• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.5
• /
• pp.2641-2654
• /
• 2014

In the smart media environment, magazine industry has been experiencing a transition to ecosystem of value network, which includes high complexity and ambiguity. Using case study method, this article conducts research on digital convergence, the model of magazine ecosystem and adaptation strategy of global magazine companies. Research findings have it that the way of contents production of global magazines has been based on collaborative production system within communities, expert communities, creative users, media contents companies and magazine platform. The system shows different patterns and characteristics depending on magazine-driven platform, Platform-driven platform or user-driven platform. Collaboration system has been confirmed in various cases: Huffington Post and Zinio which collaborate with media contents companies, Amazon magazines and Bookish with magazine companies, Huffington Post and Wired with expert communities, and Flipboard with creative users and communities. Foreign magazine contents diverge into (paper, electronic, app and web magazine) as they start the lively trades of their contents on the magazine platform. In the area of contents uses, readers employ smart media technology effectively such as cloud computing, artificial intelligence and module individualization, making it possible for the virtuous cycle to remain in the relationship within communities, expert communities and creative users.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.20 no.3
• /
• pp.311-317
• /
• 2010

In the paper, we propose an improved mobility method of modular robots by physical docking in the uneven environments. The modular robot system consists of autonomous docking device, 3 DOF robotic arm, motion controller, and main controller. Real-time location and direction of the robot are estimated using inner GPS and they are used to control direction and path of each robot for physical docking between modular robots. We design a navigation algorithm of modular robot using physical docking and cooperative navigation in the environment with broken road and low stair. The proposed method is verified by navigation experiments of three developed modular robots in the uneven environments.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2018.07a
• /
• pp.536-539
• /
• 2018

4차 산업혁명에서 핵심은 지능정보기술이다. 데이터를 처리하고 활용하며 새로운 유형의 정보를 생성하여 보유하여야만 급변하는 정보사회를 선도할 수 있다. 이제는 자급자족식의 데이터 수집에서 벗어나, 개방형 혁신환경을 조성하여 외부의 기술, 지식, 아이디어 등을 수용하여야 한다. 내 외부의 유연한 관계형성과 소통창구를 마련하여 유기적인 협의체를 구성하는 것이 중요하다. 이를 위하여 인천국제공항공사에서는 개방형 혁신 플랫폼 기반의 연구관리지원시스템을 구축하였다. 스타트업 플랫폼, R&D 협업지원, 연구관리지원 등 세 가지 요소로 구성된 본 시스템을 통해 기업의 사회적 책임 완수와 일자리 창출에 기여할 것으로 기대된다. 또한 프로세스 정립을 통한 연구 관리로 표준화된 연구과제관리 플랫폼을 마련하였다. 이를 통해 인천공항은 미래를 준비하는 공항 R&D 추진 체계 구축을 완료함으로써 일류 혁신 기업의 기반을 조성하였다.

• 한국HCI학회:학술대회논문집
• /
• 2007.02b
• /
• pp.171-178
• /
• 2007

유비쿼터스 컴퓨팅 환경은 오늘날 인간생활 행태의 변화뿐만 아니라 미래건축 공간에도 큰 변화를 가져오고 있다. 그 중에서도 인간의 가장 기본적인 생활환경인 주거 공간의 변화는 빠르게 이루어지고 있다. 지능형 홈, 사이버 아파트 등의 새로운 미래주거 공간이 나타나고 홈네트워크 시스템을 이용한 다양한 유비쿼터스 서비스가 적용되고 있다. 그러나 거주자를 위한 유비쿼터스 서비스가 공간에 적절히 적용되지 않는 사례가 발생하고 있고 수요자 요구와 관련 없이 서비스가 설계되어 있어 효율성이 떨어지는 문제점도 나타나고 있다. 이는 현재의 홈네트워크가 IT 업계가 제공한 솔루션과 기기 중심으로 서비스가 개발되고, 공급자 위주의 기능의 편리성을 위주로 제공되고 있기 때문이며, 주택을 잘 알고 있는 건설인과의 협업이나 최종 사용자의 기호가 고려되고 있지 않기 때문이다. 더욱 중요한 것은 초기 디자인 단계에서 이러한 문제점들이 수용되고 있지 못하고 공간 디자인과 서비스 개발이 유리화되어 선 시공 후, 서비스를 끼워 맞추기식으로 계획하고 있기 때문이다. 본 연구는 서비스 개발의 관점에서 접근하여 공동 주택의 초기 디자인 단계에서 수요자가 원하는 서비스를 공간에 적용하여 부합되는지를 살펴보고, 이에 따라 적절한 미래공간을 디자인 할 수 있도록 하기 위하여 공간정보 데이터베이스를 기반으로 한 공동주택 유비쿼터스 서비스 개발을 위한 기초 연구에 그 목적을 둔다.

