• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.17-25
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• 2006

An intelligent game has been studied for some time, and the main purpose of the study was to win against human by enhancing game skills. But some commercial games rather focused on adaptation of the user's behavior in order to bring interests on the games. In this study, we are suggesting an adaptive reinforcement learning algorithm, which focuses on the adaptation of user behavior. We have designed and developed the Othello game, which provides large state spaces. The evaluation of the experiment was done by playing two reinforcement learning algorithms against Min-Max algorithm individually. And the results show that our approach is playing more improved learning rate, than the previous reinforcement learning algorithm.

• The KIPS Transactions:PartD
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• v.11D no.2
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• pp.443-452
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• 2004

The paper presents an intelligent space management system for ubiquitous computing. The system is basically a home/office automation system that could control light, electronic key, and home appliances such as TV and audio. On top of these basic capabilities, there are four elegant features in the system. First, we can access the system using either a cellular Phone or using a browser on the PC connected to the Internet, so that we control the system at any time and any place. Second, to provide more human-oriented interface, we integrate voice recognition functionalities into the system. Third, the system supports not only reactive services but also proactive services, based on the regularities of user behavior. Finally, by exploiting embedded technologies, the system could be run on the hardware that has less-processing power and storage. We have implemented the system on the embedded board consisting of StrongARM CPU with 205MHz, 32MB SDRAM, 16MB NOR-type flash memory, and Relay box. Under these hardware platforms, software components such as embedded Linux, HTK voice recognition tools, GoAhead Web Server, and GPIO driver are cooperated to support user-friendly intelligent space.

• Spatial Information Research
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• v.16 no.4
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• pp.385-400
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• 2008

This study investigates how Korean Land Spatialization Group (KLSG) has controlled and managed its own R&D effectively and what particular elements have made it practical. Korean Land Spatialization Program (KLSP) has produced large amounts of practical outcomes for the intelligent land by collaborative 5 core research projects. The well-harmonized cooperations between proving ground, practical business model, and services oriented integration and others have driven the KLSG to an succeedable management. The active role for practicality is prominent with its usefulness and competitiveness. KLSP is financially and administratively supported from Ministry of Land Transport and Maritime Affairs of Korea (MLTM). By investigating relationships between 5 core research projects involved in the R&D projects of KLSG, it has been found out that R&D projects of KLSG bring forth practicality and commercialization. The results of this study presents strategies of KLSP in conducting research for practicality and commercialization of GIS technology and integrated geospatial information.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.474-484
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• 2019

In this age, where the social environment is changing rapidly and unpredictably, interest in safety from crime is increasing in Korean society. As the desire to live a life free from the fear of crime increases, interest in the construction of safe cities is also rising nationwide. To meet the national demand, the Korean government is promoting a project to link public disaster safety systems by involving municipalities, 112, 119, and other emergency services and institutions through the Smart City Integrated Platform in order to construct a smart safety net. This study investigates the linking of theSmart City Integrated Platform and theIntelligent Security Project. The results are as follows. 1. The linkage's objective is clear. 2. The system sector can provide information to accident-related organizations. 3. The scenario area can be expanded to a crime-prevention sector, and a long-term urban information integration infrastructure can be created. 4. Product testing is enabled by a smart city road map and through continuous consultation with relevant organizations. 5. Project diffusion to other local governments can be promoted with the continued addition of commercial products.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.476-481
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• 2008

The necessity of intelligent surveillance system is gradually considered seriously from the space where the security is important. From this paper will load Network Camera in Mobile Robot based on embedded Linux and Goal is in the system embodiment will be able to track the intruder. From Network Camera uses Wireless Lan transmits an image with server, grasps direction of the intruder used Block Matching algorithms from server, transmits direction information and tracks an intruder. The robot tracks the intruder according to gets the effective image of an intruder. In compliance with this paper the system which is embodied is linked with a different surveillance system and as intelligent surveillance system there is a possibility of becoming worse a reliability.

• Review of KIISC
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• v.28 no.2
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• pp.12-19
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• 2018

사이버 침해공격은 단순히 사이버 공간에만 피해를 주는 것이 아니라, IoT/CPS와 연결되면서 실생활에 큰 피해를 줄 수 있는 중요한 문제로 대두되었다. 이러한 사이버 침해공격의 대부분은 악성코드를 사용하고 있으며, 점차 지능화된 형태로 발전하고 있다. 이에 대응하고자 다양한 악성코드 분석기술이 출현해왔으며, 최근의 연구들은 대부분 머신러닝을 이용하여 기존에 진행했던 Pattern, Heuristic 기반의 한계들을 보완하려 노력하고 있다. 본 논문에서는 머신러닝을 이용한 악성코드 분석기술의 동향을 기술하였다. 특히, 머신러닝을 이용한 악성코드 분석 목적을 7개로 분류하였고, 악성코드 분석에 핵심이 되는 Key Feature들에 대해 소개하였다. 본 논문을 통해, 다양한 악성코드 분석 방법에 있어 새로운 Approach로 연결되는 계기가 되기를 기대한다.

