Comparisons of three modelling methods for the forward problem in three‐dimensional electrical capacitance tomography

Yan, Hua; Yi, Sun; Wang, Yi Fan; Zhou, Ying

doi:10.1049/iet-smt.2014.0252

Cited by 11 publications

(12 citation statements)

References 13 publications

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“…It was found that the ELS method was recommended due to its very short computational time. However, MLR is found to give better results compared with the other two methods, although it takes a little longer to compute compared with the ELS method [17].…”

Section: (C) Back-end Electronics In Electrical Capacitance Tomographmentioning

confidence: 90%

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Electronic hardware design of electrical capacitance tomography systems

Saied¹,

Meribout²

2016

Phil. Trans. R. Soc. A.

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Electrical tomography techniques for process imaging are very prominent for industrial applications, such as the oil and gas industry and chemical refineries, owing to their ability to provide the flow regime of a flowing fluid within a relatively high throughput. Among the various techniques, electrical capacitance tomography (ECT) is gaining popularity due to its non-invasive nature and its capability to differentiate between different phases based on their permittivity distribution. In recent years, several hardware designs have been provided for ECT systems that have improved its resolution of measurements to be around attofarads (aF, 10(-18) F), or the number of channels, that is required to be large for some applications that require a significant amount of data. In terms of image acquisition time, some recent systems could achieve a throughput of a few hundred frames per second, while data processing time could be achieved in only a few milliseconds per frame. This paper outlines the concept and main features of the most recent front-end and back-end electronic circuits dedicated for ECT systems. In this paper, multiple-excitation capacitance polling, a front-end electronic technique, shows promising results for ECT systems to acquire fast data acquisition speeds. A highly parallel field-programmable gate array (FPGA) based architecture for a fast reconstruction algorithm is also described. This article is part of the themed issue 'Supersensing through industrial process tomography'.

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Section: (C) Back-end Electronics In Electrical Capacitance Tomographmentioning

confidence: 90%

“…A study was performed to compare three main methods for ECT forward problem modelling: sensitivity distribution, multiple linear regression (MLR) and electric field strength (ELS) [17]. It was found that the ELS method was recommended due to its very short computational time.…”

Section: (C) Back-end Electronics In Electrical Capacitance Tomographmentioning

confidence: 99%

Electronic hardware design of electrical capacitance tomography systems

Saied¹,

Meribout²

2016

Phil. Trans. R. Soc. A.

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“…where x = ℛx and J = Jℛ −1 are the re-scaled pixel values and the normalised sensitivity matrix, respectively. With the new formulation in (8), the estimated solution x λ can be further analysed in the SVD domain analogous to (6) and 7where the new normalised sensitivity matrix is used which is defined as…”

Section: Analysis Of the Regularisation Matricesmentioning

confidence: 99%

“…Other possible applications are the study of cerebral activity of brain function and tumour localisation [3][4][5]. In the industrial application, the electrical capacitance tomography, which is a relative imaging technique to the EIT, is used for process monitoring where transmitted gas or liquid inside a pipeline can be monitored real time [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Hybrid sensitivity‐correlation regularisation matrix for electrical impedance tomography

Borijindargoon¹,

Ng²

2019

IET Science, Measurement & Technology

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“…A typical ECT system includes a multi-electrode capacitance sensor, a data acquisition system and an imaging computer [25]. ECT image reconstruction belongs to an inverse problem, which is usually realised by means of an ECT forward problem model.…”

Section: Brief Introduction To Ectmentioning

confidence: 99%