Prof. Yunsick Sung (성연식, 成演植)

Associate Professor,

The head of Entertainment Technology Major,
The head of Intelligence Robot Convergence Major

Multimedia Software Engineering Major (MME),
Entertainment Technology Major (ETM)
of School of AI Convergence(ati.dongguk)

Dept. of Multimedia Engineering (MME) &
Dept. of AI (AIX) &
Dept. of Autonomous Things Intelligence (ATI)
of Graduate School (gs.dongguk.edu)



E-mail: sung@mme.dongguk.edu
Tel : +82-2-2260-3338 Website Under Construction
Curriculum Vitae

Metaverse Micro Degree (dei.dongguk.edu)​
Convergence Major of Intelligence Robot (robot.dongguk.edu)


Superintelligence Laboratory (http://si.dongguk.edu)
with Demonstration-based Learning & Deep Learning

Autonomous THings Human Resorce Group (at.dongguk.edu)
& Autonomous Things Competition (competition.dongguk.edu)
for Driving, Picking & Flying Robots

Autonomous Things MOOC, LMS< & Video Conf. System
(AtMOOC, LMS, meeting.dongguk.edu)

in Dongguk University-Seoul, Republic of Korea (dongguk.edu)

#4113, New Engineering Building, 30, Dongguk University-Seoul
Pildong-ro 1-gil, Jung-gu, Seoul, Republic of Korea



Research Fields: Demonstration-based Learning, Deep Learning
 

Short Biography : He is currently an Associate Professor in the division of AI Software Convergence and in the major of Intelligence Robot Convergence at Dongguk University-Seoul, Seoul, Republic of Korea. He received the BS degree in the Division of Electrical and Computer Engineering from Pusan National University, Busan, Republic of Korea in 2004, the MS degree in Computer Engineering from Dongguk University-Seoul, Seoul, Republic of Korea in 2006, and the Ph.D degree in Game Engineering from Dongguk University-Seoul, Seoul, Republic of Korea in 2012. He was employed as a Member as the researcher at Samsung Electronics in Republic of Korea between 2006 and 2009. He was the postdoctoral fellow at University of Florida, Florida, USA between 2012 and 2013. His research interests are focused on Superintelligence based on deep learning and demonstration-based learning. 


He is a topic editor in Sensors (MDPI) from 2020, a managing editor in Human-centric Computing and Information Sciences (Springer) from 2015, a managing/senior editor in Journal of Information Processing Systems (KIPS) from 2017. He was a guest editor in one of special issues of Symmetry (MDPI) in 2017 and is a guest editor in one of special issues of Journal of Ambient Intelligence & Humanized Computing (Springer) in 2018. He has the diverse kinds of conference service experiences.

 

A superintelligence is a hypothetical agent that possesses intelligence far surpassing that of the brightest and most gifted human minds. "Superintelligence" may also refer to a property of problem-solving systems whether or not these high-level intellectual competencies are embodied in agents that act in the world. A superintelligence may or may not be created by an intelligence explosion and associated with a technological singularity. (from WIKI)

 

Superintelligence Lab. do research on Superintelligence based on deep learning and demonstration-based learning. Please refer the following research topics.​
 

 

약력 : 현재 동국대학교 AI소프트웨어융합학부 및 지능로봇융합전공 부교수로 재직 중이다. 2004년 부산대학교 전기컴퓨터공학과 학사, 2006년 동국대학교 컴퓨터공학과 석사, 2012년 동국대학교 게임공학과 박사학위를 취득하였습니다. 2012년부터 2013년까지 미국 플로리다주 플로리다 대학에서 박사후 연구원으로 있었습니다. 연구 관심 분야는 딥러닝 기반 Superinteligence와 모방 학습입니다.


2020년부터 Sensors(MDPI)의 주제 편집자, 2015년부터 Human-centric Computing and Information Sciences(Springer)의 편집장, 2017년부터 Journal of Information Processing Systems(KIPS)의 관리/수석 편집자입니다. 2018. 다양한 종류의 학술 서비스 경험을 가지고 있습니다.


Superintelligence은 인간의 지능을 훨씬 능가하는 지능입니다(출처:위키). Superintelligence​ 연구실은 딥러닝 기반의 Superintelligence​와 모방 학습을 연구합니다. 다음 연구 주제를 참조하십시오.​ (머신러닝, 강화학습, 딥러닝)


 

Research Topics

Virtual Simulations/Games: This research topic is related to Virtual simulations and Games. In these searches, scenario generation, virtual agent controls, learning systems are researched and developed. Recently, AI is applied to the diverse kinds of game fields such as animations, designs, programming.

NUI/NUX & Image/Vision:
 We focus on the research about Motion Recognition & Estimation approaches. Motion Recognition approach is for utilizing motions as user interfaces and Motion Estimation approach is for providing the diverse kinds of services by analyzing users’ motions. Both approaches are usually based on long short-term memory (LSTM).

Music Generation Services: This research topic contains music generation, music evaluation, music recommendation approaches. In our lab., new trendy music is generated by Generative Adversarial Network (GAN). For the music generation services, we also research on music classification, evaluation, recommendation approaches.
Unmanned System Controls: Unmnned Systems are unmanned aerial vehicles, unmanned surface vehicle, and autonomous cars. This research topic contains autonomous control & virtual AI simulation approaches. In our lab., the autonomous control approach has developed based on the integration of deep learning and demonstration-based learning. It is possible by the virtual AI simulation approach to make agents learn with the huge amount of learning data such as deep learning in virtual environments.


가상 시뮬레이션/게임: 이 연구 주제는 가상 시뮬레이션 및 게임과 관련이 있습니다. 시나리오 생성, 가상 에이전트 제어, 학습 시스템이 연구 및 개발됩니다. 최근 AI는 애니메이션, 디자인, 프로그래밍 등 다양한 게임 분야에 적용되고 있습니다.

NUI/NUX & Image/Vision
: 모션 인식 및 추정 접근 방식에 대한 연구에 중점을 둡니다. 모션 인식 방식은 모션을 사용자 인터페이스로 활용하기 위한 것이고, 모션 추정 방식은 사용자의 모션을 분석하여 다양한 서비스를 제공하기 위한 것입니다. 두 접근 방식은 일반적으로 장단기 기억(LSTM)을 기반으로 합니다.

음악 생성 서비스
: 이 연구 주제에는 음악 생성, 음악 평가, 음악 추천 접근 방식이 포함됩니다. GAN(Generative Adversarial Network)에 의해 새로운 트렌디한 음악이 생성됩니다. 음악 생성 서비스를 위해 음악 분류, 평가, 추천 접근 방식에 대해서도 연구합니다.

무인 시스템 제어
: 무인 시스템은 무인 공중 차량, 무인 지상 차량 및 자율 자동차입니다. 자율 제어 및 가상 AI 시뮬레이션 접근 방식을 포함합니다. 우리 연구실에서는 딥 러닝과 데모 기반 학습의 통합을 기반으로 자율 제어 접근 방식을 개발했습니다. 가상 AI 시뮬레이션 방식을 통해 에이전트가 가상 환경에서 딥 러닝과 같은 방대한 학습 데이터로 학습하도록 하는 것이 가능합니다.​