• Electronics and Telecommunications Trends
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• v.30 no.4
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• pp.162-173
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• 2015

약 20년 전 세상의 이목을 끌었다가 무너진 꿈으로 남아있는 위성벤처 1.0. 즉 텔레데식, 이리디움 등으로 대표되던 대규모 저궤도 위성군(Big LEO) 통신시스템이 다시 추진되고 있다. 이른바 위성벤처 2.0으로 불리우는 스페이스엑스(Space Exploration Technologies: SpaceX). 원웹(OneWeb), 리오셋(Leosat) 등의 저궤도 위성군 통신시스템의 경쟁적 재등장이다. 이 배경에는 인터넷 업체인 구글(Google)과 페이스북(Facebook)이 관련되어 있다. 이미 과거의 경험이 있는 상황이기 때문에 성공가능성에 대한 기대가 상당히 크지만 회의론도 만만치 않다. 이들의 등장배경과 각 시스템의 기술적 제안, 비지니스 모델, 그리고 네트워크 구축 및 운용경비 절감을 위한 각각의 전략적 접근에 대해서 간략히 알아본다. 기존 이리디움이나 글로벌스타도 1세대 위성수명이 다하여 이제 2세대 위성군으로 업그레이드된 상태이라 신구 시스템 간의 차별적 경쟁도 이제 피할 수 없게 되었다. 신규제안 시스템의 성공적 안착을 위한 고려사항과 이러한 흐름에 대한 우리나라의 대응도 살펴보았다.

• Journal of IKEEE
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• v.14 no.2
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• pp.33-40
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• 2010

OPC is open connectivity in industrial automation and the enterprise systems that support industry. OPC XML-DA provides a platform-independent on web service-based communication. This paper propose Power IT system integration platform using OPC that is to integration between DLMS protocol used in AMI system and Distribute protocol DNP3.0 throught IEC61850 using Substation Automation Protocol.

• Journal of The Korean Association of Information Education
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• v.15 no.2
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• pp.179-188
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• 2011

The purpose of this thesis was to implement effective blended PBL(Problem-Based Learning) systems for information communication ethics education. The proposed systems, Online learning and face-to-face classes were systematically combined and Moodle is used for online learning platform. We proposed the use of wikis and blogs not just for creation of knowledge, but as active learning tool to support PBL. In the proposed system, learners used the web 2.0 as a open place to create new knowledge and experience various effects of PBL, such as (1) Improvement of problem solving ability, (2) Understanding of cooperative learning. The blended PBL systems with teaching and learning model were evaluated learners' level of satisfaction and educational achievement in the study of information communication ethics. The result shows that blended PBL learning method is more effective in cultivating consciousness of information communication ethics and showed more higher level of learners' satisfaction and educational achievement than the face-to-face PBL learning method.

• Asia pacific journal of information systems
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• v.28 no.3
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• pp.183-203
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• 2018

In the era of Web 2.0, social media advertising can simultaneously stimulate consumers' brand purchase intention and brand information sharing intention. Product sales and brand information diffusion are equally important for a company that conducts advertising. This study investigates how features of brand content influence social media advertising effectiveness by integrating the stimulus-organism-response model and classic advertising effectiveness models. An analysis of 267 survey questionnaires shows that brand content-related cues, including perceived uniqueness, perceived vividness, and perceived interactivity have significant effects on consumers' affective and cognitive involvement, which then affect their attitude toward brand content. As a result, the consumers' attitude toward the brand and their brand purchase intention, as well as their brand content sharing intention, are positively affected by attitude toward brand content. This study contributes to a better understanding of how social advertising works, which suggests that managers should effectively use social media to conduct advertising.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.377-379
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• 2022

POI refers to the point of Interest in Location-Based Social Networks (LBSNs). With the rapid development of mobile devices, GPS, and the Web (web2.0 and 3.0), LBSNs have attracted many users to share their information, physical location (real-time location), and interesting places. The tremendous demand of the user in LBSNs leads the recommendation systems (RSs) to become more widespread attention. Recommendation systems assist users in discovering interesting local attractions or facilities and help social network service (SNS) providers based on user locations. Therefore, it plays a vital role in LBSNs, namely POI recommendation system. In the machine learning model, most of the training data are stored in the centralized data storage, so information that belongs to the user will store in the centralized storage, and users may face privacy issues. Moreover, sharing the information may have safety concerns because of uploading or sharing their real-time location with others through social network media. According to the privacy concern issue, the paper proposes a recommendation model to prevent user privacy and eliminate traditional RS problems such as cold-start and data sparsity.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.243-250
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• 2008

This paper proposes a system which extracts necessary information from call-for-paper (CFP) documents using a hidden Markov model (HMM). Even though a CFP does not follow a strict form, there is, in general, a relatively-fixed sequence of information within most CFPs. Therefore, a hiden Markov model is adopted to analyze CFPs which has an advantage of processing consecutive data. However, when CFPs are intuitively modeled with a hidden Markov model, a problem arises that the boundaries of the information are not recognized accurately. In order to solve this problem, this paper proposes a two-phrase hidden Markov model. In the first step, the P-HMM (Phrase hidden Markov model) which models a document with phrases recognizes CFP documents locally. Then, the D-HMM (Document hidden Markov model) grasps the overall structure and information flow of the document. The experiments over 400 CFP documents grathered on Web result in 0.49 of F-score. This performance implies 0.15 of F-measure improvement over the HMM which is intuitively modeled.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.617-627
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• 2016

