• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.175-178
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• 2005

스마트 홈과 같은 유비쿼터스 환경은 다양한 센서 및 제어 네트워크가 밀집되어 있는 복잡한 시스템이다. 본 논문에서는 이러한 환경하에서 복잡한 인터페이스의 사용에 대한 사용자의 인지 부담(cognitive load)를 줄이고 개인화된(personalized) 서비스를 자율적으로 제공하기 위한 사용자 행동 패턴 선호도 학습 기법을 제안한다. 이를 위해 지식 발견(Knowledge Discovery)을 위한 평생 학습(life-long learning)의 관점에서 퍼지 귀납(Fuzzy Inductive)학습 방법론을 제안하며, 이것은 수치 데이터로부터 입력 공간에 대한 효율적인 퍼지 분할(fuzzy partition)을 얻어내고 일관성있는(consisitent) 퍼지 상관 룰(fuzzy association rule)을 얻어내도록 한다.

• Annual Conference of KIPS
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• 2006.05a
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• pp.303-306
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• 2006

유비쿼터스 컴퓨팅의 핵심은 네트워크 환경에 대한 고 가용성이라 할 수 있다. 이러한 사실은 사용자 컨텍스트(Context)가 반영된 서비스를 제공하기 위한 필수조건이 이미 갖추어져 있다는 것을 시사한다. 지금까지 상황인지(Context-Aware) 서비스를 위한 여러 응용들이 제시되어 왔지만, 동적으로 변화하는, 즉 예측하기 어려운 환경을 충분히 반영할 만큼의 유연성을 제공하지 못했다. 왜냐하면, 응용 태스크 시나리오가 시작단계부터 이미 정해져 있었기 때문이다. 여기에, 본 고는 평생동안 개인화된 태스크를 동적으로 생성, 제공할 수 있는 멀티 에이전트 시스템 구조를 제안하고자 한다. 평생 개인화 태스크(Life Long Personalized Task)는 끊임없이 변화하는 사용자의 행동패턴을 반영할 수 있도록, 동적으로 생성, 제공되는 태스크를 의미한다. 이는 태스크 시나리오가 컴파일 타임에 이미 결정되지 않고, 실행 시간 중에 자동으로 생성된다는 것을 의미한다. 이러한 유연성은 평생학습 엔진(Life Long Learning Engine)을 활용함으로써 가능하다. 이 엔진은 사용자의 행동패턴을 학습하며, 결과적으로 사용자 행동패턴 규칙들을 생성한다.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.3
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• pp.21-29
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• 2015

The Internet of Things (IoT) is an Infrastructure which allows to connect with each device in physical world through the Internet. Thus IoT enables to provide meahup services or intelligent services to human user using collected data from those devices. Due to these advantages, IoT is used in divers service domains such as traffic, distribution, healthcare, and smart city. However, current IoT provides restricted services because it only supports monitor and control devices according to collected data from the devices. To resolve this problem, we propose a design and implementation of personalized IoT service base on service orchestration. The proposed service allows to discover specific services and then to combine the services according to a user location. To this end, we develop a service ontology to interpret user information according to meanings and smartphone web app to use the IoT service by human user. We also develop a service platform to work with external IoT platform. Finally, to show feasibility, we evaluate the proposed system via study.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.131-141
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• 2012

In this paper, we propose a new method for collaborative filtering (CF)-based recommender systems. Traditional CF-based recommendation algorithms have applied constant settings such as a reference group (neighborhood) size and a significance level to all users. In this paper we develop a new method that identifies optimal personalized settings for each user and applies them to generating recommendations for individual users. Personalized parameters are identified through iterative simulations with 'training' and 'verification' datasets. The method is compared with traditional 'constant settings' methods using Netflix data. The results show that the new method outperforms traditional, ordinary CF. Implications and future research directions are also discussed.

