• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.739-744
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• 2002

Recently we have discovered that sediments should be separated from lithosphere, and soil should be separated from biosphere, both sediment and soil will be mixed sediments-soil-sphere (Seso-sphere), which is using particulate mechanics to be solved. Erosion and sediment both are moving by particulate matter with water or wind. But ancient sediments will be erosion same to soil. Nowadays, real soil has already reduced much more. Many places have only remained sediments that have ploughed artificial farming layer. Thus it means sediments-soil-sphere. This paper discusses sediments-soil-sphere erosion modeling. In fact sediments-soil-sphere erosion is including water erosion, wind erosion, melt-water erosion, gravitational water erosion, and mixed erosion. We have established geographical remote sensing information modeling (RSIM) for different erosion that was using remote sensing digital images with geographical ground truth water stations and meteorological observatories data by remote sensing digital images processing and geographical information system (GIS). All of those RSIM will be a geographical multidimensional gray non-linear equation using mathematics equation (non-dimension analysis) and mathematics statistics. The mixed erosion equation is more complex that is a geographical polynomial gray non-linear equation that must use time-space fuzzy condition equations to be solved. RSIM is digital image modeling that has separated physical factors and geographical parameters. There are a lot of geographical analogous criterions that are non-dimensional factor groups. The geographical RSIM could be automatic to change them analogous criterions to be fixed difference scale maps. For example, if smaller scale maps (1:1000 000) that then will be one or two analogous criterions and if larger scale map (1:10 000) that then will be four or five analogous criterions. And the geographical parameters that are including coefficient and indexes will change too with images. The geographical RSIM has higher precision more than mathematics modeling even mathematical equation or mathematical statistics modeling.

• Korean Management Science Review
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• v.32 no.4
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• pp.109-133
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• 2015

This paper considers the allocation and engagement scheduling of air defense missiles by using MIP (mixed integer programming). Specifically, it focuses on developing a realistic MIP model for a real battle situation where multiple enemy missiles are headed toward valuable defended assets and there exist multiple air defense missiles to counteract the threats. In addition to the conventional objective such as the minimization of surviving target value, the maximization of total intercept altitude is introduced as a new objective. The intercept altitude of incoming missiles is important in order to minimize damages from debris of the intercepted missiles and moreover it can be critical if the enemy warhead contains an atomic or chemical bomb. The concept of so called the time window is used to model the engagement situation and a continuous time is assumed for flying times of the both missiles. Lastly, the model is extended to simulate the situation where the guidance radar, which guides a defense missile to its target, has the maximum guidance capacity. The initial mathematical model developed contains several non-linear constraints and a non-linear objective function. Hence, the linearization of those terms is performed before it is solved by a commercially available software. Then to thoroughly examine the MIP model, the model is empirically evaluated with several test problems. Specifically, the models with different objective functions are compared and several battle scenarios are generated to evaluate performance of the models including the extended one. The results indicate that the new model consistently presents better and more realistic results than the compared models.

• Journal of the Korean Operations Research and Management Science Society
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• v.3 no.2
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• pp.95-108
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• 1978

This paper discusses an application of mathematical programming techniques in the paper industry in determining optimal parent roll widths. Parent rolls are made from the reels produced at wide paper machines by slitting them to more manageable widths. The problem is finding a set of the slitting patterns that will minimize the trim loss involved in the sheeting operation. Two programming models, one linear and one mixed integer linear, are presented in this paper. Also presented are the computational experience, the model sensitivity, and the comparison of the optimal solutions with the simulated operational data.

• 한국데이터정보과학회:학술대회논문집
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• 2006.04a
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• pp.37-37
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• 2006

• Journal of Korean Dental Science
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• v.15 no.1
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• pp.1-8
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• 2022

