• Journal of the Korean Geographical Society
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• v.48 no.2
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• pp.259-271
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• 2013

This paper addresses the efficient facility placements to adopt a super WiFi network, taking significant considerations as the next generation 'information highway'. Since the super WiFi has a wider geographic coverage by utilizing the white spaces of TV broadcasting which are empty and available frequencies for the wireless communications, it would play an important role in releasing digital divide of the internet access for low populated or mountainous areas. The purpose of this paper is to explore systematic and efficient spatial plans for the super WiFi. For doing this, we applied optimal location covering models to Gurye-gun, Jeonlanamdo. From the application, we presented optimal locations for super WiFi facilities and significant analytical results, such as the tradeoff between the number of facilities and coverage and marginal coverage for establishing super WiFi network. The results of this research would be usefully utilized for decision makers who wish to adopt a super WiFi, to extend wireless networks in a city or build a regional infrastructure of wireless facilities.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1076-1084
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• 2019

In this paper, we introduced a system that extracts metadata by recognizing characters and objects in media using deep learning technology. In the field of broadcasting, multimedia contents such as video, audio, image, and text have been converted to digital contents for a long time, but the unconverted resources still remain vast. Building media archives requires a lot of manual work, which is time consuming and costly. Therefore, by implementing a deep learning-based metadata generation system, it is possible to save time and cost in constructing media archives. The whole system consists of four elements: training data generation module, object recognition module, character recognition module, and API server. The deep learning network module and the face recognition module are implemented to recognize characters and objects from the media and describe them as metadata. The training data generation module was designed separately to facilitate the construction of data for training neural network, and the functions of face recognition and object recognition were configured as an API server. We trained the two neural-networks using 1500 persons and 80 kinds of object data and confirmed that the accuracy is 98% in the character test data and 42% in the object data.

• Journal of KIISE:Information Networking
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• v.33 no.1
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• pp.38-48
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• 2006

For extending of hot-spot of WLAN, (2) proposes internetworking scheme between wireless LAN (WLAN) and mobile ad-hoc network (MANET), which employ the same layer-2 protocol with different mode. Compared to internetworking schemes between UMTS (Universal Mobile Telecommunications Systems) and WLAN (3-4), the scheme from (2) has relatively low overhead and latencies because WLAN and MANET are physically and logically similar to each other. However, the mode switching algorithm proposed in r2] for internetworking between WLAN and MANET only considers signal strength and determines handoff, and mobile nodes following a zigzag course in pollution area may perform handoff at short intervals. Furthermore, (2) employs mobile IPv6 (MIPv6) at base, which brings still high delay on handoff and overhead due to signal message exchange. In this paper, we present optimized internetworking scheme between WLAN and MANET, modified from (2). To settle ping-pong handoff from (2), we propose adaptive mode switching algorithm. HMIPv6 is employed for IP connectivity and mobility service in WLAN, which solves some shortcomings, such as high handoff overhead and vulnerable security. For routing in MANET, OLSR is employed, which is a proactive Protocol and has optimally reduced signal broadcasting overhead. OLSR operates with current P protocol compatibly with no change or modification. The proposed internetworking scheme based on adaptive mode switching algorithm shows better performance than scheme from (2).

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.57-71
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• 2013

