• Journal of Korea Multimedia Society
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• v.9 no.9
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• pp.1150-1159
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• 2006

This paper proposed a curve progress control function of the area base instead of the existing edge indication function, in order to detect the brain ventricle area by utilizing a geodesic active contour model. The proposed curve progress control function is very effective in detecting the brain ventricle area and this function is based on the average brightness of the brain ventricle area which appears brighter in MRI images. Compared numerically by using various measures, the proposed method in this paper can detect brain ventricle areas better than the existing method. By examining images of normal and diseased brain's images by brain tumor, we compared the several brain ventricle detection algorithms with proposed method visually and verified the effectiveness of the proposed method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.39-49
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• 2013

This paper presents a method of improving the performance of a day-ahead 24-h load curve and peak load forecasting. The next-day load curve is forecasted using radial basis function (RBF) neural network models built using the best design parameters. To improve the forecasting accuracy, the load curve forecasted using the RBF network models is corrected by the weighted sum of both the error of the current prediction and the change in the errors between the current and the previous prediction. The optimal weights (called "gains" in the error correction) are identified by differential evolution. The peak load forecasted by the RBF network models is also corrected by combining the load curve outputs of the RBF models by linear addition with 24 coefficients. The optimal coefficients for reducing both the forecasting mean absolute percent error (MAPE) and the sum of errors are also identified using differential evolution. The proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange. Simulation results reveal satisfactory forecasts: 1.230% MAPE for daily peak load and 1.128% MAPE for daily load curve.

• Journal of ILASS-Korea
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• v.4 no.1
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• pp.55-61
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• 1999

The first maximum point in the stability curve of liquid jet, i.e., the critical point is associated with the critical Reynolds number. This critical Reynolds number should be predicted by simple means. In this work, the critical Reynolds number in the stability curve of liquid jet are predicted using the empirical correlations and the experimental data reported in the literatures. The critical Reynolds number was found to be a function of the Ohnesorge number, nozzle lengh-to-diameter ratio, ambient Weber number and nozzle inlet type. An empirical correlation for the critical Reynolds number as a function of the Ohnesorge number and nozzle length-to-diameter ratio is newly proposed here. Although an empirical correlation proposed in this work may not be universal because of excluding the effects of ambient pressure and nozzle inlet type, it has reasonably agrees with the measured critical Reynolds number.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.1
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• pp.18-20
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• 2017

In this paper, we propose a new classification method to complement a $na{\ddot{i}}ve$ Bayesian classifier. This classifier assumes data distribution to be Gaussian, finds the discriminant function, and derives the decision curve. However, this method does not investigate finding the decision curve in much detail, and there are some minor problems that arise in finding an accurate discriminant function. Our findings also show that this method could produce errors when finding the decision curve. The aim of this study has therefore been to investigate existing problems and suggest a more reliable classification method. To do this, we utilize the gradient to find the decision curve. We then compare/analyze our algorithm with the $na{\ddot{i}}ve$ Bayesian method. Performance evaluation indicates that the average accuracy of our classification method is about 10% higher than $na{\ddot{i}}ve$ Bayes.

• Advances in materials Research
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• v.11 no.4
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• pp.351-374
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• 2022

Softening function is the primary input for modeling the fracture of concrete when the cohesive crack approach is used. In this paper, based on the laboratory data on notched beams, an inverse algorithm is proposed that can accurately find the softening curve of the concrete. This algorithm uses non-linear finite element analysis and the damage-plasticity model. It is based on the kinematics of the beam at the late stages of loading. The softening curve, obtained from the corresponding algorithm, has been compared to other softening curves in the literature. It was observed that in determining the behavior of concrete, the usage of the presented curve made accurate results in predicting the peak loads and the load-deflection curves of the beams with different concrete mixtures. In fact, the proposed algorithm leads to softening curves that can be used for modeling the tensile cracking of concrete precisely. Moreover, the advantage of this algorithm is the low number of iterations for converging to an appropriate answer.

