Modified iterative Landweber method in electrical capacitance tomography

Jang, Jaeduck; Lee, S H; Kim, Kyung Youn; Choi, Bong-Yeol

doi:10.1088/0957-0233/17/7/032

Cited by 101 publications

(38 citation statements)

References 16 publications

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“…The convergence rate of iterative techniques depends on the accuracy of the first image (Hytros et al 1999, Chen et al 2004. The main idea of using a fuzzy system is to assist the iterative reconstruction techniques (Fabian et al 1998, Jang et al 2006 to converge faster by providing a more accurate first image. The better the first image is the lower of the iterations number (Xie et al 1992).…”

Section: Metal Fill Visualization Expert Based Fuzzy Assisted Image Rmentioning

confidence: 99%

In-Situ Real Time Monitoring and Control of Mold Making and Filling Processes: Final Report

Abdelrahman¹,

Currie²

2010

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Section: Metal Fill Visualization Expert Based Fuzzy Assisted Image Rmentioning

confidence: 99%

In-Situ Real Time Monitoring and Control of Mold Making and Filling Processes: Final Report

Abdelrahman¹,

Currie²

2010

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“…Usually, the image reconstruction consists in determining the permittivity distribution by solving an underdetermined set of linear equations obtained by a spatial disretization using the finite element method. Thus, an iterative algorithm [15,5] is implemented to find a solution by minimizing a cost function.…”

Section: Introductionmentioning

confidence: 99%

One or two circular shapes? A binary detection for electrical capacitance tomography sensors

Oussar

Lucas

Holé

2017

Int J Numerical Modelling

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Image reconstruction using ECT sensors rises several drawbacks. Recently, the authors proposed a novel methodology dedicated to a fast detection of the configuration: size and position, of a single circular form using basic algebraic operations. However, this technique is efficient when a single form is present in the sensor cross-section under interest. The present work proposes two different techniques that determine if the sensor cross-section contains one or two circular shapes. Both methods are applied to simulated measurements. They operate much differently and each comes with its own pros and cons.The first method considers that the sensor signal presents an axis of symmetry if a single circular object lies in the sensor. Otherwise, the symmetry is broken. It achieves a recognition rate of 73.9%. In the second method, machine learning techniques are employed to perform a binary supervised classification. Promising rates of recognition over 99% are obtained. The present study also reveals that a 4-electrode sensor leads to the best recognition rates with both methods. This result was also established by the authors in a different framework when dealing with physical limitations on spatial resolution in ECT sensors.

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“…Usually in the iLW method, a sensitivity matrix or a Jacobian matrix is not updated, so the computational time is relatively fast, however, the convergence rate is slow. To speed up the convergence rate, optimal steplengths can be computed at every iteration [8,9]. However, to apply real applications using the iLW method, this method needs to be modified as one-step algorithm.…”

Section: Introductionmentioning

confidence: 99%