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Relevant bibliographies by topics / Tensor Method

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Academic literature on the topic 'Tensor Method'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Tensor Method"

1

Yang, Hye-Kyung, and Hwan-Seung Yong. "Multi-Aspect Incremental Tensor Decomposition Based on Distributed In-Memory Big Data Systems." Journal of Data and Information Science 5, no. 2 (2020): 13–32. http://dx.doi.org/10.2478/jdis-2020-0010.

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AbstractPurposeWe propose InParTen2, a multi-aspect parallel factor analysis three-dimensional tensor decomposition algorithm based on the Apache Spark framework. The proposed method reduces re-decomposition cost and can handle large tensors.Design/methodology/approachConsidering that tensor addition increases the size of a given tensor along all axes, the proposed method decomposes incoming tensors using existing decomposition results without generating sub-tensors. Additionally, InParTen2 avoids the calculation of Khari–Rao products and minimizes shuffling by using the Apache Spark platform.

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Diner, Çağrı. "The Structure of Moment Tensors in Transversely Isotropic Focal Regions." Bulletin of the Seismological Society of America 109, no. 6 (2019): 2415–26. http://dx.doi.org/10.1785/0120180316.

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Abstract Full moment tensor inversion has become a standard method for understanding the mechanisms of earthquakes as the resolution of the inversion process increases. Thus, it is important to know the possible forms of non–double‐couple (non‐DC) moment tensors, which can be obtained because of either the different source mechanisms or the anisotropy of the focal regions. In this study, the form of the moment tensors of seismic sources occurring in transversely isotropic (TI) focal regions is obtained using the eigendecomposition of the elasticity tensor. More precisely, a moment tensor is ob

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Kuznetsov, Maxim A., and Ivan V. Oseledets. "Tensor Train Spectral Method for Learning of Hidden Markov Models (HMM)." Computational Methods in Applied Mathematics 19, no. 1 (2019): 93–99. http://dx.doi.org/10.1515/cmam-2018-0027.

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AbstractWe propose a new algorithm for spectral learning of Hidden Markov Models (HMM). In contrast to the standard approach, we do not estimate the parameters of the HMM directly, but construct an estimate for the joint probability distribution. The idea is based on the representation of a joint probability distribution as an N-th-order tensor with low ranks represented in the tensor train (TT) format. Using TT-format, we get an approximation by minimizing the Frobenius distance between the empirical joint probability distribution and tensors with low TT-ranks with core tensors normalization

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Katoch, Nitish, Bup-Kyung Choi, Ji-Ae Park, In-Ok Ko, and Hyung-Joong Kim. "Comparison of Five Conductivity Tensor Models and Image Reconstruction Methods Using MRI." Molecules 26, no. 18 (2021): 5499. http://dx.doi.org/10.3390/molecules26185499.

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Imaging of the electrical conductivity distribution inside the human body has been investigated for numerous clinical applications. The conductivity tensors of biological tissue have been obtained from water diffusion tensors by applying several models, which may not cover the entire phenomenon. Recently, a new conductivity tensor imaging (CTI) method was developed through a combination of B1 mapping, and multi-b diffusion weighted imaging. In this study, we compared the most recent CTI method with the four existing models of conductivity tensors reconstruction. Two conductivity phantoms were

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Chang, Hua Jian, and Shu Wen Zhan. "A Method to Evaluate the Elastic Properties of Ceramics-Enhanced Composites Undertaking Interfacial Delamination." Key Engineering Materials 336-338 (April 2007): 2513–16. http://dx.doi.org/10.4028/www.scientific.net/kem.336-338.2513.