Over a billion people in the world generate new news minute by minute. People forecasts some news but most news are from unexpected events such as natural disasters, accidents, crimes. People spend much time to watch a huge amount of news delivered from many media because they want to understand what is happening now, to predict what might happen in the near future, and to share and discuss on the news. People make better daily decisions through watching and obtaining useful information from news they saw. However, it is difficult that people choose news suitable to them and obtain useful information from the news because there are so many news media such as portal sites, broadcasters, and most news articles consist of gossipy news and breaking news. User interest changes over time and many people have no interest in outdated news. From this fact, applying users' recent interest to personalized news service is also required in news service. It means that personalized news service should dynamically manage user profiles. In this paper, a content-based news recommendation system is proposed to provide the personalized news service. For a personalized service, user's personal information is requisitely required. Social network service is used to extract user information for personalization service. The proposed system constructs dynamic user profile based on recent user information of Facebook, which is one of social network services. User information contains personal information, recent articles, and Facebook Page information. Facebook Pages are used for businesses, organizations and brands to share their contents and connect with people. Facebook users can add Facebook Page to specify their interest in the Page. The proposed system uses this Page information to create user profile, and to match user preferences to news topics. However, some Pages are not directly matched to news topic because Page deals with individual objects and do not provide topic information suitable to news. Freebase, which is a large collaborative database of well-known people, places, things, is used to match Page to news topic by using hierarchy information of its objects. By using recent Page information and articles of Facebook users, the proposed systems can own dynamic user profile. The generated user profile is used to measure user preferences on news. To generate news profile, news category predefined by news media is used and keywords of news articles are extracted after analysis of news contents including title, category, and scripts. TF-IDF technique, which reflects how important a word is to a document in a corpus, is used to identify keywords of each news article. For user profile and news profile, same format is used to efficiently measure similarity between user preferences and news. The proposed system calculates all similarity values between user profiles and news profiles. Existing methods of similarity calculation in vector space model do not cover synonym, hypernym and hyponym because they only handle given words in vector space model. The proposed system applies WordNet to similarity calculation to overcome the limitation. Top-N news articles, which have high similarity value for a target user, are recommended to the user. To evaluate the proposed news recommendation system, user profiles are generated using Facebook account with participants consent, and we implement a Web crawler to extract news information from PBS, which is non-profit public broadcasting television network in the United States, and construct news profiles. We compare the performance of the proposed method with that of benchmark algorithms. One is a traditional method based on TF-IDF. Another is 6Sub-Vectors method that divides the points to get keywords into six parts. Experimental results demonstrate that the proposed system provide useful news to users by applying user's social network information and WordNet functions, in terms of prediction error of recommended news.

• Journal of Korean Society of Transportation
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• v.20 no.5
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• pp.33-43
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• 2002

Travel choice behavior is affected by real-time traffic information. Recently, in urban area, real-time traffic information is provided by several instruments such as transportation broadcasting, internet PC network and variable message sign, etc. Furthermore, it has been increasing for urban travelers to use real-time traffic information provided by several instruments. The purpose of this study is to analyze the effects of advanced traveler information on urban worker's travel choice behavior. Among several Advanced Traveler Information System(ATIS) employed in urban area. This study focuses on examining the effects of transportation broadcasting on urban worker's travel choice behavior. This study attempts to examine traveler's mode change behavior in the pre-trip stage and traveler's route change behavior in the on-route stage. For this study, the survey data collected from Daegu City in 2000 is used. For empirical analysis, several nested logit models are estimated, and among them, the best models are reported in this paper. Furthermore, based on the empirical models estimated for this research, important findings and their policy implications are discussed.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.6
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• pp.661-683
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• 2001

There is a rapid increase in the use of digital video information in recent years, it becomes more important to manage video databases efficiently. The development of high speed data network and digital techniques has emerged new multimedia applications such as internet broadcasting, Video On Demand(VOD) combined with video data processing and computer. Video database should be construct for searching fast, efficient video be extract the accurate feature information of video with more massive and more complex characteristics. Video database are essential differences between video databases and traditional databases. These differences lead to interesting new issues in searching of video, data modeling. So, cause us to consider new generation method of database, efficient retrieval method of video. In this paper, We propose the construction and generation method of the video database based on contents which is able to accumulate the meaningful structure of video and the prior production information. And by the proposed the construction and generation method of the video database implemented the video database which can produce the new contents for the internet broadcasting centralized on the video database. For this production, We proposed the video indexing method which integrates the annotation-based retrieval and the content-based retrieval in order to extract and retrieval the feature information of the video data using the relationship between the meaningful structure and the prior production information on the process of the video parsing and extracting the representative key frame. We can improve the performance of the video contents retrieval, because the integrated video indexing method is using the content-based metadata type represented in the low level of video and the annotation-based metadata type impressed in the high level which is difficult to extract the feature information of the video at he same time.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.2 s.302
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• pp.11-20
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• 2005

