas G.P., Using ERT for Multi-Phase Flow Monitioring,Processing of 1st World Congress on Industrial Tomography,UK,1999,47~53
[102] G. P. Lucas, J. Cory, R. C. Waterfall, W. W. Loh, F. J. Dickin, Measurement of the Solids Volume Fraction and Velocity Distribution in Solids-Liquid Flows using Dual-Plane Electrical Resistance Tomography. Flow Measurement and Instrumentation, 1999,10: 249~258
[103] Artur J Jaworski, Tomasz Dyakowski, Application of Electrical Capacitance Tomography For Measurement of Gas-solid Flow Characteristic in a Pneumatic Conveying System, Measurement Science And Technology. 2001, 12, 1109~1111
[104] Deng X, Dong F, Xu L J, et al. The design of a dual-plane ERT system for cross correlation measurement of bubbly gas/liquid pipe flow. Measurement Science and Technology, 2001, 12: 1024~1031.
[105] V Mosorov, D Sankowski, ?Mazurkiewicz and T Dyakowski, The‘best-correlated pixels’method for solid mass flow measurements using electrical capacitance tomography, Meas. Sci. Technol. 13 (2002) 1810-1814
[106] Dai Y, Wang M, Panayotopoulos N, et al. 3-D visualisation of a swirling flow using electrical resistance tomography. 4th World Congress on Industrial Process Tomography, Aizu, Japan, 2005: 362~367.
[107] Shirhatti V S, Wang Mi, Williams R A. Visualisation of dispersion, dissolution and settling of powders in a stirred mixing vessel by electrical resistance tomography. 4th World Congress on Industrial Process Tomography, Aizu, Japan, 2005: 474-479.
[108] Zhang Yan, Yao Jun, Wang Chi Hwa, Electrical capacitance tomography measurements on inclined conveying pipes. 4th World Congress on Industrial Process Tomography, Aizu, Japan, 2005: 404~409
[109] LI Hua, WANG Mi , WU Ying-xiang, MA Yi-xin , WILLIAMS Richard, Measurement of oil volume fraction and velocity distributions in vertical oil-in-water flows using ERT and a local probe, Journal of Zhejiang University SCIENCE, 2005 6A(12):1412~1415
[110] Wang M, Lucas G, Dai Y, et al. Visualisation of bubbly velocity distribution in a swirling flow using electrical resistance tomography. Particle & Particle Systems Characterization, 2006, 23: 321~329.
[111] Urmila Datta, Tomasz Dyakowski, Saba Mylvaganam, Estimation of particulate velocity components in pneumatic transport using pixel based correlation with dual plane ECT, Chemical Engineering Journal, 2007, 130: 87~99
[112] S.A. Razzak, S. Barghi, J.-X. Zhu, Electrical resistance tomography for flow characterization of a gas–liquid–solid three-phase circulating fluidized bed, Chemical Engineering Science, 2007, 62: 7253~7263
[113] G. Vilar, R.A. Williams, M. Wang, R.J. Tweedie, On line analysis of structure of dispersions in an oscillatory baffled reactor using electrical impedance tomography, Chemical Engineering Journal, 2008, 141: 58~66
[114] Dong F, Xu Y B, Xu L J, et al. Application of dual-plane ERT system and cross-correlation technique to measure gas-liquid flows in vertical upward pipe. Flow Measurement and Instrumentation, 2005, 16(2-3): 191~197.
[115]张贤达,时间序列分析——高阶统计量方法,北京:清华大学出版社,1996
[116] D.B.Percival, A.T.Walden, Wavelet Methods for Time Series Analysis, Cambridge University Press, Cambridge, England, 2000, 2~8
[117] Roger Baker, Multi-phase flow moves on, C&I, 1989, 2:35~36
[118] Vapnik V N著,张学工译,统计学习理论的本质,北京:清华大学出版社,2000,20~50
[119] Cherkassky V, Mulier F, Guest editoral Vapnik-Cherkassky(VC) learning theory and its application, Transaction on Neural Networks, 1999, 10(5):985~987
[120]邓乃扬,田英杰,数据挖掘中的新方法——支持向量机,北京:科学出版社,2004,174-180
[121] A.E.E.Roger, R.P.Ingalls. Venus: mapping the surface reflectivity by radar interferometry, ln science, 165(1969):797-799.