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A micromechanical approach is developed to investigate the behavior of composite materials, which undergo interfacial delamination. The main objective of this approach is to build a bridge between the intricate theories and the engineering applications. On the basis of the spring-layer model, which is useful to treat the interfacial debonding and sliding, the present paper proposes a convenient method to assess the effects of delamination on the overall properties of composites. By applying the Equivalent Inclusion Method (EIM), two fundamental tensors are derived in the present model, the mod

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Wu, Zhan Xiong, and Xun Li. "Tri-Linear Regulation of Diffusion Tensor Field for WM Fiber Tracking." Advanced Materials Research 998-999 (July 2014): 503–6. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.503.

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In this paper we present a method for regulating diffusion tensor magnetic resonance (DT-MRI) data. The method is divided in two steps. First, decomposed diffusion tensor and found out the tensors whose eigenvalue was non-positive. The second step was to apply tri-linear interpolation to regularize the diffusion tensor field. We show that the process is a convenient framework to regulate DT-MR data. Both steps are illustrated on real diffusion tensor magnetic resonance data.

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Liu, Hongyi, Hanyang Li, Zebin Wu, and Zhihui Wei. "Hyperspectral Image Recovery Using Non-Convex Low-Rank Tensor Approximation." Remote Sensing 12, no. 14 (2020): 2264. http://dx.doi.org/10.3390/rs12142264.

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Low-rank tensors have received more attention in hyperspectral image (HSI) recovery. Minimizing the tensor nuclear norm, as a low-rank approximation method, often leads to modeling bias. To achieve an unbiased approximation and improve the robustness, this paper develops a non-convex relaxation approach for low-rank tensor approximation. Firstly, a non-convex approximation of tensor nuclear norm (NCTNN) is introduced to the low-rank tensor completion. Secondly, a non-convex tensor robust principal component analysis (NCTRPCA) method is proposed, which aims at exactly recovering a low-rank tens

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Roy, George. "Complementarity of experimental and numerical methods for determining residual stress states." Powder Diffraction 24, S1 (2009): S3—S10. http://dx.doi.org/10.1154/1.3139050.

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Residual stress states in engineering structures are usually determined by measuring components of stress tensors with depth below the material surface. There are destructive and nondestructive methods to measure strain tensor components and convert them into stress tensor components by a variety of techniques derived from constitutive (material) equations. In this study, four methods for determining the strain tensor components are presented: X-ray diffraction method (XRDM), magnetic Barkhausen noise method (MBNM), hole drilling method (HDM), and cut-and-section method (CSM); the first two ar

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Gre´diac, M., A. Vautrin, and G. Verchery. "A General Method for Data Averaging of Anisotropic Elastic Constants." Journal of Applied Mechanics 60, no. 3 (1993): 614–18. http://dx.doi.org/10.1115/1.2900848.

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Redundant experimental data are usually required to determine the best value for the whole set of compliances of an anisotropic laminate. A method is presented here to optimize the compliance tensor values using the five invariants of fourth-rank compliance tensors. A vectorial representation of those invariants is given. It provides a compact presentation of the data and reveals the experimental scatter. Experimental data obtained with bending tests on plates are used as an example to optimize the flexural compliance tensor of a laminate and to show the relevance of the method in practice.

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Wimalawarne, Kishan, Makoto Yamada, and Hiroshi Mamitsuka. "Scaled Coupled Norms and Coupled Higher-Order Tensor Completion." Neural Computation 32, no. 2 (2020): 447–84. http://dx.doi.org/10.1162/neco_a_01254.

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Recently, a set of tensor norms known as coupled norms has been proposed as a convex solution to coupled tensor completion. Coupled norms have been designed by combining low-rank inducing tensor norms with the matrix trace norm. Though coupled norms have shown good performances, they have two major limitations: they do not have a method to control the regularization of coupled modes and uncoupled modes, and they are not optimal for couplings among higher-order tensors. In this letter, we propose a method that scales the regularization of coupled components against uncoupled components to prope

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Dissertations / Theses on the topic "Tensor Method"

1

Dinckal, Cigdem. "Decomposition Of Elastic Constant Tensor Into Orthogonal Parts." Phd thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612226/index.pdf.

