• 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.

• Journal of Technology Innovation
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• v.15 no.2
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• pp.83-102
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• 2007

This study extends the existing method of Technology Forecasting with Data Envelopment Analysis (TFDEA) by incorporating a ranking method into the model so that we can reduce the required number of DMUs (Decision Making Units). TFDEA estimates technological rate of change with the set of observations identified by DEA(Data Envelopment Analysis) model. It uses an excessive number of efficient DMUs(Decision Making Units), when the number of inputs and outputs is large compare to the number of observations. Hence, we investigated the possibility of incorporating CCCA(Constrained Canonical Correlation Analysis) into TFDEA so that the ranking of DMUs can be made. Using the ranks developed by CCCA(Constrained Canonical Correlation Analysis), we could limit the number of efficient DMUs that are to be used in the technology forecasting process. The proposed hybrid model could establish technology frontiers with the efficient DMUs for each generation of technology with the help of CCCA that uses the common weights. We applied our hybrid model to forecast the technological progress of main battle tank in order to demonstrate its forecasting capability with practical application. It was found that our hybrid model generated statistically more reliable forecasting results than both TFDEA and the regression model.

• 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.

• 한국해양학회지
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• v.22 no.1
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• pp.34-42
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• 1987

Canonical correlation analysis was applied on phytoplankton species and associated physico-chemical environmental factors of the surface mixed-later in the southwestern waters of the East Sea (Sea of Japan), Korea. Water temperature was the most significant environmental factor for the distribution of phytoplandton species among the seven factors examined in spring, and salinity in autumn. The importance of these two environmental factors was discussed with the seasonal variations of the hydrographical regime. Kuroshio indicators' and two protoperidinium species were positively associated with high water temperatures and high salinity. Small pennate diatoms and silicoflagellates seem to prefer lower temperatures, and they might compete each other for silicate nutrient resulting in lower K value in silicoflagellate species than in small diatoms. A nitrogen fixing blue-green alga, Oscillatoria erythraea, was found to be positively associated with phosphate concentration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.810-831
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• 2019

Recently, continuous dimensional emotion recognition from audiovisual clues has attracted increasing attention in both theory and in practice. The large amount of data involved in the recognition processing decreases the efficiency of most bimodal information fusion algorithms. A novel algorithm, namely the incomplete Cholesky decomposition based kernel cross factor analysis (ICDKCFA), is presented and employed for continuous dimensional audiovisual emotion recognition, in this paper. After the ICDKCFA feature transformation, two basic fusion strategies, namely feature-level fusion and decision-level fusion, are explored to combine the transformed visual and audio features for emotion recognition. Finally, extensive experiments are conducted to evaluate the ICDKCFA approach on the AVEC 2016 Multimodal Affect Recognition Sub-Challenge dataset. The experimental results show that the ICDKCFA method has a higher speed than the original kernel cross factor analysis with the comparable performance. Moreover, the ICDKCFA method achieves a better performance than other common information fusion methods, such as the Canonical correlation analysis, kernel canonical correlation analysis and cross-modal factor analysis based fusion methods.

• 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 Korean Data and Information Science Society
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• v.15 no.2
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• pp.297-306
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• 2004

It is usual that multivariate data analysis produces related (small number of) artificial variates for data reduction. Among them, refer to MDS(multidimensional scaling), MDPREF(multidimensional preference analysis), CDA(canonical discriminant analysis), CCA(canonical correlation analysis) and FA(factor analysis). Varimax rotation of artificial variables which is originally invented in FA for easy interpretations is applied to diverse multivariate techniques mentioned above. Real data analysisis is performed in order to manifest that rotation improves interpretations of artificial variables.

• Journal of Families and Better Life
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• v.27 no.3
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• pp.159-170
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• 2009

The purpose of this study was (1) to investigate the difference of temperament, self-regulation, and problem behavior according to children's gender and age, (2) to investigate the relationship among three variables by use of canonical correlation analysis. The subjects were 210 children ages from 3 to 5 in five day-care center in Incheon city. The SPSS WIN 12.0 Program was used to analyze the collected data. The statistical methods were Frequency, Percentage, Cronbach's ${\alpha}$, Two-way ANOVA, Correlation analysis, and Canonical Correlation analysis. The major results of this research are summarized as follows: (1) Children's temperament were significantly different by their gender and age. The boys got higher score than girls in adaptability, activity, and total temperament. Girls got higher score than boys in reactivity. The age 3 got higher score than the age 5 in activity and the ages 3, 4 got higher than the age 5 in total temperament. (2) Children's self-regulation were significantly different by their age. The children who were older got higher scores in self-regulation ability. (3) Children's problems behavior were significantly different by their gender and age. The boys got higher score than girls in anxiety behavior, aggressive behavior, hyperactive/distractive behavior, and total problem behavior. And the ages 3, 4 got higher score than the age 5 in anxiety behavior and hyperactive/distractive behavior. The age 4 got higher score the ages 3, 5 in aggressive behavior and the age 3 got higher score the ages 4, 5 in total problem behavior. (4) Canonical correlation results showed that stable temperament and self-regulation were positively correlated and self-regulation and problem behavior were negatively correlated.

• Journal of the military operations research society of Korea
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• v.33 no.2
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• pp.61-73
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• 2007

We examined technological forecasting of extended TFDEA(Technological Forecasting with Data Envelopment Analysis) and thereby apply the extended method to the technological forecasting problem of main battle tank. The TFDEA has the possibility of using comparatively inefficient DMUs(Decision Making Units) because it is based on DEA(Data Envelopment Analysis), which usually leads to multiple efficient DMUs. Therefore, TFDEA may result in incorrect technological forecasting. Instead of using the simple DEA, we incorporated the concept of Super-efficiency, Cross-efficiency, and CCCA(Constrained Canonical Correlation Analysis) into the TFDEA respectively, and applied each method to the case study of main battle tank using verifiable practical data sets. The comparative analysis shows that the use of CCCA with TFDEA results in very comparable prediction accuracies with respect to MAE(Mean Absolute Error), MSE(Mean Squared Error), and RMSE(Root Mean Squared Error) than using the concept of Super-efficiency and Cross-efficiency.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.208-221
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• 2012

The purpose of this study is to investigate the antecedent variables of customer citizenship behavior selected by a social exchange point of view on the Boryeong Mud Festival. Of all the questionnaires collected, 239 were included in the analysis which was performed using SPSS. Date analysis method used to frequency analysis, factor analysis, reliability analysis and canonical correlation analysis. As the results of the canonical correlation analysis between the customer citizenship behavior and the social exchange point of view, there was a significant relationship between the attributes of customer citizenship behavior - loyalty, cooperation, Participation and social exchange point of view - customer satisfaction, customer confidence, perceived support of customers, justice perception. Therefore, marketing practitioner should recognize the importance of customer's perception in the service circumstance and make a managerial effort to offer the best service to customer. The result of this paper may help local festival planner and marketers for festival marketing strategy.