• The Journal of Korean Association of Computer Education
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• v.7 no.6
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• pp.117-128
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• 2004

Compared with traditional face-to-face instruction, online learning causes learners to experience more severe feeling of isolation and results in higher dropout rate. This is due to the lack of interaction, sense of belonging, membership, interdependency, cooperation among members and social environment that enables persistence in online learning. Therefore, it is very important for grouping e-learning community to lower the dropout rate and eliminate feeling of isolation. In this paper, the research has been done on the inclination test list to be applied for grouping the desirable learning community. And on the basis of this research, the grouping system for e-learning community(GSE) based on intelligent multi agents for an inclination test using homogeneous and heterogeneous items has been developed. GSE system has such properties that construct a personalized user profile by an agent, and then make groupings according to users' inclination. When this system was evaluated, about 88% of learners were satisfied, and they wanted the group not to be disorganized but to be maintained.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.272-275
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• 2007

유비쿼터스 환경은 보이지 않는 수많은 장치들과 소프트웨어들이 서로 연결되어 각각의 사용자들에게 편리한 서비스를 제공한다. 이러한 서비스를 제공 받기 위해서는 사용자와 서비스간의 매개체 역할을 하는 모바일 디바이스가 필요하다. 하지만 자원이 제한적인 모바일 디바이스의 특성상 다양한 서비스를 이용할 수는 없다. 따라서 본 논문에서는 사용자의 프로파일을 고려한 사용자 맞춤 차원 공유 시스템을 개발하여 주변의 다양한 자원을 실시간으로 공유 할 수 있도록 함으로써 모바일 디바이스의 제한적인 리소스 문제를 해결 하고자한다. 본 논문에서는 테스트 시나리오를 이용하여 제안하는 사용자 맞춤 자원 공유 시스템을 검증한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.29-33
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• 2004

I propose a personalized digital library system (PDLS) based on an advanced distributed agent platform. The new platform is developed by improving the DECAF (Distributed Environment-Centered Agent Framework) which is one of the conventional distributed agent development toolkits. Also, a mobile ORB (Object Request Broker), Voyager, and a new multi agent negotiation algorithm are adopted to develop the advanced platform. The new platform is for mobile multi agents as well as the distributed environment, whereas the DECAF is for the distributed and non-mobile environment. From the results of the simulation the searched time of PDLS is lower, as the numbers of servers and agents are increased. And the user satisfaction is four times greater than the conventional client-server model. Therefore, the new platform has some optimality and higher performance in the distributed mobile environment.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.242-247
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• 2009

Recently an intelligent system is developed for the service what users want not a passive system which just answered user's request. This intelligent system is used for personalized recommendation system and representative techniques are content-based and collaborative filtering. In this study, we propose a prediction system which is based on the techniques of recommendation system using a collaborative filtering and a fuzzy system to solve the collaborative filtering problems. In order to verify the prediction system, we used the data that is user's rating about movies. We predicted the user's rating using this data. The accuracy of this prediction system is determined by computing the RMSE(root mean square error) of the system's prediction against the actual rating about the each movie and is compared with the existing system. Thus, this prediction system can be applied to base technology of recommendation system and also recommendation of multimedia such as music and books.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1494-1502
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• 2016

With the increasing availability of medical sensors and Internet of Things(IoT) devices for personal use, considering the interaction with users, it is planned to add intelligent function design to the fashionable jewelry, and develop composite multi-function intelligent jewelry through sensors identification. By means of IoT technology, while possessing communication function of intelligent jewelry, the function of intelligent jewelry can be expanded to the linkage network. In order to rapidly manage the mass data produced by intelligent jewelry sensors based on IoT, an intelligent jewelry system for health management is designed and an ontology model of intelligent jewelry system based on IoT is worked out. After the state of the services through the smart phone application is shown. The application provides a personalized service to the user and to determine the risk to show the guide lines according to the disease.

• Journal of Intelligence and Information Systems
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• v.11 no.3
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• pp.1-23
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• 2005

Personalized reminder systems have to identify the user's current needs dynamically and proactively based on the user's current context. However, need identification methodologies and their feasible architectures for personalized reminder systems have been so far rare. Hence, this paper aims to propose a proactive need awaring mechanism by applying agent, semantic web technologies and RFID-based context subsystem for a personalized reminder system which is one of the supporting systems for a robust ubiquitous service support environment. RescorlaWagner model is adopted as an underlying need awaring theory. We have created a prototype system called NAMA(Need Aware Multi-Agent)-RFID, to demonstrate the feasibility of the methodology and of the mobile settings framework that we propose in this paper. NAMA considers the context, user profile with preferences, and information about currently available services, to discover the user's current needs and then link the user to a set of services, which are implemented as web services. Moreover, to test if the proposed system works in terms of scalability, a simulation was performed and the results are described.