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All procedures in the literature for decomposing symmetric second rank (stress) tensor and symmetric fourth rank (elastic constant) tensor are elaborated and compared which have many engineering and scientific applications for anisotropic materials. The decomposition methods for symmetric second rank tensors are orthonormal tensor basis method, complex variable representation and spectral method. For symmetric fourth rank (elastic constant) tensor, there are four mainly decomposition methods namely as, orthonormal tensor basis, irreducible, harmonic decomposition and spectral. Those are applie

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Jacot, Benjamin (Benjamin Paul Emmanuel). "A strain tensor method for three-dimensional optimal Michell structures." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104125.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2016.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 94-95).<br>In the design of discrete structures such as trusses and frames, important quantitative goals such as minimal weight or minimal compliance often dominate. Many numerical techniques exist to address these needs. However, a

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Moore, Stan G. "Chemical Potential Perturbation: A Method to Predict Chemical Potential Using Molecular Simulations." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3248.

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A new method, called chemical potential perturbation (CPP), has been developed to predict the chemical potential as a function of composition in molecular simulations. The CPP method applies a spatially varying external potential to the simulation, causing the composition to depend upon position in the simulation cell. Following equilibration, the homogeneous chemical potential as a function of composition can be determined relative to some reference state after correcting for the effects of the inhomogeneity of the system. The CPP method allows one to predict chemical potential for a wide ran

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4

Fan, Li 1958. "Solution of the ill-conditioned load flow problem by the tensor method." Thesis, McGill University, 1989. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=62002.

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Zhu, Lierong. "Topological visualization of tensor fields using a generalized Helmholtz decomposition." Morgantown, W. Va. : [West Virginia University Libraries], 2010. http://hdl.handle.net/10450/10962.

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Thesis (M.S.)--West Virginia University, 2010.<br>Title from document title page. Document formatted into pages; contains viii, 75 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 72-75).

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Furuhashi, Takeshi, Tomohiro Yoshikawa, and Hiroaki Masai. "A visualization method of third-order tensor for knowledge extraction from questionnaire data." IEEE, 2013. http://hdl.handle.net/2237/20712.

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Spencer, Timothy J. "Lattice Boltzmann method for Q-tensor nemato-dynamics in liquid crystal display devices." Thesis, Sheffield Hallam University, 2005. http://shura.shu.ac.uk/20393/.

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Nematic liquid crystals are fluids whose anisometric molecules show long range orientational order but no positional order. The orientational order gives rise to anisotropic properties that have widely been exploited as the basis for liquid crystal display devices. The Ericksen-Leslie director theory has successfully been used to describe many dynamic properties of liquid crystals however there are situations in which a more complete description may be given in terms of the second rank traceless symmetric Q-tensor. The development of a liquid crystal device solver is described. The solver calc

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Ballani, Jonas. "Fast Evaluation of Near-Field Boundary Integrals using Tensor Approximations." Doctoral thesis, Universitätsbibliothek Leipzig, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-97317.

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In this dissertation, we introduce and analyse a scheme for the fast evaluation of integrals stemming from boundary element methods including discretisations of the classical single and double layer potential operators. Our method is based on the parametrisation of boundary elements in terms of a d-dimensional parameter tuple. We interpret the integral as a real-valued function f depending on d parameters and show that f is smooth in a d-dimensional box. A standard interpolation of f by polynomials leads to a d-dimensional tensor which is given by the values of f at the interpolation points. T

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Carvajal, Orlando A. "A Hybrid Symbolic-Numeric Method for Multiple Integration Based on Tensor-Product Series Approximations." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/1157.