Many kinds of OGC-based web standards have been utilized in the lots of geo-spatial application fields for sharing and interoperable processing of large volume of data sets containing satellite images. As well, the number of cloud-based application services by on-demand processing of virtual machines is increasing. However, remote sensing applications using these two huge trends are globally on the initial stage. This study presents a practical linkage case with both aspects of OGC-based standard and cloud computing. Performance test is performed with the implementation result for cloud detection processing. Test objects are WPS 2.0 and two types of geo-based service environment such as web server in a single core and multiple virtual servers implemented on OpenStack cloud computing environment. Performance test unit by JMeter is five requests of GetCapabilities, DescribeProcess, Execute, GetStatus, GetResult in WPS 2.0. As the results, the performance measurement time in a cloud-based environment is faster than that of single server. It is expected that expansion of processing algorithms by WPS 2.0 and virtual processing is possible to target-oriented applications in the practical level.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.1-18
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• 2015

Collaborative filtering(CF) algorithm has been popularly used for recommender systems in both academic and practical applications. A general CF system compares users based on how similar they are, and creates recommendation results with the items favored by other people with similar tastes. Thus, it is very important for CF to measure the similarities between users because the recommendation quality depends on it. In most cases, users' explicit numeric ratings of items(i.e. quantitative information) have only been used to calculate the similarities between users in CF. However, several studies indicated that qualitative information such as user's reviews on the items may contribute to measure these similarities more accurately. Considering that a lot of people are likely to share their honest opinion on the items they purchased recently due to the advent of the Web 2.0, user's reviews can be regarded as the informative source for identifying user's preference with accuracy. Under this background, this study proposes a new hybrid recommender system that combines with users' review mining. Our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and his/her text reviews on the items when calculating similarities between users. In specific, our system creates not only user-item rating matrix, but also user-item review term matrix. Then, it calculates rating similarity and review similarity from each matrix, and calculates the final user-to-user similarity based on these two similarities(i.e. rating and review similarities). As the methods for calculating review similarity between users, we proposed two alternatives - one is to use the frequency of the commonly used terms, and the other one is to use the sum of the importance weights of the commonly used terms in users' review. In the case of the importance weights of terms, we proposed the use of average TF-IDF(Term Frequency - Inverse Document Frequency) weights. To validate the applicability of the proposed system, we applied it to the implementation of a recommender system for smartphone applications (hereafter, app). At present, over a million apps are offered in each app stores operated by Google and Apple. Due to this information overload, users have difficulty in selecting proper apps that they really want. Furthermore, app store operators like Google and Apple have cumulated huge amount of users' reviews on apps until now. Thus, we chose smartphone app stores as the application domain of our system. In order to collect the experimental data set, we built and operated a Web-based data collection system for about two weeks. As a result, we could obtain 1,246 valid responses(ratings and reviews) from 78 users. The experimental system was implemented using Microsoft Visual Basic for Applications(VBA) and SAS Text Miner. And, to avoid distortion due to human intervention, we did not adopt any refining works by human during the user's review mining process. To examine the effectiveness of the proposed system, we compared its performance to the performance of conventional CF system. The performances of recommender systems were evaluated by using average MAE(mean absolute error). The experimental results showed that our proposed system(MAE = 0.7867 ~ 0.7881) slightly outperformed a conventional CF system(MAE = 0.7939). Also, they showed that the calculation of review similarity between users based on the TF-IDF weights(MAE = 0.7867) leaded to better recommendation accuracy than the calculation based on the frequency of the commonly used terms in reviews(MAE = 0.7881). The results from paired samples t-test presented that our proposed system with review similarity calculation using the frequency of the commonly used terms outperformed conventional CF system with 10% statistical significance level. Our study sheds a light on the application of users' review information for facilitating electronic commerce by recommending proper items to users.

• 한국ITS학회:학술대회논문집
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• 2008.11a
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• pp.463-466
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• 2008

기존의 교통상황판운영에 사용하는 지도는 2D를 기본으로 하는 전자지도를 중심으로 표준노드링크의 속성을 반영하는 형태이다. 2D형태의 교통전자지도는 그래픽형식에 운영자에게 실시간으로 교통상황을 직관적으로 판단하는데 도움을 제공 하였으나 2D형식이라는 한계가 존재할 수밖에 없었다. 점차적으로 IT기술의 고도화, 하드웨어, 통신기술의 발달 등으로 과거에 다룰 수 없었던 대용량데이터처리가 원활해지고, 다양한 도로이용자의 고급화된 교통수요에 대응하기 위해서는 점차적으로 교통관리자나 운영자들이 교통정보관련 장비들이나 운영시나리오에 대해 다각적으로 분석을 할 수 있는 방안이 강구되어야 한다. 이에 위도, 경도, 고도를 제공하는 3D형식의 전자지도에 실시간적으로 교통정보를 제공하는 4D형식의 교통상황판이 운영될 수 있도록 하는 방안을 제공할 수 있는 기술적 내용과 교통시나리오를 제공하고자 한다.

• The Journal of Korean Institute for Practical Engineering Education
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• v.2 no.1
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• pp.64-70
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• 2010

The purpose of this study is to understand students' use intentions of Web Based Collaborative Learning (WBCL) system. To meet this purpose, we developed a research model based on the Decomposed TPB. This model contains 5 influencing factors: Explicit social influence(EXSI) and Implicit social influence(IMSI), Perceived Usefulness (PU), Perceived easy of use(PEOU), Perceived Playfulness(PP). Data was collected 254 university students from two different institutions. Also, the analysis is conducted to do the hypothesis testing by using PLS 3.0. The result shows that influence factors except PEOU have a important and significant impact on user Behavior Intention(BI). Using WBCL system and learning tool, team leader(that is referent) and members can be a good interaction. For these same reasons, We found that especialy Explicit social influence(EXSI) and Implicit social influence(IMSI) are special influence factors in reference group.