Performance of neural network in image reconstruction and interpretation for electrical capacitance tomography

Nooralahiyan, Amir Y.; Hoyle, Brian; Bailey, Nick

doi:10.1049/ic:19950640

Cited by 10 publications

(12 citation statements)

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“…Most often, electrical capacitance was considered. For example, [10][11][12] are theoretical works in which neural network are trained on two-dimensional finite element simulations blurred with some artificial noise and in [13] difference measurements obtained from the tomograph with the help of neural networks. After proper design and suitable training (described below) of the neural network, we can extract the average gas hold-up and the parameter n from the measurement.…”

Section: Neural Network Analysis Of Electrical Measurementsmentioning

confidence: 99%

Analysis of electrical resistance tomography measurements obtained on a bubble column

Fransolet

Crine

L'Homme

et al. 2001

Meas. Sci. Technol.

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Measurements of gas hold-up obtained by using pressure transducers and an optical probe are compared with those obtained by an electrical resistance tomography method. The use of a neural network, the input of which is the electrical measurements, can lead to quantitative results for the gas hold-up distribution. An interesting qualitative diagnosis of the sparger functioning is observed from reconstructed images.

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Section: Neural Network Analysis Of Electrical Measurementsmentioning

confidence: 99%

Analysis of electrical resistance tomography measurements obtained on a bubble column

Fransolet

Crine

L'Homme

et al. 2001

Meas. Sci. Technol.

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“…Artificial neural networks have been used in the optimization reconstruction techniques to simulate the non-linearity of the ECT system (Duggan et al 1995, Nooralahiyan et al 1994, Nooralahiyan et al 1995. It has proved the ability to provide both reasonable accuracy and high speed.…”

Section: Nn-moirtmentioning

confidence: 99%

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

Abdelrahman¹,

Currie²

2010

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“…Low complexity reconstruction is typically achieved with non-iterative algorithms such as linear backprojection (LBP), offline iteration/online reconstruction (OIOR) or fast Bayesian methods (Bayesian linear Minimum Mean Square Error (BMMSE)) such as optimal first/ second-order approximation (OFOA, OSOA). Also approaches based on neural networks as suggested for electrical impedance tomography Adler and Guardo (1994) and Jeon et al (2005) and also for capacitance tomography Nooralahiyan et al (1995) and Zang et al (2006) may be suitable for resource limited systems and implemented, e.g. in an FPGA.…”

Section: Introductionmentioning

confidence: 99%

Artefact reduction in fast Bayesian inversion in electrical tomography

Zangl

Mühlbacher-Karrer

2015

COMPEL

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Purpose-The purpose of this paper is to reduce the artifacts in fast Bayesian reconstruction images in electrical tomography. This is in particular important with respect to object detection in electrical tomography applications. Design/methodology/approach-The authors suggest to apply the Box-Cox transformation in Bayesian linear minimum mean square error (BMMSE) reconstruction to better accommodate the non-linear relation between the capacitance matrix and the permittivity distribution. The authors compare the results of the original algorithm with the modified algorithm and with the ground truth in both, simulation and experiments. Findings-The results show a reduction of 50 percent of the mean square error caused by artifacts in low permittivity regions. Furthermore, the algorithm does not increase the computational complexity significantly such that the hard real time constraints can still be met. The authors demonstrate that the algorithm also works with limited observations angles. This allows for object detection in real time, e.g., in robot collision avoidance. Originality/value-This paper shows that the extension of BMMSE by applying the Box-Cox transformation leads to a significant improvement of the quality of the reconstruction image while hard real time constraints are still met.

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Performance of neural network in image reconstruction and interpretation for electrical capacitance tomography

Cited by 10 publications

References 0 publications

Analysis of electrical resistance tomography measurements obtained on a bubble column

Analysis of electrical resistance tomography measurements obtained on a bubble column

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

Artefact reduction in fast Bayesian inversion in electrical tomography

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