• 한국HCI학회:학술대회논문집
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• 2006.02b
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• pp.9-13
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• 2006

본 연구는 HD 복합TV 사용자의 시청행태 및 사용자 니즈 분석을 통해 사용자 시나리오를 도출하여 실제 제품에 적용할 수 있는 DTV 인터페이스 디자인의 일반적인 원리를 발견하고 대형 DTV 화면에 적합한 OSD디자인을 개발하여 디지털 복합 TV에 적합한 새로운 디자인 가이드라인을 제안하는 것을 그 목적으로 하고 있다. 이를 위해 DTV 시청환경을 기반으로 PVR, EPF, 녹화, 1394 등 다양한 기능에 대한 사용 시나리오를 정의하고 유저 내비게이션 특성을 반영한 OSD디자인 방향을 검토하였고 개발환경인 50인치 PDP 화면에 적합한 레이아웃을 제작하여 사용자조사를 통해 검증하였다. 사례연구로 최근 이슈가 되고 있는 타임머신 기능 X Canvas PDP의 OSD 디자인을 개발하여 양산용 제품에 적용할 수 있었다. 대표적인 특징으로 복합 TV 의 Gateway 인 홈메뉴, PVR 기능의 핵심인 프로그래스바, 모든 컨텐츠에 일관되게 적용할 수 있는 목록화면, 녹화관련화면에 공통적으로 적용할 수 있도록 폰트,칼라,레이아웃,위젯 정의를 수행하여 각 기능별 화면들이 Package개념으로 Identity를 갖추면서 사용하기 편리하도록 구성하였다. 그리고 이를 통하여 DTV Application Design에 일반적으로 적용할 수 있는 메뉴구조 및 화면디자인 가이드라인을 도출하였고 세계최초로 자사에서 개발한 하드디스크 내장 HD TV Application GUI 의 사용성 및 심미성 측면을 모두 만족시키는 등 DTV GUI 분야에서 주목할 만한 성과를 거두었다.

• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.534-539
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• 2008

Nowadays, UI(User Interface)'s main target area is getting larger from user's convenience oriented interface, to user's satisfaction oriented interface. In this study, we attempted 'Storytelling User Interface' for fulfillment of user's satisfaction. We analyzed UI in games, distinguished and clarified its effectiveness that conveys main contents and special features to its audience. The most important element in storytelling UI is 'Inherent Drama'. This was proved through the game analysis with Blizzard's 'Diablo', Sierra's 'Home world', Eden Games' 'Test Drive Unlimited', EA's 'Black And White', and Sony Entertainment's 'Eye Of Judgment'. And we divided storytelling elements into 3 pieces, Mood Design - that shows story's background, or traits, Natural Control - for user's feeling as a hero in the content, and Flow Directing - using animation, visual design and sound, etc. Via these elements, we can make the best use of background, icon, animation, every structures and flow in games. Finally, we embodied three pieces for putting in practice for effective improving interface.

• The Journal of Korean Association of Computer Education
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• v.5 no.4
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• pp.134-145
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• 2002

The purpose of this study is to present the functional and technical solutions for text learning of web-based textbook in which each letter has its own focal point. The solutions help learners not to lose the main focus when eye moves to the next letter or line. The text screen of the electronic textbook automatically scrolls the text to up and down or left and right directions which are preassigned by learner. It doesn't need the operation of mouse or keyboard. And learner can change scroll speed and types anytime during scrolling. Automatic text scroll function is a solution for controlling data and screen to reflect the personal favor and ability. It contains the content structure of the text(characteristics, categorizations etc.), the appearance of the text(density, size, font etc.), scroll options(scroll, speed etc.), program control type(ram resident program etc.), and the application of the screen design principles(legibility etc.). To resolve these functional problems, technical 8 phases are provided, which are environment setting, scroll option setting, copy, data analysis, scroll coding, centered focus coding, left and right focus coding, implementation. The learner can focus on text without dispersion because the text focal points stay in the fixed area of screen. 1bey read the text following their preferences for fonts, sizes, line spacing and so on.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.12
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• pp.499-508
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• 2022

A lot of research has been carried out on the Hangeul generation model using deep learning, and recently, research is being carried out how to minimize the number of characters input to generate one set of Hangul (Few-Shot Learning). In this paper, we propose a CKFont2 model using only 14 letters by analyzing and improving the CKFont (hereafter CKFont1) model using 28 letters. The CKFont2 model improves the performance of the CKFont1 model as a model that generates all Hangul using only 14 characters including 24 components (14 consonants and 10 vowels), where the CKFont1 model generates all Hangul by extracting 51 Hangul components from 28 characters. It uses the minimum number of characters for currently known models. From the basic consonants/vowels of Hangul, 27 components such as 5 double consonants, 11/11 compound consonants/vowels respectively are learned by deep learning and generated, and the generated 27 components are combined with 24 basic consonants/vowels. All Hangul characters are automatically generated from the combined 51 components. The superiority of the performance was verified by comparative analysis with results of the zi2zi, CKFont1, and MX-Font model. It is an efficient and effective model that has a simple structure and saves time and resources, and can be extended to Chinese, Thai, and Japanese.