• Communications for Statistical Applications and Methods
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• 제8권1호
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• pp.65-71
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• 2001

In this paper, optimal block designs for complete diallel crosses are proposed. These optimal block designs are constructed by using triangular partially balanced incomplete designs derived from symmetric balanced incomplete block designs. Also, it is shown that block designs for complete dialle crosses derived from complementary designs of triangular designs are optimal block designs.

• Journal of the Korean Statistical Society
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• 제25권2호
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• pp.277-288
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• 1996

The minimum aberration criterion is commonly used for selecting good fractional factorial designs. In this paper we give same necessary conditions for $3^{n-k}$ fractional factorial designs. We obtain minimum aberration $3^{n-k}$ designs for k = 2 and any n. For k > 2, minimum aberration designs have not found yet. As an alternative, we select a design with minimum aberration among minimum-variance designs.

• Communications for Statistical Applications and Methods
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• 제19권6호
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• pp.877-884
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• 2012

Phase I trials determine the maximum tolerated dose(MTD) and the recommended dose(RD) for subsequent Phase II trials. In this paper, a MTD estimation method applied to a biased coin design is proposed for Phase I Clinical Trials. The suggested MTD estimation method is compared to the SM3 method and the NM method (Lee and Kim, 2012) using a Monte Carlo simulation study.

• Journal of the Korean Statistical Society
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• 제14권1호
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• pp.48-55
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• 1985

In clinical trials subjects are avalible sequentially and must be assigned to treatments immediately. Completely randomized procedure for the allocation of treatments to each subject may result in severe imbalance among the number of subjects in treatment groups, especially for small experiments or interim analyses of large experiments. In this study, restricted randomization methods such as biased coin designs (Efron, 1971), permuted block design, and truncated binomial design are compared to teh completely randomized design in the presence of selection and/or accidential bias by Monte Carlo simulations.

• Journal of the Korean Statistical Society
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• 제25권4호
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• pp.499-514
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• 1996

Consider an additive regression model of Y on X = (X$_1$,X$_2$,. . .,$X_p$), Y = $sum_{j=1}^pf_j(X_j) + $\varepsilon$$, where $f_j$s are smooth functions to be estimated and $\varepsilon$ is a random error. If $X_j$s are fixed design points, we call it the fixed design additive model. Since the response variable Y is observed at fixed p-dimensional design points, the behavior of the nonparametric regression estimator depends on the design. We propose a fixed design called permutation fixed design, and fit the regression function by the kernel method. The estimator in the permutation fixed design achieves the univariate optimal rate of convergence in mean squared error for any p $\geq$ 2.

• 한국정밀공학회지
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• 제15권9호
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• pp.15-24
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• 1998

In this research. we have proposed a new technique to determine .the combination of design parameters for the process design of a hot forged product using artificial neural network(ANN) and statistical design of experiments(DOE). The investigated problem involves the adequate selection of the aspect ratio of billet, the ram velocity and the friction factor as design parameters. An optimal billet satisfying the forming limitation, die filling, load and energy as well as more uniform distribution of effective strain, is determined by applying the ability of artificial neural network and considering the analysis of mean and variation on the functional requirement. This methodology will be helpful in designing and controlling parameters on the shop floor which would yield the best design solution.

• Communications for Statistical Applications and Methods
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• 제16권1호
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• pp.195-208
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• 2009

A conventional single design optimality criterion has been used to select an efficient experimental design. But, since an experimental design is constructed with respect to an optimality criterion pre specified by investigators, an experimental design obtained from one optimality criterion which is superior to other designs may perform poorly when the design is evaluated by another optimality criterion. In other words, none of these is entirely satisfactory and even there is no guarantee that a design which is constructed from using a certain design optimality criterion is also optimal to the other design optimality criteria. Thus, it is necessary to develop certain special types of experimental designs that satisfy multiple design optimality criteria simultaneously because these multi-optimal designs (MODs) reflect the needs of the experimenters more adequately. In this article, we present a heuristic approach to construct second-order response surface designs which are more flexible and potentially very useful than the designs generated from a single design optimality criterion in many real experimental situations when several competing design optimality criteria are of interest. In this paper, over cuboidal design region for $3\;{\leq}\;k\;{\leq}\;5$ variables, we construct multi-optimal designs (MODs) that might moderately satisfy two famous alphabetic design optimality criteria, G- and IV-optimality criteria using a GA which considers a certain amount of randomness. The minimum, average and maximum scaled prediction variances for the generated response surface designs are provided. Based on the average and maximum scaled prediction variances for k = 3, 4 and 5 design variables, the MODs from a genetic algorithm (GA) have better statistical property than does the theoretically optimal designs and the MODs are more flexible and useful than single-criterion optimal designs.

