• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.1059-1063
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• 2009

모바일 폰의 메뉴 구조는 하이퍼텍스트 형태로 정보를 제공하며 링크의 연결로 페이지 정보를 전달하거나 하부 카테고리로 연결하는 역할을 한다. 모바일 폰 사용자는 메뉴 조작을 통해 기능을 실행하며, 이 과정에서 선형구조, 계층형 구조, 대화형 구조, 데이터베이스 구조, 혼합구조와 같은 다양한 형태를 경험하게 된다. 사용자는 모바일 폰의 다양한 기능을 사용하면서 추상적 개념의 공간 인지를 구체적인 공간으로 이해하려 한다. 즉 UI, GUI에서 제시하고 있는 Label이나 방향표시를 따라 상하좌우라는 공간적 개념으로 은유하여 이해하는 것이다. 하드키 단말의 경우는 상하좌우키를 이용하여 공간을 이해했다면 최근에는 터치 및 제스처 동작 인식이 가능한 폰이 등장하면서 좌우 flick, 상하 flick 등과 같은 구체적인 행동으로 디바이스 화면에서 공간이 이동하는 것으로 이해하고 있다. 본 논문에서 사용자의 공간 은유를 이해하기 위해 상위 depth로 이동하는 [이전] 키의 기능을 중심으로 살펴보고자 한다. 첫째, 기능을 수행하기 위해 순차적으로 진행하는 방법보다는 [취소]를 이용하여 depth를 이동하는 것이 사용자의 모바일 폰의 공간 은유 파악에 더욱 도움을 줄 수 있을 것이라고 예상되기 때문이다. 둘째, [이전], [취소]라는 Label이 가지고 있는 모호성 때문이다. 모바일 폰의 다양한 기능 중에서 [이전]는 전체 사용에 있어 아주 작은 요소에 불과하지만 사용자는 정해진 순서의 process에 따라 기능을 수행할 때와는 다르게 역 방향 Process를 사용하면서 모바일 폰의 구조를 이해하고 모바일 공간을 인지하는 중요한 요소로 사용될 수 있을 것이라고 예상된다. 본 논문에서는 이전으로 돌아가는 [이전]의 기능을 통해 사용자가 메뉴의 층위 구조 및 공간 인지에 영향을 미칠 수 있을 것이다라는 가설을 설정하여 이를 실험을 통해 증명하고자 한다. 실험을 위해 모바일 폰을 자주 사용하고 있는 20~30대 남녀 10명의 피험자에게 전화번호부, 앨범, 문자메시지의 목록화면과 상세보기 화면을 제시한 후 마지막 상세보기 화면에서 실험에서 제시하는 제시어를 보고 어느 화면으로 이동하게 될 것인지를 예상하는 질문을 하였으며, 그러한 이유에 대해서는 인터뷰를 통해 확인하였다. 이 실험을 통해 사용자의 모바일 폰 공간인지는 동일한 레벨의 단계 즉 상세보기의 경우는 수평의 관계라고 보고 있으며, 목록화면과 상세화면의 관계는 상하의 관계라고 이해하고 있다. 이러한 이유를 인터뷰를 통해 질문하였을 때, 상세화면에서 좌우 방향표시가 존재하기 때문이라는 응답이 높았으며, 상세보기 화면이 좌우라고 인식하면 목록화면은 상하의 관계로 이해하고 있다고 응답하였다. 즉 하나의 정해진 공간인지를 통해 다른 공간을 유추하여 생각하고 있다고 볼 수 있다. 또한 결과적으로 실제 단말에서 [이전] 기능을 상위 depth로 이동하도록 설계하였다면 [한단계위]라는 Label 또는 e번과 같은 상위의 개념을 포함한 아이콘을 사용한다면 혼란을 줄일 수 있는 방법으로 활용될 수 있을 것으로 예상된다. 현재 논문에서는 [이전]라는 기능으로만 사용자의 공간 개념을 예측할 수 있었지만, 터치스크린의 등장과 함께 플릭과 같은 다양한 제스처에 의한 인터렉션이 가능한 현 시점에서 추상적인 공간은 방향성을 가진 제스처에 의해 구체적인 물리적 공간으로 인식하는 경향이 더욱 뚜렷이 나타나고 있다. 이러한 시점에서 사용자의 공간인지에 도움을 줄 수 있는 Label과 방향성 표시는 더욱 절실히 요구되고 있는 시점이며, 이후 모바일 환경에서 사용자의 공간인지에 대한 구체적인 연구가 필요하리라고 예상된다.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.6
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• pp.32-40
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• 2015