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.71-80
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• 2001

Logistic regression model is one of the most popular linear models for a binary response variable and used for the estimation of probability function. In many practical situations, the probability function can be expressed by a bell shaped curve and such a function can be estimated by a second order logistic regression model. However, when the probability curve is asymmetric, the estimation results using a second order logistic regression model may not be precise because a second order logistic regression model is a symmetric function. In addition, even if a second order logistic regression model is used, the interpretation for the effect of second order term may not be easy. In this paper, in order to alleviate such problems, an estimation method for asymmetric probabiity curve based on a first order logistic regression model and iterative bi-section method is proposed and its performance is compared with that of a second order logistic regression model by a simulation study.

• Journal of the Korea Computer Graphics Society
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• v.16 no.4
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• pp.41-50
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• 2010

We present a new technique for multilevel editing of hierarchical B-spline curves. At each level, control points of a displacement function are expressed in the rotation minimizing frames (RMFs) [1] which are computed on nodal points of the curve at previous level. When the curve is edited at previous level, the corresponding RMFs are updated and the control points of the displacement function at current level are applied to the new RMFs, which maintains the relative details of the curve at current level to those of previous level. We demonstrate the effectiveness and robustness of the proposed technique using several experimental results.

• Journal of the Korean Geotechnical Society
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• v.29 no.2
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• pp.57-64
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• 2013

In this paper, in order to solve high water content of water sludge and promote its recycle, sludge mixtures with various mixing ratios were produced. Sludge mixture consisted of water sludge and weathered granite soil. Their physical properties and unsaturated characteristics (soil-water characteristic curve, and unsaturated permeability function) were investigated by laboratory tests. Experimental test results indicated that at a given matric suction volumetric water content of sludge mixture increased as water sludge content increased. Air entry values of sludge mixture increased from 0.9 kPa to 2.4 kPa with an increase in water sludge content or fine content. In addition, unsaturated permeability function, which is an important factor for performing infiltration analysis, was predicted using saturated permeability and soil-water characteristic curve of sludge mixture.

• Journal of the Korean Solar Energy Society
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• v.33 no.4
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• pp.51-58
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• 2013

A study on power performance testing of a wind turbine which has no met-mast at a distance of 2~4 rotor diameter was carried out using the Nacelle Transfer Function, NTF, according to IEC 61400-12-2. The wind data for this study was measured at HanKyoung wind farm of Jeju Island. The NTF was modeled using the correlation between wind speeds from the met-mast and from the wind turbine nacelle within 2~4 rotor diameter from the met-mast. The NTF was verified by the comparison of estimated Annual Energy Productions, AEPs, and binned power curves. The Nacelle Power Curve, NPC, was derived from the nacelle wind speed data corrected by NTF. The NPC of wind turbine under test and the power curve offered by the turbine manufacturer were compared to check whether the wind turbine is properly generating electricity. Overall the NPC was in good agreement with the manufacturer's power curve. The result showed power performance testing for a wind turbine which has no met-mast at a distance of 2~4 rotor diameter was successfully carried out in compliance with IEC 61400-12-2.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.05a
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• pp.334-337
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• 2006

Generally, fragility curve has been used in predicting failure of structures due to seismic actions. In this research, the method of drawing fragility curve has been applied to evaluating success/failure of structures and satisfactory/unsatisfactory of concrete mixture performance based on material parameters. In the paper, a detailed explanation of the procedure of drawing fragility curve based on material parameter has been introduced. Fragility curve generating procedure includes generation of virtual data points from limited number of actual data points by bell curve implementation, determination of success/failure status of each data point by assigned criterion, and completion of final fragility curve. For practical applications, workability of concrete mixture content based on "unit water" has been used to obtain fragility curve. Detailed explanation of fragility curve drawing procedure for material parameters is presented.