[122] J.Hérault and C.Jutten. Space or time adaptive signal processing by neural network models. In DenkerJ.S.(ed), editor, Neural networks for computing: AIP conference processings 151, New York.1986.
[123] P.Comon, C.Jutten, J.Hérault. Blind separation of sources, PartⅡ: Problems statement. Signal Proccssing, 1991,24:11-20
[124] P.Comon. Independent component analysis—a new concept? In Signal Processing, 1994(36):287-314
[125] C. Jutten, J. Hérault. Blind separation of sources, PartⅠ: An adaptive algorithm based on neuromimetic architecture. In signal Processing, 1991,24:1-10
[126] C. Jutten, J. Hérault. Independent component analysis versus principle component analysis. In Proceeding of European symposium on Signal Processing,2(1988):643-646.
[127] E. Sorouchyari. Blind separation of sources, PartⅢ:Stability analysis. Signal Processing, 1991,24:21-29
[128] G.Burel. Blind separation of sources: a nonlinear neural algorithm. Neural Networks, l992, 5(6):937-947
[129] J.P.Nadal and N.Parga. Non-linear neurons in the low noise limit: a factorial code maximizes information transfer. Network, 1994, 5:565-581.
[130] A.Cichocki and L.Moszczynski. A new learning algorithm for blind separation of sources. Electronics letter, 1992, 28(21):1386-1387.
[131] A.Cichocki, R.Unbehauen, E.Rummert. Robust learning algorithm for blind separation of signals. Electronics letters, 1994, 30(17):1986-1987.
[132] A.Cichocki, R.Unbehauen. Robust neural networks with on-line learning for blind identification and blind separation of sources. IEEE Transactions on Circuits and Systems, 1996, 43(11):894-906.
[133] J.Karhunen, J.Loutsensalo. Representation and separation of signals using nonlinear PCA type learning. Neural Networks, 1994,7(1):113-127
[134] E.oja, H.Ogawa, J.Wangviwattana. Learning in nonlinear constrained Hebbian networks. In Proc. Int. Conf.on Artificial Neural Netwofks(ICANN’91), Espoo, Finland, 1991:385-390
[135] A.Bell, T.Sejnowski. An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 1995, 7(6):1004-1034
[136] S.I. Amari, A.Cichocki, H.Yang. A new learning algorithm for b1ind source separation. Advances in Neural Information Processing System, MIT Press, Cambridge, MA, 1996, 8:757-763
[137] A.Hyv?rinen, E.Oja. A fast fixed-point algorithm for independent component analysis. Neural Computation, 1997, 9(7): 483~1492.
[138] A.Hyv?rinen. Fast and robust fixed-point algorithm for independent component analysis. IEEE Transactions on Neural Networks, 1999, 10(3):626~634
[139] A.Hyv?rinen, E.Oja. Independent component analysis: algorithms and applications. Neural Networks, 2000, 13: 411~430.
[140] J.F.Cardoso. Source separation using higher order moments. In Proc of Lcassp[C]. Glasgow.UK, 1989, 2109-2112
[141] J.F.Cardoso. The invariant approach to source separation, In International Symposium on Nonlinear Theory and ites Application(NoLTA), LasVegas, NV, Dec 10-14, 1995, 55-60.
[142] R.Linsker. Local synaptic learning rules suffice to maximize information in a linear network. In Neural Computation, 1992, 4(30):491~702
[143] J.P.Nadal, N.Brunel, N.Parga, Nonlinear Feedforward Networks with Stochastic lnputs: Infomax lmplies Redundancy Reduction. InNetwork: Computation in Neural systems, 1998,9(2).
[144] A.J.Bell, T.Sejnowski. An Information maximization approach to blind separation and blind deconvolution, In Neural Computation, 1996, 7(6):1129~1159
[145] Z.Roth, Y.Baram. Multidimensional density shaping by sigmoids, ln IEEE Transactions on Neural Networks, 1996, 7(5):1291~1298
[146] J.F.Cardoso. Informax and maximum likelihood for source separation, In IEEE Signal Processing Letters, Vol.4, No.4, Appril 1997, 112-114
[147] D.Obradovic, G.Deco. Information maximization and independent component analysis: Is there a difference? Neural Computation, 1998, 10(8):2085~2101
[148] M.Girolami. An alternative perspective on adaptive independent component analysis algorithms. Neural Computation, 1998, 10:2103~2114.
