• Journal of Families and Better Life
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• v.14 no.4
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• pp.1-12
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• 1996

The purpose of this study were i)to investigate children's perceived social support and satisfaction with level of social support and ii) to investigate the relations between children's social support and their adjustment. Subjects of this study were 412 children from the 5-6th grades of elementary school and the data were analyzed by GLM analysis canonical correlation analysis using SAS. The results were as follows: 1)Children's perceived social support levels differed across support providers and support types. 2) A canonical correlation analysis of the children's social support and the children's adjustment demonstrated that perceived maternal and peer support levels were most highly correlated to children's adjustment and satisfaction with the social support of providers in this study was highly correlated to children's adjustment.

• Industrial Engineering and Management Systems
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• v.12 no.1
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• pp.2-8
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• 2013

Uncertainty theory is a branch of mathematics based on normolity, duality, subadditivity and product axioms. Uncertain process is a sequence of uncertain variables indexed by time. Canonical Liu process is an uncertain process with stationary and independent increments. And the increments follow normal uncertainty distributions. Uncertain differential equation is a type of differential equation driven by the canonical Liu process. Stability analysis on uncertain differential equation is to investigate the qualitative properties, which is significant both in theory and application for uncertain differential equations. This paper aims to study stability properties of linear uncertain differential equations. First, the stability concepts are introduced. And then, several sufficient and necessary conditions of stability for linear uncertain differential equations are proposed. Besides, some examples are discussed.

• Culinary science and hospitality research
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• v.23 no.4
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• pp.105-115
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• 2017

Water is an essential ingredient to brew coffee. Mineral contents in the water can affect both water quality and taste quality of coffee. The effects of minerals in the water on sensory characteristics of coffee were investigated in different water samples (A: Arisu, B: Claris, C: Spring water, D: Samdasoo, E: Evian, Distilled water as control). Based on the results of quantitative descriptive analysis (QDA), there were statistically significant (p<0.01) in flavor, acidity, bitterness, sweetness, body and aftertaste according to different water samples used to brew coffee. The canonical correlation analysis of minerals (Ca, Mg, Na, K) and taste (acidity, bitterness, sweetness) indicated that there were highly correlated in the relationship between bitterness and Mg content. A strong negative relationship was shown between bitterness and acidity, sweetness. A result of preference test using hedonic scale showed an inverse linear relationship between taste quality and total mineral contents.

• 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.

• Atmosphere
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• v.25 no.1
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• pp.179-183
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• 2015

Even though state-of-the-art general circulation models is improved step by step, the seasonal predictability of the East Asian summer monsoon still remains poor. In contrast, the seasonal predictability of western North Pacific and Indian monsoon region using dynamic models is relatively high. This study builds canonical correlation analysis model for seasonal prediction using wind fields over western North Pacific and Indian Ocean from the Global Seasonal Forecasting System version 5 (GloSea5), and then assesses the predictability of so-called hybrid model. In addition, we suggest improvement method for forecast skill by introducing the lagged ensemble technique.

• Korean Journal of Child Studies
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• v.16 no.1
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• pp.99-112
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• 1995

This study aimed (1) to classify Korean kindergarten childrens' attachment to their mothers based on a system for classifying attachment organization developed by Main and Cassidy (1988), and (2) to investigate the relationship of attachment and temperament and mothers' child rearing behavior. 76 kindergarteners and their mothers were observed and videotaped in the strange situation. The modified PTQ(Parent and Teacher Temperament Questionnaire) for children 3-7 years of age and the IPBI(Iowa Parent Behavior Inventory: Mother Form) were administered respectively to 76 mothers to assess their parenting behavior and their children's temperament. The data were analyzed by percentiles, Pearson's correlations, and canonical correlation analysis. Results indicated that there was a little difference between the attachment classification of Main and Cassidy(1988) and that of Korean kindergarten children. There were significant correlations between children's temperament and the attachment to their mother. And mothers' parenting behavior was significantly related to the security of attachment. The canonical correlation analysis indicated that independent variables all together accounted for about 7.5% of the variation in attachment-variables.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.751-759
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• 2008

Mixtures of factor analyzers(MFA) is useful to model the distribution of high-dimensional data on much lower dimensional space where the number of observations is very large relative to their dimension. Mixtures of common factor analyzers(MCFA) can reduce further the number of parameters in the specification of the component covariance matrices as the number of classes is not small. Moreover, the factor scores of MCFA can be displayed in low-dimensional space to distinguish the groups. We propose the factor scores of MCFA as new low-dimensional features for classification of high-dimensional data. Compared with the conventional dimension reduction methods such as principal component analysis(PCA) and canonical covariates(CV), the proposed factor score was shown to have higher correct classification rates for three real data sets when it was used in parametric and nonparametric classifiers.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.1
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• pp.101-121
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• 2009

The research literature presents that Total Quality Management (TQM) has been successfully embraced by firms for strategic and operational management in the last decades. However few studios analyze the role of TQM practices in the R&D environment. The objective of this study is to evaluate TQM practices implemented in manufacturing-based R&D organizations with respect to R&D performance. Our research model is primarily based on Malcolm Baldrige Criteria for Performance Excellence framework, including three moderating variables (industry type, organization size, quality management system certification), to propose hypotheses on the relationships between leadership practice, management practice and R&D performance. Results of canonical correlation analysis using data from 130 R&D organizations show that in aggregate leadership practice is positively related to management practice which is, in turn, positively related to R&D performance. These relationships are, however, affected by the moderating variables. Our results imply that TQM practices can be strategically adopted in the R&D organization.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1143-1152
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• 2009

A canonical correlation analysis(CCA)-based method is proposed for prediction of future climate change which combines information from ensembles of atmosphere-ocean general circulation models(AOGCMs) and observed climate values. This paper focuses on predictions of future climate on a regional scale which are of potential economic values. The proposed method is obtained by coupling the classical CCA with empirical orthogonal functions(EOF) for dimension reduction. Furthermore, we generate a distribution of climate responses, so that extreme events as well as a general feature such as long tails and unimodality can be revealed through the distribution. Results from real data examples demonstrate the promising empirical properties of the proposed approaches.