• Journal of the Korea Computer Graphics Society
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• v.11 no.3
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• pp.41-48
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• 2005

시청자의 개입에 따라 내용이 변하는 영상은 영상산업 및 컴퓨터 그래픽스 분야에서 대중들의 새로운 흥미를 불러일으킨다. 그러나, 그러한 영상을 생성하는 시스템을 만드는 일은 제한된 개수의 영상으로 다양하면서 온전한 영상의 변화를 만들어야 하고, 사용자에게 그 변화를 조정하는 권한을 주어야 하기 때문에 어려운 일이다. 본 논문에서는 이 문제를 해결하는 한 방법으로 개별적인 실사 촬영물을 이용하여 인터랙티브 드라마를 생성하는 시스템을 제안한다. 이 시스템은 개별적인 영상 알갱이들을 부드럽게 연결하여, 다양한 줄거리와 완결된 구조를 갖춘 드라마를 생성한다. 또한, 사용자는 생성된 드라마의 내용, 길이, 장르, 등장인물을 원하는 대로 바꿀 수 있다. 이러한 시스템을 만들기 위하여 씬(scene)을 새로운 방법으로 모델링 하였고, 씬들을 적절히 선택하여 연결하기 위한 방법으로 씬 그래프(scene graph)를 제안한다. 최종 영상과 사용자와의 상호 작용을 위해서는 비젼, 모션, 그리고 스케치 기반 인터페이스를 제시한다. 끝으로 설문 조사를 통해 이 시스템의 유용성을 평가한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.781-783
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• 2016

유니티는 2D, 3D 게임 개발 도구로 다양한 물리적인 기능과 쉬운 UI와 에셋을 제공하여 개발자가 쉽게 게임을 제작할 수 있도록 지원한다. 유니티가 제공하는 주요 기능 중 하나인 에셋은 게임 개발과정에서 생성되는 스크립트, 리소스, 프리팹 등의 컴포넌트를 통칭한 것으로 개발자가 만들거나 에셋 스토어를 통해 수집하여 게임 제작에 활용할 수 있다. 즉, 에셋 스토어의 에셋들을 활용할 경우 좀 더 효율적으로 게임을 제작할 수 있다. 그러나 에셋 스토어의 구조상 개발하려고 하는 게임 장르 및 타입에 따라 어떠한 에셋을 적용할 것인지를 구분하기 쉽지 않고 적용 에셋을 찾더라도 이를 응용하여 개발 시스템에 적용하기에 상당한 시간 및 노력이 요구되는 경우도 많다. 본 논문에서는 이러한 에셋 적용문제를 해결하고 개발자의 적은 제어를 통해 효율적인 에셋을 제공할 수 있는 ACE(Adventure Create Engine)에셋 생성 엔진에 대하여 제안한다. ACE는 Unity의 상위 레벨에 존재하는 개발 도구로 최종 결과물로 생성된 패키지를 Unity에 적용할 수 있는 구조로 되어 있다. ACE를 활용할 경우 개발자의 요구에 적절한 에셋을 구축할 수 있으며 게임 개발 시간을 단축할 수 있다.

• Journal of Convergence for Information Technology
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• v.10 no.10
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• pp.40-46
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• 2020

With the development of the Internet, users share their experiences and opinions. Since related keywords are used witho0ut considering information such as the general emotion or genre of an unstructured document such as a movie review, the sensitivity accuracy according to the appropriate emotional situation is impaired. Therefore, we propose a system that predicts emotions based on information such as the genre to which the unstructured document created by users belongs or overall emotions. First, representative keyword related to emotion sets such as Joy, Anger, Fear, and Sadness are extracted from the unstructured document, and the normalized weights of the emotional feature words and information of the unstructured document are trained in a system that combines CNN and LSTM as a training set. Finally, by testing the refined words extracted through movie information, morpheme analyzer and n-gram, emoticons, and emojis, it was shown that the accuracy of emotion prediction using emotions and F-measure were improved. The proposed prediction system can predict sentiment appropriately according to the situation by avoiding the error of judging negative due to the use of sad words in sad movies and scary words in horror movies.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.401-408
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• 2023

The purpose of this study is to design textbook metadata as a basic task for building a textbook database. To this end, reading textbooks were defined as a category of textbooks, and a metadata development methodology was established through previous research. In order to ensure that bibliographically essential elements are not omitted, the catalog description elements of institutions that collect, accumulate, and service textbooks such as the National Library of Korea were investigated. The elements of Dublin Core, MODS, and KEM were mapped to derive elements suitable for describing textbooks. Finally, a set of textbook metadata elements consisting of 14 elements in three categories - bibliography, context, and textbook characteristics were presented by adding publication type, genre, and curriculum period elements. The 14 elements are titles, authors, publications, formats, identification sign, languages, locations, subject names, annotation, genres, table of contents, subjects, curriculum period, and curriculum information. In this study, we contributed to this field by discussing how to organize textbook resources with national knowledge resources, and in future studies, we proposed to evaluate usability by applying metadata elements to actual textbooks and revise and supplement them according to the evaluation results.

