• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.6A
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• pp.524-532
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• 2002

This paper reports that the Lambert W function applies to a non-iterative design of minimum mean square-error scalar quantizers for a Laplacian source. Specifically, it considers a non-iterative design algorithm for optimum quantizers for a Laplacian source; it finds that the solution of the recursive nonlinear equation in the non-iterative design is elegantly expressed in term of the principal branch of the Lambert W function in a closed form; and it proves that the non-iterative algorithm applies only to exponential or Laplacian sources. The contribution of the paper is in the reduction of the time needed for the design and the increased accuracy in resulting quantization points and thresholds, because the algorithm is non-iterative and the Lambert W function can be evaluated as accurately as desired. Also, numerical results show how optimal quantization distortion converges monotonically to the Panter-Dite constant and help derive an approximation formula for the key parameters of optimum quantizers.

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.967-975
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• 2010

Since the least squares estimation is not appropriate when multicollinearity exists among the regressors of the linear regression model, the principal components regression is used to deal with the multicollinearity problem. This article suggests a new procedure for the selection of suitable principal components. The procedure is based on the condition index instead of the eigenvalue. The principal components corresponding to the indices are removed from the model if any condition indices are larger than the upper limit of the cutoff value. On the other hand, the corresponding principal components are included if any condition indices are smaller than the lower limit. The forward inclusion method is employed to select proper principal components if any condition indices are between the upper limit and the lower limit. The limits are obtained from the linear model which is constructed on the basis of the conjoint analysis. The procedure is evaluated by Monte Carlo simulation in terms of the mean square error of estimator. The simulation results indicate that the proposed procedure is superior to the existing methods.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.413-413
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• 2011

강우의 공간분포에 대한 신뢰성 있는 추정은 수자원 해석 및 설계에 있어서 필수적인 요소이다. 강우장의 공간변동성에 대한 고해상도 추정은 홍수, 특히 돌발홍수의 원인이 되는 국지성 호우의 확인 및 분석에 있어서 중요하다. 또한 강우의 공간 변동성에 대한 고려는 면적평균강우량 추정의 정확도를 향상시키는데 있어서 중요하며, 강우-유출모델의 모의결과에 대한 신뢰도를 향상시키는데 큰 영향을 미친다. 최근 공간자료에 대한 공간분포예측에 있어서 공간상관성을 고려할 수 있는 공간통계학적 기법의 적용이 증가하고 있으며, 이러한 공간통계학적 기법의 적용에 있어서 신뢰성 있는 모델 매개변수의 추정 및 불확실성 평가는 공간분포 예측결과에 대한 신뢰성을 향상시키는데 중요한 역할을 한다. 외국의 경우 공간분포예측 및 모의, 매개변수의 불확실성 평가 등과 관련하여 활발한 연구가 이루어지고 있는 반면 국내 수자원 분야에서는 아직까지 활발한 연구가 이루어지고 있지 않은 실정이다. 따라서 본 연구에서는 계층구조로 구성된 가우시안 공간선형혼합모델을 적용하여 확률강우량의 공간분포를 추정함에 있어서 모델 매개변수에 대한 추정기법을 비교하였으며, 매개변수 추정기법으로서 경험베리오그램에 대한 곡선적합기법인 보통최소제곱법 및 가중최소제곱법, 우도함수를 기반으로 하는 최우도법 및 REML과 같은 기존의 매개변수 추정기법들과 최근 공간통계학 분야에서 적용이 증가하고 있는 Bayesian 기법을 비교하였다. 이로부터 매개변수 추정기법 간의 매개변수 추정치에 대한 정량적 비교결과를 제시하였으며, Bayesian 기법의 적용을 통해 매개변수에 대한 불확실성 추정결과를 제시하였다. 이러한 결과들은 확률강우량의 공간분포 추정에 있어서 공간예측모델의 매개변수 추정 및 예측에 대한 신뢰성을 향상시킬 수 있는 기초자료로 활용될 수 있을 것이다.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.5
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• pp.416-422
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• 2016

