Nonstationary bubble shape determination in Electrical Impedance Tomography combining Gauss–Newton Optimization with particle filter

Moura, Bruno Furtado de; Martins, Márcio Ferreira; Palma, Francisco Hernán Sepúlveda; Silva, Wellington Betencurte da; Cabello, Jorge Acevedo; Ramos, Rogério

doi:10.1016/j.measurement.2021.110216

Cited by 10 publications

(6 citation statements)

References 54 publications

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“…where Equation (8) represents the distance between an individual and its prey, and Equation ( 9) is the position update formula for the grey wolf, where t is the number of generations of the current iteration, A and C are the coefficient vectors, and X and X p are the position vector of the prey and the position vector of the grey wolf, respectively. A and C are calculated as follows:…”

Section: Candidate Solutionsmentioning

confidence: 99%

“…The traditional image reconstruction methods can be divided into dynamic algorithms (isotropic inverse projection method [6], sensitive matrix method, and conjugate gradient method [7] and static algorithms (Gaussian Newton method [8], layer peeling method [9], static imaging algorithm for isotropic inverse projection, etc.). Dynamic algorithms are Micromachines 2022, 13, 1120 2 of 10 quicker to image and are often used for online reconstruction, but they need less precision from the data gathering apparatus and produce images of lower quality, as well.…”

Section: Introductionmentioning

confidence: 99%

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Electrical Impedance Tomography Based on Grey Wolf Optimized Radial Basis Function Neural Network

2022

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Electrical impedance tomography (EIT) is a non-invasive, radiation-free imaging technique with a lot of promise in clinical monitoring. However, since EIT image reconstruction is a non-linear, pathological, and ill-posed issue, the quality of the reconstructed images needs constant improvement. To increase image reconstruction accuracy, a grey wolf optimized radial basis function neural network (GWO-RBFNN) is proposed in this paper. The grey wolf algorithm is used to optimize the weights in the radial base neural network, determine the mapping between the weights and the initial position of the grey wolf, and calculate the optimal position of the grey wolf to find the optimal solution for the weights, thus improving the image resolution of EIT imaging. COMSOL and MATLAB were used to numerically simulate the EIT system with 16 electrodes, producing 1700 simulation samples. The standard Landweber, RBFNN, and GWO-RBFNN approaches were used to train the sets separately. The obtained image correlation coefficient (ICC) of the test set after training with GWO-RBFNN is 0.9551. After adding 30, 40, and 50 dB of Gaussian white noise to the test set, the attained ICCs with GWO-RBFNN are 0.8966, 0.9197, and 0.9319, respectively. The findings reveal that the proposed GWO-RBFNN approach outperforms the existing methods when it comes to image reconstruction.

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Section: Candidate Solutionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Electrical Impedance Tomography Based on Grey Wolf Optimized Radial Basis Function Neural Network

2022

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“…We can obtain P(x, y, z) by inserting Equation ( 32) into Equation (31). Under the parameters: d = 12, e = 3, f = 9, p 1 = 2, q 1 = 3, p 2 = 3, q 1 = 2, and a noise with s = 0.4.…”

Section: Examplementioning

confidence: 99%

“…For the applications of EIT, de Moura et al [31] used EIT in multiphase flow for identifying flow patterns and void-fraction calculation. An image made of the conductivity of the domain was formed by combining an inverse issue and measurements.…”

Section: Introductionmentioning

confidence: 99%

A New Meshless Method for Solving 3D Inverse Conductivity Issues of Highly Nonlinear Elliptic Equations

Chang

2022

Symmetry

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In this research, the 3D inverse conductivity issues of highly nonlinear elliptic partial differential equations (PDEs) are investigated numerically. Even some researchers have utilized several schemes to overcome these multi-dimensional forward issues of those PDEs; nevertheless, an effective numerical algorithm to solve these 3D inverse conductivity issues of highly nonlinear elliptic PDEs is still not available. We apply two sets of single-parameter homogenization functions as the foundations for the answer and conductivity function to cope with the 3D inverse conductivity issue of highly nonlinear PDEs. The unknown conductivity function can be retrieved by working out another linear system produced from the governing equation by collocation skill, while the answer is acquired by dealing with a linear system to gratify over-specified Neumann boundary condition on a fractional border. As this new computational approach is based on a concrete theoretical foundation, it can result in a deeper understanding of 3D inverse conductivity issues with symmetry and asymmetry geometries. The homogenization functions method is rather stable, effective, and accurate in revealing the conductivity function when the over-specified Neumann data with a large level of noise of 28%.

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“…Newton's one-step error reconstruction (NOSER) algorithm proposed by Cheney only needs to iterate once based on the initially set resistivity distribution to obtain the resistivity distribution results [9]. Compared with other soft imaging algorithms that require multiple iterations [10][11][12], NOSER has great advantages in terms of computing speed and efficiency [13]. Therefore, NOSER is suitable for situations with a large finite element scale.…”

Section: Introductionmentioning

confidence: 99%

Fast artifact filtering algorithm for electrical resistivity tomography

Han

Lü

et al. 2023

Meas. Sci. Technol.

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Owing to the ill condition of electrical resistivity tomography and the measurement error of the hardware equipment, the reconstructed resistivity distribution image often contains artifacts of varying degrees. Other soft-field imaging technologies, such as electrical impedance tomography and electrical capacitance tomography also encounter artifacts. Artifacts interfere with the assessment of the real damaged area. To eliminate the influence of artifacts on the reconstructed image, a novel artifact elimination algorithm called the Fast Artifact Filtering (FAF) algorithm was proposed. Based on the calculation results of existing algorithms (such as the NOSER algorithm), the FAF algorithm can remove the damaged areas with low confidence from the potential damaged areas, and only retain the damaged areas with high confidence for final imaging. Several simulation models were used to test the effectiveness of the artifact elimination algorithm proposed in this study. The test results show that the number of artifacts in the final reconstructed image was significantly eliminated after the NOSER algorithm was combined with the FAF algorithm. In addition, when the number of finite element model division elements was 4802, the refresh time of a single image only increased by approximately 1ms. A structural health monitoring test for hollow structure is provided. The results show that the FAF also performs well on the measured voltage data.

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Nonstationary bubble shape determination in Electrical Impedance Tomography combining Gauss–Newton Optimization with particle filter

Cited by 10 publications

References 54 publications

Electrical Impedance Tomography Based on Grey Wolf Optimized Radial Basis Function Neural Network

Electrical Impedance Tomography Based on Grey Wolf Optimized Radial Basis Function Neural Network

A New Meshless Method for Solving 3D Inverse Conductivity Issues of Highly Nonlinear Elliptic Equations

Fast artifact filtering algorithm for electrical resistivity tomography

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