scholarly journals A Progress Report on Numerical Solutions of Least Squares Adjustment in GNU Project Gama

10.14311/662 ◽  
2005 ◽  
Vol 45 (1) ◽  
Author(s):  
A. Čepek ◽  
J. Pytel
Keyword(s):  
Singular Value Decomposition ◽  
Numerical Solution ◽  
Least Squares ◽  
Numerical Solutions ◽  
Singular Systems ◽  
Progress Report ◽  
Singular Value ◽  
Geodetic Networks ◽  
Least Squares Adjustment ◽  
Value Decomposition

GNU project Gama for adjustment of geodetic networks is presented. Numerical solution of Least Squares Adjustment in the project is based on Singular Value Decomposition (SVD) and General Orthogonalization Algorithm (GSO). Both algorithms enable solution of singular systems resulting from adjustment of free geodetic networks. 

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 Radar rainfall image repair techniques

2004 ◽  
Vol 8 (2) ◽  
pp. 220-234 ◽  
Author(s):  
Stephen M. Wesson ◽  
Geoffrey G. S. Pegram
Keyword(s):  
Singular Value Decomposition ◽  
Ordinary Kriging ◽  
Singular Systems ◽  
Ground Level ◽  
Singular Value ◽  
Rainfall Data ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Ground Clutter ◽  
Value Decomposition

Abstract. There are various quality problems associated with radar rainfall data viewed in images that include ground clutter, beam blocking and anomalous propagation, to name a few. To obtain the best rainfall estimate possible, techniques for removing ground clutter (non-meteorological echoes that influence radar data quality) on 2-D radar rainfall image data sets are presented here. These techniques concentrate on repairing the images in both a computationally fast and accurate manner, and are nearest neighbour techniques of two sub-types: Individual Target and Border Tracing. The contaminated data is estimated through Kriging, considered the optimal technique for the spatial interpolation of Gaussian data, where the "screening effect" that occurs with the Kriging weighting distribution around target points is exploited to ensure computational efficiency. Matrix rank reduction techniques in combination with Singular Value Decomposition (SVD) are also suggested for finding an efficient solution to the Kriging Equations which can cope with near singular systems. Rainfall estimation at ground level from radar rainfall volume scan data is of interest and importance in earth bound applications such as hydrology and agriculture. As an extension of the above, Ordinary Kriging is applied to three-dimensional radar rainfall data to estimate rainfall rate at ground level. Keywords: ground clutter, data infilling, Ordinary Kriging, nearest neighbours, Singular Value Decomposition, border tracing, computation time, ground level rainfall estimation

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