• The Journal of Engineering Geology
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• 제15권4호
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• pp.463-473
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• 2005

Because of using the same rating ranges for every rock types the RMR or the Q-system could not usually consider local geological characteristics They also could not present sufficiently the engineering anisotropy of rocks. The canonical correlation analysis was carried out with 3 kinds of face mapping data obtained from granite, sedimentary rock and phyllite in order to clarify a discrepancy between rock types. According to analysis results, as a type of rocks changes, RM factors have different influences on the total rating of RMR.

• Journal of Korean Society for Geospatial Information Science
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• 제4권2호
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• pp.181-193
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• 1996

A new technique for land cover classification which applies digital image pre-classified by unsupervised classification technique, clustering, to Canonical Correlation Analysis(CCA) was proposed in this paper. Compared with maximum likelihood classification, the proposed technique had a good flexibility in selecting training areas. This implies that any selected position of training areas has few effects on classification results. Land cover of each cluster designated by CCA after clustering is able to be used as prior information for maximum likelihood classification. In case that the same training areas are used, accuracy of classification using Canonical Correlation Analysis after cluster analysis is better than that of maximum likelihood classification. Therefore, a new technique proposed in this study will be able to be put to practical use. Moreover this will play an important role in the construction of GIS database

• Journal of Korean Institute of Industrial Engineers
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• 제41권6호
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• pp.579-587
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• 2015

Semiconductor manufacturing industry is a high density integration industry because it generates a vest number of data that takes about 300~400 processes that is supervised by numerous production parameters. It is asked of engineers to understand the correlation between different stages of the manufacturing process which is crucial in reducing production costs. With complex manufacturing processes, and defect processing time being the main cause. In the past, it was possible to grasp the corelation among manufacturing process stages through the engineer's domain knowledge. However, It is impossible to understand the corelation among manufacturing processes nowadays due to high density integration in current semiconductor manufacturing. in this paper we propose a canonical correlation analysis (CCA) using both wafer test voltage variables and fail bit counts variables. using the method we suggested, we can increase the semiconductor yield which is the result of the package test.

• Journal of the East Asian Society of Dietary Life
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• 제18권6호
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• pp.1089-1098
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• 2008

The principal objective of this study was to reveal the relationship between the menu management indicators and menu performance indicators in contract-foodservice operations. Menu indicators differed according to the type of business, type of contract, type of serving, and number of service lines. In accordance with the results of our correlation analysis, we noted significant correlations between menu performance indicators and menu management indicators. The first of these was the correlation between the food cost ration and meal counts, food loss, and the use of prepared vegetables. The second of these was the correlation between food cost per meal and forecasting error, food loss, and inventory turnover. The last of these correlations was the negative correlation between menu CSI(customer satisfaction index) and the use of prepared vegetables. According to the results of our canonical correlation analysis, 2 significant functions were identified. In the first function, we noted significant correlations between meal counts, use of prepared vegetables, food loss, and food cost ratio. Additionally, we noted significant correlations between forecasting error, inventory turnover, food loss, and food cost per meal in the second function. Menu management indicators had no influence on customer satisfaction.

• The Korean Journal of Applied Statistics
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• 제30권1호
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• pp.83-93
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• 2017

A typical approach to visualizing k (${\geq}2$)-group multidimensional data is to use Fisher's canonical discriminant analysis (CDA). CDA finds the best low-dimensional subspace that accommodates k group centroids in the Mahalanobis space. This paper proposes an alternative visualization procedure functioning in the Euclidean space, which finds the primary dimension with maximum discrimination of k group centroids and the secondary dimension with maximum dispersion of all observational units. This hybrid procedure is especially useful when the number of groups k is two.

• The Journal of the Acoustical Society of Korea
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• 제36권4호
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• pp.279-284
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• 2017

The localization of sources has a numerous number of applications. To estimate the position of sources, the relative time delay between two or more received signals for the direct signal must be determined. Although the GCC (Generalized Cross-Correlation) method is the most popular technique, an approach based on CCA (Canonical Correlation Analysis) was also proposed for the TDE (Time Delay Estimation). In this paper, we propose a new adaptive algorithm based on CCA in order to utilized the sparsity in the eigenvector of CCA based time delay estimator. The proposed algorithm uses the eigenvector corresponding to the maximum eigenvalue with log-sum regularization in order to utilize the sparsity in the eigenvector. We have performed simulations for several SNR(signal to noise ratio)s, showing that the new CCA based algorithm can estimate the time delays more accurately than the conventional CCA and GCC based TDE algorithms.