Purpose: Intraoral scanners, desktop scanners, and cone-beam computed tomography (CBCT) are being used in a complementary way for diagnosis and treatment planning. Limited patient-based results are available about dimensional reproducibility among different three-dimensional imaging systems. This study aimed to evaluate dimensional reproducibility among patient-derived digital models created from an intraoral scanner, desktop scanner, and two CBCT systems. Materials and Methods: Twenty-nine arches from sixteen patients who were candidates for implant treatments were enrolled. Different types of CBCT systems (KCT and VCT) were used before and after the surgery. Polyvinylsiloxane impressions were taken on the enrolled arches after the healing period. Gypsum casts were fabricated and scanned with an intraoral scanner (CIOS) and desktop scanner (MDS). Four test groups of digital models, each from CIOS, MDS, KCT, and VCT, respectively, were compared to the reference gypsum cast group. For comparison of linear measurements, intercanine and intermolar widths and left and right canine to molar lengths were measured on individual gypsum cast and digital models. All measurements were triplicated, and the averages were used for statistics. Bland-Altman plots were drawn to assess the degree of agreement between each test group with the reference gypsum cast group. A linear mixed model was used to analyze the fixed effect of the test groups compared to the reference group (α=0.05). Result: The Bland-Altman plots showed that the bias of each test group was -0.07 mm for CIOS, -0.07 mm for MDS, -0.21 mm for VCT, and -0.25 mm for KCT. The linear mixed model did not show significant differences between the test and reference groups (P>0.05). Conclusion: The linear distances measured on the digital models created from CIOS, MDS, and two CBCT systems showed slightly larger than the references but clinically acceptable reproducibility for diagnosis and treatment planning.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1211-1218
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• 2008

Maximum likelihood estimator (MLE) and restricted maximum likelihood estimator (REMLE) approaches are available in analyzing the linear mixed model (LMM) like bioequivalence trials. US FDA (2001) guides that REMLE may be useful to assess bioequivalence (BE) test. This paper studies the statistical behaviors of the methods in assessing BE tests when some of observations are missing at random. The simulation results show that the REMLE maintains the given nominal level well and the MLE gives a bit higher power. Considering the levels and the powers, the REMLE approach is recommended when the sample sizes are small to moderate and the MLE approach should be used when the sample size is greater than 30.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.193-200
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• 2000

Modelling the dependence via random effects in censored multivariate survival data has recently received considerable attention in the biomedical literature. The random effects models model not only the conditional survival times but also the conditional hazard rate. Systematic likelihood inference for the models with random effects is possible using Lee and Nelder's (1996) hierarchical-likelihood (h-likelihood). The purpose of this presentation is to introduce Ha et al.'s (2000a,b) inferential methods for the random effects models via the h-likelihood, which provide a conceptually simple, numerically efficient and reliable inferential procedures.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.759-771
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• 2005

Twin studies are one of the most widely used methods for quantifying the influence of genetic and environmental factors on some traits such as a life span or a disease. In this paper we propose a genetic mixed linear model for twin survival time data, which allows us to separate the genetic component from the environmental component. Inferences are based upon the hierarchical likelihood (h-likelihood), which provides a statistically efficient and simple unified framework for various random-effect models. We also propose a simple and fast computation method for analyzing a large data set on twin survival study. The new method is illustrated to the survival data in Swedish Twin Registry. A simulation study is carried out to evaluate the performance.

• Journal of the Korean Operations Research and Management Science Society
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• v.11 no.2
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• pp.37-50
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• 1986

In combining dispersed optimization models, either primal or dual(or both) decomposition method widely used as an organizing device. Interpreting the methods economically, the concepts of price and resource-directive coordination are generally well accepted. Most of deomposition/ integration methods utilize either primal information of dual information, not both, from subsystems, while some authors have developed mixed decomposition approaches employing two master problems dealing primal and dual proposals separately. In this paper a hybrid decomposition method is introduced, where one hybrid master problem utilizes the underlying relationships between primal and dual information from each subsystem. The suggested method is well justified with respect to the flexibility in information flow pattern choice (some prices and other quantities) and to the compatibility of subdivision's optimum to the systemwide optimum, that is often lacking in conventional decomposition methods such as Dantzig-Wolfe's. A numerical example is also presented to illustrate the suggested approach.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.11-18
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• 2003

Quite often normality assumptions are not satisfied in practical applications. In this paper, an estimated generalized least squares(EGLS) analysis are considered in two way mixed linear models with arbitrary types of distributions for random effects. We investigate the power performance of EGLS analysis based on Henderson's method III, ML, REML and MINQUE(1). The power performances depend on the imbalance of design, on the actual values of ratio of variance components, and on the skewness and kurtosis parameters of the underlying distributions slightly. Results of our limited simulation study suggest that the EGLS F-statistics using four estimators and arbitrary distributions produce similar type I error rates and power performance.