Relevant bibliographies by topics / Karhunen-Loéve

Academic literature on the topic 'Karhunen-Loéve'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Karhunen-Loéve"

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Tian, Wenbiao, Guosheng Rui, Daoguang Dong, and Jian Kang. "Efficient blind adaptive Karhunen–Loéve transform via parallel search." International Journal of Distributed Sensor Networks 14, no. 6 (June 2018): 155014771878237. http://dx.doi.org/10.1177/1550147718782371.

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This article introduces a new algorithm that constructs an efficient search strategy, called parallel search, for blind adaptive Karhunen–Loéve transform. Unlike anterior Karhunen–Loéve transform, the proposed algorithm converges quickly by searching for solutions in different directions simultaneously. Moreover, the process is “blind,” which means that minimal information about the original data is used. The new algorithm also avoids repeating the Karhunen–Loéve transform basis learning step in data compression applications. Numerical simulation results verify the validity of the theory and illustrate the capability of the proposed algorithm.
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Zhou, Xing-Gui, and Wei-Kang Yuan. "Control Vector Parametrization with Karhunen−Loéve Expansion." Industrial & Engineering Chemistry Research 43, no. 1 (January 2004): 127–35. http://dx.doi.org/10.1021/ie0210558.

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He, Jun. "Karhunen-Loéve expansion for random earthquake excitations." Earthquake Engineering and Engineering Vibration 14, no. 1 (February 20, 2015): 77–84. http://dx.doi.org/10.1007/s11803-015-0007-4.

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TARMAN, I. HAKAN. "A KARHUNEN-LOÉVE ANALYSIS OF TURBULENT THERMAL CONVECTION." International Journal for Numerical Methods in Fluids 22, no. 1 (January 15, 1996): 67–79. http://dx.doi.org/10.1002/(sici)1097-0363(19960115)22:1<67::aid-fld332>3.0.co;2-c.

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Chambers, D. H., R. J. Adrian, P. Moin, D. S. Stewart, and H. J. Sung. "Karhunen–Loéve expansion of Burgers’ model of turbulence." Physics of Fluids 31, no. 9 (September 1988): 2573–82. http://dx.doi.org/10.1063/1.866535.

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Webber, G. A., R. A. Handler, and L. Sirovich. "The Karhunen–Loéve decomposition of minimal channel flow." Physics of Fluids 9, no. 4 (April 1997): 1054–66. http://dx.doi.org/10.1063/1.869323.

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Zhang, Weihua, and Bernd Michaelis. "Shape Control with Karhunen-Loéve-Decomposition: Theory and Experimental Results." Journal of Intelligent Material Systems and Structures 14, no. 7 (July 2003): 415–22. http://dx.doi.org/10.1177/1045389x03034059.

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Suvorova, Sofia, and Jim Schroeder. "Automated Target Recognition Using the Karhunen–Loéve Transform with Invariance." Digital Signal Processing 12, no. 2-3 (January 2002): 295–306. http://dx.doi.org/10.1006/dspr.2002.0445.

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Sung, H. J., and R. J. Adrian. "Karhunen–Loéve expansion of the derivative of an inhomogeneous process." Physics of Fluids 6, no. 6 (June 1994): 2233–35. http://dx.doi.org/10.1063/1.868173.

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Kale, Mehmet Cemil. "A general biorthogonal wavelet based on Karhunen–Loéve transform approximation." Signal, Image and Video Processing 10, no. 4 (January 11, 2016): 791–94. http://dx.doi.org/10.1007/s11760-016-0860-2.

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Dissertations / Theses on the topic "Karhunen-Loéve"

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Reed, Richard L. "Applications of the Karhunen-Loéve transform for basis generation in the response matrix method." Thesis, Kansas State University, 2015. http://hdl.handle.net/2097/19081.

