• Korean Journal of Veterinary Research
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• v.56 no.4
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• pp.269-271
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• 2016

A female domestic ferret (Mustela putorius furo) presented to a veterinary clinic with a clinical history of anorexia and poor body condition. Due to gradual deterioration of the body condition, explorative laparotomy was performed. Diffusely, the mesentery was severely thickened and adhered with prominent mesenteric lymph nodes. A portion of the mesentery and mesenteric lymph nodes were biopsied and fixed. Microscopic analysis revealed severe pyogranulomatous peritonitis and lymphadenitis, but staining revealed no bacterial organisms. However, immunohistochemistry for feline coronavirus exhibited strong immunoreactivity, primarily in the macrophages. Based on these results, the case was diagnosed as ferret coronavirus infection.

• 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.

• Knowledge Management Research
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• v.21 no.1
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• pp.41-59
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• 2020

This explorative study examines the difference in firm performance according to the adoption of the core technology of the Fourth industrial revolution, including artificial intelligence(AI), internet of things (IoT), cloud computing, and big data technology. Additionally, we investigate the importance of internal organizational structure exclusively responsible for information security. We analyze unique microdata offered by the Korea Information Society Development Institute to examine the impact of the adoption of the new technologies and the existence of organizational structure for information protection on firm performance, i.e., firm sales. By considering the core information technology as powerful knowledge assets, we argue that the adoption of such technology leads firms to have comparative advantage comparing to the competitors. Also, we emphasize the need to consider the organizational structure suitable for information security, which can become a structural asset of a firm.

• 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.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.366-372
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• 2002

본 연구는 제 1보와 2보로 구성되었으며, 본 연구의 목적은 1) 동작 인식형 입력기술에 기반한 디지털웨어(이하, DMDI로 약칭함)의 의류상품화 가능성을 탐색하고, 2) 소비자의 잠재적 수요에 기초하여 DMDI의 디자인을 개발하는 것이다. 제 1 보에서는 소비자의 DMDI 에 대한 잠재수요를 고찰하기 위하여, 디자인 에스노 그래피적 견지에 기초하여 개발된 심층면접 방식 및 범주분석 방식을 취하였다 그 분석 결과를 토대로 하여, DMDI를 위한 7가지의 가능성있는 애플리케이션 영역과 DMDI의 6가지 디자인 방향이 제안되었으며, 이를 토대로 디자인 프로토타입 개발을 위한 기본형 디자인을 제시하였다. 제 2보에서는 제 1보의 결과를 토대로 하여 DMDI의 디자인 프로토타입을 개발하였다. 본 학술발표는 제 1보의 내용을 중심으로 하여 DMDI에 대한 소비자 수요의 분석결과와 DMDI를 위한 기본형 디자인을 주요 내용으로 구성하였다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.216-220
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• 2002

The main objective of this studty is to design a conceptual framework of ${\ulcorner}Creativity\;Management\;System:\;CMS{\lrcorner}$. This study consists of (1) defining concept of CMS (2) sellecting ${\ulcorner}Structuring\;method{\lrcorner}$ for designing CMS (3) designing ${\ulcorner}Conceptual\;Framework{\lrcorner}$ for CMS. A conceptual framework of the CMS was designed by system design method based on ${\ulcorner}Normative\;approach{\lrcorner}$, and ${\ulcorner}Explorative\;approach{\lrcorner}$; that is ${\ulcorner}Ddeductive\;method{\lrcorner}$ and this is ${\ulcorner}Inductive\;method{\lrcorner}$.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.830-835
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• 2005

4D models help construction planners to develop and evaluate construction plans. However, current analyses using 4D models are mainly visual and limit the quantitative comparison of construction alternatives. This paper explores the usefulness of extracting quantitative information from 4D models to support time-space analyses. We use two 4D models of an industry test case to illustrate how to analyze 4D content quantitatively (i.e., work space areas and distances between concurrent activities). This paper shows how these two types of 4D content can be extracted from 4D models to support 4D-based-analysis and novel presentation of construction planning information. We suggest further research to formalize the content of 4D models to enable comparative quantitative analyses of construction planning alternatives. Formalized 4D content will enable the development of reasoning mechanisms that automate 4D-model-based analyses and provide the information content for informative presentations of construction planning information.