• 디지털산업정보학회논문지
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• 제14권4호
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• pp.35-45
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• 2018

Users are sharing many of contents such as text, image, video, and so on in SNS. There are various information as like as personal interesting, opinion, and relationship in social media contents. Therefore, many of recommendation systems or search systems are being developed through analysis of social media contents. In order to extract subject-related topics of social context being collected from social media channels in developing those system, it is necessary to develop ontologies for semantic analysis. However, it is difficult to develop formal ontology because social media contents have the characteristics of non-formal data. Therefore, we develop a social topic system based on semantic and subject correlation. First of all, an extracting system of social topic based on semantic relationship analyzes semantic correlation and then extracts topics expressing semantic information of corresponding social context. Because the possibility of developing formal ontology expressing fully semantic information of various areas is limited, we develop a self-extensible architecture of ontology for semantic correlation. And then, a classifier of social contents and feed back classifies equivalent subject's social contents and feedbacks for extracting social topics according semantic correlation. The result of analyzing social contents and feedbacks extracts subject keyword, and index by measuring the degree of association based on social topic's semantic correlation. Deep Learning is applied into the process of indexing for improving accuracy and performance of mapping analysis of subject's extracting and semantic correlation. We expect that proposed system provides customized contents for users as well as optimized searching results because of analyzing semantic and subject correlation.

• 한국의류학회지
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• 제43권3호
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• pp.440-458
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• 2019

Based on a 3D body data and pattern comparison analysis, this study developed a formal jacket pattern for men in their late 30s. In order to select the representative type of men in their late 30s, factor analysis and cluster analysis were conducted on data form 319 men, 35 to 39 years old using the anthropometric data from The 7th Size Korea (2015) as the representative body type. The surface of the body surface was developed using a 3D human shape of a male in his 30s in The 6th Size Korea (2010). Then the shape was changed to a flat pattern that confirmed the necessary elements for setting the shape and dimension. Cluster analysis revealed type B as the representative type because it showed the best shape characteristics for men in the late 30s. The drafting method of the final research pattern is as follows. Jacket length: stature/2.5cm, back length: stature/5+8.5cm (constant)], armhole depth: [stature/ 7-1.5cm (constant)], back width: [C/9+9.5cm (constant)]+1cm (ease), front width: [C/9+8.5cm (constant)]+1cm (ease), armscye depth: C/8, front waist darts: 1cm, front closure amount: 2cm.

• Journal of Computing Science and Engineering
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• 제3권2호
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• pp.90-108
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• 2009

In this paper, we present an ontology mediation solution based on the methods frequently used in Formal Concept Analysis. Our approach of mediation is based on the existence of instances associated to two source ontologies, then we can generate concepts in a new ontology if and only if they share the same extent. Hence our approach creates a merged ontology which captures the knowledge of these two source ontologies. The main contributions of this work are (i) to enable the creation of concepts not originally in the source ontologies, (ii) to propose a solution to label these emerging concepts and finally (iii) to optimize the resulting ontology by eliminating redundant or non pertinent concepts. Another contribution of this work is to emphasize that several forms of mediated ontology can be defined based on the relaxation of certain criteria produced from our method. The solution that we propose for tackling these issues is an automatic solution, meaning that it does not require the intervention of the end-user, excepting for the definition of the common set of ontology instances.

• 패션비즈니스
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• 제14권3호
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• pp.75-89
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• 2010

The purpose of this study is to provide information how to locate a apparel store based on target age, merchandise's type and price. Three trade areas in Seoul are chosen: Myeong-dong which is the biggest trade area in Korea; Kangnam station one of representative Kangnam trade areas; and Myeongil-Dong a neighborhood trade area. This study is mainly performed by analysing the locations of the stores in the given areas. The result shows that the main apparel stores in Myeong-dong are casual wear stores for young people with the range of mid-high price. The stores in Kangnam station trade area also sell the casual wear for young people but they are mostly mid-low priced. In the trade area of Myeongil-dong, however, there are various kinds of mid-priced apparel stores for residents of all ages. Apparel stores for formal wear, casual wear, and formal-casual wear tend to be located side by side. But other kinds do not. These results show that affinity is found in some types of apparel stores by the analysis of the next-door apparel stores.

• 복식문화연구
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• 제13권6호통권59호
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• pp.990-998
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• 2005

The purpose of the study was to examine Korean elderly women's attitudes toward fashion and to determine formal jacket designs preferred by them. The subjects of the study were older than 50 years who will become aging population in 2014. For data collection, interview investigated 200 copies of questionnaire were collected, and available data used final analysis were 174 volumes. As the results of the principal components factor analysis, it revealed 4 attitudinal factors including cautious attitude, fashion-conscious attitude, ostentatious attitude, and easy-conscious attitude. The subjects divided into two groups, highly involved group and lowly involved group, for each factor. As the result of preference differences by the degree of each attitude, it revealed that the attitudes toward fashion products would significantly influence elderly women's jacket design preferences. The results of the study show that they are a diverse group whose consumer needs and wants vary dramatically and give initial information to assist designing appropriate formal jacket for elderly women.

