• International Journal of Contents
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• v.1 no.1
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• pp.1-5
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• 2005

This paper presents the development and application of electrical resistance imaging techniques for the visualization of two-phase flow fields. Two algorithms, the so-called the mesh grouping and the boundary estimation, are described for potential applications of electrical resistance tomography (ERT) and results from extensive numerical simulations are also presented. In the electrical resistance imaging for two-phase flows, numerical meshes fairly belonging to each phase can be grouped to improve the reconstruction performance. In many cases, the detection of phase boundary is a key subject and a mathematical model to estimate phase boundary can be formulated in a different manner. Our results indicated that the mesh grouping algorithm is effective to enhance computational performance and image quality, and boundary estimation algorithm to determine the phase boundary directly.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.7
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• pp.427-433
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• 1988

This paper presents a partitioned state estimation algorithm on the basis of network reduction by using a estimation technique of boundary line flows. The network is partitioned into several subnetworks, which generates boundary lines. The accurate estimation of boundary line flows enables us to perform state estimation on each sub-system independently. A precise method to estimate boundary line flows is presented for the partitioned state estimation. The proposed algorithm redices computation time and memory requirements remarkably. The proposed algorithm have been tested for IEEE sample system and verified to be applicable to practical power systems.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.118-121
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• 2003

In the conventional boundary estimation in EIT (Electrical Impedance Tomography), the interface between anomalies and background is expressed in usual as Fourier series and the boundary is reconstructed by obtaining the Fourier coefficients. This paper proposes a method for the boundary estimation, where the boundary of anomaly is approximated as the interpolation of front points located discretely along the boundary and is imaged by tracking the points in the region of interest. In the solution to the inverse problem to estimate the front points, the multi-layer neural network is introduced. For the verification of the proposed method, numerical experiments are conducted and the results indicate a good performance.

• Journal of IKEEE
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• v.25 no.4
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• pp.650-663
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• 2021

Subsurface topology estimation is an important factor in the geophysical survey. Electrical impedance tomography is one of the popular methods used for subsurface imaging. The EIT inverse problem is highly nonlinear and ill-posed; therefore, reconstructed conductivity distribution suffers from low spatial resolution. The subsurface region can be approximated as piece-wise separate regions with constant conductivity in each region; therefore, the conductivity estimation problem is transformed to estimate the shape and location of the layer boundary interface. Each layer interface boundary is treated as an open boundary that is described using front points. The subsurface domain contains multi-layers with very complex configurations, and, in such situations, conventional methods such as the modified Newton Raphson method fail to provide the desired solution. Therefore, in this work, we have implemented a 7-layer artificial neural network (ANN) as an inverse problem algorithm to estimate the front points that describe the multi-layer interface boundaries. An ANN model consisting of input, output, and five fully connected hidden layers are trained for interlayer boundary reconstruction using training data that consists of pairs of voltage measurements of the subsurface domain with three-layer configuration and the corresponding front points of interface boundaries. The results from the proposed ANN model are compared with the gravitational search algorithm (GSA) for interlayer boundary estimation, and the results show that ANN is successful in estimating the layer boundaries with good accuracy.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.553-558
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• 2003

The boundary estimation problem is used to estimate the shape of organic depend on the phase of the cardiac cycle or interested in the detection of the location and size of anomalies with resistivity values different from the background tissues such as nuclear reactor. And we can use the method to solve the optimal solution such as modified Newton raphson, kalman filter, extended kalman filter, etc. But, this method consumes much time and is sensitive to the initial value and noise in the estimation of the unknown shape. In the paper, we propose that multi-layer neural networks estimate the boundary of the unknown object using Fourier coefficient. This method can be used at the real time estimation and have strong characteristics at the noise and initial value. It uses voltage change; difference the homogeneous voltage to the non-homogeneous voltage, and change of Fourier coefficient change to train multi-layer neural network. After train, we can have real time estimation using this method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.1
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• pp.185-192
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• 1997

