• Journal of the Korean Data and Information Science Society
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• 제17권3호
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• pp.805-809
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• 2006

In this paper a shrinkage estimator for the measure of dispersion of the inverse Gaussian distribution with known mean is proposed. Also we compare the relative bias and relative efficiency of the proposed estimator with respect to minimum variance unbiased estimator.

• Communications for Statistical Applications and Methods
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• 제12권1호
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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.

• Communications for Statistical Applications and Methods
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• 제20권4호
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• pp.329-337
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• 2013

Consider a p-variate($p{\geq}3$) normal distribution with mean ${\theta}$ and covariance matrix ${\sum}={\sigma}^2{\mathbf{I}}_p$ for any unknown scalar ${\sigma}^2$. In this paper we improve the James-Stein estimator of ${\theta}$ in cases of shrinking toward some vectors using the Stein variance estimator. It is also shown that this domination does not hold for the positive part versions of these estimators.

• Journal of the Korean Statistical Society
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• 제19권2호
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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.

• Industrial Engineering and Management Systems
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• 제16권1호
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• pp.22-43
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• 2017

It is urgently-needed to construct a green supply chain (GSC) from collection of used products through recycling of them to sales of products using the recycled parts. Besides, it is necessary to consider the uncertainty in product demand as a risk in a GSC. This study proposes the optimal operations for a GSC with a retailer and a manufacturer. A retailer pays an incentive for collection of used products from customers and sells a single type of products in a market. A manufacturer produces the products ordered by the retailer, using recyclable parts with acceptable quality and compensates the collection cost of used products as to the recycled parts. This paper discusses the following risk attitudes: risk-neutral attitude, risk-averse attitude, and risk-prone attitude. Using mean-variance analysis, the optimal decisions for product order quantity, collection incentive, and lower limit of quality level, in the decentralized GSC (DGSC) and the integrated GSC (IGSC) are made. DGSC optimizes the utility function of each member. IGSC does that of the whole system. The analysis numerically investigates how (i) risk attitude and (ii) quality of recyclable parts affect the optimal operations. Supply chain coordination between GSC members to shift IGSC from DGSC is discussed.

• 대한산업공학회지
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• 제39권5호
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• pp.361-366
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• 2013

Prediction of process parameters is very important in parameter design. If predictions are fairly accurate, the quality improvement process will be useful to save time and reduce cost. The concept of dual response approach based on response surface methodology has widely been investigated. Dual response approach may take advantages of optimization modeling for finding optimum setting of input factor by separately modeling mean and variance responses. This study proposes an alternative dual response approach based on machine learning techniques instead of statistical analysis tools. A hybrid neural network-genetic algorithm has been proposed for the purpose of parameter design. A neural network is first constructed to model the relationship between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Using empirical process data, process parameters can be predicted without performing real experimentations. A genetic algorithm is then applied to find the optimum settings of input factors, where the neural network is used to evaluate the mean and variance response. A drug formulation example from pharmaceutical industry has been studied to demonstrate the procedures and applicability of the proposed approach.

• Journal of the Korean Data and Information Science Society
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• 제20권1호
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• pp.89-96
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• 2009

이 논문에서 우리는 1961년부터 2008년 사이의 서울지역 온도변화를 스펙트럴 이분산성 모델을 이용하여 연구하였다. 제안한 모델에서 평균 함수는 계절효과를 주기함수를 이용하여 설명하였고, 온도의 전체적인 상승을 이차 회귀 스플라인 곡선을 이용하여 설명하였다. 분산함수 또한 분산의 계절성을 설명하기 위하여 주기함수를 사용하였다. 우리는 연평균온도가 과거 48년 동안 약 1.5도 가량 증가했음을 알 수 있었다. 연평균온도의 상승은 겨울 온도가 상승하는 것에 기인하는 것이었고, 이는 연중 온도변화의 진폭이 줄어들게 만들었다.

• Journal of Electrical Engineering and Technology
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• 제8권1호
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• pp.80-89
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• 2013

This paper presents an efficient approach for solving economic dispatch (ED) problems with nonconvex cost functions using a 'Mean-Variance Optimization (MVO)' algorithm with Kuhn-Tucker condition and swap process. The aim of the ED problem, one of the most important activities in power system operation and planning, is to determine the optimal combination of power outputs of all generating units so as to meet the required load demand at minimum operating cost while satisfying system equality and inequality constraints. This paper applies Kuhn-Tucker condition and swap process to a MVO algorithm to improve a global minimum searching capability. The proposed MVO is applied to three different nonconvex ED problems with valve-point effects, prohibited operating zones, transmission network losses, and multi-fuels with valve-point effects. Additionally, it is applied to the large-scale power system of Korea. The results are compared with those of the state-of-the-art methods as well.

• 한국인터넷방송통신학회논문지
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• 제22권1호
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• pp.103-110
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• 2022

본 논문은 단일 종목에 투자금을 전액 투자하는 것에 비해 다수의 종목에 분산투자하는 것이 투자 위험을 보다 감소시킬 수 있다는 포트폴리오 최적화 문제를 다룬다. 널리 알려진 Markowitz의 수익률에 대한 평균-분산 기법(MV)은 위험요인인 분산(또는 표준편차)을 감소시키기 위해 지배원리를 적용하여 효율적 투자선에 있는 종목들을 대상으로 분산투자하는 포트폴리오를 구성하였다. 반면에, 본 논문에서는 최소표준편차를 가진 종목을 필수 투자종목으로 선정하고, 필수 투자종목과 비양(음의, 무)의 상관관계를 갖는 종목들을 대상으로 포트폴리오를 형성하였다. 제안된 방법을 실험한 결과 MV에 비해 보다 적은 위험(표준편차)을 보였다.

• 한국정밀공학회지
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• 제12권5호
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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.