• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.4
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• pp.698-702
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• 2005

As a rapidly progressing of globalization and real time Prcessing in market, Korea's IT is developing, which is aimming at world market. In addition, propagation of cultivation is forming natually by lead to CT in the Asia. Latest in '90's hallyu(korean culture wave) was generated from in the east-north China province and moved to the east south Asia. It is also spreading form TV drama, music to movie, game, food, fashion and so on. This paper analyzed the outcome related with hallyu and IT innovation in knowledge-based economic society. It also addressed some effective strategies for advancing world market incorporating hallyu waves into IT.

• Journal of the Korean Society of Clothing and Textiles
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• v.32 no.7
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• pp.1149-1159
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• 2008

This study aims at examining how male college students' extent of mass media exposure affects their sociocultural attitude toward appearance and appearance orientation. It also investigates how these variables show difference depending on the individual's self-efficacy. For the study, data were collected from 397 male students by means of stratified random sampling. The results are as follows: 1. Male college students 'sociocultural attitude toward appearance was shown in two factors of appearance internalization and appearance awareness. Appearance orientation was shown in two factors of interest in appearance and interest in body weight. 2. Male college students were exposed to mass media in order of videos, movies, TV entertainment shows, music and movie magazines. The influence of video media was strong. 3. Male college students' extent of mass media exposure exerted indirect influence through sociocultural attitude toward appearance rather than exerting direct influence on appearance orientation. 4. Male college students' appearance orientation varied depending on the extent of self-efficacy, and higher self-efficacy showed higher appearance orientation and appearance attitude. Especially the group with higher self-efficacy showed higher appearance attitude when the extent of exposure to mass media increased.

• Journal of Fashion Business
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• v.21 no.2
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• pp.45-60
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• 2017

This study analyzed the type of format and content expression of fashion films, from the perspective of promotion and advertisement. For the analysis, a reference research and case study were employed. Our results showed that the format type of fashion films are categorized as movie type; films that convey a story through lines and provide an interesting element as well as artistic value;, video clip films which consist of intriguing sections in a short-length film;, animation type; films that deliver a message through the virtualized world and a created character;, and music video fashion films that use the musical technique to convey how the story of fashion film is structured. The type of content expression of fashion films are divided into direct expression, which exposes the collection of clothes and accessories directly in fashion films;, metaphorical type; which visually delivers the brand image and product;, and the documentary type; that delivers the brand story and historical facts related to the brand. The study on the analysis of fashion films through type of format and content expression shows how fashion brands effectively and strongly promote their products, enhance their brand values, and increase an interest among the customers.

• Proceedings of the Korea Society for Simulation Conference
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• 2005.11a
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• pp.180-184
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• 2005

These days, home network connects all home appliances using one of broadband convergence network, is constructed and propagated to more than 10 million house hold. Users can monitor and control statuses of home appliances using mobile terminal through homeserver. For active propagation of home network, high-quality multimedia service is very important. Specially, as digital recorder and digital camera is propagated, new paradigm that private DVDs can be shared in many household shows up. The homeserver is the main part of UbiHome, which can store much multimedia content and through which the user can search and share these contents. For searching and sharing, the metadata of contents is supposed to keep the consistency. These metadata include the description to different format such as Image, movie, and music. Therefore, we intend to provide a RDF model for effectively storing, searching and managing high-quality contents in UbiHome. In this paper, we propose to make Ontology to close semantic approach using RDF/RDF Schema for managing multimedia data in UbiHome. we propose RDF-Based Local Ontology and merging these ontology to RDF-Based Global Ontology.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.1
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• pp.38-43
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• 2001

This Paper suggests a new feature for shot detection, using the proposed robust feature from the DC image constructed by DCT DC coefficients in the MPEG video stream, and proposes the characterizing value that reflects the characteristic of kind of video (movie, drama, news, music video etc.). The key frames are pulled out from many frames by using the local minima and maxima of differential of the value. After original frame(not do image) are reconstructed for key frame, indexing process is performed through computing parameters. Key frames that are similar to user's query image are retrieved through computing parameters. It is proved that the proposed methods are better than conventional method from experiments. The retrieval accuracy rate is so high in experiments.