• 스마트미디어저널
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• 제3권3호
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• pp.26-35
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• 2014

통계그래픽스는 정량적인 데이터를 이용하여 정보 분석, 추출, 시각화의 과정을 거쳐 정확한 정보전달과 효과적인 이해를 위해 사용자 인지측면에 초점을 둔 디자인 분야이다. 이러한 통제그래픽스에 빅데이터의 구성요소들 내포하게 될 경우 빅데이터 통제그래픽스라고 할 수 있다. 통계그래픽스에서 시각적 요소는 인지부분에 대한 오류를 줄이고 성공적으로 정보를 전달하기 위해 사용되어야 하지만, 빅데이터 통계그래픽스에서는 방대한 데이터로 인해 시각적 요소가 오히려 인지적 방해를 일으키고 있다. 본 연구는 빅데이터 통계 그래픽스에서 나타날 수 있는 인지적 방해요소를 도출하여 제시하는 것을 목적으로 한다. 빅데이터의 통계그래픽스의 유형을 구조적 형태를 바탕으로 '네트워크 유형', '세그먼트 유형', '혼합유형' 세 가지로 분류하였고, 그에 따른 특징들을 탐색하였다. 특히, 빅데이터 통계그래픽스에서 시각적 주요요소를 기반으로 시각화의 고도화 시 나타날 수 있는 인지적 방해요소를 '다차원 범례', '다양한 색채', '정보의 중첩', '서체의 가독성' 네 가지로 도출하여 제시하였다.

• 한국산학기술학회논문지
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• 제17권5호
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• pp.645-652
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• 2016

입력 랜덤 변수(input random variable)의 통계 모델링은 기계시스템의 신뢰성 해석(reliability analysis), 신뢰성 기반 설계(reliability-based design optimization), 해석모델의 통계적 검정(validation) 및 보정(calibration)을 위해 반드시 필요하다. 대표적인 통계모델링 기법에는 Akaike Information Criterion (AIC), AIC correction (AICc), Bayesian Information Criterion, Maximum Likelihood Estimation (MLE), Bayesian 방법 등이 있다. 이러한 방법들은 기본적으로 주어진 데이터로부터 후보 모델의 우도함수값을 이용하여 후보 모델 중 가장 적합한 모델을 선택하는 방법이며, 방법에 따라 데이터 수 혹은 파라미터의 수를 고려하여 모델을 선정한다. 하지만 실제 현장에서 데이터의 통계모델링을 하는 엔지니어는 각 방법의 장단점에 대한 이해가 부족하여 어떤 방법이 정확한 방법인지 몰라 통계모델링 수행 시 어려움이 있다. 본 논문에서는 다양한 통계모델링 방법들을 비교하고 각 방법의 장단점 분석을 통해 가장 적합한 모델링 기법을 제안하고자 한다. 각 방법의 검증을 위해 다양한 모분포를 가정하고 다양한 사이즈의 샘플을 임의로 생성하여 시뮬레이션을 수행하였으며, 실제 공학 데이터를 사용하여 통계모델링 방법의 유효성을 검증하였다.

• 소성∙가공
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• 제20권2호
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• pp.124-131
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• 2011

A flexible stretch forming process is useful for small quantity batch production because various shape changes of the flexible die can be achieved conveniently. In this study, the design variables, namely, the punch size, curvature radius and elastic pad thickness, were quantitatively evaluated to understand their influence on sheet formability using statistical methods such as the correlation and regression analyses. Forming simulations were designed and conducted by a three-way factorial design to obtain numerical values of a shape error. Linear relationships between the design variables and the shape error resulted from the Pearson correlation analysis. Subsequently, a regression analysis was also conducted between the design variables and the shape error. A regression equation was derived and used in the flexible die design stage to estimate the shape error.