• Composites Research
• /
• 제31권5호
• /
• pp.251-259
• /
• 2018

본 논문에서는 섬유강화복합재의 멀티스케일 해석을 위해 필요한 대규모/소규모 통계적 체적요소 모델을 개발하였다. 미시영역모델의 크기효과를 최소화하기 위해서 섬유를 최대한 포함한 거대모델을 구성하였다. 이를 위해 국부 영역의 요소 절단법을 이용하여 전체 유한요소 크기에 상관없이 신속한 격자 섬유/기지의 모델링이 가능한 요소생성기를 구성하였다. 이를 통해 대규모 통계 체적 모델을 도출하여 체적모델의 크기에 따른 국부하중 공유의 차이를 고찰하고, 섬유방향의 연속체손상역학모델을 BSVEs 모델 해석으로부터 도출 하였다. BSVEs 모델을 보편적인 RVE모델과 비교 검증하였다.

• 한국산학기술학회논문지
• /
• 제10권11호
• /
• pp.3302-3307
• /
• 2009

본 연구에서는 다양한 입지분석에서 이용되고 있는 인구추정의 정확도와 신뢰성의 향상을 기하고자, GIS와 국가인구통계자료를 이용하여 정밀한 인구통계지도를 구축할 수 있는 방법을 제시하였다. 제시한 방법은 다음과 같다. 주거지, 상업 및 업무지의 토지이용이 인구와 상관성이 높다는 것을 분석 도출하여, 세부 토지이용(비오톱) 유형별로 다중회귀분석을 실시하였다. 그런 후 인구거주 밀도 별로 가중치를 부여하여 인구를 동별 토지이용 유형별로 재분배하는 방법으로 정밀한 인구통구통계 지도를 구축하였다. 본 연구의 방법은 그간 다양한 입지분석에서 이용되었던 인구추정 방법보다 정확도와 신뢰성의 향상을 가져올 것으로 기대된다.

• Communications for Statistical Applications and Methods
• /
• 제3권3호
• /
• pp.125-133
• /
• 1996

In this paper, we present a algebraic technique for computing system reliability for complex system. The method was originally developed as an aid to fault tree analysis but it applies to general problems of reliability assessment. A success expression which directly gives the reliability expression is formed and simplified by the procedure. Several algorithms and examples are illustrated.

• Communications for Statistical Applications and Methods
• /
• 제8권2호
• /
• pp.319-325
• /
• 2001

In this note, we propose a simple fuzzy linear regression model for fuzzy input-output data based on Tanaka's approach. Then an LP-based method to derived the satisfying solution of the decision making is developed.

• Journal of the Korean Statistical Society
• /
• 제16권2호
• /
• pp.55-70
• /
• 1987

The principal components regression is one of the substitues for least squares method when there exists multicollinearity in the multiple linear regression model. It is observed graphically that the performance of the principal components regression is strongly dependent upon the values of the parameters. Accordingly, a new deletion criterion which determines proper principal components to be deleted from the analysis is developed and its usefulness is checked by simulations.

• 대한의용생체공학회:의공학회지
• /
• 제22권4호
• /
• pp.369-375
• /
• 2001

The statistical regression model is one of the most frequently used clinical analysis methods. It has basic assumption of linearity, additivity and normal distribution of data. However, most of biological data in medical field are nonlinear and unevenly distributed. To overcome the discrepancy between the basic assumption of statistical model and actual biological data, we propose a new analytical method based on artificial neural network. The newly developed multilayer perceptron(MLP) is trained with 120 data set (60 normal, 60 patient). On applying test data, it shows the discrimination power of 0.76. The diabetic risk factors were also identified from the MLP neural network model and the logistic regression model. The signigicant risk factors identified by MLP model were post prandial glucose level(PP2), sex(male), fasting blood sugar(FBS) level, age, SBP, AC and WHR. Those from the regression model are sex(male), PP2, age and FBS. The combined risk factors can be identified using the MLP model. Those are total cholesterol and body weight, which is consistent with the result of other clinical studies. From this experiment we have learned that MLP can be applied to the combined risk factor analysis of biological data which can not be provided by the conventional statistical method.