[149] M.Girolami. Self-organising neural networks-independent component analysis and blind source separation. Springer-Verlag.1999
[150] T.W. Lee, M.Girolami. Independent component analysis using an extended informax algorithm for mixed subgaussian and supergaussian sources. In Neural Computation, 1999, 11(2):417-441
[151] K.Waheed, F.M.Salem. Algebraic Independent Component Analysis. Proceedings of the 2003 IEEE, International Conference on Robotics , Intelligent Systems and Signal Procecsing,4724~477
[152] J.Porill, J.V.Stone(1998). Undercomplete independent component analysis for signal separation and dimension reduction. Technical report, University of Shefield, Department of Psychology
[153] S.Amari. Natural Gradient Learning for Over-and Under-Complete Bases in ICA. Neural Computalion.1999;11:1875~1883
[154] F.J.Theis, E.W.Lang, C.G.Puntonet. A geometric algorithm for overcomplete linear ICA. NeurocomPuting, 2004, 56:381~398
[155] H.Attias. Independent factor analysis. Neural Computation, 1999, 11(5):803~852
[156] L.B. Almeida. Linear and nonlinear ICA based on mutual information- the MISEP method. Signal Processing, 2004, 84:231~245
[157] J.Karhunen, A.Hyv?rinen, et al. Application of neural blind separation to signal and image processing. In: Proceedings of the IEEE 1997 International Conference on Acoustics, Speech, and Signal Processing,Munich,Germany, 1997, to appear:53
[158] M.B.Zadeh, C.Jutten. A general approach for mutual information minimization and its application to blind source separation. Signal Processing, 2005, 85:975~995
[159] S.Dodel, J.M. Herrmann, T.Geisel. Localization of brain activity-blind separation for fMRI data. Neurocomputing, 2000, 32-33:701~708
[160] V.D.Calhoun, T.A.Dali, etal. Semi-blind ICA of fMRI-A method for utilizing hypothesis-derived time courses in a spatial ICA analysis. NeuroImage, 2005, 25:527~53
[161] T.P.Jung, et al. Removing eletroncephalographic artifacts:Comparison between ICA and PCA. Neural Network for signal Processing, 1998, VⅢ,63~72
[162] C.J. Jame, et al. Temporally constrained ICA: An application to artifacts rejection in electromagnetic brain signal analysis. IEEE Trans.on BME, 2003, 50(9):1008-1116
[163] C.J.James, et al. Independent component analysis for biomedical signals (topical Review). Physiological Measurement, 2005, 26(1):R15~R39
[164] S.Akaho. Conditionally independent component analysis for supervised feature extraction. Neurocomputing, 2002, 49:139~150
[165] J.Lin, A.Zhang. Fault feature separation using wavelet-ICA filter. NDT&E lnternational, 2005, 38: 421~427
[166] N Kwak. C.HChoi, J.Choi. Feature Extraction Using ICA.LNCS, 2001, 2130:568~573
[167] K.Torkko la. Blind separation for audio signals-are we there yet? In:Proc lnt Workshop on Independent Component Analysis and Signal Separation(ICA’99). Aussiois, France, 1999, 239~244
[168] T.Kim, S.Y.Lee. Learning self-organized topology-preserving complex speech features at primary auditory cortex. Neurocomputing, 2005, 65:793~800
[169] D.E.Callan, A.M.Callan, et al. Multimodal contribution to speech perception revealed by independent component analysis: a single-sweep EEG case study.Cognitive Brain Research, 2001, 10:349~353
[170] D.R.Tailor, L.H.Finkel, G.Buchsbaum. Color-opponent receptive fields derived from independent component analysis of natural images. Vision Research, 2005, 40:2671-2676