• 지역과문화
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• v.7 no.4
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• pp.69-81
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• 2020

As internet companies advance into the film industry, China's IP movies have grown rapidly and brought huge business benefits to the movie market. This paper tried to diagnose the phenomenon of the Chinese IP film craze and derive its meaning and implications. To this end, we selected representative cases to examine the problems and the corresponding improvement measures. Among the three genres of novels, cartoons, and music that are currently drawing the most attention in IP film adaptation, the examples were "Wu Kong", "Take My Brother Away", "My Old Classmate". The research results show that there are problems such as the unification of Chinese IP films, the lack of screenplay ability, and the copyright. Some improvement measures such as establishing IP copyright trading platform, strengthening IP creation, and promoting the value chain of IP industry are put to solve these problems. We hope that China's IP film market create a value chain on the basis of this paper.

• CELLMED
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• v.12 no.4
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• pp.17.1-17.1
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• 2022

PoongLyuYak exists everywhere. If you read the poem and get inspired, it becomes poem Yak (詩藥, poem medicine). If you look at the picture and fall it, it becomes picture Yak. When you feel refreshed by travelling, it becomes travel Yak. Music, dance, movie, nature, etc. are very nice examples. Even a leisurely life of driving, eating out, and drinking coffee is good PoongLyuYak for the body. Whether you're alone or in a group, if you can enjoy it, it's good medicine. According to the logic of the windy and flowing world, anything that can transform one's emotions, whether happy, sad, or trivial, can be a PoongLyuYak. This PoongLyuYak may be more effective than oral medications, inhalants, ointments, or injections. Rather than simply watching, listening, or imitating others, the effect is enormous when participating directly and performing and creative activities. The pleasure of seeing Heung-min Son playing soccer is also a PoongLyuYak, but it can be more enjoyable and the medicinal effect is much greater if you enjoy soccer yourself. Here are some examples of PoongLyu medicines the authors took with the joy of creation.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.139-152
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• 2011

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors "Recommender system", "Recommendation system", "Personalization system", "Collaborative filtering" and "Contents filtering". The full text of each article was reviewed to eliminate the article that was not actually related to recommender systems. Many of articles were excluded because the articles such as Conference papers, master's and doctoral dissertations, textbook, unpublished working papers, non-English publication papers and news were unfit for our research. We classified articles by year of publication, journals, recommendation fields, and data mining techniques. The recommendation fields and data mining techniques of 187 articles are reviewed and classified into eight recommendation fields (book, document, image, movie, music, shopping, TV program, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). The results represented in this paper have several significant implications. First, based on previous publication rates, the interest in the recommender system related research will grow significantly in the future. Second, 49 articles are related to movie recommendation whereas image and TV program recommendation are identified in only 6 articles. This result has been caused by the easy use of MovieLens data set. So, it is necessary to prepare data set of other fields. Third, recently social network analysis has been used in the various applications. However studies on recommender systems using social network analysis are deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed in the recommender systems. So, it will be an interesting and further research area to evaluate the recommendation system researches using social method analysis. This result provides trend of recommender system researches by examining the published literature, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this research helps anyone who is interested in recommender systems research to gain insight for future research.

• Journal of Broadcast Engineering
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• v.13 no.5
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• pp.602-611
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• 2008

