• The KIPS Transactions:PartB
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• v.9B no.5
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• pp.587-598
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• 2002

In multicast environment, the main objective of group key management is to provide security services to group communications by sharing a single group key among all the members of the group and subsequently encrypting and decrypting all the communication messages exchanged among the members of the group. Up to now, there has been no effort to develop group key management mechanism that considers the rate of users' join/leave operations. Hence, in this research, we propose group key management mechanisms that consider the rate of user's join/leave operations. We also define a new tree structure called variable tree which is much more flexible than full regular trees and show that variable trees are more efficient than full regular trees for group key management. Especially, we propose an algorithm that minimizes the necessary number of rekey messages according to the rate of join and leave operations. We also shows that if the rate of leave operation is greater than 50%, then the tree structure with degrees 2 or 3 are the optimal structures.

• Journal of Satellite, Information and Communications
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• v.7 no.3
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• pp.1-7
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• 2012

In this paper, we was performed the interference analysis to determine an optimal coexisting criteria for the european digital video broadcasting service (DVB-T2) and the IMT-advanced LTE based TV Band Device (TVBD). The TVBD was equipped with the spectrum cognition method. To this end, we set the various transmission parameters, that includes the emission and blocking mask, antenna height and gain, transmission power and bandwidth, channel model etc. Based on this parameters, we were calculated the allowable transmit power, the number of TVBD and the change in probability of interference for the TVBD user operating in the adjacent channels of the DVB-T2 user. Also this paper presents how many TVBD users can sharing with DVB-T2 for spectral cognition performance.

• Korean Journal of Cognitive Science
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• v.10 no.1
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• pp.25-43
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• 1999

The focus of this study is on the elements of structure design that facilitate u user interaction with applications within cyberspace Structure design entails decisions regarding the optimal classification and hierarchical organization of information into s successively higher units. i.e .. the grouping of highly related information in the form of nodes of a site and the subsequent connection of nodes that are inter-related. The decisions are based on the designer's subjective classification framework. which is not always compatible with that of the user. We propose that the ensuing cognitive dissonance can be reduced via the employment of multiple hierarchies and cross-referencing links. Multiple hierarchies represent a single information space in terms of a number of single hierarchies. each of which represent a different perspective Cross-referencing refers to the inter-connection between the constituent hierarchies by providing a link to the alternate hierarchy for information that is most likely to be categorized in diverse manners by users with differing perspectives. In this study we conducted two empirical studies to gauge the effectiveness of multiple hierarchies and Cross-referencing links in the domain of cyber shopping malls. In the first phase. an experiment was conducted to determine how subjects classified given products with respect to two different perspectives for categorization. Experimental cyber malls were developed based on the results from the first phase to test the effectiveness of multiple hierarchies and cross-referencing links. Results show that the ease of navigation was higher for cyber malls that had implemented cross-referencing links are of greater value when used in conjunction with single hierarchical designs rather than multiple hierarchies. Users satisfaction with and ease of navigation was higher for cyber malls that had not implemented multiple hierarchies. This paper concludes with discussion of these results and their implications for designers of cyber malls.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.137-146
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• 2020

VR(Virtual Reality), which has drawn attention as a major area in ICT, is currently being used in various fields, including medical care, movies and architecture. Although VR technology is used in various fields, contents are mainly developed by creators where needs of users are easily left out of consideration leading to failure in forming a consensus between UI(User Interface) and UX(user experience). To identify the consumer preference and attribute level of VR game content, which is responsible for the largest proportion of VR contents, this study was designed to examine the consumers' preference properties of VR game contents through a Conjoint Analysis and derive the relative importance and weightings of each group. The study collected 166 questionnaires over a total of three months from May to July 2019, 150 of which were completed (90.4%). Statistic analysis was conducted using SPSS Ver. 25.0. The results of the study showed that the genre of the game (42.6%), number of players (24.0%), price for payment (20.3%) and game planning (13.1%) were important attributes in choosing VR games. The optimal mix of attributes was derived with new games, RPGs, multi-play and medium price (22,000 KRW). Before mentioning technology in the expectations of users who use VR game content, which is the most preferred among VR contents, this study recognized the need to have a fun and new experience through VR game content. Therefore, it is expected that this will serve as a reference for consumer behavior of VR game contents and research on VR game contents development.