In this paper, the optimum antenna placement is analyzed by considering the interference between airborne antennas mounted on the unmanned aerial vehicle(UAV). The analysis is implemented by selecting the antennas that the distance and operational frequency band between airborne antennas is close to each other among the omni-directional antennas. The analyzed antennas are the control datalink, TCAS(Traffic Collision & Avoidance System), IFF(Identification Friend or Foe), GPS(Global Positioning System), and RALT(Radar ALTimeter) antennas. There are three steps for the optimum antenna placement analysis. The first step is selecting the antenna position having the optimum properties by monitoring the variation of radiation pattern and return loss by the fuselage of UAV after selecting the initial antenna position considering the antenna use, type, and radiation pattern. The second one is analyzing the interference strength between airborne antennas considering the coupling between airborne antennas, spurious of transmitting antenna, and minimum receiving level of receiving antenna. In case of generating the interference, the antenna position without interference is selected by analyzing the minimum separation distance without interference. The last one is confirming the measure to reject the frequency interference by the frequency separation analysis between airborne antennas in case that the intereference is not rejected by the additional distance separation between airborne antennas. This analysis procedure can be efficiently used to select the optimum antenna placement without interference by predicting the interference between airborne antennas in the development stage.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.3A
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• pp.232-239
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• 2011

IEEE 802.11n standard provides a framework for new link adaptation. A station can request that another station provide a Modulation and Coding Scheme (MCS) feedback, to fully exploit channel variations on a link. However, if the time elapsed between MCS feedback request and the data frame transmission using the MCS feedback becomes bigger, the previously received feedback information may be obsolete. In that case, the effectiveness of the feedback-based link adaptation is compromised. If a station can estimate how fast the channel quality to the target station changes, it can improve accuracy of the link adaptation. The contribution of this paper is twofold. First, through a thorough NS-2 simulation, we show how the coherence time affects the performance of the MCS feedback based link adaptation of 802.11n networks. Second, this paper proposes an effective algorithm for coherence time estimation. Using Allan variance information statistic, a station estimates the coherence time of the receiving link. A proposed link adaptation scheme considering the coherence time can provide better performance.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.4
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• pp.284-288
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• 2009

With explosively growing PPI databases, the computational approach for a prediction and configuration of PPI network has been a big stream in the bioinformatics area. Recent researches gradually consider physicochemical properties of proteins and support high resolution results with integration of experimental results. With regard to current research trend, it is very close future to complete a PPI network configuration of each organism. However, direct applying the PPI network to real field is complicated problem because PPI network is only a set of co-expressive proteins or gene products, and its network link means simple physical binding rather than in-depth knowledge of biological process. In this paper, we suggest a protein functional flow model which is a directed network based on a protein functions' relation of signaling transduction pathway. The vertex of the suggested model is a molecular function annotated by gene ontology, and the relations among the vertex are considered as edges. Thus, it is easy to trace a specific function's transition, and it can be a constraint to extract a meaningful sub-path from whole PPI network. To evaluate the model, 11 functional flow models of Homo sapiens were built from KEGG, and Cronbach's alpha values were measured (alpha=0.67). Among 1023 functional flows, 765 functional flows showed 0.6 or higher alpha values.