This paper presents preliminary performance analysis of a satellite formation flying testbed, which is under development by Astrodynamics and Control Laboratory, Department of Astronomy, Yonsei University. A model reference adaptive controller (MRAC) with a first-order reference model is chosen to enhance the response of reaction wheel system which is subject to uncertainties caused by unmodelled dynamics and measurement noise. In addition, an on-line parameter estimation (OPE) technique based on the least square is combined to eliminate the effect of angular measurement noise by estimating the moment of inertia. Both numerical simulations and hardware experiments with MRAC support the effectiveness and applicability of the adaptive control scheme, which maintains the tracking error below $0.25^{\circ}$ for the entire time span. However, the high frequency control input generated in hardware experiment strongly suggests design modifications to reduce the effect of deadzone.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.205-213
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• 2012

Transforming response variable is a general tool to adapt data to a linear regression model. However, it is well known that response transformations in linear regression are very sensitive to one or a few outliers. Many methods have been suggested to develop transformations that will not be influenced by potential outliers. Recently Cheng (2005) suggested to using a trimmed likelihood estimator based on the idea of the least trimmed squares estimator(LTS). However, the method requires presetting the number of outliers and needs many computations. A new method is proposed, that can solve the problems addressed and improve the robustness of the estimates. The method uses a stepwise procedure, suggested by Hadi and Simonoff (1993), to detect outliers that determine response transformations.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.901-910
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• 2008

Incidence of Plasmodium vivax malaria in South Korea have been reemerged from mid-1990 and infected around 1600 patients annually recent years. The authors calculated the distribution of malaria infection and prevalence in South Korea using incidence (2001-2006) and incubation period distributions by a back-calculation formula and the least squares estimation method. The estimated infection has a normal distribution with a mean 207 and a standard deviation 30.7 days. In addition, the authors found the estimated daily average prevalence is 628.8 patients.

• Journal of Soil and Groundwater Environment
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• v.7 no.4
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• pp.17-23
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• 2002

Pumping tests were carried out from seven wells in fractured rocks. The time-drawdown data were obtained from pumping wells and corrected for the elapsed time of step drawdown test using Cooper-Jacob's method. A statistical method. the least square of error, was used to yield the coefficient of aquifer losses, the coefficient of well losses, and the power which indicates the severity of the turbulence. The values of the power range from 1.65 to 6.48. The well losses result mainly from turbulent flow caused by radial flow nearby pumping wells. The turbulent flow depends on Reynolds number. Since the hydraulic characteristics of fractured rocks control the fluid velocity, the value of the power is an important factor to understand the aquifer system of fractured rocks.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.2
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• pp.139-148
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• 2012

The MLS(Moving Least Squares) Difference Method is a numerical scheme that combines the MLS method of Meshfree method and Taylor expansion involving not numerical quadrature or mesh structure but only nodes. This paper presents an dynamic algorithm of MLS difference method for solving transient solid mechanics problems. The developed algorithm performs time integration by using Newmark method and directly discretizes strong forms. It is very convenient to increase the order of Taylor polynomial because derivative approximations are obtained by the Taylor series expanded by MLS method without real differentiation. The accuracy and efficiency of the dynamic algorithm are verified through numerical experiments. Numerical results converge very well to the closed-form solutions and show less oscillation and periodic error than FEM(Finite Element Method).

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1171-1180
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• 2010

It is well known that the asymptotic convergence rates of nonparametric regression estimator gets worse as the dimension of covariates gets larger. One possible way to overcome this problem is reducing the dimension of covariates by using single index models. Two coefficient estimation methods in single index models are introduced. One is semiparametric least square estimation method, which tries to find approximate solution by using iterative computation. The other one is weighted average derivative estimation method, which is non-iterative method. Both of these methods offer the parametric convergence rate to normal distribution. However, practical comparison of these two methods has not been done yet. In this article, we compare these methods by examining the variances of estimators in various models.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.4C
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• pp.254-264
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• 2011

In this paper, we propose two algorithm for improving the performance of wireless localization(Trilateration and Least Square) based on the range based approach method in indoor environment using RSSI for ranging distance. we propose a method to discriminate the case that has relatively large estimation errors in trilateration using Heron''s formula for the volume of a tetrahedron. And we propose the algorithm to process the discriminated types of distance using the absolute value calculated by Heron''s formula. In addition, we propose another algorithm for the case of which the number of anchor nodes larger than three. In this case, Residual Weighting Factor(RWGH) improves the performance of Least Square. However, RWGH requires many number of calculations. In this paper, we propose Iterative Weighted Centroid Algorithm(IWCA) that has better performance and less calculation than RWGH. We show the improvement of performance for two algorithms and the combination of these algorithm by using simulation results.