• Journal of applied mathematics & informatics
• /
• v.3 no.1
• /
• pp.101-115
• /
• 1996

In this paper we extend the best choice of subsample size m in the 2-stage sampling which suggested by Mohammad(1986) to the 3-stage sampling in cases of known and of unknown cost and variance ratio. We find the subsample size m.k which ensures more than the relative efficiency 90% Also we see that the choice of 3-stage subsample size depends on the design parameters using in 2-stage sampling.

• Proceedings of the KSME Conference
• /
• 2003.11a
• /
• pp.256-260
• /
• 2003

A computer program to calculate thermodynamic properties of oxygen is developed. Procedures for the calculation is briefly discussed. The program calculates unknown thermodynamic properties fixing the state with two independent input properties. If input value by user is inappropriate, it displays an error message. In addition user can change units with easy. The program developed in this work can be utilized to calculate parameters required for the simulation and design of an equipment using oxygen.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.7
• /
• pp.1290-1294
• /
• 2008

We present a novel deblurring algorithm for bi-level images blurred by some parameterizable point spread function. The proposed method iteratively searches unknown parameters in the point spread function and noise-to-signal ratio by minimizing an objective function that is based on the binariness and the difference between two intensity values of restoring image. In simulations and experiments, the proposed method showed improved performance compared with the Wiener filtering based method in terms of bit error rate after segmentation.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.36 no.4
• /
• pp.287-292
• /
• 1987

This paper deals with applications of Haar functions to parameter identification of linear systems. It is first introuduced to a new operational matrix which relates Haar functions and their integrations. The matrix can be used to identify the parameters of unknown linear systems by a least squares estimation. And then, the state equation of given systems is transformed into a computationally convenient algebraic form. This approach provides a more efficient method for the system identification problem.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.284-284
• /
• 2000

In adaptive fuzzy control systems. fuzzy systems are used to approximate the unknown plant nonlinearities. Until now. most of the papers in the field of controller design for nonlinear system using fuzzy systems considers the affine system with fixed grid-rule structure based on system state availability. This paper considers observer-based nonlinear controller and dynamic fuzzy rule structure. Adaptive laws for fuzzy parameters for state observer and fuzzy rule structure are established so that the whole system is stable in the sense of Lyapunov.

• Communications for Statistical Applications and Methods
• /
• v.8 no.1
• /
• pp.87-96
• /
• 2001

We consider the empirical Bayes estimation problems with the binomial and normal components when the prior distributions are unknown but are assumed to be in certain families. There may be the families of all distributions on the parameter space or subfamilies such as the parametric families of conjugate priors. We treat both cases and establish the asymptotic optimality for the corresponding decision procedures.

• Communications for Statistical Applications and Methods
• /
• v.2 no.2
• /
• pp.52-62
• /
• 1995

Consider an estimation problem under squared error loss in a family of non-regular densities with both terminals of the support being decreasing functions of an unknown parameter. Using Karlin's(1958) technique, sufficient conditions are given for generalized Bayes estimators to be admissible for estimating an arbitrarily positive, monotone parametric function and then treat some examples which illustrate our results.

• Communications for Statistical Applications and Methods
• /
• v.2 no.2
• /
• pp.288-295
• /
• 1995

In this paper a hypotheses test procedure based on the least absolute deviation estimators for the unknown parameters in nonlinear regression models is investigated. The asymptotic distribution of the proposed likelihood ratio test statistic are established voth under the null hypotheses and a sequence of local alternative hypotheses. The asymptotic relative efficiency of the proposed test with classical test based on the least squares estimator is also discussed.

• Journal of the Korean Statistical Society
• /
• v.28 no.2
• /
• pp.183-193
• /
• 1999

In the problem of estimating estimable functions in classification linear models, we propose a method of obtaining least squares estimators of estimable functions. This method is based on the hierarchical Bayesian approach for estimating a vector of unknown parameters. Also, we verify that estimators obtained by our method are identical to least squares estimators of estimable functions obtained by using either generalized inverses or full rank reparametrization of the models. Some examples are given which illustrate our results.

• Journal of the Korean Statistical Society
• /
• v.25 no.2
• /
• pp.161-173
• /
• 1996

When we deal with a Hilbert space-valued Stochastic Differential Equation (SDE) (or Stochastic Partial Differential Equation (SPDE)), depending on some unknown parameters, the solution usually has a Fourier series expansion. In this situation we consider the maximum likelihood method for the statistical estimation problem and derive the asymptotic properties (consistency and normality) of the Maximum Likelihood Estimator (MLE).