• Proceedings of the KOR-KST Conference
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• 2000.02a
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• pp.3-22
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• 2000

이용자의 경로선택 형태를 모사하는 통행배정모형 결과의 정확도는 교통계획에 상당한 영향을 미친다. 이용자의 경로선택 결정과정에서 가장 중요한 판단기준은 통행시간과 통행요금이다. 그런데 통행요금은 이용자의 경로거리에 따라 다양한 방식으로 부과되므로, 링크를 분석단위로 하는 기존의 통행배정모형은 현실적인 통행요금 반영이 힘들었고 또한 수요예측 결과를 이용한 다양한 분석에서 제약을 받아 왔다. 본 연구는 이러한 배경에서 경로교통량을 도출할 수 있는 경로기반 통행배정모형을 구축하였고, 또한 경로거리에 따라 결정되는 현실적인 통행요금을 반영할 수 있는 알고리즘을 개발하였다. 경로기반 배정모형에서는 GP(Gradient Projection) 알고리즘을 이용하였고, 계산상의 효율성 제고를 위해 K-최단경로 알고리즘 중 MPS(Minimal Path Search) 알고리즘을 이용하였다. 개발된 배정 모형은 현실적인 통행요금을 반영할 수 있으므로 통행배정 결과의 정밀도를 향상시켰을 뿐만 아니라 기존 배정모형에 비해 최적해로의 수렴속도도 개선되는 것으로 나타났다. 본 논문의 배정모형은 경로교통량이 도출되고 통행요금을 반영할 수 있으므로, 통행요금과 통행 거리 관계에 따른 목적함수의 규명과 그에 따른 효과척도를 계량화할 수 있다. 따라서 본 모형은 통행배정에서 실재상황을 보다 현실여건에 맞도록 규명할 수 있고, 기존의 제한적인 효과분석의 문제점을 해결할 수 있으므로 그 활용범위가 넓다. 또한 본 논문은 개발된 배정모형의 적용사례로서 고속도로 수요관리 요금체계 개선방안을 제시하였다. 기존의 고속도로 통행요금 산정 방법은 이론적 근거가 미약했던 반면, 본 논문에서 개발된 배정모형과 고속도로 수요관리 요금체계 개선방안은 고속도로 통행료 결정에 대한 과학적이고 합리적인 분석방법을 제공하였다.한 민감도 분석을 실시한 결과 대안1의 경우 교통량의 변화 및 화물통행의 시간가치의 증가시 사회적 편익이 오히려 감소하였고, 대안2와 3의 경우 사회적 편익이 증가하는 것을 알 수 있었다. 이는 경부고속도로의 화물차량의 구성비에 따라 대안 1의 경우 오히려 화물차의 통행시간이 증가함에 그 원인이 있다 할 것이다. 이상과 같은 결론을 통하여 경부고속도로상의 화물전용차선의 설치시는 수답렬 교통량의 구성비와 구간 평균교통량에 의하여 그 효과가 다르게 나타남을 알 수 있었다. 따라서 물류비용 절감차원에서의 화물전용차선의 설치는 본 연구에서 나타낸 방법과 같이 수단간의 경제적 편익을 고려한 구간별 시간대별 효과분석을 통하여 정책의 시행여부가 결정되어야 할 것이다. 한편, 화물전용차선의 설치로 인한 물류비용의 절감을 보다 효과적으로 달성하기 위해서는 종합류류 전산망의 시급한 구축과 함께 화물차의 적재율을 높이고 공차율을 낮출 수 있는 운송체계의 수립이 필요한 것으로 판단된다. 그라나 이러한 화물전용차선의 효과는 단기적인 치유책일 수밖에 없기 때문에 물류유통 시설의 확충을 위한 사회간접자본의 구축을 서둘러 시행하여야 할 것이다.으로 처리한 Machine oil, Phenthoate EC 및 Trichlorfon WP는 비교적 약효가 낮았다.>$^{\circ}$E/$\leq$30$^{\circ}$NW 단열군이 연구지역 내에서 지하수 유동성이 가장 높은 단열군으로 추정된다. 이러한 사실은 3개 시추공을 대상으로 실시한 시추공 내 물리검층과 정압주입시험에서도 확인된다.. It was resulted from increase of weight of single cocoon. "Manta"2.5ppm produced 22.2kg of co

