• 한국정보컨버전스학회:학술대회논문집
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• 2008.06a
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• pp.113-116
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• 2008

In this paper we suggest two novel methods for an implementation of the spot detection phase in the 2-DE gel image analysis program. The one is the adaptive thresholding method for eliminating noises and the other is the asymmetric diffusion model for spot matching. Remained noises after the preprocessing phase cause the over-segmentation problem by the next segmentation phase. To identify and exclude the over-segmented background regions, il we use a fixed thresholding method that is choosing an intensity value for the threshold, the spots that are invisible by one's human eyes but mean very small amount proteins which have important role in the biological samples could be eliminated. Accordingly we suggest the adaptive thresholding method which comes from an idea that is got on statistical analysis for the prominences of the peaks. There are the Gaussian model and the diffusion model for the spot shape model. The diffusion model is the closer to the real spot shapes than the Gaussian model, but spots have very various and irregular shapes and especially asymmetric formation in x-coordinate and y-coordinate. The reason for irregularity of spot shape is that spots could not be diffused perfectly across gel medium because of the characteristics of 2-DE process. Accordingly we suggest the asymmetric diffusion model for modeling spot shapes. In this paper we present a brief explanation ol the two methods and experimental results.

• Korean Journal of Computational Design and Engineering
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• v.14 no.6
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• pp.364-373
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• 2009

In this paper, we suggest the method for constructing parameterized human body model which has any required body sizes from 3D scan data. Because of well developed 3D scan technology, we can get more detailed human body model data which allow to generate precise human model. In this field, there are a lot of research is performed with 3D scan data. But previous researches have some limitations to make human body model. They need too much time to perform hole-filling process or calculate parameterization of model. Even more they missed out verification process. To solve these problems, we used several methods. We first choose proper 125 3D scan data from 5th Korean body size survey of Size Korea according to age, height and weight. We also did post process, feature point setting, RBF interpolation and align, to parameterize human model. Then principal component analysis is adapted to the result of post processed data to obtain dominant shape parameters. These steps allow to reduce process time without loss of accuracy. Finally, we compare these results and statistical data of Size Korea to verify our parameterized human model.

• Communications for Statistical Applications and Methods
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• v.19 no.6
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• pp.809-817
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• 2012

We propose a semiparametric inference for a generalized varying coefficient partially linear model(VCPLM) for negative binomial data. The VCPLM is useful to model real data in that varying coefficients are a special type of interaction between explanatory variables and partially linear models fit both parametric and nonparametric terms. The negative binomial distribution often arise in modelling count data which usually are overdispersed. The varying coefficient function estimators and regression parameters in generalized VCPLM are obtained by formulating a penalized likelihood through smoothing splines for negative binomial data when the shape parameter is known. The performance of the proposed method is then evaluated by simulations.

• Communications for Statistical Applications and Methods
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• v.24 no.2
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• pp.129-142
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• 2017

In this paper we consider the problem of the model selection/discrimination among three different positively skewed lifetime distributions. Lindley, Weibull, and gamma distributions have been used to effectively analyze positively skewed lifetime data. This paper assesses how much closer the Lindley distribution gets to Weibull and gamma distributions. We consider three techniques that involve the likelihood ratio test, asymptotic likelihood ratio test, and minimum Kolmogorov distance as optimality criteria to diagnose the appropriate fitting model among the three distributions for a given data set. Monte Carlo simulation study is performed for computing the probability of correct selection based on the considered optimality criteria among these families of distributions for various choices of sample sizes and shape parameters. It is observed that overall, the Lindley distribution is closer to Weibull distribution in the sense of likelihood ratio and Kolmogorov criteria. A real data set is presented and analyzed for illustrative purposes.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.2
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• pp.179-184
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• 2015

In the recent field of Machining, with the improving efficiency of processing, the index table is a key unit according to the increase of parts in available processing when working with the three axes at the same time. As an essential product of MCT, the index tables effect an influence on product quality of machined parts. Therefore, it is necessary to design the shape of index table with stability, high stiffness, lightweight structure. In this study, the optimal shape of index table was proposed using by design of experiment. The maximum displacement and stress analysis were carried out by using FEM software. The optimized shape was verified by using the statistical software. The results of shape optimization were confirmed that both displacement and stress were reduced in comparison with initial model.

• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.463-477
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• 2009

Parameter estimation methods such as maximum likelihood estimation method, probability weighted moments method, regression method have been popularly applied to various extreme value models in numerous literature. Among three methods above, the performance of regression method has not been rigorously investigated yet. In this paper the regression method is compared with the other methods via Monte Carlo simulation studies for estimation of parameters of the Generalized Extreme Value(GEV) distribution and the Generalized Pareto(GP) distribution. Our simulation results indicate that the regression method tends to outperform other methods under small samples by providing smaller biases and root mean square errors for estimation of location parameter of the GEV model. For the scale parameter estimation of the GP model under small samples, the regression method tends to report smaller biases than the other methods. The regression method tends to be superior to other methods for the shape parameter estimation of the GEV model and GP model when the shape parameter is -0.4 under small and moderately large samples.

• Journal of Korea Multimedia Society
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• v.12 no.3
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• pp.383-396
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• 2009

Bone age assessment has been widely used in pediatrics to identify endocrine problems of children. Since the number of trained doctors is far less than the demands, there has been numerous requests for automatic estimation of bone age. Therefore, in this paper, we propose an automatic bone age assessment method that utilizes pattern classification techniques. The proposed method consists of three modules; a finger segmentation module, a normalized shape model generation module and a bone age estimation module. The finger segmentation module segments fingers and epiphyseal regions by means of various image processing algorithms. The shape model abstraction module employ ASM to improves the accuracy of feature extraction for bone age estimation. In addition, SVM is used for estimation of bone age. Features for the estimation include the length of bone and the ratios of bone length. We evaluated the performance of the proposed method through statistical analysis by comparing the bone age assessment results by clinical experts and the proposed automatic method. Through the experimental results, the mean error of the assessment was 0.679 year, which was better than the average error acceptable in clinical practice.

• Journal of Technologic Dentistry
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• v.42 no.4
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• pp.321-325
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• 2020

Purpose: This study is to evaluate the accuracy of gypsum replica models made from various gypsum products. Methods: One main model was made of stainless steel by CNC milling process. Molds were formed from the main model, and the gypsum replica models were made using 8 types of type IV gypsum, 10 pieces each. The main model was digitized by a contact scanner (Incise; Renishaw) and the gypsum replicas were digitized by an optical scanner (E4; 3Shape A/S). The difference between the main model and the gypsum replicas were measured by inspection software (3D Systems). One-way ANOVA was performed to evaluate the statistical significance of differences between groups. In addition, the independent sample T test was performed to determine the difference between the conventional and scannable stone group (n=10, α=0.05). Results: The root mean square of the stone models were 7.24 ㎛ to 10.78 ㎛, and statistical significance was found between the two groups (SR, FR) and the other 6 groups (IS, SG, CA, CS, ER, EBG) (p<0.05). The accuracy of the gypsum replicas was 9.04 ㎛ and 7.62 ㎛ in the conventional and scannable stone group, respectively. There was statistical significance between the two groups (p<0.01). Conclusion: In the limited results of this study, the product with low setting expansion and the scannable showed high accuracy. Therefore, in order to obtain a stable and accurate scan model, it is more effective in terms of accuracy to use a scannable stone with a low setting expansion.

• Journal of Communications and Networks
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• v.5 no.4
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• pp.309-318
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• 2003

This paper presents an in-depth study of a UWB indoor radio channel between 1 and 9 GHz, which was used for the subsequent development of a new statistical UWB multipath channel model, focusing on short range indoor scenarios. The channel sounding process was carried out covering different indoor environments, such as laboratories, halls or corridors. A combination of new and traditional parameters has been used to accurately model the channel impulse response in order to perform a precise temporal estimation of the received pulse shape. This model is designed specifically for UWB digital systems, where the received pulse is correlated with an estimated replica of itself. The precision of the model has been verified through the comparison with measured data from equivalent scenarios and cases, and highly satisfactory results were obtained.

• International Journal of Reliability and Applications
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• v.15 no.1
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• pp.65-76
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• 2014

In this article, a new model based on Lomax distribution is introduced. This new model is both useful and practical in areas such as economic, reliability and life testing. Some statistical properties of this model are presented including moments, hazard rate, reversed hazard rate, mean residual life and mean inactivity time functions, among others. It is also shown that the distributions of the new model are ordered with respect to the strongest likelihood ratio ordering. The method of moment and maximum likelihood estimation are used to estimates the unknown parameters. Simulation is utilized to calculate the unknown shape parameter and to study its properties. Finally, to illustrate the concepts, the appropriateness of the new model for real data sets are included.