Application of 3D Laser Scanner on Dike Risk Analysis

2013 ◽  
Vol 341-342 ◽  
pp. 1085-1088
Author(s):  
Rui Wang ◽  
Lei Yang ◽  
Xuan Lou ◽  
Yang Zhou
Keyword(s):  
Singular Value Decomposition ◽  
Risk Analysis ◽  
Model Experiment ◽  
3D Model ◽  
Risk Model ◽  
Laser Scanner ◽  
Point Clouds ◽  
Singular Value ◽  
Before And After ◽  
Value Decomposition

A dike risk model experiment is introduced in this paper. Researchers use a 3D laser scanner to scan the dike model before and after a simulated flood event and calculate the volume of the erosional fossa. The scan work includes four scan stations located at different places surrounding the dike model. The four point clouds are merged together by ICP (singular value decomposition) and SVD (singular value decomposition) algorithm to create a complete, thorough and detailed virtual 3D model of the experiment dike. Researchers can do kinds survey and calculate work based on the 3D model instead of working on the real dike. The work efficient and data quality are increased significantly.

Polarization filter with singular value decomposition

Geophysics ◽  
2001 ◽  
Vol 66 (3) ◽  
pp. 932-938 ◽  
Author(s):  
Roberto de Franco ◽  
Gemma Musacchio
Keyword(s):  
Singular Value Decomposition ◽  
Particle Motion ◽  
Real Data ◽  
Singular Value ◽  
Correct Orientation ◽  
Principal Axes ◽  
Before And After ◽  
Polarization Filtering ◽  
Value Decomposition ◽  
Filter Algorithms

We present a singular value decomposition (SVD) based algorithm for polarization filtering of triaxial seismic recordings based on the assumption that the particle motion trajectory is essentially 2-D (elliptical polarization). The filter is the sum of the first two eigenimages of the SVD on the signal matrix. Weighing functions, which are strictly dependent on the intensity (linearity and planarity) of the polarization, are applied. The efficiency of the filter is tested on synthetic traces and on real data, and found to be superior to solely covariance‐based filter algorithms. Although SVD and covariance‐based methods have similar theoretical approach to the solution of the eigenvalue problem, SVD does not require any further rotation along the polarization ellipsoid principal axes. The algorithm presented here is a robust and fast filter that properly reproduces polarization attributes, amplitude, and phase of the original signal. A major novelty is the enhancement of both elliptical and linear polarized signals. Moreover as SVD preserves the amplitude ratios across the triaxial recordings, the particle motion ellipse before and after filtering retains a correct orientation, overcoming a typical artifact of the covariance‐based methods.

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

Multi-scale singular value decomposition polarization image fusion defogging algorithm and experiment

2020 ◽  
Vol 13 (6) ◽  
pp. 1-10
Author(s):  
ZHOU Wen-zhou ◽  
◽  
FAN Chen ◽  
HU Xiao-ping ◽  
HE Xiao-feng ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Image Fusion ◽  
Singular Value ◽  
Multi Scale ◽  
Polarization Image ◽  
Value Decomposition
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