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.725-735
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• 2022

Currently, disasters occurring in Korea are characterized by unpredictability and complexity. Due to these features, property damage and human casualties are increasing. Since the initial response process of these disasters is directly related to the scale and the spread of damage, optimal decision-making is essential, and information of the site must be obtained through timely applicable sensors. However, it is difficult to make appropriate decisions because indiscriminate information is collected rather than necessary information in the currently operated Disaster and Safety Situation Office. In order to improve the current situation, this study proposed a framework that quickly collects various disaster image information, extracts information required to support decision-making, and utilizes it. To this end, a web-based display system and a smartphone application were proposed. Data were collected close to real time, and various analysis results were shared. Moreover, the capability of supporting decision-making was reviewed based on images of actual disaster sites acquired through CCTV, smartphones, and UAVs. In addition to the reviewed capability, it is expected that effective disaster management can be contributed if institutional mitigation of the acquisition and sharing of disaster-related data can be achieved together.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.57-77
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• 2013

Social media has become one of the most popular media in web and mobile application. In 2011, social networks and blogs are still the top destination of online users, according to a study from Nielsen Company. In their studies, nearly 4 in 5active users visit social network and blog. Social Networks and Blogs sites rule Americans' Internet time, accounting to 23 percent of time spent online. Facebook is the main social network that the U.S internet users spend time more than the other social network services such as Yahoo, Google, AOL Media Network, Twitter, Linked In and so on. In recent trend, most of the companies promote their products in the Facebook by creating the "Facebook Page" that refers to specific product. The "Like" option allows user to subscribed and received updates their interested on from the page. The film makers which produce a lot of films around the world also take part to market and promote their films by exploiting the advantages of using the "Facebook Page". In addition, a great number of streaming service providers allows users to subscribe their service to watch and enjoy movies and TV program. They can instantly watch movies and TV program over the internet to PCs, Macs and TVs. Netflix alone as the world's leading subscription service have more than 30 million streaming members in the United States, Latin America, the United Kingdom and the Nordics. As the matter of facts, a million of movies and TV program with different of genres are offered to the subscriber. In contrast, users need spend a lot time to find the right movies which are related to their interest genre. Recent years there are many researchers who have been propose a method to improve prediction the rating or preference that would give the most related items such as books, music or movies to the garget user or the group of users that have the same interest in the particular items. One of the most popular methods to build recommendation system is traditional Collaborative Filtering (CF). The method compute the similarity of the target user and other users, which then are cluster in the same interest on items according which items that users have been rated. The method then predicts other items from the same group of users to recommend to a group of users. Moreover, There are many items that need to study for suggesting to users such as books, music, movies, news, videos and so on. However, in this paper we only focus on movie as item to recommend to users. In addition, there are many challenges for CF task. Firstly, the "sparsity problem"; it occurs when user information preference is not enough. The recommendation accuracies result is lower compared to the neighbor who composed with a large amount of ratings. The second problem is "cold-start problem"; it occurs whenever new users or items are added into the system, which each has norating or a few rating. For instance, no personalized predictions can be made for a new user without any ratings on the record. In this research we propose a clustering method according to the users' genre interest extracted from social network service (SNS) and user's movies rating information system to solve the "cold-start problem." Our proposed method will clusters the target user together with the other users by combining the user genre interest and the rating information. It is important to realize a huge amount of interesting and useful user's information from Facebook Graph, we can extract information from the "Facebook Page" which "Like" by them. Moreover, we use the Internet Movie Database(IMDb) as the main dataset. The IMDbis online databases that consist of a large amount of information related to movies, TV programs and including actors. This dataset not only used to provide movie information in our Movie Rating Systems, but also as resources to provide movie genre information which extracted from the "Facebook Page". Formerly, the user must login with their Facebook account to login to the Movie Rating System, at the same time our system will collect the genre interest from the "Facebook Page". We conduct many experiments with other methods to see how our method performs and we also compare to the other methods. First, we compared our proposed method in the case of the normal recommendation to see how our system improves the recommendation result. Then we experiment method in case of cold-start problem. Our experiment show that our method is outperform than the other methods. In these two cases of our experimentation, we see that our proposed method produces better result in case both cases.

