• Cartoon and Animation Studies
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• s.26
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• pp.109-130
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• 2012

The purpose of this study is to suggest the direction of the development of the feature length animation for movie theater by analyzing of the reasons of the success of . released in 2011 has broken box office records by drawing 2 millions since drew 760 thousands in 1976. This can be one of the success model of the animation for movie theater, considering it has had trouble not only in planning ability and scenario power but also in producing environment where subcontracts are prevalent. This box office hit seems to have been possible through cooperation and division of movie crews and animation crews. Many kinds of materials are reviewed and producer Kim Seonku was interviewed for analysis of the reasons of box office success. Followings are five reasons of success found as the result of analysis and the body of this article is composed of the argument and analysis of each. 1.This animation was planned and produced in the same way of commercial feature films. 2.There was detailed division of work while producing 3. Various kinds of investments were made sequentially, 4. Major film distributor like Lotte and CJ could be motivated 5. There were producers who can mediate between the animation and film field This study suggests the direction both in the aspect of industry and the aspect of training professionals as the result of analysis. In the industrial aspect, transitional cooperation is needed between animation filed and film field which can motivate distributor. Industrial approach like planning, investment, distribution and marketing is absolute for the success of animation for movie theater. Also in the aspect of training professionals, curriculum needs to be improved in the university because the ability and passion of the professionals in the field of animation industry are the most important and education is the most approachable way.

• The Journal of the Korea Contents Association
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• v.11 no.2
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• pp.238-250
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• 2011

In 2006, the number of days of the screen quota (theatrical movie screening days of Korean produced films in a year required by the law) was reduced to 73 days from 146 days per year. Three years after the reduction of screen quota, this paper studies the impact of reduction of screen quota system on Korean produced films. Using Non-parametric statistics, Gini Index and Regression analysis, this study shows that the average number of Korean moviegoer of Korean films which was released last three years(2007-2009) after the cutback of screen quota in 2006 is reduced to 640,109.9123 from 1,107,217.82 for three years(2003-2005) before the cutback. And this is significant in statistics. while Hollywood film gets 76,168,518 more audiences than the total number of audience for 2003-2005, the total numbers of Korean films is cut to 218,917,590 (2007-2009) from 245,802,356 (2003-2005). Gini Index of 2009(0.84) indicates that the inequality of a distribution of box office performance of Korean films is getting worse after the cutback. For now, the reduction of screen quota has negative effects for Korean films. Only the technological advance, education of relates personnel, redesigning of the related infrastructure, market-driven movies, creative production, and the promotion of the independent films can reduced the negative effects.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.35-47
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• 2020

Box-office prediction is important to movie stakeholders. It is necessary to accurately predict box-office and select important variables. In this paper, we propose a multivariate time series classification and important variable selection method to improve accuracy of predicting the box-office. As a research method, we collected daily data from KOBIS and NAVER for South Korean movies, selected important variables using Random Forest and predicted multivariate time series using Deep Learning. Based on the Korean screen quota system, Deep Learning was used to compare the accuracy of box-office predictions on the 73^rd day from movie release with the important variables and entire variables, and the results was tested whether they are statistically significant. As a Deep Learning model, Multi-Layer Perceptron, Fully Convolutional Neural Networks, and Residual Network were used. Among the Deep Learning models, the model using important variables and Residual Network had the highest prediction accuracy at 93%.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.98-101
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• 2003

To classify desired and undesired documents on the web according to each user's view, FOCUS (Fuzzy dOCUment ordering System) is developed based on fuzzy concept extraction, fuzzy fish eye matching, and fuzzy selection. Experiments are done using the concept-system-dictionary by EDR (Electronic Dictionary Research Institute) including 140,000 words and web-based documents related to movie.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.519-522
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• 2004

웹의 급격한 확산과 더불어 고객에게 맞춤화된 정보 제공의 필요성이 높아지고 있다. 또한 전자상거래 기업은 맞춤화와 개인화 서비스를 실현하기 위해서 웹 기반의 추천시스템에 많은 관심을 가지고 있다. 협업필터링(Collaborative filtering)은 개인화된 정보필터링 기법으로 추천시스템에서 가장 많이 사용되고 있다. 본 연구에서는 MovieLens 데이터 셋의 아이템속성을 고려하여 클러스터링 기반의 사례기반추론을 통한 협업필터링 추천시스템을 개발하고 기존의 방법과 제안된 모델의 성과를 비교 분석하였다.

