New selection methods of regularization parameter for electrical resistance tomography image reconstruction

2016 ◽  
Author(s):  
Wang ChuanLei ◽  
Yue ShiHong
Keyword(s):  
Image Reconstruction ◽  
Electrical Resistance ◽  
Regularization Parameter ◽  
Selection Methods ◽  
Electrical Resistance Tomography ◽  
Tomography Image

Fundamental Sensor Development in Electrical Resistance Tomography

2015 ◽  
Vol 73 (6) ◽  
Author(s):  
Ling En Hong ◽  
Ruzairi Hj. Abdul Rahim ◽  
Anita Ahmad ◽  
Mohd Amri Md. Yunus ◽  
Khairul Hamimah Aba ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Data Acquisition ◽  
Electrical Resistance ◽  
Sensor Array ◽  
Sensor Arrays ◽  
Region Of Interest ◽  
Display System ◽  
Electrical Resistance Tomography ◽  
Fundamental Understanding ◽  
Collection Strategies

This paper will provide a fundamental understanding of one of the most commonly used tomography, Electrical Resistance Tomography (ERT). Unlike the other tomography systems, ERT displayed conductivity distribution in the Region of Interest (ROI) and commonly associated to Sensitivity Theorem in their image reconstruction. The fundamental construction of ERT includes a sensor array spaced equally around the imaged object periphery, a Data Acquisition (DAQ), image reconstruction and display system. Four ERT data collection strategies that will be discussed are Adjacent Strategy, Opposite Strategy, Diagonal Strategy and Conducting Boundary Strategy. We will also explain briefly on some of the possible Data Acquisition System (DAQ), forward and inverse problems, different arrangements for conducting and non-conducting pipes and factors that influence sensor arrays selections. 

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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