• Journal of Korean Society of Transportation
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• v.36 no.2
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• pp.155-168
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• 2018

In this study, we studied the directional pattern of entering the intersection from the intersection upstream link prior to predicting short future (such as 5 or 10 minutes) intersection direction traffic volume on the interrupted flow, and examined the possibility of traffic volume prediction using traffic assignment model. The analysis method of this study is to investigate the similarity of patterns by performing cluster analysis with the ratio of traffic volume by intersection direction divided by 2 hours using taxi DTG (Digital Tachograph) data (1 week). Also, for linking with the result of the traffic assignment model, this study compares the impact area of 5 minutes or 10 minutes from the center of the intersection with the analysis result of taxi DTG data. To do this, we have developed an algorithm to set the impact area of intersection, using the taxi DTG data and traffic assignment model. As a result of the analysis, the intersection entry pattern of the taxi is grouped into 12, and the Cubic Clustering Criterion indicating the confidence level of clustering is 6.92. As a result of correlation analysis with the impact area of the traffic assignment model, the correlation coefficient for the impact area of 5 minutes was analyzed as 0.86, and significant results were obtained. However, it was analyzed that the correlation coefficient is slightly lowered to 0.69 in the impact area of 10 minutes from the center of the intersection, but this was due to insufficient accuracy of O/D (Origin/Destination) travel and network data. In future, if accuracy of traffic network and accuracy of O/D traffic by time are improved, it is expected that it will be able to utilize traffic volume data calculated from traffic assignment model when controlling traffic signals at intersections.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.227-240
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• 2011

New concepts and ideas often result from extensive recombination of existing concepts or ideas. Both researchers and developers build on existing concepts and ideas in published papers or registered patents to develop new theories and technologies that in turn serve as a basis for further development. As the importance of patent increases, so does that of patent analysis. Patent analysis is largely divided into network-based and keyword-based analyses. The former lacks its ability to analyze information technology in details while the letter is unable to identify the relationship between such technologies. In order to overcome the limitations of network-based and keyword-based analyses, this study, which blends those two methods, suggests the keyword network based analysis methodology. In this study, we collected significant technology information in each patent that is related to Light Emitting Diode (LED) through text mining, built a keyword network, and then executed a community network analysis on the collected data. The results of analysis are as the following. First, the patent keyword network indicated very low density and exceptionally high clustering coefficient. Technically, density is obtained by dividing the number of ties in a network by the number of all possible ties. The value ranges between 0 and 1, with higher values indicating denser networks and lower values indicating sparser networks. In real-world networks, the density varies depending on the size of a network; increasing the size of a network generally leads to a decrease in the density. The clustering coefficient is a network-level measure that illustrates the tendency of nodes to cluster in densely interconnected modules. This measure is to show the small-world property in which a network can be highly clustered even though it has a small average distance between nodes in spite of the large number of nodes. Therefore, high density in patent keyword network means that nodes in the patent keyword network are connected sporadically, and high clustering coefficient shows that nodes in the network are closely connected one another. Second, the cumulative degree distribution of the patent keyword network, as any other knowledge network like citation network or collaboration network, followed a clear power-law distribution. A well-known mechanism of this pattern is the preferential attachment mechanism, whereby a node with more links is likely to attain further new links in the evolution of the corresponding network. Unlike general normal distributions, the power-law distribution does not have a representative scale. This means that one cannot pick a representative or an average because there is always a considerable probability of finding much larger values. Networks with power-law distributions are therefore often referred to as scale-free networks. The presence of heavy-tailed scale-free distribution represents the fundamental signature of an emergent collective behavior of the actors who contribute to forming the network. In our context, the more frequently a patent keyword is used, the more often it is selected by researchers and is associated with other keywords or concepts to constitute and convey new patents or technologies. The evidence of power-law distribution implies that the preferential attachment mechanism suggests the origin of heavy-tailed distributions in a wide range of growing patent keyword network. Third, we found that among keywords that flew into a particular field, the vast majority of keywords with new links join existing keywords in the associated community in forming the concept of a new patent. This finding resulted in the same outcomes for both the short-term period (4-year) and long-term period (10-year) analyses. Furthermore, using the keyword combination information that was derived from the methodology suggested by our study enables one to forecast which concepts combine to form a new patent dimension and refer to those concepts when developing a new patent.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.57-79
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• 2016