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.441-450
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• 2003

Collaborative filtering is one of the most widely used methodologies for recommendation system. Collaborative filtering is based on a data matrix of each customer's preferences and frequently, there exits missing data problem. We introduced two imputation approach (multiple imputation via Markov Chain Monte Carlo method and multiple imputation via bootstrap method) to improve the prediction performance of collaborative filtering and evaluated the performance using EachMovie data.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.246-250
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• 2005

Recommand Systems are being used by an ever-increasing number of E-Commerce to help customers find products to purchase. Recommend Systems offer a technology that allows personalized recommendations of items of potential interest to users based on information about similarities and dissimilarities among different customers tastes. In this paper, we design and build a Recommend System using the historical customer movie purchase transactions and extracts the knowledge needed to make association recommendations to new customers.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.285-287
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• 1998

본 논문에서는 다양한 멀티미디어 스트림(multimedia stream)을 재생할 수 있는DirectShow SDK를 이용하여, 비디오의 특수효과를 구현하기 위한 변환 필터(Transform filter) 제작 방법에 대해 소개한다. DirectShow는 여러 가지 포맷(MPEG, AVI, MOV, WAV)으로 부호화된 digital movie나 sound를 재생할 수 있는 run-time(.ock)과 dynamic-link library (DLLs)를 제공하여 필터의 제작을 용이하게 한다. 본 논문에서는 디지털 영상 특수효과 중에서 Clip이라는 특수효과를 예로하여 변환 필터를 제작하는 방법을 소개한다. 또한 DirectShow Documentation에서 제공된 변환방법을 수정하여 변환필터의 입력 pin에서 출력 pin으로의 불필요한 복사를 줄임으로써 수행 속도를 개선하는 특수효과 구현 방법을 제시한다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.25-27
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• 2003

MPEG-7은 XML을 이용하여 사용자가 원하는 내용을 검색하고 브라우징할 수 있도록 하는 멀티미디어 컨텐츠의 다양한 정보를 기술하고 있다. 그러나 많은 부분으로 나누어진 세부화된 장면들을 찾는 질의가 주어졌을때 원하는 정보를 찾기 위하여 모든 문서의 내용을 검색하는 것은 상당한 시간이 요구되며, 일반적인 XML 문서의 인덱싱 기법을 적용할 경우 MPEG-7의 관점에서 부합되지 않은 점이 있다. 본 논문에서는 이를 개선하기 위하여 MPEG-7 문서의 구조적인 정보를 기반으로 하는 인덱싱 기법을 제안하고 질의를 하여 동영상내의 원하는 부분을 효율적으로 찾는 방법을 보인다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.460-462
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• 2003

본 논문에서는 모바일 환경에서. 사용자의 상황정보와 개인적 선호도 정보를 고려하여 사용자에게 적합한 영화를 선택. 추천하고 선택된 영화의 요약(Summary)을 서비스 할 수 있는 시스템을 제안한다. 제안된 시스템은 사용자가 이동하는 상황에 따라 변하는 위치, 시간 정보와 개인적 선호도인 영화장르 정보에 기반한 영화선택 서비스를 제공하고, 선택된 영화 콘텐츠의 요약을 MPEG-7 메타데이터로 기술하고, 이를 이용해 요약을 효과적으로 소모할 수 있게 한다. 제안된 시스템을 통해, 모바일 환경 기반 영화 선택 및 서비스 시스템(Mobile Environment-Based Movie Selection and Summary Service System)을 실현하고, 그 효용성을 입증하였다.