• Korean journal of communication and information
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• v.60
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• pp.172-192
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• 2012

Over the last ten years, media convergence and multiple platform expansion have affected the ways that people watch conventional television. In the post-TV era, the growing use of the Internet and mobile multi-media devices, such as smart phones, as well as the availability of abundant television content, allows television consumption to be more personalized, diversified, and linked with various media activities, especially social media uses. This study attempts to examine how television viewing experiences have been transformed with the development of the trans-media uses. Based on Walter J. Ong's concept of relation-ism, which posits that new media transform the meanings and relevance of old media rather than making old media obsolete, this study will pay particular attention to how the cultural meanings of television viewing have been redefined in the post-TV era. For the examination, this study has looked at concrete cases of the television viewing experiences of 29 young people in their twenties. Based on in-depth interview data, this study discusses the newly emerging characteristics of television viewing, its temporal and spatial experiences, and the significance of television as a medium and as a social place.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.1
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• pp.104-114
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• 1999

This paper proposes an efficient method for the automatic generation of personalized 3D face model from color image sequence. To detect a robust facial region in a complex background, moving color detection technique based on he facial color distribution has been suggested. Color distribution and edge position information in the detected face region are used to extract the exact 31 facial feature points of the facial description parameter(FDP) proposed by MPEG-4 SNHC(Synthetic-Natural Hybrid Coding) adhoc group. Extracted feature points are then applied to the corresponding vertex points of the 3D generic face model composed of 1038 triangular mesh points. The personalized 3D face model can be generated automatically in less then 2 seconds on Pentium PC.

• Smart Media Journal
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• v.7 no.2
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• pp.47-53
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• 2018

Along with the development of information and communication technology, smart education that can learn without restrictions of time, place and equipment is activated even in the field of education. Although smart education is provided with content-based training solutions, construction of a system that grasps individual characteristics of learners and provides personalized learning is relatively weak. The activity of free choice is an important play activity of early childhood education, but it is not implemented efficiently by relying on the clinical observation of the teacher. If the IoT(Internet of Things) technology based on Hyper-Connected is applied to free-choice activities, it is possible to provide the child's personalized activity type and play-form analysis based on objective and stylized data. In this paper, we design and implement a system to monitor the child's activity of free choice by building an IoT environment that is based on open source hardware. The proposed system provides children's activity information as objective data and will be used as teacher's work mitigation and custom training material for each child.

• Journal of Broadcast Engineering
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• v.23 no.2
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• pp.253-260
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• 2018

Personalized media broadcasting services can produce their own broadcasting contents with a variety of creative themes if they have just a transmission platform and devices that can obtain videos and voices of producers without the existing expensive equipment. In this paper, we develop and implement a new broadcasting system by applying this service framework to events such as seminars or academic conferences. The devices can be installed at each conference rooms and the integrated system transmitted to users. They can watch via their multi-resolution screen, such as smart-phones, laptops, and tablet PCs. It has the advantage of being able to receive real-time streaming and VOD services as well as additional information related to the conference. It is expected to provide convenience by allowing attendees to access the information via their devices, thereby creating an impact on participation and the underlying technology for the future research.

• Journal of Korea Multimedia Society
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• v.19 no.11
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• pp.1878-1890
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• 2016

Web 2.0 has features produced the content through the user of the participation and share. The content production activities have became active since social network service appear. The social bookmark, one of social network service, is service that lets users to store useful content and share bookmarked contents between personal users. Unlike Internet search engines such as Google and Naver, the content stored on social bookmark is searched based on tag keyword information and unnecessary information can be excluded. Social bookmark can make users access to selected content. However, quick access to content that users want is difficult job because of the user of the participation and share. Our paper suggests a method recommending search word to be able to access quickly to content. A method is suggested by using Collaborative Filtering and Jaccard similarity coefficient. The performance of suggested system is verified with experiments that compare by 'Delicious' and "Feeltering' with our system.

