The use of simultaneous iterative reconstruction technique for electrical capacitance tomography

Bangliang, Su; Zhang, Yiheng; Peng, Lihui; Yao, Danya; Baofen, Zhang

doi:10.1016/s1385-8947(99)00134-5

Cited by 90 publications

(23 citation statements)

References 2 publications

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“…However, SIRT is very slow speed to reconstruct an image, because it consumes much time during iteration to get relatively high precision image. Moreover, the SIRT produces blurred smoothing effect [13].…”

Section: Simultaneous Iterative Reconstruction Technique (Sirt)mentioning

confidence: 99%

“…Simultaneous Algebraic Reconstruction Technique (SART) has been proposed as improvement of ART and SIRT algorithms [13], which it takes advantageous combination of ART and SIRT. ART is fast convergence process while SIRT generates good quality image, therefore, SART is expected to have such useful properties.…”

Section: Simultaneous Algebraic Reconstruction Technique (Sart)mentioning

confidence: 99%

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On the performance of SART and ART algorithms for microwave imaging

Aprilliyani

Prabowo

Basari

2018

AIP Conference Proceedings

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Section: Simultaneous Iterative Reconstruction Technique (Sirt)mentioning

confidence: 99%

Section: Simultaneous Algebraic Reconstruction Technique (Sart)mentioning

confidence: 99%

On the performance of SART and ART algorithms for microwave imaging

Aprilliyani

Prabowo

Basari

2018

AIP Conference Proceedings

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“…An alternative way to improve accuracy was presented in (Bangliang et al, 2000). The authors present a simultaneous iterative reconstruction technique (SIRT) which uses a least-square principle and an entropic thresholding method to overcome the smoothing effect of SIRT.…”

Section: ) All Phases Move (Predominantly Along One Direction) Like Inmentioning

confidence: 99%

Inverse problems, Ill-posedness and regularization – an illustrative example

Brandstätter

2007

Elektrotech. Inftech.

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Inverse problems, Ill-posedness and regularization -an illustrative example B. Brandstä tter Whenever one is confronted with the necessity to measure some quantities, which are not accessible directly, however, are linked via a mathematical model to some measurement data, one has to solve an inverse problem. In this context we speak of a direct problem, when expected measurement data are calculated from a mathematical model, when the not directly accessible quantities are given and, on the other hand, of an inverse problem, when these quantities are calculated from measured data via the mathematical model. In this paper the principles of inverse problems are explained on the basis of a one-dimensional image restoration problem, which is a linear problem and hence is easy to understand. Furthermore, the term ill-posedness will be explained and some possibilities to attain reasonable solutions to ill-posed problems are discussed.Inverse Probleme, Schlechtgestelltheit und Regularisierung -ein anschauliches Beispiel.Wann immer man mit der Aufgabe konfrontiert ist, nicht-direkt erfassbare Daten, die aber ü ber ein mathematisches Modell mit direkt messbaren Grö ßen verlinkt sind, messen zu mü ssen, muss man ein inverses Problem lö sen. In diesem Zusammenhang spricht man von einem direkten Problem, wenn man mit Vorgabe der nicht-direkt erfassbaren Grö ßen ü ber ein mathematisches Modell die zu erwartenden Messdaten berechnet, und umgekehrt von einem inversen Problem, wenn man aus den Messdaten -wieder ü ber den Umweg ü ber dasselbe mathematische Modell -die nicht-direkt erfassbaren Grö ßen berechnet. In diesem Beitrag werden die Grundzü ge von inversen Problemen anhand eines linearen eindimensionalen Image-Restoration-Problems dargelegt. Weiters werden der Ausdruck Schlechtgestelltheit erklä rt und Mö glichkeiten, zu annehmbaren Lö sungen fü r schlechtgestellte Probleme zu kommen, diskutiert.Schlü sselwö rter: inverse Probleme; Schlechtgestelltheit; Regularisierung

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“…More advanced algorithms were quickly adapted to ECT including the algebraic reconstruction technique (ART) (Reinecke and Mewes, 1996), simultaneous iterative reconstruction technique (SIRT) (Bangliang et al, 2000), projected Landweber iteration (PLI) (Yang et al, 1999), and Tikhonov regularisation (Peng et al, 2000). These algorithms effectively reconstruct the image using an l 2 -norm penalty term, which is defined as the square root of the sum of the squared voxel-intensity values in the image.…”

Section: Introductionmentioning

confidence: 99%

Measurement of bubble sizes in fluidised beds using electrical capacitance tomography

Chandrasekera

Moody

et al. 2015

Chemical Engineering Science

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Electrical capacitance tomography (ECT) is used to characterise a fluidised bed. Here, ECT measurements are reconstructed by penalising the Total Variation. Algorithm permits characterisation of regions with sharp changes in permittivity. ECT measurements of the bubble size are compared with existing correlations. a b s t r a c tElectrical capacitance tomography (ECT) provides a means for non-invasively imaging multiphase flows, such as those in fluidised beds. Traditionally ECT images are reconstructed using the assumption that the distribution of permittivity varies smoothly throughout the sensor region. However, for many applications there are step changes in the permittivity, for example, between the bubble and particulate phases in a fluidised bed, and the assumption of smoothness is flawed. In this article a Total Variation Iterative Soft Thresholding (TV-IST) algorithm is used to reconstruct ECT images that allows for sharp transitions in the permittivity distribution. This new algorithm has been compared with established algorithms for ECT image reconstruction. It was found that the TV-IST algorithm reduced the sensitivity to the threshold level chosen when extracting measurements of bubble size from ECT data sets. Measurements of the bubble size distribution in the fluidised bed using the TV-IST algorithm agreed closely with established empirical correlations for the size of bubbles. The results demonstrate that ECT can provide accurate and high spatial resolution measurements of features such as bubbles in gas-solid fluidised beds.

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The use of simultaneous iterative reconstruction technique for electrical capacitance tomography

Cited by 90 publications

References 2 publications

On the performance of SART and ART algorithms for microwave imaging

On the performance of SART and ART algorithms for microwave imaging

Inverse problems, Ill-posedness and regularization – an illustrative example

Measurement of bubble sizes in fluidised beds using electrical capacitance tomography

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