• Annual Conference of KIPS
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• 2005.11a
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• pp.521-524
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• 2005

오늘날 컴퓨팅은 모바일과 유비쿼터스 환경으로 변화하고 있으며, 응용과 서비스는 이 같은 동적인 환경을 인식할 수 있고 적응할 수 있어야 한다. 상황해석 서비스를 구축하는 것은 복잡하고 많은 시간이 소요되는 작업이다. 본 논문에서는 동적인 환경에서 응용과 서비스를 개발하는데 필요한 상황해석 구조를 제안한다. 연구의 범위는 교육을 위해 필요한 교실, 연구실, 강의실과 같은 물리공간으로 제한하였다.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.9
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• pp.363-370
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• 2022

Grasping a target object among clutter objects without collision requires machine intelligence. Machine intelligence includes environment recognition, target & obstacle recognition, collision-free path planning, and object grasping intelligence of robot hands. In this work, we implement such system in simulation and hardware to grasp a target object without collision. We use a RGB-D image sensor to recognize the environment and objects. Various path-finding algorithms been implemented and tested to find collision-free paths. Finally for an anthropomorphic robot hand, object grasping intelligence is learned through deep reinforcement learning. In our simulation environment, grasping a target out of five clutter objects, showed an average success rate of 78.8%and a collision rate of 34% without path planning. Whereas our system combined with path planning showed an average success rate of 94% and an average collision rate of 20%. In our hardware environment grasping a target out of three clutter objects showed an average success rate of 30% and a collision rate of 97% without path planning whereas our system combined with path planning showed an average success rate of 90% and an average collision rate of 23%. Our results show that grasping a target object in clutter is feasible with vision intelligence, path planning, and deep RL.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06a
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• pp.122-124
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• 2012

최근 지식정보화 시대의 집단지성기반 교육 패러다임 변화는 큰 이슈로 떠오르고 있다. 특히 융합적 학문을 근원으로 창의성 계발과 아이디어를 중요시하고 있으며, 창조적 교육방식을 지향하고 있다. 그러나 다양한 영역에 지식전문가들과 학습자들 간에 지식을 공유하기 위한 플랫폼 공간이 제대로 제공되고 있지 못하며, 단순한 컨텐츠 제공을 목적으로 이러닝 서비스가 일부 제공되고 있는 것이 현실이다. 따라서 본 논문에서는 집단지성을 기반으로 지능형 튜터링 에이전트 시스템 설계를 제안하고, 새로운 에이전트(Agent) 개념을 통해 지식인들과 학습자들 간에 지식을 공유할 수 있을 뿐만 아니라 새로운 지식을 창출하고, 관리 및 유통할 수 있는 구조를 연구하였다. 또한 사용자들로부터 발생하는 데이터와 정보들을 자동 분석하여 지능적으로 학습상황에 대처할 수 있도록 설계하였으며, 튜터(Tutor)와 튜티(Tutee)간에 협력적인 학습 생태계가 형성될 수 있도록 하였다. 따라서 본 연구의 결과를 기반으로 미래 스마트 학습 플랫폼 발전에 많은 도움이 되길 기대한다.

• Annual Conference of KIPS
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• 2023.11a
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• pp.656-659
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• 2023

정서적 지원 대화를 위한 챗봇 개발 시, 사용자의 챗봇에 대한 사용성 및 대화 적절성을 높이기 위해서는 사용자 감정에 적합한 지원 콘텐츠를 제공하는 것이 중요하다. 이를 위해, 본 논문은 사용자 입력 텍스트의 감정 강도 예측 모델을 제안하고, 사용자 발화 맞춤형 정서적 지원 대화에 적용하고자 한다. 먼저 입력된 한국어 문장에서 키워드를 추출한 뒤, 이를 각성도 (arousal)과 긍정부 정도(valence) 공간에 투영하여 키워드가 내포하는 각성도-긍정부정도에 가장 근접한 감정을 예측하였다. 뿐만 아니라, 입력된 전체 문장에 대한 감정 강도를 추가로 예측하여, 핵심 감정 강도 - 문맥상 감정강도를 모두 추출하였다. 이러한 통섭적 감정 강도 지수들은 사용자 감정에 따른 최적 지원 전략 선택 및 최적 대화 콘텐츠 생성에 공헌할 것으로 기대한다.