• Journal of Korea Multimedia Society
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• v.14 no.5
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• pp.658-668
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• 2011

Over the past decade, the improvement of communications technologies and the rapid spread of www (World Wide Web) have brought on the exponential growth of users using Internet and real time multimedia multicast services like video conferencing, tele-immersive virtual reality, and Internet games. The dense-wavelength division multiplexing (DWDM) networks have been widely accepted as a promising approach to meet the ever-increasing bandwidth demands of Internet users, especially in next generation Internet backbone networks for nation-wide or global coverage. A major challenge in the next generation Internet backbone networks based on DWDM technologies is the resolution of the multicasting RWA (Routing and Wavelength Assignment) problem; given a set of wavelengths in the DWDM network, we set up light-paths by routing and assigning a wavelength for each connection so that the multicast connections are set-upped as many as possible. Finding such optimal multicast connections has been proven to be Non-deterministic Polynomial-time-complete. In this paper, we suggest a new heuristic multicast routing and wavelength assignment method for multicast sessions called DVS-PMIPMR (Differentiated Virtual Source-based Priority Minimum Interference Path Multicast Routing algorithm). We measured the performance of the proposed algorithm in terms of number of wavelength and wavelength channel. The simulation results demonstrate that DVS-PMIPMR algorithm is superior to previous multicast routing algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8B
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• pp.681-688
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• 2008

In this paper, we propose a new price-based call admission control algorithm for multi-class communication networks. When a call arrives at the network, it informs the network of the number of requested circuits and the minimum amount of time that it will require. The network provides the optimal price for the arrived call with which it tries to maximize its expected revenue. The optimal price is dynamically adjusted based on the information of the arrived call, and the present and the estimated future congestion level of the network during the reservation time of the call. If the call accepts the price, it is admitted. Otherwise, it is rejected. We compare the performance of our dynamic pricing algorithm with that of the static pricing algorithm by Courcoubetis and Reiman [1], and Paschalidis and Tsitsiklis [2]. By the comparison, we show that our dynamic pricing algorithm has better performance aspects such as higher call admission ratio and lower price than the static pricing algorithm, although these two algorithms result in almost the same revenue as shown in [2]. This implies that, in the competitive situation, the dynamic pricing algorithm can attract more users than the static pricing algorithm, generating more revenue. Moreover, we show that if a certain fixed connection fee is introduced to the price for a call, our dynamic pricing algorithm yields more revenue.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.147-161
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• 2010

