• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.5
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• pp.145-152
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• 2010

This paper presents a digital auto-focusing algorithm using iterative unsharp mask filter. The proposed digital auto-focusing algorithm has the advantage of low computational complexity because it uses a simple filter instead of calculating the point spread function for the estimation of image degradation. The proposed iterative algorithm can control the number of iterations for image restoration according to the objective and the subjective criterion. We show that the proposed algorithm is mathematically equivalent to the conventional image restoration. Finally, in order to evaluate the performance of the proposed algorithm, various experiments are performed so that the proposed algorithm can provide good results in the sense of subjective and objective views.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.10 no.1
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• pp.47-56
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• 1990

An inverse self-iterative procedure is developed to estimate layer moduli and stresses in cement concrete pavement systems from the falling weight deflectometer deflection basins. The existing concrete pavement highways are analyzed using coupled analysis procedure of finite element and layer elastic theory for models obtained through factorial design, from which the characteristics of deflection basins are studied and the empirical equations are proposed for the estimation of layer moduli. The empirical equations are used to assume initial moduli, and the relations between the rate of change of moduli and deflections are used in the self-iterative procedure to ensure accuracy of moduli. The developed computer program of this procedure is verified through various numerical model tests.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.203-213
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• 2017

We study estimation and inference of joint conditional distributions of bivariate longitudinal outcomes using regression models and copulas. We consider a class of time-varying transformation models and combine the two marginal models using Gaussian copulas to estimate the joint models. Our models and estimation method can be applied in many situations where the conditional mean-based models are inadequate. Gaussian copulas combined with time-varying transformation models may allow convenient and easy-to-interpret modeling for the joint conditional distributions for bivariate longitudinal data. We apply our method to an epidemiological study of repeatedly measured bivariate cholesterol data.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.201-210
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• 2015

Repeated measurements on each variables of interest often arise in bioscience or medical research. We need to account for correlations among repeated measurements to assess the correlation between two variables in the presence of replication. This paper reviews methods to estimate a correlation coefficient between two variables in repeated measurements using the variance-covariance matrix of linear mixed effect models. We analyze acoustic radiation force impulse imaging (ARFI) data to assess correlation between three shear wave velocity (SWV) measurements in liver or spleen and spleen length by ultrasonography. We present how to obtain parameter estimates for the variance-covariance matrix and correlations in mixed effects models using PROC MIXED in SAS.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.10
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• pp.889-900
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• 2012

In wireless sensor networks, the positioning scheme using received signal strength (RSS) has been widely considered. Appropriate estimation of path-loss exponent (PLE) between a sensor node and an anchor node plays a key role in reducing position error in this RSS-based positioning scheme. In the conventional researches, a sensor node directly uses the PLEs measured by its nearest anchor node to calculate its position. However, the actual PLE between a sensor node and the anchor node can be different from the PLE measured by its nearest anchor node. Thus, if a sensor node directly uses the PLEs measured by its nearest anchor node, the estimated position is different from the actual position of the sensor node with a high probability. In this paper, we describe the method how a sensor node estimates PLEs from the anchor nodes of interest by itself and calculates its position based on these self-estimated PLEs. Especially, our proposal suggests the mechanism to iteratively calculate the PLEs depending on the estimated distances between a sensor node and anchor nodes. Based on the recalculated PLEs, the sensor node reproduces its position. Through simulations, we show that our proposed positioning scheme outperforms the traditional scheme in terms of position error.

• Proceedings of the Korean Statistical Society Conference
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• 2005.11a
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• pp.91-96
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• 2005

다차원층화에서 선형계획법을 이용한 표본배정 방법은 Winkler(1990, 2001), Sitter와 Skinner(1994, 2002)가 제안하였다. 이 방법들은 표본크기가 층 개수보다 크지 않는 경우에 공통적으로 선형계획법을 이용하여 표본배정을 실시하였다. 반복 비율 적합방법(IPF), 일반화 반복 비율 적합(GIFP), SS 방법을 통해 셀 값을 결정하고 선형계획법을 이용하여 표본의 배정확률을 통해 표본배정을 실시한다. 이 3가지 방법들로 표본을 배정하고 평균 및 분산추정량을 비교한다.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.565-578
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• 2006

Repeated measurement data between two group is often used in the field of medicine study. In this paper, we suggest a method for comparison of the trend between two groups based on repeated measurement data. First, we estimate regression coefficient of linear regression model from each subject and generate samples using the regression coefficient estimated previous. And then, we test the difference between two groups by unpaired t-test, Wilcoxon rank sum test and placement test using generated samples. Monte Carlo Simulation is adapted to examine the power and experimental significance levels of several methods in various combinations.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.689-700
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• 2016

We study estimation and inference of the joint conditional distributions of bivariate longitudinal outcomes using regression models and copulas. For the estimation of marginal models we consider a class of time-varying transformation models and combine the two marginal models using nonparametric empirical copulas. Regression parameters in the transformation model can be obtained as the solution of estimating equations and our models and estimation method can be applied in many situations where the conditional mean-based models are not good enough. Nonparametric copulas combined with time-varying transformation models may allow quite flexible modeling for the joint conditional distributions for bivariate longitudinal data. We apply our method to an epidemiological study of repeatedly measured bivariate cholesterol data.

• Journal of Internet Computing and Services
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• v.15 no.5
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• pp.17-24
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• 2014

Blind algorithms designed to maximize the probability that constant modulus errors become zero carry out some summation operations for a set of constant modulus errors at an iteration time inducing heavy complexity. For the purpose of reducing this computational burden induced from the summation, a new approach to the estimation of the zero-error probability (ZEP) of constant modulus errors (CME) and its gradient is proposed in this paper. The ZEP of CME at the next iteration time is shown to be calculated recursively based on the currently calculated ZEP of CME. It also is shown that the gradient for the weight update of the algorithm can be obtained by differentiating the ZEP of CME estimated recursively. From the simulation results that the proposed estimation method of ZEP-CME and its gradient produces exactly the same estimation results with a significantly reduced computational complexity as the block-processing method does.

• Journal of Internet Computing and Services
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• v.15 no.4
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• pp.119-124
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• 2014

Blind adaptive systems based on the Euclidean distance (ED) between the distribution function of the output samples and that of a set of random symbols generated at the receiver matching with the distribution function of the transmitted symbol points estimate the ED at each iteration time to examine its convergence state or its minimum ED value. The problem is that this ED estimation obtained by block?data processing requires a heavy calculation burden. In this paper, a recursive ED estimation method is proposed that reduces the computational complexity by way of utilizing the relationship between the current and previous states of the datablock. The relationship provides a ground that the currently estimated ED value can be used for the estimation of the next ED without the need for processing the whole new data block. From the simulation results the proposed recursive ED estimation shows the same estimation values as that of the conventional method, and in the aspect of computational burden, the proposed method requires only O(N) at each iteration time while the conventional block?processing method does $O(N^2)$.