[171] Z.Wang, C.S.Leung, et al. Data compression on the illumination adjustable images by PCA and ICA. Signal Processing: Image Communication, 2004, 19:939~954
[172] N.Katsumata, Y.Matsuyama. Database retrieval for similar images using ICA and PCA bascs. Engineering Applications of Artificial Intelligence, 2005, 18:705~717
[173] P.C.Yuen, J.H.Lai. Face representation using independent component analysis. Pattern Recognition, 2002, 35:1247~1257
[174] O.Déniz, M.Castrillón, M.Hernández. Face recognition using independent component analysis and support vector machines. Pattern Recognition Letters, 2003, 24:2153~2157
[175] H.K.Ekenel, B.Sankur. Feature selection in the independent component subspace for face recognition. Pattern Recognition Letters, 2004, 25:1377~1388
[176] B.A.Drapter, K.Baek, et al. Recognizing faces with PCA and ICA. Computer vision and Image Understanding, 2003, 91:115~137
[177]彭佩星,王保良,李海青,基于ICA和ES-SVM的油气两相流空隙率测量,化工自动化及仪表,2007,34(4):60~62
[178]吴新杰,石玉珠,陈跃宁,藏树良,独立成分分析在两相流速度测量中的应用,仪器仪表学报,27(2): 115~117
[179] Aapo Hyvarinen, Juha Karhunen, Erkki Oja, Independent Component Analysis, New YorK: John, Wiley&Sons, 2001
[180] K. J. Hay, Z. C. Liu, T. J. Hanratty, A backlighted imaging technique for particle size measurements in two-phase flows. Experiments in Fluids, 1998, 25: 226-232
[181] W. A. Gardner. Introduction to random processes with application to signals and systems. New York: Macmillan, 1985
[182] W. A. Gardner. The spectral correlation theory of cyclostationary time-series. Signal Processing [EURASIP], 1986,11(1): 13~36
[183] W. A. Gardner. Measurement of spectral correlation. IEEE Trans. ASSP, 1986, ASSP -34(10):1111~1123
[184] M.S.Beck,A.Plaskowski,徐苓安,相关流量计的设计与应用,天津:天津大学出版社,1992
[185]邓湘,基于层析成像技术的智能化两相流测量系统研究:博士学位论文,天津,天津大学,2001
[186] F sanchez Silva, M ToledoV, P Quinto D, J Cruz Maya, Experimental Slug Flow Characterization in a Horizontal pipe, Experimental Heat Transfer, Fluid Mechanics and Thermodynamics, pp 893~900, 1997.
[187]蔡鸿宇等,波形法与相关法在气、固两相流速度测量中的比较,多相流检测技术进展,李海青、乔贺堂主编,石油工业出版社,1996,70~74,
[188] Lin P.Y. et al, Detection of slug flow from pressure measurements, Int. J. Multiphase Flow, 1987, 13(1):13~21
[189]赵国文,向阳,彭勇,王利,卢艳辉,互相关和高阶谱时延估计在混凝土板冲击反射法测量中的应用,无损检测,25(10),2003,524~527
[190]李英伟,孔令富,刘兴斌,胡金海,基于互相关理论的油井流量测量系统,电子器件,2007,30(4), 1458~1461
[191] Nydal O.J., Pintos S. and Andreussi P, Statistical characterization of slug flow in horizontal pipes, Int. J. Multiphase Flow, 1992, Vol.18, No.3, pp .439~453
[192] Barnea D. and Taitel Y. A model for slug length distribution in gas-liquid slug flow. Int. J. Multiphase Flow, 1993, Vol.19, No.5, pp.829-838
[193] Dukler A.E. and Hubbard, M.D. A model for gas-liquid slug flow in horizontal and near horizontal tubes, Ind. Eng.Chem Fundamentals, 1975, Vol.14, No.4, pp.337-347
[194] Nicholson M.K., Aziz K. and Gregory G.A. Intermittent two phase flow in horizontal pipes: predictive models. Can. J. Chem Eng, 1978, Vol.56, N o.12, pp.653-663
[195] Barnea, D. and Brauner, N. Holdup of the liquid slug in two-phase intermittent flow. Int.J. Multiphase Flow, 1985,Vol.11, No.1, pp.43~49
[196] Cook, M. and Behnia, M. Slug length prediction in near horizontal g as-liquid intermittent flow, Chem.Eng.Sci., 2000, Vo1.55, pp.2009~2018