• Proceedings of the KIEE Conference
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• 2008.04c
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• pp.106-109
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• 2008

Piecewise linear diode models are widely used for large-signal circuit analyses, especially power electronic circuit simulations. When using a piecewise linear diode model for simulation, a switching method to select a proper one among linear models is needed. The conventional switching method keeps the previous ON, OFF state information, and applies different switching conditions according to the state. However, this method has difficulties especially in extending to multi-piecewise linear models. This paper presents a switching method which appropriately divides the v-i plane into regions and select a linear model according to the region where the operating point(the voltage and the current of the diode) belongs. This switching method is easily extended to multi-Piecewise linear models. An example using the tableau analysis and the backward Euler integration is presented for verification.

• Communications for Statistical Applications and Methods
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• v.6 no.2
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• pp.523-532
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• 1999

Generalized linear models have various applications for data arising from many kinds of statistical studies. Although the response variable is generally assumed to be generated from a wide class of probability distributions we focus on count data that are most often analyzed using binomial models for proportions or poisson models for rates. The methods and results presented here also apply to many other categorical data models in general due to the relationship between multinomial and poisson sampling. The novelty of the approach suggested here is that all conditional distribution s can be specified directly so that staraightforward Gibbs sampling is possible. The prior distribution consists of two stages. We rely on a normal nonconjugate prior at the first stage and a vague prior for hyperparameters at the second stage. The methods are demonstrated with an illustrative example using data collected by Rosenkranz and raftery(1994) concerning the number of hospital admissions due to back pain in Washington state.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.10a
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• pp.295-302
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• 2002

This paper introduces a method to determine strut-tie models in reinforced concrete (RC) structures using the evolutionary structural optimization (ESO). Even though strut-tie models are broadly adapted in design of reinforced concrete members subjected to shear and torsion, conventional methods can hardly give correct models in RC members subjected to complex loadings and geometry conditions. In this paper, the basic idea of the ESO method is used to determine more rational strut-tie models. Since an optimum topology of structures, finally obtained by the ESO method, usually represents a truss-like structure, the ESO method can effectively be used in finding the best strut-tie model in RC structures. Several example structures are provided to demonstrate the capability of the proposed method in finding the best strut-tie model of each RC structure and to verify its efficiency in application to real design problems.

• Communications for Statistical Applications and Methods
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• v.26 no.4
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• pp.359-370
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• 2019

Grouping structures in covariates are often ignored in regression models. Recent statistical developments considering grouping structure shows clear advantages; however, reflecting the grouping structure on the quantile regression model has been relatively rare in the literature. Treating the grouping structure is usually conducted by employing a group penalty. In this work, we explore the idea of group penalty to the quantile regression models. The grouping structure is assumed to be known, which is commonly true for some cases. For example, group of dummy variables transformed from one categorical variable can be regarded as one group of covariates. We examine the group quantile regression models via two real data analyses and simulation studies that reveal the beneficial performance of group quantile regression models to the non-group version methods if there exists grouping structures among variables.

• Communications for Statistical Applications and Methods
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• v.18 no.3
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• pp.319-331
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• 2011

In this paper, we propose automatic procedures for the model selection of various univariate time series data. Automatic model selection is important, especially in data mining with large number of time series, for example, the number (in thousands) of signals accessing a web server during a specific time period. Several methods have been proposed for automatic model selection of time series. However, most existing methods focus on linear time series models such as exponential smoothing and autoregressive integrated moving average(ARIMA) models. The key feature that distinguishes the proposed procedures from previous approaches is that the former can be used for both linear time series models and nonlinear time series models such as threshold autoregressive(TAR) models and autoregressive moving average-generalized autoregressive conditional heteroscedasticity(ARMA-GARCH) models. The proposed methods select a model from among the various models in the prediction error sense. We also provide an R package autots that implements the proposed automatic model selection procedures. In this paper, we illustrate these algorithms with the artificial and real data, and describe the implementation of the autots package for R.

• Journal of the Korea Computer Graphics Society
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• v.5 no.1
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• pp.11-17
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• 1999

This paper proposes example guided inverse kinematics (EGIK) which extends and enhances existing inverse kinematics technique. In conventional inverse kinematics, redundancy in the model produces an infinite number of solutions. The motion could be jerky depending on the choice of solutions at each frame. EGIK exploits the redundancy for imitating an example motion (a premeasured motion data) so that a unique solution is chosen. To minimize the gap between the goal and current end-effector position and imitate the original motion at the same time, nonlinear optimization technique is employed. So, the resulting motion resembles the original one in an optimal sense. Experiments prove that the method is a robust and effective technique to animate high DOF articulated models from an example motion.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.209-216
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• 2003

Contingent valuation method (CVM) is a main evaluation method of nonmarket goods for which markets either do not exist at all or do exist only incompletely; an example is environmental good. A dichotomous choice approach, the most popular type of CVM in environmental economics, employs binary discrete choice models as statistical estimation models. In this paper, we propose a semiparametric dichotomous choice CVM method using local linear model of Fan and Gijbels (1996) in which probability distribution of error term is specified parametrically but latent structural function is specified nonparametrically. The computation procedures of the proposed method are illustrated with a simple design of simulations.

• Korean Management Science Review
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• v.19 no.2
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• pp.91-124
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• 2002

The use of network planning models and tools is essential for effective KII (Korea Information Infrastructure) planning and analysis in that it will significantly reduce the risk and uncertainty embeded in the development and the provision of future broadband services. The purpose of this study is to develop a theoretical framework and a computer tool for modeling the various aspects of the KII topology and architecture and evaluating the techno-economic feasibility of the KII implementation strategy.

• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.99-106
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• 2010

In this paper, we present two methods for obtaining prediction intervals for the times to failure of units censored in multiple stages in a progressively censored sample from proportional hazard rate models. A numerical example and a Monte Carlo simulation study are presented to illustrate the prediction methods.

• Journal of the Korea Society for Simulation
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• v.3 no.1
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• pp.75-88
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• 1994

Discrete event modeling is finding ever more application to anlysis and design of complex manufacturing, communication, computer systems, etc. This paper shows how packet network systems may be advantageously represented as DEVS (Discrete Event System Specification) models by employing System Entity structure / Model base (SES/MB) framework developed by Zeigler. DEVS models and network structure representations support a strong basis for performance analysis of packet network systems. This approach is illustated in a typical packet network example with several routing strategies.