Subspace constrained regularization for corrosion detection with magnetic induction tomography

Polydorides, Nick; Georghiou, George E.; Kim, D; Won, Chul Ho

doi:10.1016/j.ndteint.2008.05.006

Cited by 11 publications

(4 citation statements)

References 16 publications

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“…In general applications, many groups of excitation–detection combination coils are used to obtain induced magnetic field signals near the imaging body [ 2 ], and then the conductivity distribution of the range to be measured is calculated by the reconstruction algorithm [ 3 , 4 ]. Because of its non-invasive and non-contact characteristics, MIT is suitable for geological exploration [ 5 ], industrial flaw detection [ 6 ], impurity detection [ 7 ], and medical imaging [ 8 ]. The research on it can be roughly divided into a positive problem and an inverse problem [ 9 ].…”

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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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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“…Zhang [ 28 ] investigated the fundamental relationship between corrosion rate and magnetic induction surrounding steel reinforcement. N. Polydorides [ 29 ] realized the magnetic induction tomography scanning of corroded reinforced concrete columns. Sun [ 30 ] had quantitatively studied the magnetic anomalies of reinforcement rods in bored in situ concrete piles for the first time and summarized their magnetic anomaly character.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study on Residual Bending Strength of Corroded Reinforced Concrete Beam Based on Micromagnetic Sensor

Zhou

Qiu

Zhou³

et al. 2018

Sensors

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This paper presents a nondestructive test method to evaluate the residual bending strength of corroded reinforced concrete beam by analyzing the self-magnetic flux leakage (SMFL) signals. The automatic scanning device was equipped with a micromagnetic sensor and sensor-based experimental details were introduced. Next, the theoretical formula of the normal component HS(z) of the SMFL signal that originated from the corroded region was derived based on the magnetic dipole model and the experimental results were discussed. The results indicate that the experimental data of HS(z) are consistent with the theoretical calculations, both location and extent of the steel bars corrosion can be qualitatively determined by using HS(z). The gradient K of HS(z) is approximately linearly related to the loss rate, S, of the bending strength, which can be used to evaluate the residual bending strength of the corroded reinforced concrete beam. This work lays the foundation for evaluating the residual bending strength of corroded reinforced concrete beams using the SMFL signal; the micromagnetic sensor is further applied to the civil engineering.

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“…[6] There are many medical imaging applications including cross-well tomography,[7] ground penetrating radar surface imaging,[89] ultrasound tomography,[10] optical microscopy,[1112] photon imaging,[13] induction imaging,[14] positron emission tomography (PET)[15] and electrical impedance tomography (EIT). [6161718192021]…”

Section: Introductionmentioning

confidence: 99%

Accurate D-bar reconstructions of conductivity images based on a method of moment with sinc basis

Abbasi

2014

J Med Signals Sens

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Planar D-bar integral equation is one of the inverse scattering solution methods for complex problems including inverse conductivity considered in applications such as Electrical impedance tomography (EIT). Recently two different methodologies are considered for the numerical solution of D-bar integrals equation, namely product integrals and multigrid. The first one involves high computational burden and the other one suffers from low convergence rate (CR). In this paper, a novel high speed moment method based using the sinc basis is introduced to solve the two-dimensional D-bar integral equation. In this method, all functions within D-bar integral equation are first expanded using the sinc basis functions. Then, the orthogonal properties of their products dissolve the integral operator of the D-bar equation and results a discrete convolution equation. That is, the new moment method leads to the equation solution without direct computation of the D-bar integral. The resulted discrete convolution equation maybe adapted to a suitable structure to be solved using fast Fourier transform. This allows us to reduce the order of computational complexity to as low as O (N2log N). Simulation results on solving D-bar equations arising in EIT problem show that the proposed method is accurate with an ultra-linear CR.

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Subspace constrained regularization for corrosion detection with magnetic induction tomography

Cited by 11 publications

References 16 publications

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

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

Experimental Study on Residual Bending Strength of Corroded Reinforced Concrete Beam Based on Micromagnetic Sensor

Accurate D-bar reconstructions of conductivity images based on a method of moment with sinc basis

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