• Journal of Korea Technology Innovation Society
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• v.14 no.4
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• pp.1050-1072
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• 2011

It is well known that international cooperation is considerably more difficult because of the difference of interests, level of technology of the partners, social and culture as well as various macro-environmental conditions. This case study aims to investigate the success factors of international R&D collaboration between two firms. For it, an in-dept interview was conducted to the Etri-Microsoft Information Security Lab. A comprehensive research on theories, such as resource-based theory, network theory, relationship marketing theory and market power perspective was done as well. The results show clear objectives, communications based on trust, core competence on which the counterpart can rely on, consideration on the government decision process as well as the environment are important for success.

• The Journal of the Korea Contents Association
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• v.18 no.3
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• pp.19-33
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• 2018

Collaborative consumption was suggested as a new paradigm of consumption and business model that focuses on sharing and using resources to reduce the risks of resource depletion and environmental impact. Individual consumers carry out a role not only as users but also as providers for collaborative consumption. In the present study, the authors investigated consumers' intention to participate in collaborative consumption both as providers and users. The effects of perceived benefit of collaborative consumption, subjective norms and ease of participation on the intention to participate in collaborative consumption were analyzed. The perceptions on benefits and subjective norms were relatively high, while the perception on ease of participation was relatively low. The effects of subjective norms and ease of participation on the intention to participate in collaborative consumption were significant. The result shows that support from one's reference group and the perception on ease of participation have significant positive effects on the intent to participate in collaborative consumption. Therefore, communication strategies for developing a positive consumer attitude and a social consensus about the necessity of collaborative consumption are needed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.3-6
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• 2004

본 논문에서는 퍼지 로직을 이용하여 로봇과 공의 상태에 따른 로봇 행동의 선택 알고리즘을 제시한다. 전략 및 전술 알고리즘으로 많이 알려진 Modular Q-학습 알고리즘은 개체의 수에 따른 상태수를 지수 함수적으로 증가시킬 뿐만 아니라, 로봇이 협력하기 위해 중재자 모듈이라는 별도의 알고리즘을 필요로 한다. 그러나 앞으로 제시하는 퍼지 로직을 적용한 로봇축구 전략 및 전술 알고리즘은 퍼지 로직을 이용하여 로봇의 주행 알고리즘을 선택하는 과정과 로봇의 행동을 협력하는 과정을 동시에 구현함으로써, 계산 양을 줄여 로봇 축구에 보다 적합하게 해준다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04d
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• pp.289-291
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• 2003

Peer-to-Peer Network에서의 object위치 정보의 배치 및 라우팅 방법인 Chord시스템은 node ID와 object ID생성에 있어서 SHA-1과 같은 해쉬 함수를 사용한다. Chord object위치 정보 관리에 있어서 해쉬에 의해 저절로 load-balancing을 기대하나 실험에 의하면 노드별 load에는 상당한 편차가 있음이 나타난다. 그러므로 이 논문에서는 각 node간 협력을 통하여 보다 효과적인 load-balancing을 제공하는 CLCS 기법을 제안한다.

• Annual Conference of KIPS
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• 2022.05a
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• pp.379-380
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• 2022

KAIST 캠퍼스 기반의 실습환경 구축을 위하여 캠퍼스 전체를 스마트시티 테스트베드로 사용하며 CCTV 네트워크 기반 모니터링/관제 시스템 구축, 교통, 방범, 가로등, CCTV, 교내 버스 등 인프라 통합 관제 및 보안 실습실 구축하고 교내 자율주행 기술 연구진과 실습 협력 추진을 통한 캠퍼스 기반의 실전 스마트 환경을 토대로 다각도의 보안 공격/방어 실습을 진행하고 지자체 및 컨소시엄 기업들과 산학협력 프로젝트를 진행하기 위하여 구축한 내용을 설명한다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2021.11a
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• pp.164-166
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• 2021

크라우드 펀딩 방식을 통한, 해양 산업/교육 분야, 특화된 사업을 위한 플랫폼으로써 해양분야에서 특화된 사업 개척하기 위한 시스템이다. RND 공동 개발, 투자자 유치를 공동 목표하고 (연결/소통)제안자와 참여자간의 연결 및 소통 플랫폼, (발굴/지원) 아이디어 발굴과 사업화를 위한 지원 플랫폼, (교육/협력) 창업을 위한 교육 및 기술개발 협력 플랫폼이용하여 스마트취업·창업을 위한 해양크라우드 펀딩 플랫폼을 활용할수 있게 시스템을 제공한다.

