• Korean Journal of Human Ecology
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• v.20 no.4
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• pp.801-817
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• 2011

The purpose of this research was to explore the levels of ethical consumption of the college students and classify their types on ethical consumption behaviors. This research was conducted with university students living in Gwangju. Statistical analysis was achieved by using t-test, one-way ANOVA, Duncan's multiple range test, $X^2$, and Ward' hierarchical cluster analysis with a total of 761 questionnaires. The research results are summarized as follows: First, the overall ethical consumption average mark of college students was 3.14. Second, all surveyed college students were classified into five types based on the means scores of three dimension ethical consumption behaviors. A total 16.7% of students belonged to Type 1 (named as entire region active group) where students scored high points on three dimension ethical consumption behaviors. Type 2 (named as entire region average group) had about 41.6% of students whose scores were the average mark level in three dimension ethical consumption behaviors. Type 3 (named as future-oriented group) occupied 13.9% and this group scored low on the ethical consumption in commercial transaction but high on the ethical consumption for the future generation. Type 4 (named as commercial transaction oriented group) occupied 9.1% and this group scored low on the ethical consumption for contemporary humankind and the ethical consumption for the future generation but high on the ethical consumption in commercial transaction. Type 5 (named as entire region passive group) had 18.7% of students whose scores of three dimension ethical consumption behaviors were low.

• Journal of the Korean Society of Clothing and Textiles
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• v.28 no.910
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• pp.1329-1340
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• 2004

The purpose of this study is to survey about the images of the sexy clothes that young women sympathize in modern society, that is, to make a research into the cognitive dimensions of 'Sexy image' and the constructive peculiarity of clothing. The researches into dimension of image by sexy clothing make that female mainly consist of in their twenties during May to October in 2003. The independent variables were: 1) The physical beauty dimension, 2) The negative estimate dimension, 3) The characteristic devaluation dimension,4) The elegance/ high grade dimension,5) The freshness/ beauty dimension,6) The sex appeal/ exposure dimension. Judging from each characteristic of costume in classified by articles, that is, colors, texture, shapes, details, and others-phenomenon of outerization of under clothing, print. We can know the clothes showing up our nice figures are the sleeveless design tied with strap, soft texture as silk chiffon and the colors that are not strong. And there were no embarrassed, ugly and pitiful clothing. Expressing sexy beauty by clothing is the result that the wearer produces intentionally by compounding design factors, it's the ideal when the observer's reaction and the wearer's intention correspond. Therefore, it will be helpful in expressing ideally if the subject, what kinds of sexy beauty the compounding design factors express, is studied carefully. Also it is expected to be used in grasping modem consumers' desires that want to look sexy, finding out how to express, dealing with the consumers' desires correctly, and the reference materials of the planning goods, sales promotion, and selecting of a medium of advertisement according to the consumers' desires.

• 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.

• Korean Journal of Computational Design and Engineering
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• v.1 no.2
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• pp.122-132
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• 1996

Features are generic shapes with which engineers associate certain attributes and knowledge useful in reasoning about the product. Feature-based modeling systems support additional levels of information beyond those available in geometric modelers. The objective of this study is to develop a PC level feature-based modeling system which explicitly represents dimensions of the part. The feature-based modeler retains all the benefits of traditional B-rep. solid models, and represents the dimensions at a high level of a abstraction so that dimension driven geometry can be achieved.

• Journal of Industrial Technology
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• v.20 no.B
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• pp.207-211
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• 2000

For efficient massive image retrieval, an image retrieval requires that several important objectives are satisfied, namely: automated extraction of features, efficient indexing and effective retrieval. In this work, we present a technique for extracting the 4-dimension directional feature. By directional detail, we imply strong directional activity in the horizontal, vertical and diagonal direction present in region of the image texture. This directional information also present smoothness of region. The 4-dimension feature is only indexed in the 4-D space so that complex high-dimensional indexing can be avoided.

• International Journal of Contents
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• v.3 no.2
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• pp.1-5
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• 2007

This paper proposes a technique for principal component analysis (PCA) to raise the recognition rate of a front face in a low dimension by hierarchical image and parallel processing structure. The conventional PCA shows a recognition rate of less than 50% in a low dimension (dimensions 1 to 6) when used for facial recognition. In this paper, a face is formed as images of 3 fixed-size levels: the 1st being a region around the nose, the 2nd level a region including the eyes, nose, and mouth, and the 3rd level image is the whole face. PCA of the 3-level images is treated by parallel processing structure, and finally their similarities are combined for high recognition rate in a low dimension. The proposed method under went experimental feasibility study with ORL face database for evaluation of the face recognition function. The experimental demonstration has been done by PCA and the proposed method according to each level. The proposed method showed high recognition of over 50% from dimensions 1 to 6.

