scholarly journals Algoritmo para a compressão de sinais da rede elétrica baseado na combinação da técnica Compressive Sensing com uma abordagem dissociativa

2020 ◽  
Author(s):  
Jorge Cormane ◽  
Camila Franco de Sousa
Keyword(s):  
Compressive Sensing ◽  
Least Squares ◽  
Conjugate Gradient ◽  
Iteratively Reweighted Least Squares

Este trabalho apresenta um método de compressão de sinais da rede elétrica baseado na técnica de Compressive Sensing combinada com uma abordagem dissociativa. Para isso, utilizam-se os algoritmos Iteratively Reweighted Least-Squares e o Conjugate Gradient. O primeiro adequado para a reconstrução de sinais unidimensionais, enquanto que o segundo é adequado para a reconstrução do sinal em um formato bidimensional. Os resultados demonstram a preservação do sinal após a reconstrução (SNR > 40 dB), além da redução da complexidade computacional, a partir da dissociação do sinal segundo seu comportamento: regime permanente ou disturbio.

Compressive Sensing Reconstruction With Prior Information by Iteratively Reweighted Least-Squares

2009 ◽  
Vol 57 (6) ◽  
pp. 2424-2431 ◽  
Author(s):  
C.J. Miosso ◽  
R. von Borries ◽  
M. Argaez ◽  
L. Velazquez ◽  
C. Quintero ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Least Squares ◽  
Prior Information ◽  
Iteratively Reweighted Least Squares

scholarly journals Analytical quality assessment of iteratively reweighted least-squares (IRLS) method

2014 ◽  
Vol 20 (1) ◽  
pp. 132-141 ◽  
Author(s):  
Jianfeng Guo
Keyword(s):  
Least Squares ◽  
Order Approximation ◽  
Diagnostic Tools ◽  
Iteratively Reweighted Least Squares ◽  
First Order ◽  
Iterative Reweighting ◽  
First Order Approximation ◽  
Detectable Bias ◽  
Theoretical Analyses ◽  
Exact Analytical Method

The iteratively reweighted least-squares (IRLS) technique has been widely employed in geodetic and geophysical literature. The reliability measures are important diagnostic tools for inferring the strength of the model validation. An exact analytical method is adopted to obtain insights on how much iterative reweighting can affect the quality indicators. Theoretical analyses and numerical results show that, when the downweighting procedure is performed, (1) the precision, all kinds of dilution of precision (DOP) metrics and the minimal detectable bias (MDB) will become larger; (2) the variations of the bias-to-noise ratio (BNR) are involved, and (3) all these results coincide with those obtained by the first-order approximation method.

Plane-wave least-squares reverse time migration with a preconditioned stochastic conjugate gradient method

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. S33-S46 ◽  
Author(s):  
Chuang Li ◽  
Jianping Huang ◽  
Zhenchun Li ◽  
Rongrong Wang
Keyword(s):  
Plane Wave ◽  
Least Squares ◽  
Conjugate Gradient ◽  
Sampling Method ◽  
Singular Spectrum Analysis ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Numerical Tests ◽  
Time Migration

This study derives a preconditioned stochastic conjugate gradient (CG) method that combines stochastic optimization with singular spectrum analysis (SSA) denoising to improve the efficiency and image quality of plane-wave least-squares reverse time migration (PLSRTM). This method reduces the computational costs of PLSRTM by applying a controlled group-sampling method to a sufficiently large number of plane-wave sections and accelerates the convergence using a hybrid of stochastic descent (SD) iteration and CG iteration. However, the group sampling also produces aliasing artifacts in the migration results. We use SSA denoising as a preconditioner to remove the artifacts. Moreover, we implement the preconditioning on the take-off angle-domain common-image gathers (CIGs) for better results. We conduct numerical tests using the Marmousi model and Sigsbee2A salt model and compare the results of this method with those of the SD method and the CG method. The results demonstrate that our method efficiently eliminates the artifacts and produces high-quality images and CIGs.

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