• Journal of The Korean Association of Information Education
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• v.1 no.1
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• pp.74-81
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• 1997

The World Wide Web (Web) is one of the most popular internet tool on now, In this reason most of common user, they understand the Web is internet and Web content is also important issues on this side. However, commonly Web content created by one of Web content creator and sometime they refer the another document and link. In these kinds of environments cause tile delivery of incorrect information or linking to another Web user. There are lots of way to protect the incorrect information deliveries to Web user and the most famous one is Computer Supported Cooperation Work (CSCW). This supports the multi-user environment on single system environment, but this needs more additional things in the current internet environment Current internet defined as distributed information network not tile traditional client-server environment. Specially, Intranet environments need to support the heterogeneous system environment like several Rinds of database, systems like PC, Mac and UNIX workstation, and etc. In this reason, we need collaboration and this would serve the common user interface to all of Web user. In these paper, we review the current concept of CSCW and grouoware that are major concept of collaboration and definition, classification and problem analysis of the collaboration. Finally, we suggest the construction method of collaboration environment for Web.

• Journal of Satellite, Information and Communications
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• v.9 no.1
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• pp.97-101
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• 2014

We propose a timing-offset resilient OFDMA with achieving full diversity for uplink cooparative base station (BS) systems. In uplink OFDMA, timing offset (TO) among multiple users destroys subcarrier orthogonality and thus, it degrades the performance. In order to avoid this performance degradation, the accurate processing, so called 'ranging', is required to synchronize. However, in cooparative BS systems, it is difficult to perform ranging scheme. This is because if the ranging scheme is performed for a specific BS, timing offset has to occur for other BSs. Thus, the conventional ranging method cannot achieve full diversity gain in cooperative BS systems. By employing TO resilient OFDMA, so called, 'ZCZ time-spread OFDMA'. we achieve full diversity gain even with TO among multiple users. We show that the proposed scheme achieves the same performance with case of no multiple acces interference.

• The KIPS Transactions:PartB
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• v.19B no.1
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• pp.31-36
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• 2012

In recent years, there has been increasing interest in recommender systems which provide users with personalized suggestions for products or services. In particular, researches of collaborative filtering analyzing relations between users and items has become more active because of the Netflix Prize competition. This paper presents the reinforcement learning approach for collaborative filtering. By applying reinforcement learning techniques to the movie rating, we discovered the connection between a time sequence of past ratings and current ratings. For this, we first formulated the collaborative filtering problem as a Markov Decision Process. And then we trained the learning model which reflects the connection between the time sequence of past ratings and current ratings using Q-learning. The experimental results indicate that there is a significant effect on current ratings by the time sequence of past ratings.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.189-194
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• 2010

In the recommendation system that recommends services to a specific user by using the estimation value of other users for users' recommendation service, collaborative filtering methods are widely used. But such recommendation systems have problems that exact classification is not possible because a specific user is classified to already classified group in the course of clustering and inexact result can be recommended in case of big errors in users' estimation values. In this paper, in order to increase estimation accuracy, the researchers suggest a recommendation system that applies collaborative filtering after reclassifying on the basis of a specific user's classification items and then finding and correcting the estimation values of the users beyond the critical value of time. This system uses a method where a specific user is not classified to already classified group in the course of clustering but a group is reorganized on the basis of the specific user. In addition, the researchers correct estimation information by cutting off the subordinate 10% from the trimmed mean of samples and then applies weight over time to the remaining data. As the result of an experiment, the suggested method demonstrated about 14.9%'s more accurate estimation result in case of using MAE than general collaborative filtering method.