• 한국공작기계학회:학술대회논문집
• /
• 한국공작기계학회 1998년도 춘계학술대회 논문집
• /
• pp.100-105
• /
• 1998

Image processing was used to recognize parts at various disposition. Non-transpatent tachometer panel for automobile and semi-transparent panel have been used as test specimen. Laplacian filter and various threshold values have been applied for preprocessing and edge following algorithm has been applied. Series of length data between edges have been generated from each image and compared using cross correlation coefficient. The result using cross correlation coefficient. The result using both edge following and cross correlation coefficient was proven to be the best fit for the proposed parts.

• 한국수자원학회논문집
• /
• 제38권10호
• /
• pp.851-859
• /
• 2005

정규 상호 상관 (상관계수)은 입자영상유속계(PIV) 분석에서 형태 분석을 위한 가장 정확하고 적합한 척도이다. 그러나 상관계수는 주파수 영역에서 그에 상당하는 간단한 수식 표현이 없기 때문에, 빠르지만 부정확한 척도들이 종종 이용된다. 이러한 척도 중에서 선정된 세 가지 방법과 상관계수법을 상호 비교하였다. 그 결과 상관계수법을 제외한 나머지 척도들은 모두 종종 부정확한 결과를 도출함을 알 수 있었다. 그러나 상관계수법은 계산 시간이 많이 걸린다는 단점을 지니고 있다. 이 문제를 해결하기 위해, 상관계수법을 계산하는 빠르고 정확한 방법을 제시하였다. 이 방법은 상관계수의 분산을 계산하는 Fn 알고리듬과 분모를 계산하는 순차가감법을 결합한 것이다. 시험 결과 이 방법은 상관계수를 빠르고 정확하게 계산할 수 있음을 보였다.

• Communications for Statistical Applications and Methods
• /
• 제14권2호
• /
• pp.301-307
• /
• 2007

A effective teaching method on correlation coefficient for elementary level statistics course is discussed in this article. The well known inequalities, such as Theorem 368 of Hardy et al. (1952), are used for the interpretation of concept of covariance. An Excel example is provided for the illustration of concept of correlation coefficient.

• 응용통계연구
• /
• 제29권7호
• /
• pp.1445-1458
• /
• 2016

본 논문에서는 1976년부터 2015년까지 40년 간, 59개 지역 기온 시계열을 대상으로 degrended cross-correlation analysis(DCCA) 방법을 이용한 상관 계수를 도출하였다. 4년 단위의 평균기온, 최고기온, 최저기온 시계열을 분석하여 상관계수 값이 0.9 이상이면 단위 기간 동안 두 지역의 온도 상관성이 존재하는 것으로 판단하고, 두 지역 간의 연결선을 만드는 방식으로 네트워크를 구축하였다. 이후 네트워크 이론을 바탕으로 평균 경로 길이, 결집 계수, 유사성, 모듈성 등의 값들을 도출하였다. 그 결과, 기온 네트워크는 좁은 세상 성질을 만족하고, 유사성과 모듈성이 높은 네트워크임을 알 수 있었다.

• Journal of the Korean Wood Science and Technology
• /
• 제34권2호
• /
• pp.46-57
• /
• 2006

To estimate nondestructively strength performances of laminated woods, 3-ply parallel- and cross-laminated wood specimens exposed under atmosphere conditions after bending creep test were prepared for this study. The effects of density of species, arrangement of laminae and lamination types on dynamic MOE obtained by flexural vibration were investigated, and regression analyses were conducted in order to estimate static bending strength and bending creep performances. Dynamic MOE of parallel-laminated woods showed 1.0~1.2 times higher values than static bending MOE, and those of cross-laminated woods showed 1.0~1.4 times higher values than static bending MOE. The degree of anisotropy of dynamic MOE perpendicular to the grain of face laminae versus that parallel to the grain of face laminae was markedly decreased by cross-laminating. There were strong correlations between dynamic MOE by flexural vibration and static bending MOE (correlation coefficient r = 0.919~0.972) or bending MOR (correlation coefficient r = 0.811~0.947) of 3-ply laminated woods, and the correlation coefficient were higher in parallel-laminated woods than in cross-laminated woods. It indicated that static bending strength performances were able to be estimated from dynamic MOE by flexural vibration. Also, close correlations between the reciprocal of dynamic MOE by flexural vibration and initial compliance at 0.008 h of 3-ply laminated woods were found (correlation coefficient r = 0.873~0.991). However, the correlation coefficient between the reciprocal of dynamic MOE and creep compliance at 168 h of 3-ply laminated woods was considerably lower than those between dynamic MOE and initial compliance, and it was hard to estimate creep compliance with a high accuracy from dynamic MOE due to the variation of creep deformation.