• Journal of Intelligence and Information Systems
• /
• v.15 no.2
• /
• pp.15-31
• /
• 2009

The next generation IT environment is expected to be an open computing environment based on Grid computing technologies, which allow users to access to any type of computing resources through networks. The open computing environment has benefits in aspects of resource utilization, collaboration, flexibility and cost reduction. Due to the variation in performance of open computing resources, however, resource allocation simply based on users' budget and time constraints often fails to meet the Service Level Agreement(SLA). This paper proposes the Mean-Absolute Deviation(MAD) portfolio optimization approach, in which service brokers consider the uncertainty of performance of resources, and compose resource portfolios that minimize the uncertainty. In order to investigate the effect of this approach, we simulate an open computing environment with varying uncertainty levels, users' constraints, and brokers' optimization strategies. The simulation result concludes threefolds. First, the MAD portfolio optimization improves the success ratio of delivering the required performance to users. Second, the success ratio depends on the accuracy in predicting the variability of performance. Thirdly, the measured variability can also help service brokers expand their service to cost-critical users by discounting the access cost of open computing resources.

• Journal of Intelligence and Information Systems
• /
• v.21 no.1
• /
• pp.119-142
• /
• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of Internet Computing and Services
• /
• v.18 no.2
• /
• pp.21-30
• /
• 2017

IoT(Internet of Things) can enable networking and computing using any devices is rapidly proliferated. In the existing IoT environment, bottlenecks and service delays can occur because it processes data and provides services to users using central processing based on Cloud. For this reason, Edge Computing processes data directly in IoT nodes and networks to provide the services to the users has attracted attention. Also, numerous researchers have been attracted to intelligent service efficiently based on Edge Computing. In this paper, expert system-based context awareness scheme for Edge Computing in IoT environment is proposed. The proposed scheme can provide customized services to the users using context awareness and process data in real-time using the expert system based on efficient cooperations of resource limited IoT nodes. The context awareness services can be modified by the users according to the usage purpose. The three service modes in the security system based on smart home are used to test the proposed scheme and the stability of the proposed scheme is proven by a comparison of the resource consumptions of the servers between the proposed scheme and the PC-based expert system.

• Journal of the Korea Convergence Society
• /
• v.8 no.10
• /
• pp.29-35
• /
• 2017

In this paper, we describe certificate policy and characteristics in cooperation condition with Cooperative intelligent transport system and autonomous driving vehicle. Among the authentication functions of the vehicle, there is a pseudonym authentication function. This pseudonymity is provided for the purpose of protecting the privacy of information that identifies the vehicle driver, passenger or vehicle. Therefore, the purpose of the pseudonym certificate is to be used for reporting on BSM authentication or misbehavior. However, this pseudonym certificate is used in the OBE of the vehicle and does not have a cryptographic key. In this paper, we consider a method for managing a pseudonym authentication function, which is a key feature of the pseudonym certificate, such as location privacy protection, pseudonym function, disposition of linkage value or CRL, request shuffling processing by registry, butterfly key processing, The authentication policy and its characteristics are examined in detail. In connection with the management of pseudonymes of the vehicle, the attacker must record the BSM transmission and trace the driver or vehicle. In this respect, the results of this study are contributing.

• Journal of Intelligence and Information Systems
• /
• v.13 no.1
• /
• pp.1-14
• /
• 2007

Character images for the background screen of cell phones are one of the fast growing sectors of the mobile content market. However, character image buyers currently experience tremendous difficulties in searching for desired images due to the awkward image search process. Content-based image retrieval (CBIR) widely used for image retrieval could be a good candidate as a solution to this problem, but it needs to overcome the limitation of the mobile Internet environment where an initial query set (IQS) cannot be easily provided as in the PC-based environment. We propose a new approach, IQS-AutoGen, which automatically generates an initial query set for CBIR on the mobile Internet. The approach applies the collaborative filtering (CF), a well-known recommendation technique, to the CBIR process by using users' preference information collected during the relevance feedback process of CBIR. The results of the experiment using a PC-based prototype system show that the proposed approach successfully satisfies the initial query requirement of CBIR in the mobile Internet environment, thereby outperforming the current image search process on the mobile Internet.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.24 no.3
• /
• pp.77-85
• /
• 2024

In this dissertation, a we designed and implemented a patrol robot that integrates a thermal imaging camera, speed dome camera, PTZ camera, radar, lidar sensor, and smartphone. This robot has the ability to monitor and respond efficiently even in complex environments, and is especially designed to demonstrate high performance even at night or in low visibility conditions. An orbital movement system was selected for the robot's mobility, and a smartphone-based control system was developed for real-time data processing and decision-making. The combination of various sensors allows the robot to comprehensively perceive the environment and quickly detect hazards. Thermal imaging cameras are used for night surveillance, speed domes and PTZ cameras are used for wide-area monitoring, and radar and LIDAR are used for obstacle detection and avoidance. The smartphone-based control system provides a user-friendly interface. The proposed robot system can be used in various fields such as security, surveillance, and disaster response. Future research should include improving the robot's autonomous patrol algorithm, developing a multi-robot collaboration system, and long-term testing in a real environment. This study is expected to contribute to the development of the field of intelligent surveillance robots.