• The Journal of the Korea Contents Association
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• v.20 no.5
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• pp.291-302
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• 2020

Korean popular music industry, which is lead by "Idol group", has forsaken their fandom all over the world. Therefore, idol groups has become not only an artist but also the most influential people in the Korean economy. A global idol group with a strong fandom can earn more than a trillion-dollar by attracting their global fan's interest in Korea. In other words, it is considerably important to carry the idol to a successful conclusion. This study tries to expect whether the idols can be survived or not at a certain point after their debut by ANN, Decision Tree, Random Forest. We decide that certain point as the three-year and eight-year after their debut, because it is their break-even point year and the year after their average renewal of the contract. In addition, this study also explains which feature is the most important to their survival by feature importance and Logistic regression. In conclusion, features like the number of idol competitors, the number of debut members and the number of the genre are significant. These results shed light on the efficient management of K-Pop idol to improve industrial competitiveness.

• The KIPS Transactions:PartB
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• v.13B no.1 s.104
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• pp.15-20
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• 2006

This paper describes research on intelligent game character through performance enhancements of physics engine in computer games. The algorithm that recognizes the physics situation uses momentum back-propagation neural networks. Also, we present an experiment and its results, integration methods that display optimum performance based on the physics situation. In this experiment on integration methods, the Euler method was shown to produce the best results in terms of fps in a simulation environment with collision detection. Simulation with collision detection was shown similar fps for all three methods and the Runge-kutta method was shown the greatest accuracy. In the experiment on physics situation recognition, a physics situation recognition algorithm where the number of input layers (number of physical parameters) and output layers (destruction value for the master car) is fixed has shown the best performance when the number of hidden layers is 3 and the learning count number is 30,000. Since we tested with rigid bodies only, we are currently studying efficient physics situation recognition for soft body objects.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.1
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• pp.121-127
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• 2009

The Multistage Feature-based Classification Model(MFCM) is proposed in this paper. MFCM does not use whole feature vectors extracted from the original data at once to classify each data, but use only groups related to each feature vector to classify separately. In the training stage, the contribution rate calculated from each feature vector group is drew throughout the accuracy of each feature vector group and then, in the testing stage, the final classification result is obtained by applying weights corresponding to the contribution rate of each feature vector group. In this paper, the proposed MFCM algorithm is applied to the problem of music genre classification. The results demonstrate that the proposed MFCM outperforms conventional algorithms by 7% - 13% on average in terms of classification accuracy.

• The Journal of the Korea Contents Association
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• v.20 no.2
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• pp.572-580
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• 2020

Recently released AI speakers show a pattern of interacting with the user by mainly with voice and simultaneously displaying simple and formal visual feedback through status LED light. This is due to the limitations of the product characteristics of the speaker, which makes it difficult to interact variously, and even such visual feedback is not standardized for each product, and thus does not give a consistent user experience. By maximizing the visual elements that can be expressed through color and abstract movement to assist voice feedback, the product can provide the user with an extended experience that includes not only functional satisfaction but also emotional satisfaction. In this study, after analyzing the interaction methods of the existing AI speakers, we examined the theory of color communication in order to expand the visual feedback effect, and examined the meaning and expression technique of Color Field Painting, an art genre that maximizes the emotional experience by using only color. Through this, the AI speaker's visual communication function was expanded by designing a way to feedback communication status using LED light.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.36-41
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• 2010

An advanced form of Multistage Feature-based Classification Model(AMFCM), called AMFCM, is proposed in this paper. AMFCM like MFCM does not use the concatenated form of available feature vectors extracted from original data to classify each data, but uses only groups related to each feature vector to classify separately. The prpposed AMFCM improves the contribution rate used in MFCM and proposes a confusion table for each local classifier using a specific feature vector group. The confusion table for each local classifier contains accuracy information of each local classifier on each class of data. The proposed AMFCM is applied to the problem of music genre classification on a set of music data. The results demonstrate that the proposed AMFCM outperforms MFCM by 8% - 15% on average in terms of classification accuracy depending on the grouping algorithms used for local classifiers and the number of clusters.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.4
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• pp.1582-1588
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• 2013

This study investigated whether artists' education level, college of graduation, and gender have any influences on artists' premium which leads to higher sales possibility and art price using Art Fair Market data. We found that artists' education level do not have statistically significant effects on either sales possibility or art price. But, artists' college of graduation which was measured by artists' undergraduate school level(high or low) has significant effects on both sales possibility and art price. Artists' gender also has a significant effect on art price, although it does not have any significant effect on sales possibility. These results imply that artists' educational level or degree does not exert significant impacts on artists' premium, while the level of college that artists attended indeed has a significant effect on artists' premium. They also imply that gender plays a role in Korean art market.