PORE-SCALE VISUALIZATION ON POLYMER FLOODING: APPLICATION OF SINGULAR VALUE DECOMPOSITION-BASED IMAGE ANALYSIS METHOD

2020 ◽  
Vol 23 (6) ◽  
pp. 531-543 ◽  
Author(s):  
Behbood Abedi ◽  
Eliana P. Marin Castano ◽  
Ehsan Heidaryan ◽  
Mostafa Safdari Shadloo
Keyword(s):  
Image Analysis ◽  
Singular Value Decomposition ◽  
Singular Value ◽  
Polymer Flooding ◽  
Pore Scale ◽  
Analysis Method ◽  
Image Analysis Method ◽  
Value Decomposition

scholarly journals Image analysis of the tree test using wavelet transform, singular value decomposition, and Fourier transform

2019 ◽  
Vol 90 (3) ◽  
pp. 284-293
Author(s):  
Keita Kawasugi ◽  
Kazuhisa Takemura ◽  
Yumi Iwamitsu ◽  
Hitomi Sugawara ◽  
Sakura Nishizawa ◽  
...  
Keyword(s):  
Image Analysis ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Singular Value ◽  
Value Decomposition

The Algebraic Operation of SVD

2011 ◽  
Author(s):  
Gidon Eshel
Keyword(s):  
Data Analysis ◽  
Singular Value Decomposition ◽  
Singular Value ◽  
Algebraic Operation ◽  
Analysis Method ◽  
Data Analysis Method ◽  
Value Decomposition ◽  
Data Matrices

Chapter 4 discussed the eigenvalue/eigenvector diagonalization of a matrix. Perhaps the biggest problem for this to be very useful in data analysis is the restriction to square matrices. It has already been emphasized time and again that data matrices, unlike dynamical operators, are rarely square. The algebraic operation of the singular value decomposition (SVD) is the answer. Note the distinction between the data analysis method widely known as SVD and the actual algebraic machinery. The former uses the latter, but is not the latter. This chapter describes the method. Following the introduction to SVD, it provides some examples and applications.

Factor analysis of ambiguity in geophysics

Geophysics ◽  
1994 ◽  
Vol 59 (7) ◽  
pp. 1083-1091 ◽  
Author(s):  
Simone G. C. Fraiha ◽  
João B. C. Silva
Keyword(s):  
Factor Analysis ◽  
Singular Value Decomposition ◽  
Finite Number ◽  
Analytical Methods ◽  
Decomposition Analysis ◽  
Singular Value ◽  
Analysis Method ◽  
Singular Value Decomposition Analysis ◽  
Value Decomposition

We present an empirical ambiguity analysis method based on a finite number of acceptable solutions that are representative of the ambiguity region. These solutions are submitted to a Q‐mode factor analysis that indicates which parameters are ambiguous and their ambiguity range. We illustrate, with a synthetic nonlinear example, that our method is more effective than singular value decomposition analysis in producing an average trend of the ambiguity region. It requires less restrictive hypotheses and is more robust than analytical methods of ambiguity analysis, in the sense of being applicable to a broader class of problems.

scholarly journals Proposal of Golf Swing Analysis Method Using Singular Value Decomposition

Proceedings ◽  
2020 ◽  
Vol 49 (1) ◽  
pp. 91
Author(s):  
Kenta Matsumoto ◽  
Nobutaka Tsujiuchi ◽  
Akihito Ito ◽  
Hiroshi Kobayashi ◽  
Masahiko Ueda ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Motion Capture ◽  
Singular Value ◽  
Golf Swing ◽  
Motion Capture System ◽  
Analysis Method ◽  
Main Mode ◽  
Value Decomposition ◽  
The Relationship ◽  
Positional Data

We analyzed the relationship between the cooperative actions of golf swings and the differences in swing trajectory. To extract cooperative actions from different swings, we acquired swing data in an experiment on an experienced golfer who swung with two different trajectories. We measured the swings with motion capture system (VICON). We built an observance matrix from the collected positional data and conducted singular value decomposition (SVD) on it. The SVD yielded the cooperative actions as independent modes. Next, we compared the cooperative actions of different swing trajectories in the main mode. The results indicate that the analysis of the golf swing could be divided into a dominant behavior and an accompanying behavior.

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