• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.4
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• pp.491-508
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• 2010

In peer-to-peer (P2P) on-demand streaming networks, the alleviation of server load depends on reciprocal stream sharing among peers. In general, on-demand video services enable clients to watch videos from beginning to end. As long as clients are able to buffer the initial part of the video they are watching, on-demand service can provide access to the video to the next clients who request to watch it. Therefore, the key challenge is how to keep the initial part of a video in a peer's buffer for as long as possible, and thus maximize the availability of a video for stream relay. In addition, to address the issues of delivering data on lossy network and providing scalable quality of services for clients, the adoption of multiple description coding (MDC) has been proven as a feasible resolution by much research work. In this paper, we propose a novel caching scheme for P2P on-demand streaming, called Dynamic Buffering. The proposed Dynamic Buffering relies on the feature of MDC to gradually reduce the number of cached descriptions held in a client's buffers, once the buffer is full. Preserving as many initial parts of descriptions in the buffer as possible, instead of losing them all at one time, effectively extends peers’ service time. In addition, this study proposes a description distribution balancing scheme to further improve the use of resources. Simulation experiments show that Dynamic Buffering can make efficient use of cache space, reduce server bandwidth consumption, and increase the number of peers being served.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.859-872
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• 2016

Land cover classification is an important tool for preventing natural disasters, collecting environmental information, and monitoring natural resources. Hyperspectral imaging is widely used for this task thanks to sufficient spectral information. However, the curse of dimensionality, spatiotemporal variability, and lack of labeled data make it difficult to classify the land cover correctly. We propose a novel classification framework for land cover classification of hyperspectral data based on convolutional neural networks. The proposed framework naturally incorporates full spectral features with the information from neighboring pixels and has advantages over existing methods that require additional feature extraction or pre-processing steps. Empirical evaluation results show that the proposed framework provides good generalization power with classification accuracies better than (or comparable to) the most advanced existing classifiers.

• Journal of the military operations research society of Korea
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• v.33 no.2
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• pp.75-86
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• 2007

This paper presents the overall valuation framework of the Army Tactical Command Information System(ATCIS) with its empirical data which is developed by the ROK Army in 2005 with its own R&D efforts over the past 10 years. The valuation with the appropriate methods is conducted through the full and relentless support of ROK Army HQs, defense agencies, and related firms. And the results include both qualitative and quantitative values of the ATCIS in perspectives of economic, technical, and defense strength effects. As a result, the development of ATCIS is valued over 1.7291 trillion Won which is over 6.55 times benefit and cost ratio comparing with other country's similar system. The worth of this paper includes the actual framework and methods to apply the valuation of other weapon systems and its preliminary research, defense offset valuation, performance evaluation and others.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.1
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• pp.63-73
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• 2010

A comprehensive mapping project for agroclimatic zoning in South Korea will end by April 2010, which has required 4 years, a billion won (ca. 0.9 million US dollars) and 22 experts from 7 institutions to complete it. The map database from this project may be categorized into primary, secondary and analytical products. The primary products are called "high definition" digital climate maps (HD-DCMs) and available through the state of the art techniques in geospatial climatology. For example, daily minimum temperature surfaces were prepared by combining the climatic normals (1971-2000 and 1981-2008) of synoptic observations with the simulated thermodynamic nature of cold air by using the raster GIS and microwave temperature profiling which can quantify effects of cold air drainage on local temperature. The spatial resolution of the gridded climate data is 30m for temperature and solar irradiance, and 270m for precipitation. The secondary products are climatic indices produced by statistical analysis of the primary products and includes extremes, sums, and probabilities of climatic events relevant to farming activities at a given grid cell. The analytical products were prepared by driving agronomic models with the HD-DCMs and dates of full bloom, the risk of freezing damage, and the fruit quality are among the examples. Because the spatial resolution of local climate information for agronomic practices exceeds the current weather service scale, HD-DCMs and the value-added products are expected to supplement the insufficient spatial resolution of official climatology. In this lecture, state of the art techniques embedded in the products, how to combine the techniques with the existing geospatial information, and agroclimatic zoning for major crops and fruits in South Korea will be provided.