• Communications for Statistical Applications and Methods
• /
• 제30권1호
• /
• pp.37-48
• /
• 2023

Shape analysis is a method for measuring, describing and comparing the shape of objects in geometric space. An important aspect is to obtain Procrustes distance based on least square method. We note that the shape is all the geometrical information that remains when location, scale and rotational effects are filtered out from an object. However, and unfortunately, when we cannot measure some landmarks which are some biologically or geometrically meaningful points of any object, it is not possible to measure the variation of all shapes of an object, including that of the incomplete object. Hence, we need to replace the missing landmarks. In particular, Albers and Gower (2010) studied the missing rows of configurations in Procrustes analysis. They noted that the convergence of their approach can be quite slow. In this study, alternatively, we derive an algorithm for estimating the missing landmarks based on the pre-shapes. The pre-shape is invariant under the location and scaling of the original configuration with the centroid size of the pre-shape being one. Therefore we expect that we can reduce the amount of total computing time for obtaining the estimate of the missing landmarks.

• 대한기계학회논문집A
• /
• 제33권11호
• /
• pp.1314-1319
• /
• 2009

The design methods of mechanical systems are largely classified into deterministic methods and stochastic methods. In deterministic methods, design parameters are assumed to have fixed values. On the other hand, in stochastic methods, design parameters are assumed to be statistically distributed. When a stochastic method is employed, statistical characteristics of the populations of design variables are assumed to be known. However, very often, it is almost impossible or very expensive to obtain the statistical characteristics of the populations. Therefore a sample survey method is usually employed for stochastic methods. This paper describes the procedure of estimating the statistical characteristics of populations by employing sample data sets. An example of AFM micro cantilever beam is employed to show the effectiveness of the procedure.

• Communications for Statistical Applications and Methods
• /
• 제25권3호
• /
• pp.307-319
• /
• 2018

High-throughput technologies enable the simultaneous evaluation of thousands of genes that could discriminate different subclasses of complex diseases. Ranking genes according to differential expression is an important screening step for follow-up analysis. Many statistical measures have been proposed for this purpose. A good ranked list should provide a stable rank (at least for top-ranked gene), and the top ranked genes should have a high power in differentiating different disease status. However, there is a lack of emphasis in the literature on ranking genes based on these two criteria simultaneously. To achieve the above two criteria simultaneously, we proposed to apply a previously reported metric, the modified area under the receiver operating characteristic cure, to gene ranking. The proposed ranking method is found to be promising in leading to a stable ranking list and good prediction performances of top ranked genes. The findings are illustrated through studies on both synthesized data and real microarray gene expression data. The proposed method is recommended for ranking genes or other biomarkers for high-dimensional omics studies.

• 한국석유지질학회:학술대회논문집
• /
• 한국석유지질학회 1998년도 제5차 학술발표회 발표논문집
• /
• pp.10-14
• /
• 1998

A systematic methodology is developed for the electrofacies determination from wireline log data using multivariate statistical analysis. To consider corresponding contribution of each log and reduce the computational dimension, multivariate logs are transformed into a single variable through principal components analysis. Resultant principal components logs are segmented using the statistical zonation method to enhance the efficiency and quality of the interpreted results. Hierarchical cluster analysis is then used to group the segments into electrofacies. Optimal number of groups is determined on the basis of the ratio of within-group variance to total variance and core data. This technique is applied to the wells in the Korea Continental Shelf. The results of field application demonstrate that the prediction of lithology based on the electrofacies classification matches well to the core and the cutting data with high reliability This methodology for electrofacies classification can be used to define the reservoir characteristics which are helpful to the reservoir management.