• Journal of Information Technology Applications and Management
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• v.26 no.3
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• pp.135-157
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• 2019

Information technology has economic, social and cultural impacts is closely linked to our lives. This information technology is becoming a key to the change of human civilization through connecting people and objects on the earth. In addition, future information technology is becoming more intelligent and personalized with the development of computing technology, and due to the rapid development of alcohol, environment without time and space constraint is realized, Is spreading. Since existing portable storage media are made of physical form, there is a limit to usage due to the risk of loss and limitation of capacity. Cloud services can overcome these limitations. Due to the problems of existing storage media, it is possible to overcome the limitations of storing, managing and reusing information through cloud services. Despite the large number of cloud service users, the existing research has focused mainly on the concept of cloud service and the effect of introduction on the companies. This study aims to conduct a study on individual characteristics that affect the degree of cloud use. We will conduct research on the causes of IT knowledge, personal perception of security, convenience, innovation, economical trust, and platform dependency affecting the intention to use the cloud. These results show that the variables affecting individual 's use of cloud service are influenced by individuals, and this study can be used as a basic data for individuals to use cloud service.

• Journal of Broadcast Engineering
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• v.9 no.4 s.25
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• pp.305-321
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• 2004

With the advent of the digital broadcasting, the audiences can access a large number of TV programs and their information through the multiple channels on various media devices. The access to a large number of TV programs can support a user for many chances with which he/she can sort and select the best one of them. However, the information overload on the user inevitably requires much effort with a lot of patience for finding his/her favorite programs. Therefore, it is useful to provide the persona1ized broadcasting service which assists the user to automatically find his/her favorite programs. As the growing requirements of the TV personalization, we introduce our automatic user preference learning algorithm which 1) analyzes a user's usage history on TV program contents: 2) extracts the user's watching pattern depending on a specific time and day and shows our automatic TV program recommendation system using MPEG-7 MDS (Multimedia Description Scheme: ISO/IEC 15938-5) and 3) automatically calculates the user's preference. For our experimental results, we have used TV audiences' watching history with the ages, genders and viewing times obtained from AC Nielson Korea. From our experimental results, we observed that our proposed algorithm of the automatic user preference learning algorithm based on the Bayesian network can effectively learn the user's preferences accordingly during the course of TV watching periods.

• 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.

• The Journal of the Korea Contents Association
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• v.15 no.7
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• pp.403-413
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• 2015

Social media has recently become one of the primary tools for viral marketing as an effective advertising channel, in order to maximize effects of world-of-mouth through social media, it is very important to obtain customer experience-centric differentiated contents in offline. However, viral marketing is still being implemented mainly based on online channels because of the lack of effective services and methods to support social medial marketing in conjunction with offline. On that account, this study proposes O2O(Online to Offline) based social media marketing method allowing customers to connect their personal experience conveniently and easily in offline in which they voluntarily participate. Furthermore, this study aims to verify the effectiveness of the proposed method by analyzing the empirical cases thereof. This study would eventually contribute to the vitalization of social media marketing market by enabling customers to obtain personalized posts as connecting online to offline organically and also by allowing corporations to get an ample amount of useful CRM information for planning marketing strategies.

• Journal of Convergence for Information Technology
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• v.9 no.5
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• pp.1-6
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• 2019

As the mobile market expands, a variety of platforms are available to provide multimodal media content. Multimodal media content contains heterogeneous data, accordingly, user requires much time and effort to select preferred content. Therefore, in this paper we propose multimodal media content classification using keyword weighting for recommendation. The proposed method extracts keyword that best represent contents through keyword weighting in text data of multimodal media contents. Based on the extracted data, genre class with subclass are generated and classify appropriate multimodal media contents. In addition, the user's preference evaluation is performed for personalized recommendation, and multimodal content is recommended based on the result of the user's content preference analysis. The performance evaluation verifies that it is superiority of recommendation results through the accuracy and satisfaction. The recommendation accuracy is 74.62% and the satisfaction rate is 69.1%, because it is recommended considering the user's favorite the keyword as well as the genre.