It is a rising trend that the number of users using one of the social media channels, the Social Network Service, so called the SNS, is getting increased. As per to this social trend, more companies have interest in this networking platform and start to invest their funds in it. It has received much attention as a tool spreading and expanding the message that a company wants to deliver to its customers and has been recognized as an important channel in terms of the relationship marketing with them. The environment of media that is radically changing these days makes possible for companies to approach their customers in various ways. Particularly, the social network service, which has been developed rapidly, provides the environment that customers can freely talk about products. For companies, it also works as a channel that gives customized information to customers. To succeed in the online environment, companies need to not only build the relationship between companies and customers but focus on the relationship between customers as well. In response to the online environment with the continuous development of technology, companies have tirelessly made the novel marketing strategy. Especially, as the one-to-one marketing to customers become available, it is more important for companies to maintain the relationship marketing with their customers. Among many SNS, Facebook, which many companies use as a communication channel, provides a fan page service for each company that supports its business. Facebook fan page is the platform that the event, information and announcement can be shared with customers using texts, videos, and pictures. Companies open their own fan pages in order to inform their companies and businesses. Such page functions as the websites of companies and has a characteristic of their brand communities such as blogs as well. As Facebook has become the major communication medium with customers, companies recognize its importance as the effective marketing channel, but they still need to investigate their business performances by using Facebook. Although there are infinite potentials in Facebook fan page that even has a function as a community between users, which other platforms do not, it is incomplete to regard companies' Facebook fan pages as communities and analyze them. In this study, it explores the relationship among customers through the network of the Facebook fan page users. The previous studies on a company's Facebook fan page were focused on finding out the effective operational direction by analyzing the use state of the company. However, in this study, it draws out the structural variable of the network, which customer committment can be measured by applying the social network analysis methodology and investigates the influence of the structural characteristics of network on the business performance of companies in an empirical way. Through each company's Facebook fan page, the network of users who engaged in the communication with each company is exploited and it is the one-mode undirected binary network that respectively regards users and the relationship of them in terms of their marketing activities as the node and link. In this network, it draws out the structural variable of network that can explain the customer commitment, who pressed "like," made comments and shared the Facebook marketing message, of each company by calculating density, global clustering coefficient, mean geodesic distance, diameter. By exploiting companies' historical performance such as net income and Tobin's Q indicator as the result variables, this study investigates influence on companies' business performances. For this purpose, it collects the network data on the subjects of 54 companies among KOSPI-listed companies, which have posted more than 100 articles on their Facebook fan pages during the data collection period. Then it draws out the network indicator of each company. The indicator related to companies' performances is calculated, based on the posted value on DART website of the Financial Supervisory Service. From the academic perspective, this study suggests a new approach through the social network analysis methodology to researchers who attempt to study the business-purpose utilization of the social media channel. From the practical perspective, this study proposes the more substantive marketing performance measurements to companies performing marketing activities through the social media and it is expected that it will bring a foundation of establishing smart business strategies by using the network indicators.