• Journal of Korean Academy of Nursing Administration
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• v.17 no.4
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• pp.524-537
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• 2011

Purpose: The purpose of this study was to analyze the role performance and related factors of the Clinical Research Coordinator (CRC). Methods: A quantitative, cross-sectional design was used. Data were collected using a structured questionnaire from 134 Clinical Research Coordinators in 4 hospitals and analyzed using descriptive statistics, t-test, ANOVA, and Pearson correlation coefficient. Results: The scores for role performance of CRC were high (average 336.51 points of a possible 420 points, mean average $5.74{\pm}0.67$ of a possible 7) and the degree of role performance in the management task sub dimension (mean average $6.12{\pm}0.77$ of a possible 7) was higher than any other sub dimension. Role performance of the CRC was also related to job satisfaction (r=.198, p=.023). Conclusion: The study results indicate that most CRC are nurses and perceive their role as very important. Further the CRC performance of activities of management task and the Coordination/Advocacy task sub dimension were high and higher levels of job satisfaction were related to higher levels of role performance of CRC was. Therefore, strategies related to improvement in the fringe benefit system and work environment should be developed to increase job satisfaction and thus increase job performance.

• Communications for Statistical Applications and Methods
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• v.23 no.3
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• pp.259-268
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• 2016

High-dimensional survival data with large numbers of predictors has become more common. The analysis of such data can be facilitated if the dimensions of predictors are adequately reduced. Recent studies show that a method called sliced inverse regression (SIR) is an effective dimension reduction tool in high-dimensional survival regression. However, it faces incapability in implementation due to a double categorization procedure. This problem can be overcome in the right-censoring type by transforming the observed survival time and censoring status into a single variable. This provides more flexibility in the categorization, so the applicability of SIR can be enhanced. Numerical studies show that the proposed transforming approach is equally good to (or even better) than the usual SIR application in both balanced and highly-unbalanced censoring status. The real data example also confirms its practical usefulness, so the proposed approach should be an effective and valuable addition to usual statistical practitioners.

• Journal of The Korean Astronomical Society
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• v.49 no.6
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• pp.255-259
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• 2016

We estimate the fractal dimension of the ${\rho}$ Ophiuchus Molecular Cloud Complex, associated with star forming regions. We selected a cube (${\upsilon}$, l, b) database, obtained with J = 1-0 transition lines of $^{12}CO$ and $^{13}CO$ at a resolution of 22" using a multibeam receiver system on the 14-m telescope of the Five College Radio Astronomy Observatory. Using a code developed within IRAF, we identified slice-clouds with two threshold temperatures to estimate the fractal dimension. With threshold temperatures of 2.25 K ($3{\sigma}$) and 3.75 K ($5{\sigma}$), the fractal dimension of the target cloud is estimated to be D = 1.52-1.54, where $P{\propto}A^{D/2}$, which is larger than previous results. We suggest that the sampling rate (spatial resolution) of observed data must be an important parameter when estimating the fractal dimension, and that narrower or wider dispersion around an arbitrary fit line and the intercepts at NP = 100 should be checked whether they relate to firms noise level or characteristic structure of the target cloud. This issue could be investigated by analysing several high resolution databases with different quality (low or moderate sensitivity).

• Journal of Korean Elementary Science Education
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• v.31 no.1
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• pp.1-12
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• 2012

The purpose of this study was to explore instructional objectives of 'Wise Life' based on Bloom's Revised Taxonomy of Educational Objectives. It was to extend understanding the 7th and the revised Wise Life 2007 as well. The written objectives of two curriculum of Wise Life were divided into two, the 'knowledge' dimension and the 'cognitive process' dimension based on Bloom's Revised Taxonomy of Educational Objectives. Major activity subjects presented in the 7th and the revised Wise Life 2007 were dominantly the basic inquiry activity types. It was found from this research that the 'factual' knowledge of the 'knowledge' dimension and the 'understand' of the 'cognitive process' dimension took a large proportion of the revised Wise Life 2007 objectives. The 'meta-cognitive' knowledge was increased. 'Remember' of the 'cognitive process' dimension was decreased and the high level objectives as 'analyze' and 'create', in the 'cognitive process' dimension was increased from the 7th to the revised curriculum. The 'factual' knowledge, 'conceptual' knowledge and 'metacognitive' knowledge were engaged with 'understand' and the 'procedural' knowledge were engaged with 'apply'. The 'meta- cognitive' were engaged with 'create' is the 'procedural' knowledge in the revised Wise Life 2007. 'Remember' was decreased. 'Create' was increased in the basic inquiry activities. It was analysed in zero percent of 'Observing', 'Investigating and Presenting', and 'Making'.