• Magazine of the Korean Society of Agricultural Engineers
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• v.44 no.6
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• pp.49-60
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• 2002

This study was conducted to estimate the design flood by the determination of best fitting order of LH-moments of the annual maximum series at six and nine watersheds in Korea and Australia, respectively. Adequacy for flood flow data was confirmed by the tests of independence, homogeneity, and outliers. Gumbel (GUM), Generalized Extreme Value (GEV), Generalized Pareto (GPA), and Generalized Logistic (GLO) distributions were applied to get the best fitting frequency distribution for flood flow data. Theoretical bases of L, L1, L2, L3 and L4-moments were derived to estimate the parameters of 4 distributions. L, L1, L2, L3 and L4-moment ratio diagrams (LH-moments ratio diagram) were developed in this study. GEV distribution for the flood flow data of the applied watersheds was confirmed as the best one among others by the LH-moments ratio diagram and Kolmogorov-Smirnov test. Best fitting order of LH-moments will be derived by the confidence analysis of estimated design flood in the second report of this study.

• Journal of the Korean Society of Clothing and Textiles
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• v.17 no.3
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• pp.459-469
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• 1993

The purpose of this study is to investigate the relationship between body-types and skirt-patterns by analyzing various fitting conditions of the skirt patterns in relation to the body-types. To achieve this, fitting tests were done on six types of skirt-patterns. The tests included four body-types selected according to the existing classification of the types of under part of the body. Sensory tests were performed on the existing patterns and new drafting method based on the results of the fitting tests. The results of the fitting tests and sensory test are as follows. 1. The existing skirt-patterns were relatively well-fitted except for certain parts in standard somatotype (body-type A). 2. The new drafting method, designed on the basis of the fitting test on the existing patterns, scored high on all of the eighteen items included in the test This signifies the excellence of the new drafting method. 3. In the case of the allocation between the front and the back, which is the difference between the educational patterns and industrial patterns, the best silhouette was achieved when the hip-line and the waist-line were same sized both in the back and in the front. However, in the case of body-type D it was better when there was a 1cm difference between the back and the front. 4. The number of darts that best fits young persons were found to be eight, to keep their side hip-curve smooth. Further, it is desirable to set the center line of the dart as the vertical waist-line. 5. The best length of the darts were determined to be 12.5cm, 11ch in the back, and 11cm, 9.5cm in the front. Adjustments would be made according to the differing body-types in the range of 0.5cm. 6. The lowerness of the back waist-line for the normal was determined at 1.5cm, with some variations in other body-types. 7. The front waist-line was needed to be 1cm lowered in the case of body-type B, whereas, in the case of body-type D, it was needed to be 1cm raised.

• Journal of Fashion Business
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• v.17 no.2
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• pp.63-81
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• 2013

A Study using compare and analyze about differences among three patterns(shape and size of pattern, fitting and amount of air gap and so on) through 3D simulation. This study use 3D simulation program, DC Suite version 3.0. The results are as a follows: The results of estimation about total appearance, the pattern of Bunka is best of all pattern about total fitting and silhouette of front and back side. The pattern of armstrong and on&on stand low in estimation. As the total fitting, the pattern of Bunka is 4.40, the pattern of Armstrong is 2.60 and the pattern of On&on is 1.60. The result show better pattern of Bunka than pattern of Armstrong and On&on. When we examine about space between body and cloth, the pattern of Bunka is best. The pattern of Armstrong don't have problems about back side but front side have some problem of getting loose. Because it only have a waist dart so dart size is too big. The pattern of On&on have so much space because it don't have a waist dart. On the amount of air gap, the pattern of Bunka squash up body so it have the amount of air gap 0.08 at bust circumference and underbust circumference. Next is the pattern of Armstrong, amount of air gap is 0.14 at bust circumference and 0.23 at underbust circumference. The pattern of On&on's amount of air gap is 0.30 at bust circumference and 0.37 at underbust circumference. So the pattern of Bunka is bodice block of the best closing adhesion and the On&on is a loose-fitting pattern.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.905-906
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• 2008

This paper presents a method where the best focues points are calculated using line fitting. Two datasets are selected for each pixel based on the maximum value which is calculated using Laplacian operator. Then linear regression model is used to find lines that approximate these datasets. The best fit lines are found using least squares method. After approximating the two lines, their intersection point is calculated and weights are assigned to calculate the new value for the depth map.