• Food Science of Animal Resources
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• 제29권1호
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• pp.132-139
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• 2009

The objective of this study was to investigate the most effective analysis methods for palatability grading of Hanwoo beef by comparing the results of discriminant analysis with sensory data. The sensory data were obtained from sensory testing by 1,300 consumers evaluated tenderness, juiciness, flavor-likeness and overall acceptability of Hanwoo beef samples prepared by boiling, roasting and grilling cooking methods. For the discriminant analysis with one factor, overall acceptability, the linear discriminant functions and the non-parametric discriminant function with the Gaussian kernel were estimated. The linear discriminant functions were simple and easy to understand while the non-parametric discriminant functions were not explicit and had the problem of selection of kernel function and bandwidth. With the three palatability factors such as tenderness, juiciness and flavor-likeness, the canonical discriminant analysis was used and the ability of classification was calculated with the accurate classification rate and the error rate. The canonical discriminant analysis did not need the specific distributional assumptions and only used the principal component and canonical correlation. Also, it contained the function of 3 factors (tenderness, juiciness and flavor-likeness) and accurate classification rate was similar with the other discriminant methods. Therefore, the canonical discriminant analysis was the most proper method to analyze the palatability grading of Hanwoo beef.

• Journal of Korea Multimedia Society
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• 제19권5호
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• pp.848-855
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• 2016

Face recognition has received a great deal of attention for the wide range of applications in real-world scenario. In this scenario, mismatches (so called heterogeneity) in terms of resolution and illumination between gallery and test face images are inevitable due to the different capturing conditions. In order to deal with the mismatch problem, we propose a local feature learning method using deep canonical correlation analysis (DCCA) for heterogeneous face recognition. By the DCCA, we can effectively reduce the mismatch between the gallery and the test face images. Furthermore, the proposed local feature learned by the DCCA is able to enhance the discriminative power by using facial local structure information. Through the experiments on two different scenarios (i.e., matching near-infrared to visible face images and matching low-resolution to high-resolution face images), we could validate the effectiveness of the proposed method in terms of recognition accuracy using publicly available databases.

• Journal of Agricultural Extension & Community Development
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• 제29권2호
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• pp.51-64
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• 2022

This study aims to investigate relationships between lifestyle and benefit sought of rural healing tourists. For data collection, a total of 3,000 copies of questionnaires were collected by nationwide online survey. The data were analyzed by using SPSS 26.0. The factor analysis identified seven dimensions of the lifestyle : conservative, sports activity orientation, health orientation, consumption orientation, achievement orientation, adventure orientation, and personal orientation. Five dimensions of benefit sought were identified as psychological recovery, outdoor activities, rest, rural experience, and exercise. The results of the canonical correlation analysis indicated that adventure orientation of lifestyle and psychological recovery, outdoor activities, rural experience, exercise of benefit sought were highly correlated. This means it is important to place an emphasis on psychological recovery, outdoor activities, rural experience, and exercise for tourists looking for an adventure away from everyday life. Rural healing tourism marketers should consider lifestyle aspects as the most important factors affecting benefit sought of rural healing tourism.

• Communications for Statistical Applications and Methods
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• 제29권1호
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• pp.103-125
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• 2022

In this paper, we analyze the time series data of the case and death counts of COVID-19 that broke out in China in December, 2019. The study period is during the lockdown of Wuhan. We exploit functional data analysis methods to analyze the collected time series data. The analysis is divided into three parts. First, the functional principal component analysis is conducted to investigate the modes of variation. Second, we carry out the functional canonical correlation analysis to explore the relationship between confirmed and death cases. Finally, we utilize a clustering method based on the Expectation-Maximization (EM) algorithm to run the cluster analysis on the counts of confirmed cases, where the number of clusters is determined via a cross-validation approach. Besides, we compare the clustering results with some migration data available to the public.