• Asian Journal of Innovation and Policy
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• v.5 no.1
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• pp.35-54
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• 2016

This study starts from the perspective that preparing students for employment is the most important role of undergraduate degree programs. Therefore, we examine the determinants of undergraduate employment, especially highlighting the differences by disciplines. We classify 28 factors into five categories such as personal attributes, entrance attributes, students’ employment-related activities, regular curriculum and employability programs provided by universities. This study is based on data from 7,616 students from a Korean university over four and half years. Findings reveal that university efforts are crucial in engineering. Only exchange programs and employment programs have an impact on science. There are no specific factors in humanities and social science. Grade point average and students’ efforts are important in business. Face-to-face coaching in employability support programs is effective for securing employment except in science disciplines. The study results do not point to the absence of a role of university even in the low employment disciplines. Rather, the issue is that of an over-supply of graduates exceeding job offers that results from the worldwide expansion of higher education services.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.249-258
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• 2003

K-means clustering has been widely used in many applications, such that pattern analysis or recognition, data analysis, image processing, market research and so on. It can identify dense and sparse regions among data attributes or object attributes. But k-means algorithm requires many hours to get k clusters that we want, because it is more primitive, explorative. In this paper we propose a new method of k-means clustering using the grid-based sample. It is more fast than any traditional clustering method and maintains its accuracy.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.229-238
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• 2003

K-means clustering has been widely used in many applications, such that pattern analysis, data analysis, market research and so on. It can identify dense and sparse regions among data attributes or object attributes. But k-means algorithm requires many hours to get k clusters, because it is more primitive and explorative. In this paper we propose a new method of k-means clustering using the grid-based representative value(arithmetic and trimmed mean) for sample. It is more fast than any traditional clustering method and maintains its accuracy.

• Korean Management Science Review
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• v.33 no.3
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• pp.105-117
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• 2016

In this paper, we present empirical testing result to examine the validity of inbound supply and outbound demand risk factors in the sense of early predicting the firm's bankruptcy risk level. The risk factors are drawn from industry uncertainty attributes categorized as uncertainties of input market (inbound supply), and product market (outbound demand). On the basis of input-output table, industry level inbound and outbound sectors are identified to formalize supply chain structures, relevant inbound and outbound uncertainty attributes and corresponding risk factors. Subsequently, publicly available macro-economic indicators are used to appropriately quantify these risk factors. Total 68 industry level bankruptcy risk forecasting results are presented with the average R-square scores of between 53.4% and 37.1% with varying time lag. The findings offers useful insights to incorporate supply chain risk to the body of firm's bankruptcy risk level prediction literature.

• The Journal of Korea Robotics Society
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• v.6 no.1
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• pp.86-96
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• 2011

The human-following is one of the significant procedure in human-friendly navigation of mobile robots. There are many approaches of human-following technology. Many approaches have adopted various multiple sensors such as vision system and Laser Range Finder (LRF). In this paper, we propose detection and tracking approaches for human legs by the use of a single LRF. We extract four simple attributes of human legs. To define the boundary of extracted attributes mathematically, we used a Support Vector Data Description (SVDD) scheme. We establish an efficient leg-tracking scheme by exploiting a human walking model to achieve robust tracking under occlusions. The proposed approaches were successfully verified through various experiments.

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.535-543
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• 2003

Cluster analysis has been widely used in many applications, such as pattern analysis or recognition, data analysis, image processing, market research on on-line or off-line and so on. Clustering can identify dense and sparse regions among data attributes or object attributes. But it requires many hours to get clusters that we want, because clustering is more primitive, explorative and we make many data an object of cluster analysis. In this paper we propose a new method of clustering using sample based on grid. It is more fast than any traditional clustering method and maintains its accuracy.

• 한국데이터정보과학회:학술대회논문집
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• 2003.05a
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• pp.97-108
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• 2003

Cluster analysis has been widely used in many applications, such that pattern analysis or recognition, data analysis, image processing, market research on on-line or off-line and so on. Clustering can identify dense and sparse regions among data attributes or object attributes. But it requires many hours to get clusters that we want, because of clustering is more primitive, explorative and we make many data an object of cluster analysis. In this paper we propose a new method of clustering using sample based on grid. It is more fast than any traditional clustering method and maintains its accuracy. It reduces running time by using grid-based sample. And other clustering applications can be more effective by using this methods with its original methods.

• 한국데이터정보과학회:학술대회논문집
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• 2004.04a
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• pp.51-60
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• 2004

K-means clustering is an iterative algorithm in which items are moved among sets of clusters until the desired set is reached. K-means clustering has been widely used in many applications, such as market research, pattern analysis or recognition, image processing, etc. It can identify dense and sparse regions among data attributes or object attributes. But k-means algorithm requires many hours to get k clusters that we want, because it is more primitive, explorative. In this paper we propose a new method of k-means clustering using a center of gravity for grid-based sample. It is more fast than any traditional clustering method and maintains its accuracy.

• Journal of the Korean Home Economics Association
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• v.49 no.10
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• pp.103-113
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• 2011

This study investigated the structural relationships between attachment, social competence, and emotion regulation. A survey was administered to 233 children of elementary school age(5th-6th grades) in G-city, Korea, using the IPPA-R, the Social Competence Inventory and an Emotion Regulation Scale. The collected data were then analyzed using a Canonical Correlation Analysis. First, the relationship between attachment and social competence was analyzed. The results showed that attachment and social competence have a positively correlated relationship. Peer attachments strongly affect the attributes of social competence when using a canonical variate analysis. Secondly, the relationship between attachment and emotional regulation was analyzed. The results showed that attachment and emotion regulation are also positively correlated. Maternal attachment particularly strongly affected the attributes of emotion regulation. Thirdly, the relationship between social competence and emotional regulation was analyzed. The results showed that social competence and emotional regulation have a positive relationship.

• Fashion & Textile Research Journal
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• v.11 no.2
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• pp.252-261
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• 2009

The purpose of this study was to investigate the influence of female university students' cosmetic purchase motivation on cosmetic attribute evaluation and brand repurchase intention. Questionnaires data of 202 female university students who had purchase experience of cosmetic product in recent 6 months through off-line were analyzed. The results are as follows. First, situational purchase motivation had a positive impact on extrinsic and economic attributes. Second, intrinsic purchase motivation had a positive impact on extrinsic, utilitarian, aesthetic, and economic attributes. Third, hedonic purchase motivation had a positive impact on extrinsic attribute. Fourth, aesthetic attribute had a positive influence on brand repurchase intention and extrinsic attribute had a negative effect on brand repurchase intention. Therefore, when cosmetic companies dealing with female university students' cosmetic product establish marketing strategies, they need to pay attention to aesthetic attribute evaluation and intrinsic purchase motivation to highten their brand loyalty.