As broadcasting and communication are converged recently, communication is jointed to TV. TV viewing has brought about many changes. The IPTV (Internet Protocol Television) provides information service, movie contents, broadcast, etc. through internet with live programs + VOD (Video on demand) jointed. Using communication network, it becomes an issue of new business. In addition, new technical issues have been created by imaging technology for the service, networking technology without video cuts, security technologies to protect copyright, etc. Through this IPTV network, users can watch their desired programs when they want. However, IPTV has difficulties in search approach, menu approach, or finding programs. Menu approach spends a lot of time in approaching programs desired. Search approach can't be found when title, genre, name of actors, etc. are not known. In addition, inserting letters through remote control have problems. However, the bigger problem is that many times users are not usually ware of the services they use. Thus, to resolve difficulties when selecting VOD service in IPTV, a personalized service is recommended, which enhance users' satisfaction and use your time, efficiently. This paper provides appropriate programs which are fit to individuals not to save time in order to solve IPTV's shortcomings through filtering and recommendation-related system. The proposed recommendation system collects TV program information, the user's preferred program genres and detailed genre, channel, watching program, and information on viewing time based on individual records of watching IPTV. To look for these kinds of similarities, similarities can be compared by using ontology for TV programs. The reason to use these is because the distance of program can be measured by the similarity comparison. TV program ontology we are using is one extracted from TV-Anytime metadata which represents semantic nature. Also, ontology expresses the contents and features in figures. Through world net, vocabulary similarity is determined. All the words described on the programs are expanded into upper and lower classes for word similarity decision. The average of described key words was measured. The criterion of distance calculated ties similar programs through K-medoids dividing method. K-medoids dividing method is a dividing way to divide classified groups into ones with similar characteristics. This K-medoids method sets K-unit representative objects. Here, distance from representative object sets temporary distance and colonize it. Through algorithm, when the initial n-unit objects are tried to be divided into K-units. The optimal object must be found through repeated trials after selecting representative object temporarily. Through this course, similar programs must be colonized. Selecting programs through group analysis, weight should be given to the recommendation. The way to provide weight with recommendation is as the follows. When each group recommends programs, similar programs near representative objects will be recommended to users. The formula to calculate the distance is same as measure similar distance. It will be a basic figure which determines the rankings of recommended programs. Weight is used to calculate the number of watching lists. As the more programs are, the higher weight will be loaded. This is defined as cluster weight. Through this, sub-TV programs which are representative of the groups must be selected. The final TV programs ranks must be determined. However, the group-representative TV programs include errors. Therefore, weights must be added to TV program viewing preference. They must determine the finalranks.Based on this, our customers prefer proposed to recommend contents. So, based on the proposed method this paper suggested, experiment was carried out in controlled environment. Through experiment, the superiority of the proposed method is shown, compared to existing ways.

• Journal of the Korean Society for Railway
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• v.18 no.6
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• pp.630-638
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• 2015

The total number of passengers on the KTX since its construction in 2004 surpassed 500 million in October, 2015. The operation of KTX made it possible to reach anywhere in a country in half a day, which subsequently altered people's lifestyle. As the KTX has become an important mode of transportation, there is a growing interest in the optimal size and location of its stations. Currently, the stations are constructed through public-private partnerships since a sufficient amount of budget is hard to secure only from the public sector; however, because railway stations are traditionally aimed at promoting public interests, an emphasis on the profitability of the private sector could compromise public interests. At this juncture, this study separately computes the number of users based on each of the two primary functions of the stations-as a railway station and as a sales facility-and estimates the numbers of people according to various transportation modes that are taken to access the stations. This estimation is applied to the case of Dongdaegu Station, which will open in 2016. Such an application helps to predict and respond to possible congestion as brought about by the expansion of the sales facility.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 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 the Korea Institute of Information and Communication Engineering
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• v.10 no.11
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• pp.2091-2098
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• 2006

In this paper, it proposes a protocol of resource adaptive data dissemination for sensor nodes in a wireless sensor network. In general, each sensor node used in a wireless sensor network delivers the required information to the final destination by conducting cooperative works such as sensing, processing, and communicating each other using the battery power of a independent sensor node. So, a protocol used for transferring the acquired information to users through the wireless sensor network can minimize the power consumption of energy resource given to a sensor node. Especially, it is very important to minimize the total amount of power consumption with a method for handling the problems on implosion. data delivery overlapping, and excessive message transfer caused by message broadcasting. In this paper, for the maintaining of the shortest path between sensor nodes, maximizing of the life time of a sensor node and minimizing of communication cost, it presents a method for selecting the representative transfer node for an event arising area based on the negotiation scheme and maintaining optimal transfer path using hop and energy information. Finally, for the performance evaluation, we compare the proposed protocol to existing directed diffusion and SPIN protocol. And, with the simulation results, we show that the proposed protocol enhances the performance on the power consumption rate when the number of overall sensor nodes in a sensor network or neighbor sensor nodes in an event area are increased and on the number of messages disseminated from a sensor node.