• 한국IT서비스학회지
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• 제7권3호
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• pp.271-286
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• 2008

The core of the semantic web is ontology, which supports interoperability among semantic web applications and enables developer to reuse and share domain knowledge. It used a variety of fields such as Information Retrieval, E-commerce, Software Engineering, Artificial Intelligence and Bio-informatics. However, the reality is that various errors might be included in conceptual hierarchy when developing ontologies. Therefore, methodologies and supporting tools are essential to help the developer construct suitable ontologies for the given purposes and to detect and analyze errors in order to verify the inconsistency in the ontologies. In this paper we propose a new approach for ontology error detection based on the Concept Lattices of Formal Concept Analysis. By using the tool that we developed in this research, we can extract core elements from the source code of Ontology and then detect some structural errors based on the concept lattices. The results of this research can be helpful for ontology engineers to support error detection and construction of "well-defined" and "good" ontologies.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2005년도 추계학술발표대회 및 정기총회
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• pp.251-254
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• 2005

계층적 개념구조는 대상 도메인으로부터 지식을 표현하는데 있어서 간결하면서도 효과적으로 개념간의 구조를 설명하기 위해 사용되고, 데이터의 구조화와 요약을 제공하며 필요한 정보의 수월한 접근을 제공하기 위해 널리 적용되고 있다. 그러나, 대상도메인으로부터 계층적 개념구조를 구축하기 위해서는 많은 시간과 노력이 요구된다. 따라서 계층적 개념구조를 효율적으로 구축하기 위한 체계가 필요하다. 본 논문에서는 계층적 개념구조를 체계적으로 구축하기 위한 형식개념분석기법(FCA, Formal Concept Analysis)을 토대로, Many-valued context와 Scaling을 자동화하기 위한 도구로써 본 연구에서 개발된 FCA Wizard를 소개한다.

• Journal of Electrical Engineering and information Science
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• 제2권6호
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• pp.191-196
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• 1997

Fuzzy clustering has been playing an important role in solving many problems. Fuzzy c-Means(FCM) algorithm is most frequently used for fuzzy clustering. But some fixed point of FCM algorithm, know as Tucker's counter example, is not a reasonable solution. Moreover, FCM algorithm is impossible to perform the on-line learning since it is basically a batch learning scheme. This paper presents unsupervised learning networks as an attempt to improve shortcomings of the conventional clustering algorithm. This model integrates optimization function of FCM algorithm into unsupervised learning networks. The learning rule of the proposed scheme is a result of formal derivation based on the gradient descent procedure of a fuzzy objective function. Using the result of formal derivation, two algorithms of fuzzy cluster analysis, the batch learning version and on-line learning version, are devised. They are tested on several data sets and compared with FCM. The experimental results show that the proposed algorithms find out the reasonable solution on Tucker's counter example.

• 한국GIS학회:학술대회논문집
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• 한국GIS학회 2008년도 공동춘계학술대회
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• pp.91-96
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• 2008

국토 모니터링을 위해서는 다양한 출처와 종류의 자료를 통합하여 제공하여야 한다. 특히 국토 모니터링의 범위에는 원격탐사와 같은 공중 모니터링과 지상 모니터링 자료가 모두 포함되기 때문에 매우 다양한 종류와 속성을 가진 지리정보 자료들이 통합되어야 한다. 본 연구에서는 다양한 출처와 종류의 지리정보자료를 효과적으로 통합하는 방법으로 형식개념분석(Formal Concept Analysis, FCA)을 살펴보고 사례분석을 통해 이 기법의 적용 가능성을 파악하고자 한다. 연구결과 형식개념분석을 통하여, 다양한 종류의 자료가 가지고 있는 중복을 제거하고 이를 체계적으로 정리하여 통합할 수 있다. 본 연구에서는 형식개념 분석의 개념을 파악하고 현재 활용되고 있는 지리정보자료들에 적용하여 평가함으로써 국토 모니터링 자료의 통합기법으로 적용될 수 있는 가능성을 연구하였다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2004년도 추계학술발표논문집(상)
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• pp.275-278
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• 2004

객체지향 프로그램에 있어 클래스계층구조는 프로그램의 뼈대가 된다. 따라서 이러한 클래스계층구조를 얼마나 잘 만드느냐에 따라 프로그램의 품질이 좌우된다. 그러나 좋은 품질의 클래스계층구조를 구축하는 작업은 객체지향 초보자에게는 쉬운 일이 아니다. 본 논문에서는 FCA(Formal Concept Analysis)기법을 이용하여 클래스계층구조 설계 도구를 BlueJ 의 확장기능으로 구현하였다. 본 연구결과는 객체지향 프로그래밍 초보자들이 클래스계층구조를 보다 수월하게 설계함으로써 좀 더 좋은 프로그램을 작성 할 수 있는 지원도구로서 제공될 수 있다.