• Journal of KIISE:Computer Systems and Theory
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• v.36 no.3
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• pp.140-148
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• 2009

Recent advances in communication and multimedia technologies make it possible to provide video-on-demand(VOD) services and people can access video servers over the Internet at any time using their electronic devices, such as PDA, mobile phone and digital TV. Each device has different processing capabilities, energy budgets, display sizes and network connectivities. To support such diverse devices, multiple versions of videos are needed to meet users' requests. In general cases, VOD servers cannot store all the versions of videos due to the storage limitation. When a device requests a stored version, the server can send the appropriate version immediately, but when the requested version is not stored, the server first converts some stored version to the requested version, and then sends it to the client. We call this conversion process transcoding. If transcoding occurs frequently in a VOD server, the CPU resource of the server becomes insufficient to response to clients. Thus, to admit as many requests as possible, we need to maximize the CPU availability. In this paper, we propose a new algorithm to select versions from those stored on disk using a branch and bound technique to maximize the CPU availability. We also explore the impact of these storage management policies on streaming to heterogeneous users.

• The KIPS Transactions:PartC
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• v.11C no.6 s.95
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• pp.843-850
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• 2004

As mobile devices prevail, requests for various services using mobile devices have increased. Requests for application services that require large data space such as multimedia, game and database [1] specifically have greatly increased. However, mobile appliances have difficulty in applying various services like a wire environment, because the storage capacity of one is not enough. Therefore, research (5) which provides remote storage service for mobile appliances using iSCSI is being conducted to overcome storage space limitations in mobile appliances. But, when iSCSI is applied to mobile appliances, iSCSI I/O performance drops rapidly if a iSCSI client moves from the server to a far away position. In the case of write operation, $28\%$ reduction of I/O performance occurred when the latency of network is 64ms. This is because the iSCSI has a structural quality that is very .sensitive to delay time. In this paper, we will introduce an intermediate target server and localize iSCSI target to improve the shortcomings of iSCSI performance dropping sharply as latency increases when mobile appliances recede from a storage server.

• Journal of the Economic Geographical Society of Korea
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• v.15 no.1
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• pp.26-41
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• 2012

This study investigates the time-space characteristics of intra-urban passenger flows in the Metropolitan Seoul area. In particular, we analyze the relationships between transit ridership and land use through the use of the subway passenger flow data obtained from the transit transaction databases. For this purpose, the strength of each subway station, i.e., the number of total in-coming and out-going passengers at each station, in the morning, afternoon, and evening, is calculated and visualized, which reflects urban land use patterns. Then the subway stations are classified into four groups via a hierarchical analysis of the in-coming and out-going passenger flows at 353 stations. Each group appears to have characteristic properties according to the region, e.g., residential areas and central business districts. This has been confirmed by the analysis which probes explicitly the relationship between the local socio-economic variables and station groups. This analysis, disclosing the inter-relationship between the subway network and urban land use, may be useful at various stages in urban as well as transportation planning, and provides analytical tools for a wide spectrum of applications ranging from impact evaluation to decision-making and planning support.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.115-127
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• 2021

With the development of science and technology around the world, the realm of cyberspace, following land, sea, air, and space, is also recognized as a battlefield area. Accordingly, it is necessary to design and establish various elements such as definitions, systems, procedures, and plans for not only physical operations in land, sea, air, and space but also cyber operations in cyberspace. In this research, the importance of cyber targets that can be considered when prioritizing the list of cyber targets selected through intermediate target development in the target development and prioritization stage of targeting processing of cyber operations was selected as a factor to be considered. We propose a method to calculate the score for the cyber target and use it as a part of the cyber target prioritization score. Accordingly, in the cyber target prioritization process, the cyber target importance category is set, and the cyber target importance concept and reference item are derived. We propose a TIR (Target Importance Rank) algorithm that synthesizes parameters such as Event Prioritization Framework based on PageRank algorithm for score calculation and synthesis for each derived standard item. And, by constructing the Stuxnet case-based network topology and scenario data, a cyber target importance score is derived with the proposed algorithm, and the cyber target is prioritized to verify the proposed algorithm.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1693-1705
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• 2023

Rural areas, which account for about 90% of the country's land area, are increasing in importance and value as a space that performs various public functions. However, facilities that adversely affect residents' lives, such as livestock facilities, factories, and solar panels, are being built indiscriminately near residential areas, damaging the rural environment and landscape and lowering the quality of residents' lives. In order to prevent disorderly development in rural areas and manage rural space in a planned manner, detection and monitoring of hazardous facilities in rural areas is necessary. Data can be acquired through satellite imagery, which can be acquired periodically and provide information on the entire region. Effective detection is possible by utilizing image-based deep learning techniques using convolutional neural networks. Therefore, U-Net model, which shows high performance in semantic segmentation, was used to classify potentially hazardous facilities in rural areas. In this study, KOMPSAT ortho-mosaic optical imagery provided by the Korea Aerospace Research Institute in 2020 with a spatial resolution of 0.7 meters was used, and AI training data for livestock facilities, factories, and solar panels were produced by hand for training and inference. After training with U-Net, pixel accuracy of 0.9739 and mean Intersection over Union (mIoU) of 0.7025 were achieved. The results of this study can be used for monitoring hazardous facilities in rural areas and are expected to be used as basis for rural planning.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.2
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• pp.117-131
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• 2007

