Reconstruction method for fluorescent X-ray computed tomography by least-squares method using singular value decomposition

1997 ◽  
Vol 44 (1) ◽  
pp. 54-62 ◽  
Author(s):  
T. Yuasa ◽  
M. Akiba ◽  
T. Takeda ◽  
M. Kazama ◽  
A. Hoshino ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Singular Value Decomposition ◽  
Least Squares ◽  
Least Squares Method ◽  
Singular Value ◽  
Reconstruction Method ◽  
X Ray ◽  
X Ray Computed ◽  
Value Decomposition

scholarly journals Identification Method for Voltage Sags Based on K-means-Singular Value Decomposition and Least Squares Support Vector Machine

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1137 ◽  
Author(s):  
Haoyuan Sha ◽  
Fei Mei ◽  
Chenyu Zhang ◽  
Yi Pan ◽  
Jianyong Zheng
Keyword(s):  
Support Vector Machine ◽  
Singular Value Decomposition ◽  
Least Squares ◽  
Short Circuit ◽  
Singular Value ◽  
Support Vector ◽  
Voltage Sag ◽  
Svm Classifier ◽  
Identification Algorithm ◽  
Value Decomposition

Voltage sag is one of the most serious problems in power quality. The occurrence of voltage sag will lead to a huge loss in the social economy and have a serious effect on people’s daily life. The identification of sag types is the basis for solving the problem and ensuring the safe grid operation. Therefore, with the measured data uploaded by the sag monitoring system, this paper proposes a sag type identification algorithm based on K-means-Singular Value Decomposition (K-SVD) and Least Squares Support Vector Machine (LS-SVM). Firstly; each phase of the sag sample RMS data is sparsely coded by the K-SVD algorithm and the sparse coding information of each phase data is used as the feature matrix of the sag sample. Then the LS-SVM classifier is used to identify the sag type. This method not only works without any dependence on the sag data feature extraction by artificial ways, but can also judge the short-circuit fault phase, providing more effective information for the repair of grid faults. Finally, based on a comparison with existing methods, the accuracy advantages of the proposed algorithm with be presented.

scholarly journals Singular value decomposition as a tool for background corrections in time-resolved XFEL scattering data

2014 ◽  
Vol 369 (1647) ◽  
pp. 20130336 ◽  
Author(s):  
Kristoffer Haldrup
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Scattering Data ◽  
High Time Resolution ◽  
Light Sources ◽  
Time Resolved ◽  
X Ray ◽  
Very Large Datasets ◽  
Chaotic Nature ◽  
Value Decomposition

The development of new X-ray light sources, XFELs, with unprecedented time and brilliance characteristics has led to the availability of very large datasets with high time resolution and superior signal strength. The chaotic nature of the emission processes in such sources as well as entirely novel detector demands has also led to significant challenges in terms of data analysis. This paper describes a heuristic approach to datasets where spurious background contributions of a magnitude similar to (or larger) than the signal of interest prevents conventional analysis approaches. The method relies on singular-value decomposition of no-signal subsets of acquired datasets in combination with model inputs and appears generally applicable to time-resolved X-ray diffuse scattering experiments.

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