• Journal of the Korean Statistical Society
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• v.19 no.2
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• pp.139-144
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• 1990

For the first-order bilinear time series model $X_t = aX_{t-1} + e_i + be_{t-1}X_{t-1}$ where ${e_i}$ is a sequence of independent normal random variables with mean 0 and variance $\sigma^2$, the asymptotic distribution of sample autocarrelation function is obtained and shown to follow a normal distribution. The variance of the asymptotic distribution is of a complicated form and hence a bootstrap estimate of the variance is proposed for large sample inference. This result can be used to distinguish between different bilinear models.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.103-110
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• 2022

This paper deals with portfolio optimization problem that you could lower the total risk of an investment portfolio by adding risky assets to the mix than the minimum risk of single asset. Popular Markowitz's mean-variance(MV) model construct the portfolio with the point in the efficient frontier using principle of domination where the variance is minimized for a given mean return. While this paper suggest the portfolio with minimum risk asset with non-positive(negative and uncorrelated) correlation assets to it. As a result of experiments, the proposed method shows lower risk(standard deviation) than MV.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.5
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• pp.98-107
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• 1995

The in-process detection of drill wear and breakage is one of the most importnat technical problems in unmaned machining system. In this paper, the monitoring system is developed to monitor abnormal drilling states such as drill breakage, drill wear and unstable cutting using motor current. Drill breakage is detected by level monitoring. Tool wear is classified by fuzzy pattern recognition. The key feature for classification of tool wear is the estimated flank wear which is calculated by the proposed flank wear model. The characteristic of the model is not sensitive to the variation of cutting conditions but is sensitive to drill wear state. Unstable cutting states due to the unsmooth chip disposal and the overload are monitored by the variance/mean ratio of spindle motor current. Variance/mean ratio also includes the information about the prediction of drill wear and drill breakage. The evaluation experiments have shown that the developed system works very well.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.1033-1041
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• 2013

In this paper, we investigate several variance estimators for pps systematic sampling. Unfortunately, there is no unbiased variance estimators for a systematic sample because systematic sampling can be regarded as a random selection of one cluster. This study provides guidance on which variance estimator may be more appropriate than others in several circumstances. We judge the efficiency of variance estimators for systematic sampling based on of their relative biases and relative mean square error. Also, we investigate variance estimation problems for two-stage systematic sampling applied for the Food Raw Material Consumption Survey and the Establishment Labor Force Survey simulation study, in order to consider the popular two-stage pps systematic sample design for establishment and household survey in Korea.

• Communications for Statistical Applications and Methods
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• v.12 no.1
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• pp.47-59
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• 2005

Expected mean square(EMS) is an important part of conducting the analysis of variance in the experimental design problem, especially in mixed or random models. We present a set of EMS rules for balanced factorial designs under no restriction on interaction. Also we point out how to use the variance component of SPSS or SAS procedure to obtain EMS.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.5
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• pp.541-547
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• 2010

The rigorous validation of the accuracy of metamodels is an important topic in research on metamodel techniques. Although a leave-k-out cross-validation technique involves a considerably high computational cost, it cannot be used to measure the fidelity of metamodels. Recently, the mean$_0$ validation technique has been proposed to quantitatively determine the accuracy of metamodels. However, the use of mean$_0$ validation criterion may lead to premature termination of a sampling process even if the kriging model is inaccurate. In this study, we propose a new validation technique based on the mean and variance of the response evaluated when sequential sampling method, such as maximum entropy sampling, is used. The proposed validation technique is more efficient and accurate than the leave-k-out cross-validation technique, because instead of performing numerical integration, the kriging model is explicitly integrated to accurately evaluate the mean and variance of the response evaluated. The error in the proposed validation technique resembles a root mean squared error, thus it can be used to determine a stop criterion for sequential sampling of metamodels.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.89-96
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• 2009

We examined the characteristics of temperature variation in Seoul between 1961 to 2008 using the spectral heteroscedastic model. The mean function in the propsed model explains the season effect using periodic functions and the overall increase using the quadratic regression spline. The variance function also had periodic functions to explain the seasonality of variance. We found that there has been annual mean temperature increase by about $1.5^{\circ}C$ for the last 48 years. The increase of annual mean temperature was mainly caused by the increase in winter, which made the amplitude decreased.

• Journal of Korea Multimedia Society
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• v.18 no.10
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• pp.1197-1204
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• 2015

Smoke detection plays an important role for the early detection of fire. In this paper, we suggest a newly developed method that generated LBPV(Local Binary Pattern Variance)s as special feature vectors from RGB contrast images can be applied to detect smoke using SVM(Support Vector Machine). The proposed method rearranges mean value of the block from each R, G, B channel and its intensity of the mean value. Additionally, it generates RGB contrast image which indicates each RGB channel’s contrast via smoke’s achromatic color. Uniform LBPV, Rotation-Invariance LBPV, Rotation-Invariance Uniform LBPV are applied to RGB Contrast images so that it could generate feature vector from the form of LBP. It helps to distinguish between smoke and non smoke area through SVM. Experimental results show that true positive detection rate is similar but false positive detection rate has been improved, although the proposed method reduced numbers of feature vector in half comparing with the existing method with LBP and LBPV.

• Nuclear Engineering and Technology
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• v.43 no.3
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• pp.287-300
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• 2011

Counting statistics modified by introducing two dead times in series under a Poisson input distribution are discussed. A previous study examined the two cases of series combinations of nonextended-extended (NE-E) and extended-extended (EE) dead times. The present study investigated the remaining two cases of extended-nonextended (E-NE) and nonextended-nonextended (NE-NE) dead times. For the three time origins of the counting processes - ordinary, equilibrium, and shifted processes - a set of formulae was newly developed from a general formulation and presented for the event time interval densities, total densities, and exact mean and variance of the counts in a given counting duration. The asymptotic expressions for the mean and variance of the counts, which are most convenient for applications, were fully listed. The equilibrium mean count rates distorted by the three dead times in series were newly derived from the information obtained in these studies. An application of the derived formulae is briefly discussed.

• Communications for Statistical Applications and Methods
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• v.28 no.4
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• pp.351-368
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• 2021

For a probabilistic model with positively skewed data, a lognormal distribution is one of the key distributions that play a critical role. Several lognormal models can be found in various areas, such as medical science, engineering, and finance. In this paper, we propose a new estimator for a lognormal mean and depict the performance of the proposed estimator in terms of the relative mean squared error (RMSE) compared with Shen's estimator (Shen et al., 2006), which is considered the best estimator among the existing methods. The proposed estimator includes a tuning parameter. By finding the optimal value of the tuning parameter, we can improve the average performance of the proposed estimator over the typical range of σ². The bias reduction of the proposed estimator tends to exceed the increased variance, and it results in a smaller RMSE than Shen's estimator. A numerical study reveals that the proposed estimator has performance comparable with Shen's estimator when σ² is small and exhibits a meaningful decrease in the RMSE under moderate and large σ² values.