• Journal of applied mathematics & informatics
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• v.7 no.3
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• pp.983-994
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• 2000

We consider the problem of determining the circle of best fit to a set of data points in the plane. In [1] and [2] several algorithms already have been given for fitting a circle in least squares sense of minimizing the geometric distances to the given data points. In this paper we present another new descent algorithm which computes a parametric represented circle in order to minimize the sum of the squares of the distances to the given points. For any choice of starting values our algorithm has the advantage of ensuring convergence to a local minimum. Numerical examples are given.

• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.3
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• pp.17-29
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• 1995

Recently, neural network models have been employed as an alternative to regression analysis for point estimation or function fitting in various field. Thus far, however, no theoretical or empirical guides seem to exist for selecting the tool which the most suitable one for a specific function-fitting problem. In this paper, we evaluate performance of three major function-fitting techniques, regression analysis and two neural network models, back-propagation and linear-Hebbian-learning neural networks. The functions to be fitted are simple linear ones of a single independent variable. The factors considered are size of noise both in dependent and independent variables, portion of outliers, and size of the data. Based on comutational results performed in this study, some guidelines are suggested to choose the best technique that can be used for a specific problem concerned.

• Proceedings of the Korea Water Resources Association Conference
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• 1982.07a
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• pp.59-65
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• 1982

• Journal of Korea Multimedia Society
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• v.18 no.7
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• pp.827-833
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• 2015

In this paper, we have compared three level set-based active contour (LSAC) methods on inhomogeneous MR image segmentation which is known as an important role of brain diseases to diagnosis and treatment in early. MR image is often occurred a problem with similar intensities and weak boundaries which have been causing many segmentation methods. However, LSAC method could be able to segment the targets such as the level set based on the local image fitting energy, the local binary fitting energy, and local Gaussian distribution fitting energy. Our implemented and tested the subcortical image segmentations were the corpus callosum and hippocampus and finally demonstrated their effectiveness. Consequently, the level set based on local Gaussian distribution fitting energy has obtained the best model to accurate and robust for the subcortical image segmentation.

• Journal of Korean Ophthalmic Optics Society
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• v.13 no.2
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• pp.9-16
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• 2008

To assess the preference and efficacy of empirical fitting methods with spheric and aspheric RGP lenses. Methods: Healthy 37 subjects were fitted with spheric design (diameter 9.3 mm) on right eye and aspheric design (dia 9.6 mm) on the left eye. Base curves which were fitted empirically (using on-K, Kavg-0.50D (or 1.00D) and manufacturer's recommended fitting guide) were compared with another base curve which obtained the best diagnostic fit with spheric and aspheric RGP lenses. The preference and fitting type (lid attachment or interpalpebral) for two design lenses were investigated 2 weeks after fitting RGP lenses. Results: Of 33 successful RGP lens-wearing subjects, 76% preferred spheric design compared with 24% of aspheric RGP lens wearers. Sixty seven percent were fitted with lid-attachment in spheric lenses, whereas 64% were fitted with lid-attachment in aspheric lenses. The acceptable fit success rates within ${\pm}$0.50D of base curves were 97% for the on-K fit, 100% for the Kavg-0.50D fit and 100% of the manufacturer's guide fit compared with the diagnostic fit in spheric design, whereas 91%, 79% and 94% reported on-K, Kavg-1.00D and manufacturer's guide, respectively, in aspheric design. Conclusions: Although aspheric RGP lenses are more popular in the Korean market, it is still preferable to fit subjects with spheric RGP lenses. Empirical fitting may be best accomplished with the spheric lenses using Kavg-0.50D fit and the manufacturer's fitting guide, whereas aspheric RGP lens designs are unacceptable lens fit based on empirical fitting.

• Communications of the Korean Mathematical Society
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• v.34 no.3
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• pp.1029-1047
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• 2019

We are interested in the problem of determining the best fitted circle to a set of data points in space. This can be usually obtained by minimizing the geometric distances or various approximate algebraic distances from the fitted circle to the given data points. In this paper, we propose an algorithm in such a way that the sum of the squares of the geometric distances is minimized in ${\mathbb{R}}^3$. Our algorithm is mainly based on the steepest descent method with a view of ensuring the convergence of the corresponding objective function Q(u) to a local minimum. Numerical examples are given.