• Information Systems Review
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• v.17 no.1
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• pp.1-18
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• 2015

Recently, the number of available cloud services are increasing dramatically because many IT companies have entered into cloud service market. Due to the reason, cloud service brokers are emerging as agents to solve cloud service selection problems and to support cloud service initialization and maintenance of unskilled cloud service users. In this study, NCloud24 case in South Korea and Right Scale case in the USA are analyzed as representative examples of the collaboration between original cloud service providers and cloud service brokers. The business models of two companies are analyzed using Business Model Canvas. The emergence of cloud service brokers are interpreted as unbundling process of IaaS (Infrastructure-as-a-service) cloud service companies. Based on the comparison with the two companies, we prospect future directions of cloud service brokerage.

• Journal of Biomedical Engineering Research
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• v.26 no.6
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• pp.357-363
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• 2005

Active contour models have been extensively used to segment, match, and track objects of interest in computer vision and image processing applications, particularly to locate object boundaries. With conventional methods an object boundary can be extracted by controlling the internal energy and external energy based on energy minimization. However, this still leaves a number of problems, such as initialization and poor convergence in concave regions. In particular, a contour is unable to enter a concave region based on the stretching and bending characteristic of the internal energy. Therefore, this study proposes a method that controls the internal energy by moving the local perpendicular bisector point of each control point on the contour, and determines the object boundary by minimizing the energy relative to the external energy. Convergence at a concave region can then be effectively implemented as regards the feature of interest using the internal energy, plus several objects can be detected using a multi-detection method based on the initial contour. The proposed method is compared with other conventional methods through objective validation and subjective consideration. As a result, it is anticipated that the proposed method can be efficiently applied to the detection of the pulmonary parenchyma region in medical images.

• Korean Journal of Remote Sensing
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• v.16 no.4
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• pp.305-313
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• 2000

A vicarious calibration method was developed for the OSMI sensor calibration. Employing measured aerosol optical thickness by a sunphotometer and a sky radiometer and water leaving radiance by ship measurements as inputs, TOA (top of the atmosphere) radiance at each OSMI band was simulated in conjunction with a radiative transfer model (Rstar5b) by Nakajima and Tanaka (1988). As a case of examining the accuracy of this method, we simulated TOA radiance based on water leaving radiance measured at NASA/MOBY site and aerosol optical thickness estimated nearby at Lanai, and compared simulated results with SeaWiFS-estimated TOA radiances. The difference falls within about $\pm$5%, suggesting that OMSI sensor can be calibrated with the suggested accuracy. In order to apply this method for the OSMI sensor calibration, ground-based sun photometry and ship measurements were carried out off the east coast of Korean peninsula on May 31, 2000. Simulations of TOA radiance by using these measured data as input to the radiative transfer model show that there are substantial differences between simulated and OSMI-estimated radiances. Such a discrepancy appears to be mainly due to the cloud contamination because satellite image indicates optically thin clouds over the experimental area. Nevertheless results suggest that sensor calibration can be achieved within 5% uncertainty range if there are ground-based measurements of aerosol optical thickness, and water leaving radiances under clear-sky and optically thin atmospheric conditions.

• Atmosphere
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• v.31 no.3
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• pp.341-359
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• 2021

This technical note introduces the new Korea Meteorological Administration (KMA) Global Seasonal forecasting system version 6 (GloSea6) to provide a reference for future scientific works on GloSea6. We describe the main areas of progress and improvements to the current GloSea5 in the scientific and technical aspects of all the GloSea6 components - atmosphere, land, ocean, and sea-ice models. Also, the operational architectures of GloSea6 installed on the new KMA supercomputer are presented. It includes (1) pre-processes for atmospheric and ocean initial conditions with the quasi-real-time land surface initialization system, (2) the configurations for model runs to produce sets of forecasts and hindcasts, (3) the ensemble statistical prediction system, and (4) the verification system. The changes of operational frameworks and computing systems are also reported, including Rose/Cylc - a new framework equipped with suite configurations and workflows for operationally managing and running Glosea6. In addition, we conduct the first-ever run with GloSea6 and evaluate the potential of GloSea6 compared to GloSea5 in terms of verification against reanalysis and observations, using a one-month case of June 2020. The GloSea6 yields improvements in model performance for some variables in some regions; for example, the root mean squared error of 500 hPa geopotential height over the tropics is reduced by about 52%. These experimental results show that GloSea6 is a promising system for improved seasonal forecasts.

• Atmosphere
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• v.34 no.1
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• pp.55-67
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• 2024

This paper aims to provide a detailed introduction to the concept of the Ratio of Predictable Component (RPC) and the Signal-to-Noise Paradox. Then, we derive insights from them by exploring the paradoxical features by conducting a seasonal and regional analysis through ensemble expansion in KMA's climate prediction system (GloSea). We also provide an explanation of the ensemble generation method, with a specific focus on stochastic physics. Through this study, we can provide the predictability limits of our forecasting system, and find way to enhance it. On a global scale, RPC reaches a value of 1 when the ensemble is expanded to a maximum of 56 members, underlining the significance of ensemble expansion in the climate prediction system. The feature indicating RPC paradoxically exceeding 1 becomes particularly evident in the winter North Atlantic and the summer North Pacific. In the Siberian Continent, predictability is notably low, persisting even as the ensemble size increases. This region, characterized by a low RPC, is considered challenging for making reliable predictions, highlighting the need for further improvement in the model and initialization processes related to land processes. In contrast, the tropical ocean demonstrates robust predictability while maintaining an RPC of 1. Through this study, we have brought to attention the limitations of potential predictability within the climate prediction system, emphasizing the necessity of leveraging predictable signals with high RPC values. We also underscore the importance of continuous efforts aimed at improving models and initializations to overcome these limitations.