The sound design performed in the production of media contents, such as TV, movie, and CF, have been conducted through the experienced feeling of some experts in the aspect of auditory effects that communicates stories. Also, there have been few studies of the quantitative approach and verification to apply visual and auditory effects felt by users. This study is a non-equivalent control group pretest-posttest design and investigates the difference in communication effects in which the difference in a sound design in the production of media contents that affects users. This study analyzed the brain quotient (BQ) obtained by the measurement of brain waves during the watching of an experiment image (track A) designed by using a 60-second TV CF only and an experiment image (track B) designed by sound effects and music and investigated which sound design represents differences in communication effects for users. The results of this investigation can be summarized as follows: First, in the results of the comparison of the attention quotient (ATQ), which is the BQ of recognition effects, between A and B tracks, the track A showed a higher difference in activation than the track B. It can be analyzed that the sound design based on music showed higher levels in attention and concentration than that of the sound effect design. Second, in the results of the comparison of the emotional quotient (EQ), which is emotional effects, between A and B tracks, the track A represented a higher difference than the track B. It means that the sound design based on music showed higher contribution levels in emotional effects than that of the design based on sound effects. Third, in the results of the comparison of the left and right brain equivalent quotient (ACQ), which is memory activation effects, between A and B tracks, there were no significant differences. In the results of the experiments, although there are some constraints in TV CF based on the conventional theories in which sound effects based design affects strong concentration, and music based design affects emotional feeling, the music based design may present more effects in continued concentration. In addition, it was evident that the music based design showed higher effects in emotional aspects. However, it is necessary to continue the study by increasing the number of subjects for improving the little differences in ACQ. This study is useful to investigate the communication effects of the sound based design in media contents as a quantitative manner through measuring brain waves and expect the results of this study as the basic materials in the fields of sound production.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.57-77
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• 2013

Social media has become one of the most popular media in web and mobile application. In 2011, social networks and blogs are still the top destination of online users, according to a study from Nielsen Company. In their studies, nearly 4 in 5active users visit social network and blog. Social Networks and Blogs sites rule Americans' Internet time, accounting to 23 percent of time spent online. Facebook is the main social network that the U.S internet users spend time more than the other social network services such as Yahoo, Google, AOL Media Network, Twitter, Linked In and so on. In recent trend, most of the companies promote their products in the Facebook by creating the "Facebook Page" that refers to specific product. The "Like" option allows user to subscribed and received updates their interested on from the page. The film makers which produce a lot of films around the world also take part to market and promote their films by exploiting the advantages of using the "Facebook Page". In addition, a great number of streaming service providers allows users to subscribe their service to watch and enjoy movies and TV program. They can instantly watch movies and TV program over the internet to PCs, Macs and TVs. Netflix alone as the world's leading subscription service have more than 30 million streaming members in the United States, Latin America, the United Kingdom and the Nordics. As the matter of facts, a million of movies and TV program with different of genres are offered to the subscriber. In contrast, users need spend a lot time to find the right movies which are related to their interest genre. Recent years there are many researchers who have been propose a method to improve prediction the rating or preference that would give the most related items such as books, music or movies to the garget user or the group of users that have the same interest in the particular items. One of the most popular methods to build recommendation system is traditional Collaborative Filtering (CF). The method compute the similarity of the target user and other users, which then are cluster in the same interest on items according which items that users have been rated. The method then predicts other items from the same group of users to recommend to a group of users. Moreover, There are many items that need to study for suggesting to users such as books, music, movies, news, videos and so on. However, in this paper we only focus on movie as item to recommend to users. In addition, there are many challenges for CF task. Firstly, the "sparsity problem"; it occurs when user information preference is not enough. The recommendation accuracies result is lower compared to the neighbor who composed with a large amount of ratings. The second problem is "cold-start problem"; it occurs whenever new users or items are added into the system, which each has norating or a few rating. For instance, no personalized predictions can be made for a new user without any ratings on the record. In this research we propose a clustering method according to the users' genre interest extracted from social network service (SNS) and user's movies rating information system to solve the "cold-start problem." Our proposed method will clusters the target user together with the other users by combining the user genre interest and the rating information. It is important to realize a huge amount of interesting and useful user's information from Facebook Graph, we can extract information from the "Facebook Page" which "Like" by them. Moreover, we use the Internet Movie Database(IMDb) as the main dataset. The IMDbis online databases that consist of a large amount of information related to movies, TV programs and including actors. This dataset not only used to provide movie information in our Movie Rating Systems, but also as resources to provide movie genre information which extracted from the "Facebook Page". Formerly, the user must login with their Facebook account to login to the Movie Rating System, at the same time our system will collect the genre interest from the "Facebook Page". We conduct many experiments with other methods to see how our method performs and we also compare to the other methods. First, we compared our proposed method in the case of the normal recommendation to see how our system improves the recommendation result. Then we experiment method in case of cold-start problem. Our experiment show that our method is outperform than the other methods. In these two cases of our experimentation, we see that our proposed method produces better result in case both cases.