Satellite Fault Detection and Isolation through Decoupling Parity Space

2011 ◽  
Vol 110-116 ◽  
pp. 4406-4414
Author(s):  
Zheng Wang ◽  
Yan Jun Li ◽  
Xiao Wei Sun
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Fault Detection And Isolation ◽  
Actuator Fault ◽  
Actuator Faults ◽  
Eigen Value ◽  
Parity Space ◽  
Simulation Results ◽  
Value Decomposition ◽  
Space Method

In order to detect and isolate orbiting satellite actuator faults, a decoupling parity space method was extended. The decoupling parity vector was obtained using this method by singular value decomposition. Sometimes this vector may not exist, then by singular value substitution or generalized Eigen value method was used for solving the optimal approximation. The vector can easily make satellite actuator fault detected and isolated. The simulation results showed the effectiveness of the proposed algorithm.

A FRT - SVD Based Blind Medical Watermarking Technique for Telemedicine Applications

2021 ◽  
pp. 632-653
Author(s):  
Surekah Borra ◽  
Rohit Thanki
Keyword(s):  
Singular Value Decomposition ◽  
Medical Image ◽  
Medical Images ◽  
Image Watermarking ◽  
Region Of Interest ◽  
Singular Value ◽  
Ridgelet Transform ◽  
Simulation Results ◽  
The Individual ◽  
Value Decomposition

In this article, a blind and robust medical image watermarking technique based on Finite Ridgelet Transform (FRT) and Singular Value Decomposition (SVD) is proposed. A host medical image is first transformed into 16 × 16 non-overlapping blocks and then ridgelet transform is applied on the individual blocks to obtain sets of ridgelet coefficients. SVD is then applied on these sets, to obtain the corresponding U, S and V matrix. The watermark information is embedded into the host medical image by modification of the value of the significant elements of U matrix. This proposed technique is tested on various types of medical images such as X-ray and CT scan. The simulation results revealed that this technique provides better imperceptibility, with an average PSNR being 42.95 dB for all test medical images. This technique also overcomes the limitation of the existing technique which is applicable on only the Region of Interest (ROI) of the medical image.

Edge Resonances of Circular Cylinders

2014 ◽  
Vol 915-916 ◽  
pp. 45-48
Author(s):  
Fei Long Feng ◽  
Shu Yu Lin ◽  
Zhuang Zhi Shen
Keyword(s):  
Numerical Simulation ◽  
Singular Value Decomposition ◽  
Resonance Frequency ◽  
Singular Value ◽  
Circular Cylinders ◽  
Calculation Results ◽  
Resonance Solution ◽  
Edge Resonance ◽  
Simulation Results ◽  
Value Decomposition

Based on modal decomposition, an explicit formula for edge resonance solution in circular cylinders is presented here. Using the bi-orthogonality relation and singular value decomposition, the method could get the exact edge resonance frequency of circular cylinders. The calculation results are compared with numerical simulation results for verification.

Improving the Multi-Agent General Cooperation Model Based on SVD

2015 ◽  
Vol 738-739 ◽  
pp. 251-255
Author(s):  
Yan Chen ◽  
Yu Jing
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Multi Agent System ◽  
Feasible Method ◽  
Reliable Tool ◽  
Cooperation Model ◽  
Multi Agent ◽  
Simulation Results ◽  
Improved Model ◽  
Value Decomposition

<p class="p16">The cooperation is the most important for a multi-agent system (MAS). In order to research the cooperation, creating a multi-agent general cooperation model, that not only thinking about the cooperation solving way, but also thinking about the cooperation necessity and how to solve the conflicts, was a feasible method. However, the cooperation model might be inefficient. This paper suggested, based on the singular value decomposition (SVD), simplifying multi-agent cooperation data, and then improving the general cooperation model. According to the simulation results, the improved model had higher efficiency and can provide a more reliable tool for the study of the cooperation in MAS.</p>

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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