We propose a method of paramete estimation using order-of-magnitude analysis for optimal boundary smoothing in Mean Field Annealing(MFA) technique in this paper. We previously proposed two boundary smoothing methods for consistent object representation in the previous paper, one is using a constratined regulaization(CR) method and the other is using a MFA method. The CR method causes unnecessary smoothing effects at corners. On the other hand, the MFA method method smooths our the noise without losing sharpness of corners. The MFA algorithm is influenced by several parameters such as standard deviation of the noise, the relativemagnitude of prior ter, initial temperature and final temperature. We propose a general parameter esimation method for optimal boundary smoothing using order-of-magnitude analysis to be used for consistent object representation in this paper. In addition, we prove the effectiveness of our parameter estimation and also show the temperature parameter sensitivities of the algorithm.

• Magazine of the Korean Society of Agricultural Engineers
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• v.11 no.3
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• pp.1742-1748
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• 1969

This is a method to determine the boundary line of reservoir flooding area which will be purchased. Until now, flood water level was used as the boundary line. By lowering this line from flood water level, purchasing cost of reservoir flooding area can be cut down. Sometimes, temporary flooding of arable land outside the boundary occurs. During the life of reservoir, flood damage to crop product on of this land must be indemified with net berefit from arable land between the bovndary line and normal water level. Following is the basic formula to determine the line. (Estimated flood damage to crop production of land outside the boundary line $\leqq$ Estimated net beneift from land between the boundary line and normal water level.) Minimum difference between both sides is needed to minimize the purchasing area. Flood damage and net benefit are estimated by hydrologic estimation with rainfall data and crop production estimation.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.570-574
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• 2003

In most boundary estimation algorithms estimation in EIT (Electrical Impedance Tomography), anomaly boundaries can be expressed with Fourier series and the unknown coefficients are estimated with proper inverse algorithms. Furthermore, the number of anomalies is assumed to be available a priori. The prior knowledge on the number of anomalies may be unavailable in some cases, and we need to determine the number of anomalies with other methods. This paper presents an algorithm for the boundary estimation in EIT (Electrical Impedance Tomography) using the prior information from the conventional Newton-Raphson method. Although Newton-Raphson method generates so poor spatial resolution that the anomaly boundaries are hardly reconstructed, even after a few iterations it can give general feature of the object to be imaged such as the number of anomalies, their sizes and locations, as long as the anomalies are big enough. Some numerical experiments indicate that the Newton-Raphson method can be used as a good predictor of the unknown boundaries and the proposed boundary discrimination algorithm has a good performance.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.2
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• pp.59-68
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• 1993

The motion vector estimation and motion boundary detection have been briskly studied since they are an important clue for analysis of object structure and 3-d motion. The purpose of this researches is more exact estimation, but there are two main causes to make inaccurate. The one is the erroneous measurement of gradients in brightness values and the other is the blurring of motion boundries which is caused by the smoothness constraint. In this paper, we analyze the gradient measurement error of conventional methods and propose new technique based on it. When the proposed method is applied to the motion boundary detection in Schunck and motion vector estimation in Horn & Schunck, it is shown to have much better performance than conventional method is some artificial and real image sequences.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.30 no.3 s.246
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• pp.270-277
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• 2006

Inverse radiation problems were solved for estimating boundary temperature distribution in a way of function estimation approach in an axisymmetric absorbing, emitting and scattering medium, given the measured radiative data. In order to reduce the computational time fur the calculation of sensitivity matrix, automatic differentiation and Broyden combined method were adopted, and their computational precision and efficiency were compared with the result obtained by finite difference approximation.. In inverse analysis, the effects of the precision of sensitivity matrix, the number of measurement points and measurement error on the estimation accuracy had been inspected using quasi-Newton method as an inverse method. Inverse solutions were validated with the result acquired by additional inverse methods of conjugate-gradient method or Levenberg-Marquardt method.