• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1139-1149
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• 2014

Multivariate volatility is summarized through canonical correlation analysis (CCA). Along with the standard CCA, non-negative and sparse canonical correlation analysis (NSCCA) is introduced to make sure that volatility coefficients are non-negative and the number of coefficients in the volatility CCA is as small as possible. Various multivariate financial time series are analyzed to illustrate the main contribution of the paper.

• Earthquakes and Structures
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• v.23 no.3
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• pp.259-269
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• 2022

In this study, we introduce a canonical correlation analysis method to accurately assess the tunnel damage potential of ground motion. The proposed method can retain information relating to the initial variables. A total of 100 ground motion records are used as seismic inputs to analyze the dynamic response of three different profiles of tunnels under deep and shallow burial conditions. Nine commonly used ground motion parameters were selected to form the canonical variables of ground motion parameters (GMP_CCA). Five structural dynamic response parameters were selected to form canonical variables of structural dynamic response parameters (DRP_CCA). Canonical correlation analysis is used to maximize the correlation coefficients between GMP_CCA and DRP_CCA to obtain multivariate ground motion parameters that can be used to comprehensively assess the tunnel damage potential. The results indicate that the multivariate ground motion parameters used in this study exhibit good stability, making them suitable for evaluating the tunnel damage potential induced by ground motion. Among the nine selected ground motion parameters, peck ground acceleration (PGA), peck ground velocity (PGV), root-mean-square acceleration (RMSA), and spectral acceleration (Sa) have the highest contribution rates to GMP_CCA and DRP_CCA and the highest importance in assessing the tunnel damage potential. In contrast to univariate ground motion parameters, multivariate ground motion parameters exhibit a higher correlation with tunnel dynamic response parameters and enable accurate assessment of tunnel damage potential.

• The Journal of the Acoustical Society of Korea
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• v.36 no.4
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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.

• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.305-313
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• 2019

Generalized canonical correlation analysis (GCCA) extends the canonical correlation analysis (CCA) to the case of more than two sets of variables and there have been many studies on how two-set canonical solutions can be generalized. In this paper, we derive certain stationary equations which can lead the higher-order solutions of several GCCA methods and suggest a type of iterative procedure to obtain the canonical coefficients. In addition, with some numerical examples we present the methods for graphical display, which are useful to interpret the GCCA results obtained.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.6
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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.

• Atmosphere
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• v.26 no.4
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• pp.711-716
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• 2016

The dynamical model forecasts using state-of-art general circulation models (GCMs) have some limitations to simulate the real climate system since they do not depend on the past history. One of the alternative methods to correct model errors is to use the canonical correlation analysis (CCA) correction method. CCA forecasts at the present time show better skill than dynamical model forecasts especially over the midlatitudes. Model outputs are adjusted based on the CCA modes between the model forecasts and the observations. This study builds a canonical correlation prediction model for subseasonal (June) precipitation. The predictors are circulation fields over western North Pacific from the Global Seasonal Forecasting System version 5 (GloSea5) and observed snow cover extent over Eurasia continent from Climate Data Record (CDR). The former is based on simultaneous teleconnection between the western North Pacific and the East Asia, and the latter on lagged teleconnection between the Eurasia continent and the East Asia. In addition, we suggest a technique for improving forecast skill by applying the ensemble canonical correlation (ECC) to individual canonical correlation predictions.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.11
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• pp.1576-1584
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• 2008

In this study, canonical correlation analysis (CCA) was applied to estimate the relationship between three different sexual maturity traits (X set: days to first egg (DFE), weight of the first egg (WFE), body weight at first egg (BWFE)) and level of nutrient intake (Y set: energy (EI) and protein intake (PI)) or the egg production traits at two different periods (Z set: number of egg (NE1 and NET) and weight of egg (WE1 and WET) from 22 to 25 (Wfirst) and 22 to 33 wk of age (Wall), respectively), which were measured from 64 egg-type pullets (Isa Brown) manipulated for time of access to energy and protein sources to onset of egg production. Partial CCA (PCCA) was used to eliminate the contribution of differences in the levels of nutrient intake to canonical variables for X and Z sets at the first production period. Estimated canonical correlation coefficients between X set and Y set (0.429, p = 0.042), X set and Z set (0.390, p = 0.007 for Wfirst) and within Z set (between Wfirst and Wall; 0.780, p<0.001), and partial canonical correlation coefficient between X set and Z set (0.415, p = 0.009) were significant. Canonical weights and loadings from CCA indicated that the BWFE had the largest contribution compared to the DFE and WFE to variation of egg number produced at two different periods. The results from PCCA indicated that the contribution of PI and EI to the degree of the correlation between canonical variables for X and Z sets were unfavourable. In conclusion, the effect of body weight at sexual maturity upon the availability of nutrients can have a higher contribution to variation of egg production in pullets if the contribution of differences in nutrient intakes to onset of egg production were eliminated.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.139-147
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• 2015

Steady state visual evoked potential (SSVEP) has been actively studied because of its short training time, relatively higher signal-to-noise ratio, and higher information transfer rate. There are two popular analysis methods for SSVEP signals: power spectral density analysis (PSDA) and canonical correlation analysis (CCA). However, the PSDA is known to be vulnerable to noise due to the use of a single channel. Although conventional CCA is more accurate than PSDA, it may not be appropriate for the real-time SSVEP-based BCI system when it has short time window length because it uses sinusoidal signals as references. Therefore, the two methods are not efficient for the real-time BCI system that requires a short TW and a high recognition accuracy. To overcome this limitation of the conventional methods, this paper proposes a frequency recognition method with a combination of CCA and PSDA using the difference between powers of canonical variables obtained from the results of CCA. Experimental results show that the performance of the combination of CCA and PSDA is better than that of CCA for the case of a short TW.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.105-110
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• 2012

The CCA (canonical correlation analysis) is a well known analysis tool that measures the linear relationship between two variable sets and it can be used for blind source separation (BSS). In previous works, a blind source separation scheme based on the CCA and auto regression was proposed. Unfortunately, the proposed scheme requires high signal-to-noise ratio for successful source separation. In this paper, we propose an improved BSS scheme based on the CCA and auto regression by eliminating the main diagonal elements of auto covariance matrix. Compared to the previously proposed BSS scheme, the proposed BSS scheme not only offers better source separation performance but also requires low computational complexity.

• Food Science of Animal Resources
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• v.38 no.4
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• pp.794-805
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• 2018

To identify correlation between fresh meat and processed meat products, we performed canonical correlation analysis (CCA) to predict the relationship between pork supply and meat product production in Korea. Results of CCA showed a canonical correlation of 0.8576 in the first canonical pair (p<0.01). The production of meat products showed the highest correlation with pork import but the lowest correlation with the production of domestic pork. Although Korean consumer preferred meat products produced by fresh domestic pork, inexpensively imported pork with high share in meat products was supplied in the market. Therefore, securing domestically produced raw meat is important for expanding consumption of domestic meat products. Results of this study suggest that meat processor and pig producer can achieve the $6^{th}$ industrialization by combining the production of raw pork materials, meat processing, and sales service.