http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
이상광,장시환,양성일,Lee, S.K.,Jang, S.H.,Yang, S.I. 한국전자통신연구원 2017 전자통신동향분석 Vol.32 No.4
최근 모바일 게임 산업에서는 게임 이용 중 게임 내 필요한 아이템을 별도로 구매하는 부분 유료화 비즈니스 모델이 지속해서 성장하고 있다. 부분 유료화 게임은 이용자 측면에서 접근성이 용이하며, 게임 제공자 측면에서는 구매를 유도하는 방법들이 고도화되었다. 인기게임의 경우 부분 유료화로 인한 수입이 지속적으로 증가하고 있다. 본고에서는 모바일 부분 유료화 게임에 대해 게임 운영을 최적화하기 위한 기술들을 살펴보고자 한다. 먼저, 게임 현황 파악 및 분석을 위한 대표적인 게임운영지표 분석 솔루션들을 요약하고, 게임운영지표를 개선하기 위한 게임 이용자 행동예측 기술들을 소개한다. 또한, 최근 연구되고 있는 모바일 게임 분석 기술의 한계점을 돌아보고 향후 연구 방향에 관해 기술한다.
김종성,장시환,양성일,윤민성,Kim, J.S.,Jang, S.H.,Yang, S.I.,Yoon, M.S. 한국전자통신연구원 2021 전자통신동향분석 Vol.36 No.4
Outdoor sports activities have been restricted by serious air pollution, such as fine dust and yellow dust, and abnormal meteorological change, such as heatwave and heavy snow. These environmental problems have rapidly increased the demand for indoor sports activities. Virtual sports, such as virtual golf, virtual baseball, virtual soccer, etc., allow playing various sports games without going outdoors. Indoor sports industries and markets have seen rapid growth since the advent of virtual sports. Most virtual sports platforms use screen-based virtual reality techniques, which are why they are called screen sports. However, these platforms cannot support various sports games, especially virtual match games, such as squash, boxing, and so on, because existing screen-based virtual reality sports techniques use real balls and players. This article presents screen-based haptic-augmented reality technologies for a new virtual sports platform. The new platform does not use real balls and players to solve the limitations of previous platforms. Here, various technologies, including human motion tracking, human action recognition, haptic feedback, screen-based augmented-reality systems, and augmented-reality sports content, are unified for the new virtual sports platform. From these haptic-augmented reality technologies, the proposed platform supports sports games, including indoor virtual matches, that existing virtual sports platforms cannot support.
이상광,김대욱,장시환,양성일,Lee, S.K.,Kim, D.W.,Jang, S.H.,Yang, S.I. 한국전자통신연구원 2019 전자통신동향분석 Vol.34 No.6
Recently, reinforcement learning (RL) has expanded from the research phase of the virtual simulation environment to a wide range of applications, such as autonomous driving, natural language processing, recommendation systems, and disease diagnosis. However, RL is less likely to be used in these complex real-world environments. In contrast, inverse reinforcement learning (IRL) can obtain optimal policies in various situations; furthermore, it can use expert demonstration data to achieve its target task. In particular, IRL is expected to be a key technology for artificial general intelligence research that can successfully perform human intellectual tasks. In this report, we briefly summarize various IRL techniques and research directions.