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Master of Science
Department of Mechanical and Nuclear Engineering
Jeremy A. Roberts
A novel approach based on the Karhunen-Loéve Transform (KLT) is presented for treatment of the energy variable in response matrix methods, which are based on the partitioning of global domains into independent nodes linked by approximate boundary conditions. These conditions are defined using truncated expansions of nodal boundary fluxes in each phase-space variable (i.e., space, angle, and energy). There are several ways in which to represent the dependence on these variables, each of which results in a trade-off between accuracy and speed. This work provides a method to expand in energy that can reduce the number of energy degrees of freedom needed for sub-0.1% errors in nodal fission densities by up to an order of magnitude. The Karhunen-Loéve Transform is used to generate basis sets for expansion in the energy variable that maximize the amount of physics captured by low-order moments, thus permitting low-order expansions with less error than basis sets previously studied, e.g., the Discrete Legendre Polynomials (DLP) or modified DLPs. To test these basis functions, two 1-D test problems were developed: (1) a 10-pin representation of the junction between two heterogeneous fuel assemblies, and (2) a 70-pin representation of a boiling water reactor. Each of these problems utilized two cross-section libraries based on a 44-group and 238-group structure. Furthermore, a 2-D test problem based on the C5G7 benchmark is used to show applicability to higher dimensions.
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Connell, R. J. "Unstable equilibrium : modelling waves and turbulence in water flow." Diss., Lincoln University, 2008. http://hdl.handle.net/10182/592.

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This thesis develops a one-dimensional version of a new data driven model of turbulence that uses the KL expansion to provide a spectral solution of the turbulent flow field based on analysis of Particle Image Velocimetry (PIV) turbulent data. The analysis derives a 2nd order random field over the whole flow domain that gives better turbulence properties in areas of non-uniform flow and where flow separates than the present models that are based on the Navier-Stokes Equations. These latter models need assumptions to decrease the number of calculations to enable them to run on present day computers or super-computers. These assumptions reduce the accuracy of these models. The improved flow field is gained at the expense of the model not being generic. Therefore the new data driven model can only be used for the flow situation of the data as the analysis shows that the kernel of the turbulent flow field of undular hydraulic jump could not be related to the surface waves, a key feature of the jump. The kernel developed has two parts, called the outer and inner parts. A comparison shows that the ratio of outer kernel to inner kernel primarily reflects the ratio of turbulent production to turbulent dissipation. The outer part, with a larger correlation length, reflects the larger structures of the flow that contain most of the turbulent energy production. The inner part reflects the smaller structures that contain most turbulent energy dissipation. The new data driven model can use a kernel with changing variance and/or regression coefficient over the domain, necessitating the use of both numerical and analytical methods. The model allows the use of a two-part regression coefficient kernel, the solution being the addition of the result from each part of the kernel. This research highlighted the need to assess the size of the structures calculated by the models based on the Navier-Stokes equations to validate these models. At present most studies use mean velocities and the turbulent fluctuations to validate a models performance. As the new data driven model gives better turbulence properties, it could be used in complicated flow situations, such as a rock groyne to give better assessment of the forces and pressures in the water flow resulting from turbulence fluctuations for the design of such structures. Further development to make the model usable includes; solving the numerical problem associated with the double kernel, reducing the number of modes required, obtaining a solution for the kernel of two-dimensional and three-dimensional flows, including the change in correlation length with time as presently the model gives instant realisations of the flow field and finally including third and fourth order statistics to improve the data driven model velocity field from having Gaussian distribution properties. As the third and fourth order statistics are Reynolds Number dependent this will enable the model to be applied to PIV data from physical scale models. In summary, this new data driven model is complementary to models based on the Navier-Stokes equations by providing better results in complicated design situations. Further research to develop the new model is viewed as an important step forward in the analysis of river control structures such as rock groynes that are prevalent on New Zealand Rivers protecting large cities.
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Tu, Jia Lin, and 涂嘉琳. "Mathematical Relationship and Applications Between Human Face Orientation and Its Karhunen-Loéve Transformation Coefficients." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/73968733473380139502.