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This work presents a new hybrid symbolic-numeric method for fast and accurate evaluation of multiple integrals, effective both in high dimensions and with high accuracy. In two dimensions, the thesis presents an adaptive two-phase algorithm for double integration of continuous functions over general regions using Frederick W. Chapman's recently developed Geddes series expansions to approximate the integrand. These results are extended to higher dimensions using a novel Deconstruction/Approximation/Reconstruction Technique (DART), which facilitates the dimensional reduction of families of

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Kestler, Sebastian [Verfasser]. "On the adaptive tensor product wavelet Galerkin method with applications in finance / Sebastian Kestler." Ulm : Universität Ulm. Fakultät für Mathematik und Wirtschaftswissenschaften, 2013. http://d-nb.info/1042709130/34.

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Books on the topic "Tensor Method"

1

Griffith, J. S. The irreducible tensor method for molecular symmetry groups. Dover Publications, 2006.

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Tensor methods in statistics. Chapman and Hall, 1987.

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P, Miles J., ed. Tensor methods for engineers. Ellis Horwood, 1990.

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Montangero, Simone. Introduction to Tensor Network Methods. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01409-4.

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5

Borg, Sidney F. Matrix-tensor methods in continuum mechanics. 2nd ed. World Scientific, 1990.

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6

Greenblatt, Seth A. Tensor methods for full-information maximum likelihood estimation: Unconstrained estimation. University of Reading. Department of Economics, 1992.

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7

Giles, Peter. A basic counter-tenor method: For teacher and student. Thames Publishing, 1987.

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8

Ran, Shi-Ju. Tensor Network Contractions: Methods and Applications to Quantum Many-Body Systems. Springer Nature, 2020.

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9

Giraldo, Francis X. An Introduction to Element-Based Galerkin Methods on Tensor-Product Bases. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55069-1.

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Greenblatt, Seth A. Tensor methods for full-information maximum likelihood estimation: Estimation with parameter constraints. University of Reading. Department of Economics, 1992.

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Book chapters on the topic "Tensor Method"

1

Clermont, Jean-Robert, and Amine Ammar. "Tensor Frames." In Stream-Tube Method. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65470-2_1.

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2

Rebetsky, Yu L., and A. Yu Polets. "The Method of Cataclastic Analysis of Discontinuous Displacements." In Moment Tensor Solutions. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77359-9_6.

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Strathearn, Aidan. "Method." In Modelling Non-Markovian Quantum Systems Using Tensor Networks. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54975-6_3.

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Dreger, Douglas S. "Berkeley Seismic Moment Tensor Method, Uncertainty Analysis, and Study of Non-double-couple Seismic Events." In Moment Tensor Solutions. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77359-9_4.

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Gao, Jianshe, Baotang Wang, Weilong Zuo, and Qianyuan Yu. "Tensor Method for Mechanism Motion Analysis." In Advances in Mechanical Design. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6553-8_79.

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Itoh, Hayato, Atsushi Imiya, and Tomoya Sakai. "Mathematical Aspects of Tensor Subspace Method." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49055-7_4.

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Suk, Tomáš, and Jan Flusser. "Tensor Method for Constructing 3D Moment Invariants." In Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23678-5_24.

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Word, D. R., H. W. Smith, and F. X. Bostick. "Crustal Investigations by the Magnetotelluric Tensor Impedance Method." In Geophysical Monograph Series. American Geophysical Union, 2013. http://dx.doi.org/10.1029/gm014p0145.

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Zhou, Lihua, Guowang Du, Qing Xiao, and Lizhen Wang. "A Tensor-Based Method for Geosensor Data Forecasting." In Web and Big Data. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96893-3_23.

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Franken, Erik, Markus van Almsick, Peter Rongen, Luc Florack, and Bart ter Haar Romeny. "An Efficient Method for Tensor Voting Using Steerable Filters." In Computer Vision – ECCV 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11744085_18.

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Conference papers on the topic "Tensor Method"

1

Cheng, Miaomiao, Liping Jing, and Michael K. Ng. "A Weighted Tensor Factorization Method for Low-Rank Tensor Completion." In 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM). IEEE, 2019. http://dx.doi.org/10.1109/bigmm.2019.00-45.

