Image reconstruction for a partially immersed imperfectly conducting cylinder by genetic algorithm

2009 ◽  
Vol 19 (4) ◽  
pp. 299-305 ◽  
Author(s):  
Wei Chien ◽  
Chi-Hsien Sun ◽  
Chien-Ching Chiu
Keyword(s):  
Genetic Algorithm ◽  
Image Reconstruction ◽  
Conducting Cylinder

Image reconstruction for a partially immersed perfectly conducting cylinder using the steady state genetic algorithm

Radio Science ◽  
2004 ◽  
Vol 39 (2) ◽  
pp. n/a-n/a ◽  
Author(s):  
Ching-Lieh Li ◽  
Shao-Hon Chen ◽  
Chih-Ming Yang ◽  
Chien-Ching Chiu
Keyword(s):  
Genetic Algorithm ◽  
Steady State ◽  
Image Reconstruction ◽  
Conducting Cylinder

Frequency Dependence on Image Reconstruction for a Buried Conducting Cylinder

2013 ◽  
Vol 284-287 ◽  
pp. 473-477
Author(s):  
Wei Chien ◽  
Hsien Wei Tseng ◽  
Yung Wen Lee ◽  
Liang Yu Yen ◽  
Chien Ching Chiu
Keyword(s):  
Genetic Algorithm ◽  
Steady State ◽  
Image Reconstruction ◽  
Resonant Frequency ◽  
Frequency Dependence ◽  
Incident Wave ◽  
Optimization Problem ◽  
Quantitative Information ◽  
Frequency Range ◽  
Conducting Cylinder

Frequency dependence on image reconstruction for a buried conducting cylinder is investigated. A conducting cylinder of unknown shape scatters the incident wave in free space and measured the scattered field. By using measured fields, the imaging problem is reformulated into an optimization problem and solved by the steady-state genetic algorithm (SSGA). Numerical results show that the reconstruction is quite well in the resonant frequency range. This work provides both comparative and quantitative information

A NOVEL TWO-STAGE GENETIC ALGORITHM FOR IMAGE RECONSTRUCTION OF ELECTRICAL RESISTANCE TOMOGRAPHY

2010 ◽  
Vol 01 (04) ◽  
pp. 523-542
Author(s):  
SANAZ ASGARIFAR ◽  
JAVAD FROUNCHI ◽  
MOHAMMAD HOSSIEN ZARIFI ◽  
AMIN MAHDIZADEH
Keyword(s):  
Genetic Algorithm ◽  
Image Reconstruction ◽  
Electrical Resistance ◽  
Process Time ◽  
Initial Population ◽  
Two Stage ◽  
Electrical Resistance Tomography ◽  
Random Group ◽  
Ill Posed ◽  
Resistivity Inversion

In this paper, we present a combined GA-ERT method based on two-stage genetic algorithm for image reconstruction in electrical resistance tomography (ERT). Image reconstruction in ERT is an ill-posed inverse problem and we have replaced the reverse solver by a two-stage optimization algorithm. The first stage of GA-ERT is reach to an approximate shape and location of the object. Also in this stage, we proposed a new electrode arrangement for ERT forward solver to reduce the process time of the forward problem. In the second stage, the GA employs result of the first stage as an initial population instead of a random group. Therefore with the local zoom, the GA can be employed to obtain the shape and location of the object more precisely. Experimental results of numerically solved ERT by the GA are also presented and compared to those obtained by other more established inversion methods such as modified Newton–Raphson (mNR) and RES2DINV program which is a standard 2-D resistivity inversion program. Results show that the proposed method can efficiently improve the ill-posed condition of ERT image reconstruction problem and can superiorly enhance the quality of ERT images.

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