• Journal of Korea Water Resources Association
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• v.53 no.3
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• pp.181-191
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• 2020

In order to produce accurate initial condition of soil moisture for global Numerical Weather Prediction (NWP), spin-up experiment is carried out using Noah Land Surface Model (LSM). The model is run repeatedly through 10 years, under the atmospheric forcing condition of 2008-2017 until climatological land surface state is achieved. Spin-up time for the equilibrium condition of soil moisture exhibited large variability across Koppen-Geiger climate classification zone and soil layer. Top soil layer took the longgest time to equilibrate in polar region. From the second layer to the fourth layer, arid region equilibrated slower (7 years) than other regions. This result means that LSM reached to equilibrium condition within 10 year loop. Also, spin-up time indicated inverse correlation with near surface temperature and precipitation amount. Initialized from the equilibrium state, LSM was spun up to obtain land surface state in 2018. After 6 months from restarted run, LSM simulates soil moisture, skin temperature and evaportranspiration being similar land surface state in 2018. Based on the results, proposed LSM spin-up system could be used to produce proper initial soil moisture condition despite updates of physics or ancillaries for LSM coupled with NWP.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8A
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• pp.1238-1246
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• 2000

In a multi-channel network based on wavelength division multiplexing (WDM), an efficient protocol is needed for transmitter and receiver to be tuned to same wavelength during message transmission time. This paper proposes a message scheduling protocol that can efficiently support variable-sized messages, where tunable transceiver has nonzero tuning time. In this protocol, once a node reserves one data channel, it can persistently use the reserved channel till message transmission is finished. Therefore, overhead due to tuning time of the tunable transceiver can be reduced. Moreover, the protocol does not require any global information. Therefore, it can operate independently of the change of the number of nodes, and any new node can join the network at anytime without requiring network initialization. With this protocol, one can avoid data channel and destination conflicts. The protocol is analyzed with a finite population model and the throughput-delay characteristics are investigated as performance measures.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.289-298
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• 2009

Active Contour Model, that is, Snake algorithm is effective for detection and tracking the objects. However, this algorithm has some drawbacks; numerous parameters must be designed(weighting factors, iteration steps, etc.), a reasonable initialization must be available and moreover suffers from numerical instability. Therefore we propose a novel Energy Corrected Snake(ECS) algorithm which improved on external energy of Snake algorithm for detection and tracking the moving object more effectively. The proposed algorithm uses the difference image, getting when the object is moving. It copies four direction images from the difference image and performs the accumulating compute to erasing image noise, so that it gets external energy steadily. Then external energy united with contour that is computed by internal energy. Consequently we can detect and track the moving object more speedily and easily. To show the effectiveness of the proposed algorithm, we experiment on 3 situations. The experimental results showed that the proposed algorithm outperformed by 6$\sim$9% of detection rate and 6$\sim$11% of tracker detection rate compared with the Snake algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2577-2596
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• 2013

In this paper, we present an Energy-Aware Self-Stabilizing Distributed Clustering protocol based on message-passing model for Ad Hoc networks. The latter does not require any initialization. Starting from an arbitrary configuration, the network converges to a stable state in a finite time. Our contribution is twofold. We firstly give the formal proof that the stabilization is reached after at most n+2 transitions and requires at most $n{\times}log(2n+{\kappa}+3)$ memory space, where n is the number of network nodes and ${\kappa}$ represents the maximum hops number in the clusters. Furthermore, using the OMNeT++ simulator, we perform an evaluation of our approach. Secondly, we propose an adaptation of our solution in the context of Wireless Sensor Networks (WSNs) with energy constraint. We notably show that our protocol can be easily used for constructing clusters according to multiple criteria in the election of cluster-heads, such as nodes' identity, residual energy or degree. We give a comparison under the different election metrics by evaluating their communication cost and energy consumption. Simulation results show that in terms of number of exchanged messages and energy consumption, it is better to use the Highest-ID metric for electing CHs.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.355-362
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• 2010

Face tracking is to estimate the motion of a non-rigid face together with a rigid head in 3D, and plays important roles in higher levels such as face/facial expression/emotion recognition. In this paper, we propose an AAM-based face tracking algorithm. AAM has been widely used to segment and track deformable objects, but there are still many difficulties. Particularly, it often tends to diverge or converge into local minima when a target object is self-occluded, partially or completely occluded. To address this problem, we utilize the scale invariant feature transform (SIFT). SIFT is an effective method for self and partial occlusion because it is able to find correspondence between feature points under partial loss. And it enables an AAM to continue to track without re-initialization in complete occlusions thanks to the good performance of global matching. We also register and use the SIFT features extracted from multi-view face images during tracking to effectively track a face across large pose changes. Our proposed algorithm is validated by comparing other algorithms under the above 3 kinds of occlusions.