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.59-67
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• 2013

In this paper, we propose the LAR_CF, latent attribute rating-based collaborative filtering, that is robust to data sparsity problem which is one of traditional problems caused of decreasing rating prediction accuracy. As compared with that existing collaborative filtering method uses a preference rating rated by users as feature vector to calculate similarity between objects, the proposed method improves data sparsity problem using unique attributes of two target objects with existing explicit preference. We consider MovieLens 100k dataset and its item attributes to evaluate the LAR_CF. As a result of artificial data sparsity and full-rating experiments, we confirmed that rating prediction accuracy can be improved rating prediction accuracy in data sparsity condition by the LAR_CF.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4398-4417
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• 2017

Multi-user Multiple-Input and Multiple-Output (MU-MIMO) has potential for prominently enhancing the capacity of wireless network by simultaneously transmitting to multiple users. User selection is an unavoidable problem which bottlenecks the gain of MU-MIMO to a great extent. Major state-of-the-art works are focusing on improving network throughput by using Channel State Information (CSI), however, the overhead of CSI feedback becomes unacceptable when the number of users is large. Some work does well in balancing tradeoff between complexity and achievable throughput but is lack of consideration of fairness. Current works universally ignore the rational utilizing of time resources, which may lead the improvements of network throughput to a standstill. In this paper, we propose TOUSE, a scalable and fair user selection scheme for MU-MIMO. The core design is dynamic-time-warping-based user selection mechanism for downlink MU-MIMO, which could make full use of concurrent transmitting time. TOUSE also presents a novel data-rate estimation method without any CSI feedback, providing supports for user selections. Simulation result shows that TOUSE significantly outperforms traditional contention-based user selection schemes in both throughput and fairness in an indoor condition.

• Journal of Korea Spatial Information System Society
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• v.3 no.1 s.5
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• pp.5-16
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• 2001

ITS (Intelligent Transport System) has been developed into the full service leading to a new life style with the various services from private sector, which was intended to prevent the traffic accidents and to reduce the traffic jams. Since traveler-type components used in ITS services are not exclusively designed. but they can be one of the existing devices such as mobile phone, PDA, PC, KIOSK and etc. We need to consider that these devices are equipped to support ITS related services. Therefore, this study modeled, through UML, a framework of portable components based on the present national ITS architecture; the purpose being that private companies may be able to develop portable components with concepts of ITS services. The methodology of the modeling process we followed consisted of four steps: (1) analyzing service-related subsystems, (2) generalizing equipment-package and functional specifications. (3) describing information and function specific reference models, and (4) UML modeling of the reference model.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.4
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• pp.31-38
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• 2009

Most 3D models found in the literature focus on theoretical topology for exterior 3D volumes. Although there are a few indoor models such as CityGML or IFC, implementing a full topology for the indoor spaces is either less practical due to the complexity or not even necessary in some application domains. Moreover, current spatial DBMSs do not support functionalities explicitly for 3D topological relations. In this study, an alternative method to build a 3D indoor model with less complexity ernativespatial DBMS is suggested. Focusnation the fact that semantic attributes can be storedion the floor surface, we suggestivemulti-layered 3D model for indoor spaces. We show the process to build the proposed model in the PostGIS, a spatial DBMS. And, then, as an example application, we illustrate the process to build and run a campus building information system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.3967-3983
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• 2014

The performance of an partial relay selection on the decode-and-forward (DF) mode cognitive radio (CR) relay networks is studied, with some important factors, including the outage probability, the bit error ratio (BER), and the average channel capacity being analyzed. Different from the conventional relay selection schemes, the impact of spectrum sensing process as well as the spectrum utilization efficiency of primary users on the performance of DF-based CR relaying networks has been taken into consideration. In particular, the exact closed-form expressions for the figures of merit such as outage probability, BER, and average channel capacity over independent and identically distributed (i.i.d.) Rayleigh fading channels, have been derived in this paper. The validity of the proposed analysis is proven by simulation, which showed that the numerical results are consistent with the theoretical analysis in terms of the outage probability, the BER and the average channel capacity. It is also shown that the full spatial diversity order can always be obtained at the signal-to-noise ratio (SNR) range of [0dB, 15dB] in the presence of multiple potential relays.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4103-4121
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• 2018

Quality of Service (QoS) awareness is recognized as a key point for the success of Internet of Things (IOT).Realizing the full potential of the Internet of Things requires, a real-time task scheduling algorithm must be designed to meet the QoS need. In order to schedule tasks with diverse QoS requirements in cloud environment efficiently, we propose a task scheduling strategy based on dynamic priority and load balancing (DPLB) in this paper. The dynamic priority consisted of task value density and the urgency of the task execution, the priority is increased over time to insure that each task can be implemented in time. The scheduling decision variable is composed of time attractiveness considered earliest completion time (ECT) and load brightness considered load status information which by obtain from each virtual machine by topic-based publish/subscribe mechanism. Then sorting tasks by priority and first schedule the task with highest priority to the virtual machine in feasible VMs group which satisfy the QoS requirements of task with maximal. Finally, after this patch tasks are scheduled over, the task migration manager will start work to reduce the load balancing degree.The experimental results show that, compared with the Min-Min, Max-Min, WRR, GAs, and HBB-LB algorithm, the DPLB is more effective, it reduces the Makespan, balances the load of VMs, augments the success completed ratio of tasks before deadline and raises the profit of cloud service per second.