scholarly journals Recursive Star-Identification Algorithm Using an Adaptive Singular-Value-Decomposition-Based Angular-Velocity Estimator

2021 ◽  
pp. 1-11
Author(s):  
Hunter Johnston ◽  
Carl Leake ◽  
Marcelino M. de Almeida ◽  
Daniele Mortari
Keyword(s):  
Angular Velocity ◽  
Singular Value Decomposition ◽  
Singular Value ◽  
Identification Algorithm ◽  
Star Identification ◽  
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.

The Wood Slice Cell Image Identification Algorithm Based on Singular Value Decomposition

2015 ◽  
Vol 12 (12) ◽  
pp. 5372-5378
Author(s):  
Yongke Sun ◽  
Yong Cao ◽  
Fei Xiong ◽  
Xiaoguang Yue ◽  
Jian Qiu ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Identification Algorithm ◽  
Image Identification ◽  
Cell Image ◽  
Value Decomposition

Identification of the time-varying modal parameters of a spacecraft with flexible appendages using a recursive predictor-based subspace identification algorithm

2018 ◽  
Vol 233 (6) ◽  
pp. 2032-2050 ◽  
Author(s):  
Zhiyu Ni ◽  
Jinguo Liu ◽  
Zhigang Wu
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Noise Robustness ◽  
Modal Parameters ◽  
Subspace Identification ◽  
Identification Algorithm ◽  
Subspace Tracking ◽  
Online Identification ◽  
Projection Approximation ◽  
Value Decomposition

This study focuses on the recursive identification of the time-varying modal parameters of on-orbit spacecraft caused by structural configuration changes. For this purpose, an algorithm called recursive predictor-based subspace identification is applied as an alternative method to improve the computational efficiency and noise robustness, and to implement an online identification of system parameters. In the existing time-domain identification methods, the eigensystem realization algorithm and subspace identification methods are usually applied to obtain the on-orbit spacecraft modal parameters. However, these approaches are designed based on a time-invariant system and singular value decomposition, which require a significant amount of computational time. Thus, these methods are difficult to employ for online identification. According to the adaptive filter theory, the recursive predictor-based subspace identification algorithm can not only avoid the singular value decomposition computation but also provide unbiased estimates in a general noisy framework using the recursive least squares approach. Furthermore, in comparison with the classical projection approximation subspace tracking series recursive algorithm, the recursive predictor-based subspace identification method is more suitable for systems with strong noise disturbances. By establishing the dynamics model of a large rigid-flexible coupling spacecraft, three cases of on-orbit modal parameter variation with time are investigated, and the corresponding system frequencies are identified using the recursive predictor-based subspace identification, projection approximation subspace tracking, and singular value decomposition methods. The results demonstrate that the recursive predictor-based subspace identification algorithm can be used to effectively perform an online parameter identification, and the corresponding computational efficiency and noise robustness are better than those of the singular value decomposition and projection approximation subspace tracking series approaches, respectively. Finally, the applicability of this method is also verified through a numerical simulation.

Improving TF-IDF with Singular Value Decomposition (SVD) for Feature Extraction on Twitter

2017 ◽  
Author(s):  
Ammar Ismael Kadhim ◽  
Yu-N Cheah ◽  
Inaam Abbas Hieder ◽  
Rawaa Ahmed Ali
Keyword(s):  
Feature Extraction ◽  
Singular Value Decomposition ◽  
Singular Value ◽  
Value Decomposition
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