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碩士
亞洲大學
生物與醫學資訊學系碩士班
100
This research platform to verification on Matlab and achieve on Opencv，In this paper we presents a method to abtain face direction angle coefficients by using the Karhunen-Loève Transform(KLT)，we explore first coefficients and second coefficients after the behavior and the relationship between the angle measurements 。 And regression cosefficients of the first feature corresponding to the angle of the equation。 The Curve equation regression coefficients to test at least 3 to 6 power ，However ， the 5-th power of curve equation is the most efficiency and accuracy can be achieved the best results 。 In addition， the resolution of load into image curve of the coefficient is not much interference 。Therefore, we will transform the minimum image resolution 50x50 after using only one first feature in the case. The angel measurement error of average angel accuracy of equation returen in the case is 1.7 degree
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Book chapters on the topic "Karhunen-Loéve"

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Ochoa-Domínguez, Humberto, and K. R. Rao. "The Karhunen–Loéve Transform." In Discrete Cosine Transform, 5–21. Second edition. | Boca Raton, FL : Taylor & Francis Group, CRC Press, 2019. | Revised edition of: Discrete cosine transform : algorithms, advntages, applications / K. R. Rao, P. Yip. 1990.: CRC Press, 2019. http://dx.doi.org/10.1201/9780203729854-2.

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Choudhary, Shalu, and Debraj Ghosh. "Efficient Computation of Karhunen–Loéve Decomposition." In Proceedings of the International Symposium on Engineering under Uncertainty: Safety Assessment and Management (ISEUSAM - 2012), 879–86. India: Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-0757-3_59.

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Béreš, Michal. "Karhunen-Loéve Decomposition of Isotropic Gaussian Random Fields Using a Tensor Approximation of Autocovariance Kernel." In Lecture Notes in Computer Science, 188–202. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97136-0_14.

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Britanak, Vladimir, Patrick C. Yip, and K. R. Rao. "The Karhunen–Loéve Transform and Optimal Decorrelation." In Discrete Cosine and Sine Transforms, 51–72. Elsevier, 2007. http://dx.doi.org/10.1016/b978-012373624-6/50005-9.

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Fontanella, Lara, and Luigi Ippoliti. "Karhunen–Loéve Expansion of Temporal and Spatio-Temporal Processes." In Time Series Analysis: Methods and Applications, 497–520. Elsevier, 2012. http://dx.doi.org/10.1016/b978-0-444-53858-1.00017-x.

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Liang, Zongxia. "Karhunen-Loéve Expansion for Stochastic Convolution of Cylindrical Fractional Brownian Motions." In Recent Development in Stochastic Dynamics and Stochastic Analysis, 195–206. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814277266_0013.

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Conference papers on the topic "Karhunen-Loéve"

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Tan, Wen, Xue-Dong Liu, Chuang-Ceng Huang, and Jun Wan. "Data Independent Karhunen-Loéve Transform for Image and Video Coding." In 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC). IEEE, 2018. http://dx.doi.org/10.1109/itoec.2018.8740361.

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Rudzki, M. "Coherent Noise Attenuation in the GPR Data via the Karhunen-Loéve Transform." In Near Surface 2008 - 14th EAGE European Meeting of Environmental and Engineering Geophysics. European Association of Geoscientists & Engineers, 2008. http://dx.doi.org/10.3997/2214-4609.20146289.

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Lu, Zhiming, and Dongxiao Zhang. "Higher-Order Approximations for Saturated Flow in Randomly Heterogeneous Media via Karhunen-Loéve Decomposition." In World Water and Environmental Resources Congress 2003. Reston, VA: American Society of Civil Engineers, 2003. http://dx.doi.org/10.1061/40685(2003)14.

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Rivera-García, Diego, Luis Angel García-Escudero, Agustín Mayo-Iscar, and Joaquin Ortega. "Stationary Intervals for Random Waves by Functional Clustering of Spectral Densities." In ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/omae2020-19171.

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Abstract A new time series clustering procedure, based on Functional Data Analysis techniques applied to spectral densities, is employed in this work for the detection of stationary intervals in random waves. Long records of wave data are divided into 30-minute or one-hour segments and the spectral density of each interval is estimated by one of the standard methods available. These spectra are regarded as the main characteristic of each 30-minute time series for clustering purposes. The spectra are considered as functional data and, after representation on a spline basis, they are clustered by a mixtures model method based on a truncated Karhunen-Loéve expansion as an approximation to the density function for functional data. The clustering method uses trimming techniques and restrictions on the scatter within groups to reduce the effect of outliers and to prevent the detection of spurious clusters. Simulation examples show that the procedure works well in the presence of noise and the restrictions on the scatter are effective in avoiding the detection of false clusters. Consecutive time intervals clustered together are considered as a single stationary segment of the time series. An application to real wave data is presented.
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