• 한국방사선학회논문지
• /
• 제7권5호
• /
• pp.347-352
• /
• 2013

본 논문은 강릉 지역에서 동일 기간에 기록된 공간 감마선량률과 강수량, 일조시간, 평균풍속 사이의 교차 상관 지수 ${\rho}_{DCCA}$를 DCCA cross-correlation coefficient(DCCA ${\rho}$)방법으로 구하여 교차 상관성을 분석하였다. 우리는 이 연구를 통해 다음의 사실을 알았다. 첫 번째, 공간 감마선량률과 강수량 사이 ${\rho}_{DCCA}$는 일(4~7일), 달(30일), 계절(90일), 년(360일)에서 0.57~0.48, 0.39, 0.34, 0.26, 공간 감마선량률과 일조시간 사이 ${\rho}_{DCCA}$는 일, 달, 계절, 년에서 -0.20~-0.23, -0.22, -0.17, -0.13, 공간 감마선량률과 평균풍속 사이의 ${\rho}_{DCCA}$는 일, 달, 계절, 년에서 -0.10~-0.12, -0.11, -0.05, -0.05이다. 두 번째, ${\rho}_{DCCA}$를 통하여 공간 감마선량률과 강수량은 교차 상관성이 있으며 공간 감마선량률과 일조시간, 평균풍속 사이에는 교차 상관성이 없다는 것을 확인하였다.

• 한국콘텐츠학회논문지
• /
• 제17권1호
• /
• pp.201-212
• /
• 2017

본 연구에서는 변동계수를 이용하여 DEA 모형의 변별력 평가에 적용할 수 있는 새로운 평가기준을 제시하였다. 변별력 평가를 위해 기존 연구에서 제시한 중요도와 본 연구에서 제안한 변동계수를 이용하여 변별력을 분석하였다. 다양한 DEA 모형들 중 변별력 평가를 위해 CCR-DEA, BCC-DEA, entropy, bootstrap, super efficiency, cross efficiency DEA 모형을 선정하고 실증분석을 실시하였다. 모형들의 순위상관관계를 파악하기 위해서 CCR 모형과 BCC 모형의 효율성 값과 entropy, bootstrap, super efficiency, cross efficiency 모형의 효율성 값들 간에 순위상관분석을 실시하였다. 본 연구를 통해 도출된 연구결과를 요약하면 다음과 같다. 첫째, 중요도와 변동계수를 이용한 모형들의 변별력 순위가 동일한 것으로 분석되어 변동계수를 DEA 모형의 변별력 평가기준으로 이용할 수 있다는 것이다. 둘째, 본 연구의 실증분석 결과에 따르면 4개 모형 중 super efficiency 모형이 변별력이 가장 높은 것으로 분석되었다. 셋째, CCR 모형과 순위상관관계가 가장 높은 모형은 super efficiency 모형으로 나타났고, BCC 모형과 순위상관관계가 가장 높은 모형도 super efficiency 모형으로 분석되었다.

• Wind and Structures
• /
• 제31권6호
• /
• pp.523-532
• /
• 2020

As the height and flexibility of high-rise buildings increase, the wind loads become more dominant and the combination coefficient of Equivalent Static Wind Loads (ESWLs) should be considered when they are used in the structural design. In the first phase of the study, a brief introduction to the theory on the combination coefficient for high-rise buildings was given and then the time history of wind-induced responses of a 208-meter high-rise building with an elliptical cross-section was presented based on the wind tunnel test results for pressure measurement. The correlation between wind-induced responses was analyzed and the combination coefficients of ESWLs of the high-rise buildings using Turkstra's rule, and Asami's method, were calculated and compared with related design codes, e.g., AIJ-RLB, ASCE 7-10, and China Load Code for structural design. The results of the study showed that the combination coefficients from Asami's method are conservative compared with the other three methods. The results of this paper would be helpful to the wind-resistant design of high-rise buildings with elliptical cross-section.

• 대한원격탐사학회지
• /
• 제11권1호
• /
• pp.19-29
• /
• 1995

The surface velocities were estimated by the Maximum Cross Correlation(MCC) method and an inverse method from AVHRR/SST. In the results of MCC, discontinuous flow fields were estimated in the case that cross correlation coefficient was above 0.5 but these flow pattern disappeared when cross correlation coefficient was above 0.9. This estimation was conspicuous near SST patterns of eddies. In the results of inverse method, flow field was continuous and eddy motion was estimated definitely but the velocity was overstimated in compared with MCC result over the area of small temperature gradient. This result may be due to temperature error included in SST calculated and spatial variation of heat flux.

• 한국방재학회:학술대회논문집
• /
• 한국방재학회 2008년도 정기총회 및 학술발표대회
• /
• pp.101-104
• /
• 2008

In this study, rainfall adjust and forecasting using artificial neural network(ANN) which includes a correlation coefficient is application in Seoul region. It analyzed one-hour rainfall data which has been reported in 25 region in seoul during from 2000 to 2006 at rainfall observatory by AWS. The ANN learning algorithm apply for input data that each region using cross-correlation will use the highest correlation coefficient region. In addition, rainfall adjust analyzed the minimum error based on correlation coefficient and determination coefficient related to the input region. ANN model used back-propagation algorithm for learning algorithm. In case of the back-propagation algorithm, many attempts and efforts are required to find the optimum neural network structure as applied model. This is calculated similar to the observed rainfall that the correlation coefficient was 0.98 in missing rainfall adjust at 10 region. As a result, ANN model has been for suitable for rainfall adjust. It is considered that the result will be more accurate when it includes climate data affecting rainfall.