• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.340-342
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• 2002

다중 값이란 속성 값이 집합인 것을 말한다. 즉, 관계형 데이터베이스에서 자료 유형이 집합인 속성을 의미한다. 이러한 다중 값 속성 처리는 기존 데이터마이닝 기술 자체로는 처리한 수 없으며 후처리나 선처리 과정을 이용하여 처리하고 있다. 전처리나 후처리 과정을 통해 처리할 경우 수행과장에 있어 많은 시간이 소요되고 혹은 타당하지 않은 규칙이 생성되는 문제점을 가지고 있다. 특히 연관화 기법 특성상 분석하고자 할 항목이 증가할수록 연관성의 수가 지수(exponential)단위이기 때문에 이를 해결하는데는 상당한 어려움이 따르게 된다. 본 논문에서는 관계형 데이터베이스 테이블 구조에서 데이터 마이닝의 수행을 위한 전처리나 후처리의 과정을 고려하지 않음으로 위에서 언급된 문제점들을 해결하고자 한다. 특히 데이터 변환 작업 없이 정량적(Quantitative)연관 규칙과 연관 규칙(Market Basket Analysis)의 혼합 형태의 규칙을 생성할 수 있게끔 알고리즘을 확장하여 보다 효율적인 규칙이 생성될 수 있도록 한다. 마지막으로 Each Movie 데이터를 사용하여 확장한 알고리즘의 다중 값 속성 처리 방법의 효율성과 타탕성을 검증한다.

• Journal of Korea Society of Digital Industry and Information Management
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• v.4 no.1
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• pp.89-104
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• 2008

The aim of this study is to analyze the distribution structure of exclusive digital muti-media broadcasting's contents. This study takes into distribution wether exclusive contents create an additional window being serviced to other media. And it investigates how the contents work in window through analysis of programming table and interview with programming editors of each DMB service company. The window effect is a same notion to the one source multi use. In the film industry, window means a movie distribution that a film can be on one after the other starting the cinema, DVD, local cable channel, and terrestrial television. The key point of this paper is how to new media, DMB, creates its additional window and changes the existing window structure.

• Journal of Information Science Theory and Practice
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• v.1 no.1
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• pp.10-26
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• 2013

Domain transfer is a widely recognized problem for machine learning algorithms because models built upon one data domain generally do not perform well in another data domain. This is especially a challenge for tasks such as opinion classification, which often has to deal with insufficient quantities of labeled data. This study investigates the feasibility of self-training in dealing with the domain transfer problem in opinion classification via leveraging labeled data in non-target data domain(s) and unlabeled data in the target-domain. Specifically, self-training is evaluated for effectiveness in sparse data situations and feasibility for domain adaptation in opinion classification. Three types of Web content are tested: edited news articles, semi-structured movie reviews, and the informal and unstructured content of the blogosphere. Findings of this study suggest that, when there are limited labeled data, self-training is a promising approach for opinion classification, although the contributions vary across data domains. Significant improvement was demonstrated for the most challenging data domain-the blogosphere-when a domain transfer-based self-training strategy was implemented.

• IE interfaces
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• v.9 no.3
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• pp.202-213
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• 1996

Video-on-demand is an interactive service that provides programs (movie, home shopping, etc.) to users connected to a network. This service will require high bandwidth network and video servers with a large amount of storage capacity. From the viewpoint of system analysis, there are optimization problems to be solved. In this paper, we present a dynamic programming method for allocating the storage for programs being served in a multi-level video-on-demand network. In the optimization of the network resource, we consider the three kinds of costs: installation cost for video servers, program storage cost, and transmission (or communication) cost. The factors related to the costs are investigated. An example is shown to illustrate the proposed method.

• Journal of Fashion Business
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• v.8 no.6
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• pp.123-136
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• 2004

The purpose of this the end of 20th century fashion about various phenomenon of distopia. This study have the analysis and consideration through the elements and peculiarity of distopia. This study give us the various figures of distopia st fashion as well as the common features of distopia. The study method refers to sundry records, thesis, fashion magazine, publication, the collection works and internet. It is as follows. 'Distopia' prefix to dis of Utopia and means unknown future. Distopia trends towards future negatively. The end of 20th century, it is well brought out various cultures. Movie, novel and pop culture have effect on end of the 20th fashion with a view of distopia. It is the fear and uncertainty of the future. The characters of distopia through the works are divided into formative characteristics and aesthetics meaning. The future fashion of distopia expression mixed and various cultural life, also the mixed of utopia and distopia fashion. Distopia stimulates the designers to the new expression and expose their new areas.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.111-118
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• 1999

