• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.49.1-49
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• 2001

The skin color model is a very important concept in face detection, face recognition and face tracking. Usually, this model is obtained by estimating a probability density function of skin color distribution. In many cases, it is assumed that the underlying density function follows a Gaussian distribution. In this paper, a new method for non-parametric estimation of the probability density function, by using feed-forward neural network, is used to estimate the underlying skin color model. By using this method, the resulting skin color model is better than the Gaussian estimation and substantially approaches the real distribution. Applications to face detection and face ...

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.713-723
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• 2013

An objective Bayesian estimation procedure of the two-parameter Pareto distribution is presented under the reference prior and the noninformative prior. Bayesian estimators are obtained by Gibbs sampling. The steps to generate parameters in the Gibbs sampler are from the shape parameter of the gamma distribution and then the scale parameter by the adaptive rejection sampling algorism. A numerical study shows that the proposed objective Bayesian estimation outperforms other estimations in simulated bias and mean squared error.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.977-989
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• 2011

The half logistic distribution has been used intensively in reliability and survival analysis especially when the data is censored. In this paper, we provide Bayesian estimation of the shape parameter and reliability function in the generalized half logistic distribution based on progressively Type-II censored data under various loss functions. We here consider conjugate prior and noninformative prior and corresponding posterior distributions are obtained. As an illustration, we examine the validity of our estimation using real data and simulated data.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.597-603
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• 2011

The half logistic distribution has been used intensively in reliability and survival analysis especially when the data is censored. In this paper, we provide prole likelihood estimation of the shape parameter and scale parameter in the generalized half logistic distribution based on progressively Type-II censored data. We also introduce approximate maximum prole likelihood estimates for the scale parameter. As an illustration, we examine the validity of our estimation using real data and simulated data.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1188-1191
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• 1998

Detailed estimation of subsurface resistivity distribution and accurate estimation of actual fault current coming into the grounding system are indispensible to optimun grounding system design. Especially, it is essential for efficient grounding design to estimate subsurface resistivity distribution quantitatively and logically. Accurate estimation of subsurface resistivity distribution has an absolute influence on calculating touch voltage, step voltage and ground potential rise (GPR) which are related with grounding design standard for human safety. In this study, thirty-three electrical sounding surveys were made in Yongdam Power Station to obtain detailed subsurface resistivity distribution and the sounding data were interpreted quantitatively using multi-layered model. The results of the quantitative resistivity models were adopted practically to calculate grounding resistance values. Analytical asymptotic equations and CDEGS program were used in grounding resistance calculation and the results were compared and reviewed in the study.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.623-632
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• 2014

We shall consider the estimation for the parameter and the right tail probability in a general exponential distribution. We also shall consider the estimation of the reliability P(X < Y ) and the skewness trends of the density function of the ratio X=(X+Y) for two independent general exponential variables each having different shape parameters and known scale parameter. We then shall consider the estimation of the failure rate average and the hazard function for a general exponential variable having the density function with the unknown shape and known scale parameters, and for a bivariate density induced by the general exponential density.

• Journal of Integrative Natural Science
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• v.5 no.3
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• pp.186-189
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• 2012

For the mean vector of a p-variate normal distribution ($p{\geq}3$), the optimal estimation within the class of James-Stein type decision rules under the quadratic loss are given when the underlying distribution is that of a variance mixture of normals and when the norm ${\parallel}\underline{{\theta}}{\parallel}$ in known. It also demonstrated that the optimal estimation within the class of Lindley type decision rules under the same loss when the underlying distribution is the previous type and the norm ${\parallel}{\theta}-\overline{\theta}\underline{1}{\parallel}$ with $\overline{\theta}=\frac{1}{p}\sum\limits_{i=1}^{n}{\theta}_i$ and $\underline{1}=(1,{\cdots},1)^{\prime}$ is known.

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.1-11
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• 2021

Estimation of distribution algorithm (EDA) is a popular stochastic metaheuristic algorithm. EDA has been widely utilized in various optimization problems. However, it has been shown that the diversity of the population gradually decreases during the iterations, which makes EDA easily lead to premature convergence. This article introduces a hybrid estimation of distribution algorithm (EDA) with differential evolution (DE) based on self-adaptive strategy, namely HEDADE-SA. Firstly, an alternative probability model is used in sampling to improve population diversity. Secondly, the proposed algorithm is combined with DE, and a self-adaptive strategy is adopted to improve the convergence speed of the algorithm. Finally, twenty-five benchmark problems are conducted to verify the performance of HEDADE-SA. Experimental results indicate that HEDADE-SA is a feasible and effective algorithm.

• Proceedings of the KIEE Conference
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• 2002.11b
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• pp.206-208
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• 2002

Distributed generation (DG) is predicted to play an increasing role in the electric power system of the near future. Distributed generation is by definition that is of limited size (roughly 10MW or less) and interconnected at the substation, distribution feeder or customer load levels. The effects of generation sources within a distribution network on the system losses are investigated in this paper. WLAV state estimation is performed with the composite distribution system containing DG. Simulations with test cases are performed and the results are presented, using IEEE34 bus radial distribution system.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.285-294
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• 2005

This paper deals with the comparison of parameter estimation methods in a 3-parameter Kappa distribution which is sometimes used in flood frequency analysis. Method of moment estimation(MME), L-moment estimation(L-ME), and maximum likelihood estimation(MLE) are applied to estimate three parameters. The performance of these methods are compared by Monte-carlo simulations. Especially for computing MME and L-ME, three dimensional nonlinear equations are simplified to one dimensional equation which is calculated by the Newton-Raphson iteration under constraint. Based on the criterion of the mean squared error, L-ME (or MME) is recommended to use for small sample size( n$\le$100) while MLE is good for large sample size.