Jong Woo Kim
한양대학교 경영학부 · 컴퓨터과학
이 교수의 연구실은 지능형 시스템 및 지능형 서비스 기반의 응용 분야에서 핵심 기술을 개발하고 있습니다. 특히 개인화 추천, 웹 서비스 조합, 다중 에이전트 제어 및 자동 문서 처리 등에서 데이터 기반 및 규칙 기반의 지능형 알고리즘 설계에 중점을 두고 있습니다. 또한, 블루프 시스템의 운동 계획 및 의료 정보 자동 처리 등 실제 응용 분야에 적용 가능한 기술적 솔루션을 지속적으로 연구하고 있습니다. 특히, 사회적 미디어 데이터를 활용한 정서 분석 등 인간 중심의 지능형 응용 분야에도 기여하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Web services composition is becoming increasingly important as organizations are now getting ready to provide more complex service-based applications. Contemporary literature on the web services composition primarily addresses various aspects of composition for example, automated composition, quality-driven composition, and semantic composition. These approaches, however, do not adequately address the various dependencies among the tasks performed by the web services. Such dependencies could pot
This study is significant in that it demonstrates the possibility of determining depression from only social media users' textual data.
Reinforcement learning (RL) can be used to obtain an approximate numerical solution to the Hamilton-Jacobi-Bellman (HJB) equation. Recent advances in machine learning community enable the use of deep neural networks (DNNs) to approximate high-dimensional nonlinear functions as those that occur in RL, accurately without any domain knowledge. In the standard RL setting, both system and cost structures are unknown, and the amount of data needed to obtain an accurate approximation can be impractical
Online gamers form clans voluntarily to play together and to discuss their real and virtual lives. Although these clans have diverse goals, they seek to increase their rank in the game community by winning more battles. Communications among clan members and battles with other clans may influence the performance of a clan. In this study, we compared the effects of communication structure inside a clan, and battle networks among clans, with the performance of the clans. We collected battle histori