Representation of building internal space is an active research area as the need for more geometrically accurate and visually realistic increases. 3 dimensional representation is common ground of research for disciplines such as computer graphics, architectural design and engineering and Geographic Information System (GIS). In many cases CAD plans are the starting point of reconstruction of 3D building models. The main objectives of building reconstruction in GIS applications are visualization and spatial analysis. Hence, CAD plans need to be preprocessed and edited to adapt to the data models of GIS SW and then georeferenced to enable spatial analysis. This study automated the preprocessing of CAD data using AutoCAD VBA (Visual Basic Application), and the processed data was topologically restructured for further analysis in GIS environment. Accuracy of georeferencing CAD data was also examined by comparing the results of coordinate transformation by using digital maps and GPS measurements as the sources of ground control points. The reconstructed buildings were then applied to visualization and network modeling.

• Korean Journal of Community Nutrition
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• v.25 no.3
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• pp.189-203
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• 2020

Objectives: This study was performed to identify the current barriers of obesity management for children using Community Child Care Centers and their caregivers (parents and teachers working in the Centers). Further, this study explored the possibility of utilizing a mobile phone application for tailored obesity prevention and management programs to overcome the current difficulties associated with children's obesity management. Methods: The qualitative data were collected through in-depth interviews with 20 obese and overweight children or children who wanted to participate in this study using Community Child Care Centers, 12 teachers working at the Centers, and a focus group interview with five parents of children using the Centers. Data were analyzed with a thematic approach categorizing themes and sub-themes based on the transcripts. Results: The current barriers of obesity management of obese and overweight children using Community Child Care Centers were lack of self-directed motivation regarding obesity management (chronic obesity-induced lifestyles and reduced self-confidence due to stigma) and lack of support from households and Community Child Care Centers (latchkey child, inconsistency in dietary guidance between the Center and household, repetitive pressure to eat, and absence of regular nutrition education). Mobile phone applications may have potential to overcome the current barriers by providing handy and interesting obesity management based on visual media (real-time tracking of lifestyles using behavior records and social support using gamification), environmental support (supplementation of parental care and network-based education between the Community Child Care Center and household), and individualized intervention (encouragement of tailored and gradual changes in eating habits and tailored goal setting). It is predicted that the real-time mobile phone program will provide information for improving nutritional knowledge and behavioral skills as well as lead to sustainable children's coping strategies regarding obesity management. In addition, it is expected that environmental factors may be improved by network-based education between the Community Child Care Centers and households using the characteristics of mobile phones, which are free from space and time constraints. Conclusions: The tailored education program for children using Community Child Care Centers based on mobile phones may prevent and reduce childhood obesity by overcoming the current barriers of obesity management for children, providing environmental and individualized support to promote healthy lifestyles and quality of life in the future.

• Journal of Korea Multimedia Society
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• v.7 no.8
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• pp.1066-1077
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• 2004

Dramatic increase in the number of Internet users want highly qualified service of continuous media contents on the web. To solve these problems, we present two network caching schemes(PPC, PPCwP) which consider the characteristics of continuous media objects and user access pattern in this paper. While there are plenty of reasons to create rich media contents, delivering this high bandwidth contents over the internet presents problems such as server overload, network congestion and client-perceived latency. PPC scheme periodically calculates the popularity of objects based on the playback quantity and determines the optimal size of the initial fraction of a continuous media object to be cached in proportion to the calculated popularity. PPCwP scheme calculates the expected popularity using the series information and prefetches the expected initial fraction of newly created continuous media objects. Under the PPCwP scheme, the initial client-perceived latency and the data transferred from a remote server can be reduced and limited cache storage space can be utilized efficiently. Trace-driven simulation have been performed to evaluate the presented caching schemes using the log-files of iMBC. Through these simulations, PPC and PPCwP outperforms LRU and LFU in terms of BHR and DSR.

• The KIPS Transactions:PartD
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• v.10D no.4
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• pp.591-602
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• 2003

Delay and discontinuance phenomenon of service are cause by sudden increase of the network communication amount and the quantity consumed of resources when Internet users are driven excessively to a conventional single large database sewer. To solve these problems, spatial database cluster consisted of several single nodes on high-speed network to offer high-performance is risen. But, research about spatial join operation that can reduce the performance of whole system in case process at single node is not achieved. So, in this paper, we propose efficient parallel spatial join processing method in a spatial database cluster system that uses data partitions and replications method that considers the characteristics of space data. Since proposed method does not need the creation step and the assignment step of tasks, and does not occur additional message transmission between cluster nodes that appear in existent parallel spatial join method, it shows performance improvement of 23% than the conventional parallel R-tree spatial join for a shared-nothing architecture about expensive spatial join queries. Also, It can minimize the response time to user because it removes redundant refinement operation at each cluster node.