• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.03a
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• pp.100-105
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• 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.

• Journal of Korea Water Resources Association
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• v.38 no.10 s.159
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• pp.851-859
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• 2005

Normalized cross-correlation (correlation coefficient) is a useful measure for pattern matching in PIV (Particle Image Velocimetry) analysis. Because it does not have a corresponding simple expression in frequency domain, several fast but inexact measures have been used. Among them, three measures of correlation for PIV analysis and the normalized cross-correlation were evaluated with a sample calculation. The test revealed that all other proposed correlation measures sometimes show inaccurate results, except the normalized cross-correlation. However, correlation coefficient method has a weakpoint that it requires so long time for calculation. To overcome this shortcoming, a fast and exact method for calculating normalized cross-correlation is suggested. It adopts Fast Fourier Transform (FFT) for calculation of covariance and the successive-summing method for the denominator of correlation coefficient. The new algorithm showed that it is really fast and exact in calculating correlation coefficient.

• Communications for Statistical Applications and Methods
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• v.14 no.2
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• pp.301-307
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• 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.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1445-1458
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• 2016

This paper derives a correlation coefficient using detrended cross-correlation analysis (DCCA) method for 59 regional temperature series for 40 years from 1976 to 2015. The average temperature, maximum temperature, and minimum temperature series for 4 year units are analyzed; consequently, we estimated that a temperature correlation exists between the two regions during the unit period where the correlation coefficient is greater than or equal to 0.9; subsequently, we construct a network linking the two regions. Based on network theory, average path length, clustering coefficient, assortativity, and modularity were derived. As a result, it was found that the temperature network satisfies a small-worldness property and is a network having assortativity and modularity.

• Journal of the Korean Wood Science and Technology
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• v.34 no.2
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• pp.46-57
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• 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.

• Journal of the Korean Society of Radiology
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• v.7 no.5
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• pp.347-352
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• 2013

In this study, we analyze the cross correlation between Gamma exposure rates and Rainfall, Hours of daylight, Average wind speed using cross-correlation coefficient ${\rho}_{DCCA}$ and DCCA cross-correlation coefficient(DCCA ${\rho}$) method. Our data are measured simultaneous in Gangneung regional. First, we find the ${\rho}_{DCCA}$ between Gamma exposure rates and Rainfall is Day(3~7days) 0.57~0.48, Month(30days) 0.39, Season(90days) 0.34, Year(360days) 0.26, between Gamma exposure rates and Hours of daylight is Day -0.20~-0.23, Month -0.22, Season -0.17, Year -0.13, between Gamma exposure rates and Average wind speed is Day -0.10~-0.12, Month -0.11, Season -0.05, Year -0.05. Second, our finding is cross- correlation between Gamma exposure rates and Rainfall, is no cross-correlation between Gamma exposure rates and Hours of daylight, Average wind speed.

• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.201-212
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• 2017

This study presented the new evaluation index which can evaluate the discrimination of DEA models. To evaluate the discrimination of DEA models, data were analyzed using importance index as suggested in previous study and the coefficient of variation as suggested in this study for the discrimination evaluation. This study selected the CCR-DEA, BCC-DEA, entropy, bootstrap, super efficiency, and cross efficiency DEA model for the discrimination evaluation and accomplished empirical analysis. In order to grasp the rank correlation of the models, this study implemented the rank correlation analysis between the efficiency of CCR model and BCC model and entropy, bootstrap, super efficiency, and efficiency of the cross efficiency model. The obtained results of this study are as follows. First, the discrimination rank of models using the importance index and the coefficient of variation was shown to be identical. Therefore, the coefficient of variation can be used the discrimination evaluation index of DEA model. Second, the discrimination of the super efficiency model was found to be the highest rank among 4 models according to the analysis of this present study. Third, the highest rank correlation with CCR model was the super efficiency model. In addition, the super efficiency model was found to be the highest rank correlation with BCC model.

• Wind and Structures
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• v.31 no.6
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• pp.523-532
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• 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.

• Korean Journal of Remote Sensing
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• v.11 no.1
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• pp.19-29
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• 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
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• 2008.02a
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• pp.101-104
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• 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.