In previous video conference system, when the number of participants in video conference increases by n, the bandwidth and memory of n2 is required. And also, it brings about increase in traffic and problem of a say during a conference in aspect of transmission of voice data. In this paper, we propose an algorithm of remote video conference using silence detection algerian to resolve the questions such as buffering method of video data in server and heavy traffic detection algorithm to the increase in participants. Video data buffering algorithm is not a method of broadcasting to other client in the server, but this algorithm uses two other methods; the buffering method of receiving compressed video data from clients and the indexing method for acquiring the video data of other participants in clients according to clients' bandwidth and network transmission speed. We apply a voice transmission algerian and a channel management algorithm to the remote video conference system. The method used in the voice transmission algorithm is a silence detection algorithm which does not send silent participants' voice data to the server. The channel management algorithm is a method allocating a say to the participants who have priority. In consideration of average 20 frames and 30ms regardless of a number of participants, we can safely conclude that the transmission of video and voice data is stable.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.11 s.353
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• pp.118-127
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• 2006

The wireless transmission medium inherently broadcasts a signal to all neighbor nodes in the transmission range. Existing asynchronous MAC protocols do not provide a concrete solution for reliable broadcast in link layer. This mainly comes from that an omnidirectional broadcasting causes to reduce the network performance due to the explosive collisions and contentions. This paper proposes a directional broadcast protocol by using neighborhood information in the link layer based o,1 directional antennas, named MNDB (MAC protocol with Neighborhood for reliable Directional Broadcast). This protocol makes use of neighborhood information and DMACA (Directional Multiple Access and Collision Avoidance) scheme through 4-way handshake to support a reliable directional broadcast. To analyze its performance, MNDB protocol si compared with $RMDB^{[1]}$, the protocol 2 of reference [3], and IEEE 802.11 $protocol^{[9]}$, in terms of the number of collisions, the number of dropped packets, the number of redundant packets, and broadcast delay.

• Journal of Broadcast Engineering
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• v.22 no.4
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• pp.484-495
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• 2017

The development of display devices and the increase of network transmission bandwidth bring demands for over 2K high resolution video such as panorama video, 4K ultra-high definition commercial broadcasting, and ultra-wide viewing video. To compress these image sequences with significant amount of data, High Efficiency Video Coding (HEVC) standard with the highest coding efficiency is a promising solution. HEVC, the latest video coding standard, provides high encoding efficiency using various advanced encoding tools, but it also requires significant amounts of computation complexity compared to previous coding standards. In particular, the complexity of HEVC decoding process is a imposing challenges on real-time playback of ultra-high resolution video. To accelerate the HEVC decoding process for ultra high resolution video, this paper introduces a data-level parallel video decoding method using slice and/or tile supported by HEVC. Moreover, deblocking filter process is further parallelized. The proposed method distributes independent decoding operations of each tile and/or each slice to multiple threads as well as deblocking filter operations. The experimental results show that the proposed method facilitates executions up to 2.0 times faster than the HEVC reference software for 4K videos.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.4
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• pp.313-320
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• 2008

The dynamic clustering technique has some problems regarding energy consumption. In the cluster configuration aspect the cluster structure must be modified every time the head nodes are re-selected resulting in high energy consumption. Also, there is excessive energy consumption when a cluster head node receives identical data from adjacent cluster sources nodes. This paper proposes a solution to the problems described above from the energy efficiency perspective. The round-robin cluster header(RRCH) technique, which fixes the initially structured cluster and sequentially selects duster head nodes, is suggested for solving the energy consumption problem regarding repetitive cluster construction. Furthermore, the issue of redundant data occurring at the cluster head node is dealt with by broadcasting metadata of the initially received data to prevent reception by a sensor node with identical data. A simulation experiment was performed to verify the validity of the proposed approach. The results of the simulation experiments were compared with the performances of two of the must widely used conventional techniques, the LEACH(Low Energy Adaptive Clustering Hierarchy) and HEED(Hybrid, Energy Efficient Distributed Clustering) algorithms, based on energy consumption, remaining energy for each node and uniform distribution. The evaluation confirmed that in terms of energy consumption, the technique proposed in this paper was 29.3% and 21.2% more efficient than LEACH and HEED, respectively.