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2

Phan, Anh-Huy, Petr Tichavsky, and Andrzej Cichocki. "Rank-one tensor injection: A novel method for canonical polyadic tensor decomposition." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7472137.

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Yu, Jinshi, Guoxu Zhou, Qibin Zhao, and Kan Xie. "An Effective Tensor Completion Method Based on Multi-linear Tensor Ring Decomposition." In 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2018. http://dx.doi.org/10.23919/apsipa.2018.8659492.

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Chen, Cong, Kim Batselier, and Ngai Wong. "A novel tensor-based model compression method via tucker and tensor train decompositions." In 2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS). IEEE, 2017. http://dx.doi.org/10.1109/epeps.2017.8329724.

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Yeqing Zhu, Wenming Huang, Peizhi Wen, Yuanyuan Liang, Yaheng Ren, and Guihai Luo. "Tensor voting based method for image denoising." In 2010 International Conference on Intelligent Computing and Integrated Systems (ICISS). IEEE, 2010. http://dx.doi.org/10.1109/iciss.2010.5656824.

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Phan, Anh-Huy, Petr Tichavsky, and Andrzej Cichocki. "Deflation method for CANDECOMP/PARAFAC tensor decomposition." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6854904.

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Shakhurdin, Valintin I., and Igor A. Patrikeyev. "Reconstruction of symmetrical tensor fields by optical tomographic method." In Analytical Methods for Optical Tomography, edited by Gennady G. Levin. SPIE, 1992. http://dx.doi.org/10.1117/12.131898.

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Gur, Yaniv, and Chris R. Johnson. "Generalized HARDI invariants by method of tensor contraction." In 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014). IEEE, 2014. http://dx.doi.org/10.1109/isbi.2014.6867971.

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Xu, Meirong, and Yige Zhao. "Minimum coloring problem via semi-tensor product method." In 2017 29th Chinese Control And Decision Conference (CCDC). IEEE, 2017. http://dx.doi.org/10.1109/ccdc.2017.7979475.

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Zhao, Qiming, and Costas D. Sarris. "Generalized Tensor FDTD Method for Sloped Plasmonic Interfaces." In 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting. IEEE, 2019. http://dx.doi.org/10.1109/apusncursinrsm.2019.8888892.

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Reports on the topic "Tensor Method"

1

Mayo, Jackson R., and Tamara Gibson Kolda. Shifted power method for computing tensor eigenpairs. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/1005408.

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Manke, J. A tensor product B-spline method for numerical grid generation. Office of Scientific and Technical Information (OSTI), 1989. http://dx.doi.org/10.2172/5005256.

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Manke, J. A tensor product b-spline method for 3D multi-block elliptic grid generation. Office of Scientific and Technical Information (OSTI), 1988. http://dx.doi.org/10.2172/5536897.

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MIRZAEI, Hossein, Reza KAKAIE, Behrooz HASSANI, and Esmaeil JALALI. Determination of Stress Tensor in Multi-fractured Rock Medium by the Use of Element Free Galerkin Method. Cogeo@oeaw-giscience, 2011. http://dx.doi.org/10.5242/iamg.2011.0128.

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Xu, Yangyang, and Wotao Yin. A Block Coordinate Descent Method for Multi-Convex Optimization with Applications to Nonnegative Tensor Factorization and Completion. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada567404.

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Bouaricha, A. Tensor methods for large, sparse unconstrained optimization. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/409872.

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Schnabel, Robert B., and Ta-Tung Chow. Tensor Methods for Unconstrained Optimization Using Second Derivatives. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada213642.

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Schnabel, Robert B., and Paul D. Frank. Solving Systems of Nonlinear Equations by Tensor Methods. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada169927.

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Bouaricha, A., and R. B. Schnabel. Tensor methods for large sparse systems of nonlinear equations. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/434848.

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Haass, Michael Joseph, Mark Hilary Van Benthem, and Edward M. Ochoa. Tensor analysis methods for activity characterization in spatiotemporal data. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1200656.

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