• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.4
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• pp.491-497
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• 2018

Most of the applications for distribution system operation highly rely on the voltage and current managements from the field devices. Voltage from the remote controlled switch contains unacceptably large measurement error due to the nonlinear characteristics of the bushing potential transformer. This paper proposes a new voltage magnitude estimation method by calculating voltage drop using current measurement, line impedance and loads deployment data. Contract demand power and pole transformer capacity managed by NDIS are used as a key element to improve accuracy of the proposed method. Various case studies using Matlab simulation have been performed to verify feasibility of the propose voltage estimation method.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.531-536
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• 2022

This paper compared the performance of the parametric method and the nonparametric method when estimating the distribution for the tail of the distribution with heavy tails. For the parametric method, the generalized extreme value distribution and the generalized Pareto distribution were used, and for the nonparametric method, the kernel density estimation method was applied. For comparison of the two approaches, the results of function estimation by applying the block maximum value model and the threshold excess model using daily fine dust public data for each observatory in Seoul from 2014 to 2018 are shown together. In addition, the area where high concentrations of fine dust will occur was predicted through the return level.

• Journal of Korean Society for Quality Management
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• v.38 no.4
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• pp.480-490
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• 2010

In this paper, two estimation methods(least square estimation and maximum likelihood estimation) were compared for Weibull distribution and Type I censoring. Data obtained by Monte Carlo simulation were analyzed using two estimation methods and analysis results were compared by MSE(Mean Squared Error). Comparison results show that maximum likelihood estimator is better for censored data and complete data with more than 30 samples and least square estimator is better for small size complete data(less than and equal to 20 samples).

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.71-80
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• 2007

The present paper investigates estimation of a scale parameter of a gamma distribution using a loss function that reflects both goodness of fit and precision of estimation. The Bayes and empirical Bayes estimators rotative to balanced loss functions (BLFs) are derived and optimality of some estimators are studied.

• Journal of Korean Society for Quality Management
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• v.25 no.2
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• pp.1-14
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• 1997

This paper considers maximum likelihood (ML) estimation of lifetime distribution under stress bounded ramp tests in which the stress is increased linearly from used condition stress to the stress u, pp.r bound. The following assumptions are used: exponential lifetime distribution under a constant stress, an inverse power law relationship between stress and mean of exponential lifetime distribution, and a cumulative exposure model for the effect of changing stress. Likelihood equations for the parameters involved in the model and asymptotic distribution of the estimators are obtained, and a numerical example is given.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.1055-1065
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• 2003

We shall propose maximum likelihood, Bayesian and generalized maximum likelihood estimation for the reliability of the two-unit hot standby system with exponential lifetime distribution that switch is perfect. Each estimation will be compared numerically in terms of various mission times, parameter values and asymptotic relative efficiency through Monte Carlo simulation.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.239-250
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• 2004

We shall propose maximum likelihood, Bayesian and generalized maximum likelihood estimation for the reliability of the two-unit hot standby system with Rayleigh lifetime distribution that switch is perfect. Each estimation will be compared numerically in terms of various mission times, parameter values and asymptotic relative efficiency through Monte Carlo simulation.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.987-998
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• 2016

Auto insurance is an insurance product that requires the proper application of pricing techniques due to intense market competition and the rate regulations of financial authorities. Especially, population change according to aging and rating faction segmentation mainly affect the pricing process. This study suggests a pricing optimization methodology through the proper estimation of age factors. To properly estimate the future distribution of age factor, age change, renewal and conversion of customers are considered as main effects for the optimization of estimation and application. The properness and effectiveness for the suggested method will be proved by a comparison of results applied (one for current distribution and the other for future distribution) at the off-balance process. This study suggests an appropriate risk estimation methodology based on optimization that uses the proper estimation of future distribution to protect from the over or under estimation of risk.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.923-931
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• 2005

The two parameter generalized exponential distribution was recently introduced by Gupta and Kundu (1999). It is observed that the generalized exponential distribution can be used quite effectively to analyze skewed data set. This paper develops the accelerated life test model using generalized exponential distribution and considers maximum likelihood estimation of parameters under the tampered random variable model. To show the performance of proposed maximum likelihood estimates, some simulation will be performed. Using a real data set, an example will be given.

• Journal of the Korean Geographical Society
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• v.45 no.6
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• pp.806-819
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• 2010

Population data has been essential and fundamental in spatial analysis and commonly aggregated into political boundaries. A conventional method for population distribution estimation was a regression model with land use data, but the estimation process has limitation because of spatial autocorrelation of the population data. This study aimed to improve the accuracy of population distribution estimation by adopting a Regression-Kriging method, namely RK Model, which combines a regression model with Kriging for the residuals. RK Model was applied to a part of Seoul metropolitan area to estimate population distribution based on the residential zones. Comparative results of regression model and RK model using RMSE, MAE, and G statistics revealed that RK model could substantially improve the accuracy of population distribution. It is expected that RK model could be adopted actively for further population distribution estimation.