• Journal of IKEEE
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• v.19 no.3
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• pp.407-414
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• 2015

Electrical impedance tomography is an imaging technique to reconstruct the internal conductivity distribution based on applied small currents and measured voltages through an array of electrodes attached on the boundary of a domain of interest. In this paper, an analytical solver with complete electrode model is derived and the analytical voltage data are calculated. Moreover, the voltage data are also computed with existing numerical solvers such as finite element method and boundary element method. The forward solutions using homogeneous and inhomogeneous conditions are compared with phantom experiments through the root mean square errors.

• Journal of IKEEE
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• v.5 no.2 s.9
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• pp.153-163
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• 2001

Electrical impedance tomography(EIT) is a relatively new imaging modality in which the internal impedivity distribution is reconstructed based on the known sets of injected currents and measured voltages on the surface of the object. We describe a dynamic EIT imaging technique for the case where the resistivity distribution inside the object changes rapidly within the time taken to acquire a full set of independent measurement data. In doing so, the inverse problem is treated as the state estimation problem and the unknown state (resistivity) is estimated with the aid of extended Kalman filter in a minimum mean square error sense. In particular, additional electrodes are attached to the known internal structure of the object to enhance the reconstruction performance and modified Tikhonov regularization technique is employed to mitigate the ill-posedness of the inverse problem. Computer simulations are provided to illustrate the reconstruction performance of the proposed algorithm.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 2007.11a
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• pp.100-105
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• 2007

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 2007.11a
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• pp.106-111
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• 2007

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.4
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• pp.36-44
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• 2012

Electrical impedance tomography is a relatively new imaging modality in which the internal conductivity (or resistivity) distribution of a object is reconstructed based on the injected currents and measured voltages through the electrodes placed on the surface of the object. In this paper, it is assumed that the relationship between the resistivity distribution and the resistance of electrodes is linear. From this linear relation, the weighting matrix can be obtained and modified iterative Landweber method is applied to estimate the internal resistivity distribution. Additionally, to accelerate the convergence rate and improve the spatial resolution of the reconstructed image, optimal step lengths for the iterative Landweber method are computed from the objective function in the least-square sense. The numerical experiments have been performed to illustrate the superior reconstruction performance of the proposed scheme.

• Journal of Industrial Technology
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• v.26 no.B
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• pp.221-226
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• 2006

Electrical impedance tomography (EIT) is a technique for determining the electrical conductivity and permittivity distribution within the interior of a body from measurements made on its surface. One recent application area of the EIT is the detection of breast cancer by imaging the conductivity and permittivity distribution inside the breast. The present "gold standard" for breast cancer detection is X-ray mammography, and it is desirable that EIT and X-ray mammography use the same geometry. This paper presents a forward model of a simplified mammography geometry for EIT imaging. The mammography geometry is modeled as a rectangular box with electrode arrays on the top and bottom planes. A forward model for the electrical impedance imaging problem is derived for a homogeneous conductivity distribution and Validated by experiment using a phantom tank.

• The Journal of Korean Institute of Information Technology
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• v.16 no.11
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• pp.35-41
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• 2018

Electrical impedance tomography (EIT) is a kind of nondestructive testing technique that obtains the internal resistivity distribution from the voltages measured at the electrodes located outside the area of interest. However, an image reconstruction problem in EIT has innate non-linearity and ill-posedness, so that it is difficult to obtain satisfactory reconstructed results. In general, a neural network can efficiently model the input and output relationships of a non-linear system. This paper proposes a method for estimating the position and size of a circular target using a multi-layer neural network. To verify the performance of the proposed method, neural network was trained and various computer simulations were performed and satisfactory performance was verified.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.4
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• pp.249-256
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• 2019

This paper presents an optimization framework of electrical impedance tomography for characterizing electrical conductivity profiles of concrete structures in two dimensions. The framework utilizes a partial-differential-equation(PDE)-constrained optimization approach that can obtain the spatial distribution of electrical conductivity using measured electrical potentials from several electrodes located on the boundary of the concrete domain. The forward problem is formulated based on a complete electrode model(CEM) for the electrical potential of a medium due to current input. The CEM consists of a Laplace equation for electrical potential and boundary conditions to represent the current inputs to the electrodes on the surface. To validate the forward solution, electrical potential calculated by the finite element method is compared with that obtained using TCAD software. The PDE-constrained optimization approach seeks the optimal values of electrical conductivity on the domain of investigation while minimizing the Lagrangian function. The Lagrangian consists of least-squares objective functional and regularization terms augmented by the weak imposition of the governing equation and boundary conditions via Lagrange multipliers. Enforcing the stationarity of the Lagrangian leads to the Karush-Kuhn-Tucker condition to obtain an optimal solution for electrical conductivity within the target medium. Numerical inversion results are reported showing the reconstruction of the electrical conductivity profile of a concrete specimen in two dimensions.

• Journal of IKEEE
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• v.18 no.2
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• pp.272-281
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• 2014

In electrical impedance tomography (EIT), modified Newton Raphson (mNR) method is widely used inverse algorithm for static image reconstruction due to its convergence speed and estimation accuracy. The unknown conductivity distribution is estimated iteratively by minimizing a cost functional such that the residual error namely the difference in measured and calculated voltages is reduced. Although, mNR method has good estimation performance, EIT inverse problem still suffers from ill-conditioned and ill-posedness nature. To mitigate the ill-posedness, generally, regularization methods are adopted. The inverse solution is highly dependent on the choice of regularization parameter. In most cases, the regularization parameter has a constant value and is chosen based on experience or trail and error approach. In situations, when the internal distribution changes or with high measurement noise, the solution does not get converged with the use of constant regularization parameter. Therefore, in this paper, in order to improve the image reconstruction performance, we propose a new scheme to determine the regularization parameter. The regularization parameter is computed based on residual error and updated every iteration. The proposed scheme is tested with numerical simulations and laboratory phantom experiments. The results show an improved reconstruction performance when using the proposed regularization scheme as compared to constant regularization scheme.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.8
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• pp.1430-1435
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• 2016

Electrical impedance tomography (EIT) has been applied to measure the location of external disturbance using a phantom and conductive yarns. According to the test results, the addition of carbon nanotube particles into the phantom does not show remarkable improvement in location errors. On the other hand combined fabric, conductive yarns with fabric, and non-woven fabric, were added to evaluate its performance as a fabric sensor. The combined fabric resulted in a decrease of 21.5% in the circumferential location error and a decrease of 50% in the radial location error, compared to those of the yarns. Additionally, it was revealed that the measurement error is almost linearly proportional to the conductivity of the phantom liquid and resistance of the conductive yarns. The combined fabric can be a promising material for fabric sensors in sports utilities and medical devices.