Recently computer can make cyberspace to walk through by an interactive virtual reality technique. An a avatar in cyberspace can bring us a virtual face-to-face communication environment. In this paper, an avatar is realized which has a real face in cyberspace and a multiuser communication system is constructed by voice transmitted through network. Voice from microphone is transmitted and analyzed, then mouth shape and facial expression of avatar are synchronously estimated and synthesized on real time. And also an entertainment application of a real-time voice driven synthetic face is introduced and this is an example of interactive movie. Finally, face motion capture system using physics based face model is introduced.

• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.89-90
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• 2019

시각효과에 의한 스펙터클 요소는 영상콘텐츠에서 관객의 몰입감을 향상시키고, 작품성을 높인다. 특히 재난영화는 스펙터클 요소를 주된 연출 방법의 하나로 사용하여 관객의 기대와 감동을 키운다. 또한 영화제작기법과 함께 스펙터클 장면을 표현하기 위한 CGI기술의 발전이 영화 장르의 확장과 학문적 발전을 이끌어왔다. 그러나 스펙터클 요소가 내러티브에 영향을 끼친 연구는 지금까지 매우 미비하다. 본 논문은 스펙터클 요소가 재난영화의 내러티브에 끼치는 연구를 분석하기 위해 2019년에 개봉된 재난 영화 <유랑지구>를 분석하였다. 이를 위해 스펙터클과 내러티브의 개념과 특징을 이론적 배경으로 제시하고, <유랑지구>의 스펙터클 요소 분석과 극중의 내러티브와의 관계를 연구하였다. 본 연구를 통해 스펙터클 요소를 활용해 영화의 내러티브 강화에 도움이 되길 기대한다.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1029-1035
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• 2019

In recent years, artificial intelligence (AI) services have become one of the most essential parts to extend human capabilities in various fields such as face recognition for security, weather prediction, and so on. Various learning algorithms for existing AI services are utilized, such as classification, regression, and deep learning, to increase accuracy and efficiency for humans. Nonetheless, these services face many challenges such as fake news spread on social media, stock selection, and volatility delay in stock prediction systems and inaccurate movie-based recommendation systems. In this paper, various algorithms are presented to mitigate these issues in different systems and services. Convolutional neural network algorithms are used for detecting fake news in Korean language with a Word-Embedded model. It is based on k-clique and data mining and increased accuracy in personalized recommendation-based services stock selection and volatility delay in stock prediction. Other algorithms like multi-level fusion processing address problems of lack of real-time database.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.177-182
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• 2020

In the age of technology and information, communicating with people from different cultures is significantly important. Regarding this perspective, communicative competence has been the focus in the EFL educational context. The purpose of this study is to investigate the effectiveness of using movies in a culture-integrated English reading course at the college level. In order to achieve this purpose, pre- and post-reading comprehension tests, pre- and post-surveys and in-depth interviews were conducted. The results of data analysis showed that using movies in the culture-integrated English reading class positively affected students' interest and confidence towards English reading as well as their reading achievement. In this respect, we suggest that cultural teaching through movies in English classes should be learner-centered so that learners can proactively analyze and understand different cultural backgrounds shown in the movies and build schemas which can be used in their learning process.

• Annual Conference of KIPS
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• 2013.11a
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• pp.1245-1247
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• 2013

최근 들어 영화에 대한 수요가 증가하면서 국내 영화시장규모는 지속적으로 성장하고 있다. 이와 관련하여 여러 가지 위험요소를 제거하고 시장에서의 성공을 위해 영화의 흥행을 예측하기 위한 다양한 연구들이 진행되고 있다. 그러나 그러한 예측을 위한 관련 요소들 간의 상관관계를 정확한 수치로 표현하는 일은 매우 어려우며 관련연구 또한 아직 미흡하다. 본 논문에서는 트위터에서 발생되는 트윗을 설문 표본으로 삼고 영화 관련 트윗과 영화의 흥행을 의미하는 관객 수와의 상관관계를 분석하여 상관계수를 도출하였다. 실험 결과 실험에 사용된 영화 10편의 관객 수에 대한 데이터 모두 관련 트윗의 발생비율과 양의 상관관계를 가짐을 알 수 있었으며 이를 통해 트위터를 이용한 영화의 흥행 여부 예측에 대한 가능성을 제시했다.