Electrical resistance imaging of two-phase flow using direct Landweber method

Kim, Bong Seok; Khambampati, Anil Kumar; Kim, Sin; Kim, Kyung Youn

doi:10.1016/j.flowmeasinst.2014.10.015

Cited by 21 publications

(7 citation statements)

References 9 publications

(14 reference statements)

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“…In most real-time monitoring system, non-iterative methods are preferred for fast reconstruction speed. To improve the accuracy of non-iterative method, many researchers tried to iteratively calculate the optimal inversion operator beforehand for non-iterative online reconstruction, for instance, Offline Iteration Online Reconstruction (OIOR) [25] based on Landweber iteration, and Direct Landweber (DLW) based on modified Landweber [26]. In order to build a real-time acoustic tomography system, the offline iteration method is applied based on the SIRT method defined in equation (12) and equation (13), named offline iteration reconstruction technique using Tikhonov regularisation (OIRTT) and smooth filter (OIRTF).…”

Section: Offline Iteration Methodsmentioning

confidence: 99%

Real-time temperature field measurement based on acoustic tomography

Bao

Jia

Polydorides

2017

Meas. Sci. Technol.

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Acoustic tomography can be used to measure temperature field from the time-of-flight (TOF). In order to capture real-time temperature field change and accurately yield quantitative temperature image, two improvements on the conventional acoustic tomography system are studied: simultaneous acoustic transmission and TOF collection along multiple ray paths, and offline iteration reconstruction algorithm. In system operation, all the acoustic transceivers send the modulated and filtered wideband Kasami sequence simultaneously to facilitate fast and accurate TOF measurements using cross-correlation detection. For image reconstruction, iteration process is separated and executed offline beforehand to shorten computational time for online temperature field reconstruction. The feasibility and effectiveness of the developed methods are validated in simulation study. Simulation results show that the proposed method can reduce the processing time per frame from 160 ms to 20 ms, and the reconstruction error remains less than 5%. Hence the proposed method has the potential of monitoring rapid temperature change with good temporal and spatial resolution.

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Section: Offline Iteration Methodsmentioning

confidence: 99%

Real-time temperature field measurement based on acoustic tomography

Bao

Jia

Polydorides

2017

Meas. Sci. Technol.

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“…The general idea of traditional ones is to discretize the imaging volume and approximate the nonlinear soft field with linear equations. The Gauss-Newton [ 11 ], Landweber iteration [ 12 ], conjugate gradient (CG) [ 13 ], Tikhonov regularization method, etc. were proposed in the early stages, and current traditional algorithms are basically their superposition or improvement.…”

Section: Introductionmentioning

confidence: 99%

Application of a Generative Adversarial Network in Image Reconstruction of Magnetic Induction Tomography

Yang

Liu

Wang

et al. 2021

Sensors

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Image reconstruction of Magnetic induction tomography (MIT) is an ill-posed problem. The non-linear characteristics lead many difficulties to its solution. In this paper, a method based on a Generative Adversarial Network (GAN) is presented to tackle these barriers. Firstly, the principle of MIT is analyzed. Then the process for finding the global optimum of conductivity distribution is described as a training process, and the GAN model is proposed. Finally, the image was reconstructed by a part of the model (the generator). All datasets are obtained from an eight-channel MIT model by COMSOL Multiphysics software. The voltage measurement samples are used as input to the trained network, and its output is an estimate for image reconstruction of the internal conductivity distribution. The results based on the proposed model and the traditional algorithms were compared, which have shown that average root mean squared error of reconstruction results obtained by the proposed method is 0.090, and the average correlation coefficient with original images is 0.940, better than corresponding indicators of BPNN and Tikhonov regularization algorithms. Accordingly, the GAN algorithm was able to fit the non-linear relationship between input and output, and visual images also show that it solved the usual problems of artifact in traditional algorithm and hot pixels in L2 regularization, which is of great significance for other ill-posed or non-linear problems.

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“…The reconstructed results based on the iterative method could be high-resolution, but the processes of reconstruction might be time-consuming. Some typical methods (Hanafizadeh et al , 2016) include the truncated singular value decomposition (TSVD) method, the Tikhonov regularization method, the Newton-Raphson method and the pre-iterative Landweber (Kim et al , 2015). The TSVD method stabilizes the solution at the cost of decreasing the spatial resolution by eliminating the small singular values.…”

Section: Introductionmentioning

confidence: 99%

A novel gas/liquid two-phase flow imaging method through electrical resistance tomography with DDELM-AE sparse dictionary

Wang

et al. 2020

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Purpose The purpose of this paper is to explore gas/liquid two-phase flow is widely existed in industrial fields, especially in chemical engineering. Electrical resistance tomography (ERT) is considered to be one of the most promising techniques to monitor the transient flow process because of its advantages such as fast respond speed and cross-section imaging. However, maintaining high resolution in space together with low cost is still challenging for two-phase flow imaging because of the ill-conditioning of ERT inverse problem. Design/methodology/approach In this paper, a sparse reconstruction (SR) method based on the learned dictionary has been proposed for ERT, to accurately monitor the transient flow process of gas/liquid two-phase flow in a pipeline. The high-level representation of the conductivity distributions for typical flow regimes can be extracted based on denoising the deep extreme learning machine (DDELM) model, which is used as prior information for dictionary learning. Findings The results from simulation and dynamic experiments indicate that the proposed algorithm efficiently improves the quality of reconstructed images as compared to some typical algorithms such as Landweber and SR-discrete fourier transformation/discrete cosine transformation. Furthermore, the SR-DDELM has also used to estimate the important parameters of the chemical process, a case in point is the volume flow rate. Therefore, the SR-DDELM is considered an ideal candidate for online monitor the gas/liquid two-phase flow. Originality/value This paper fulfills a novel approach to effectively monitor the gas/liquid two-phase flow in pipelines. One deep learning model and one adaptive dictionary are trained via the same prior conductivity, respectively. The model is used to extract high-level representation. The dictionary is used to represent the features of the flow process. SR and extraction of high-level representation are performed iteratively. The new method can obviously improve the monitoring accuracy and save calculation time.

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Electrical resistance imaging of two-phase flow using direct Landweber method

Cited by 21 publications

References 9 publications

Real-time temperature field measurement based on acoustic tomography

Real-time temperature field measurement based on acoustic tomography

Application of a Generative Adversarial Network in Image Reconstruction of Magnetic Induction Tomography

A novel gas/liquid two-phase flow imaging method through electrical resistance tomography with DDELM-AE sparse dictionary

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