• Journal of KIISE:Information Networking
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• v.32 no.6
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• pp.676-686
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• 2005

The Internet users as well as network providers are eager to have different qualities of service among users beyond the best-effort. In this paper, we propose a scheduling algorithm that provides a differentiated service in the granularity of user sessions. The proposed algorithm is a Hierarchical Deficit Round-Robin (H-DRR) algorithm that is an extension of an existing DRR algorithm. A main advantage is that H-DRR provides service differentiation for throughput-intensive applications such as FTP as well as delay-sensitive applications such as telnet or VoIP without distinguishing the types of applications. The most importance in providing a service differentiation in term of network providers is to have controllability and predictability. We show that H-DRR is superior to DRR in terms of controllability and predictability through both mathematical analysis and simulation experiments. Nevertheless, H-DRR requires O(1) complexity for implementation.

• Atmosphere
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• v.30 no.3
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• pp.209-220
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• 2020

Seasonal predictability and variability of tropical storms (TCs) simulated in the Global Seasonal Forecast System version 5 (GloSea5) of the Korea Meteorological Administration (KMA) is assessed in Northern Hemisphere in 1996~2009. In the KMA, the GloSea5-Global Atmosphere version 3.0 (GloSea5-GA3) that was previously operated was switched to the GloSea5-Global Coupled version 2.0 (GloSea5-GC2) with data assimilation system since May 2016. In this study, frequency, track, duration, and strength of the TCs in the North Indian Ocean, Western Pacific, Eastern Pacific, and North Atlantic regions derived from the GloSea5-GC2 and GloSea5-GA3 are examined against the best track data during the research period. In general, the GloSea5 shows a good skill for the prediction of seasonally averaged number of the TCs in the Eastern and Western Pacific regions, but underestimation of those in the North Atlantic region. Both the GloSea5-GA3 and GC2 are not able to predict the recurvature of the TCs in the North Western Pacific Ocean (NWPO), which implies that there is no skill for the prediction of landfalls in the Korean peninsula. The GloSea5-GC2 has higher skills for predictability and variability of the TCs than the GloSea5-GA3, although continuous improvements in the operational system for seasonal forecast are still necessary to simulate TCs more realistically in the future.

• Korean Journal of Ecology and Environment
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• v.43 no.1
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• pp.161-167
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• 2010

In this study, we implemented an experimental approach of ecological model development in order to emphasize the importance of input variable selection with respect to time-delayed arrangement between input and output variables. Time-series modeling requires relevant input variable selection for the prediction of a specific output variable (e.g. density of a species). Inadequate variable utility for input often causes increase of model construction time and low efficiency of developed model when applied to real world representation. Therefore, for future prediction, researchers have to decide number of time-delay (e.g. months, weeks or days; t-n) to predict a certain phenomenon at current time t. We prepared a total of 3,900 equation models produced by Time-Series Optimized Genetic Programming (TSOGP) algorithm, for the prediction of monthly averaged density of a potamic phytoplankton species Stephanodiscus hantzschii, considering future prediction from 0- (no future prediction) to 12-months ahead (interval by 1 month; 300 equations per each month-delay). From the investigation of model structure, input variable selectivity was obviously affected by the time-delay arrangement, and the model predictability was related with the type of input variables. From the results, we can conclude that, although Machine Learning (ML) algorithms which have popularly been used in Ecological Informatics (EI) provide high performance in future prediction of ecological entities, the efficiency of models would be lowered unless relevant input variables are selectively used.

• Atmosphere
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• v.21 no.2
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• pp.163-172
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• 2011

Numerical forecasting depends on the initial condition error strongly because numerical model is a chaotic system. To calculate the sensitivity of some forecast aspects to the initial condition in the Korea Meteorological Administration (KMA) Unified Model (UM) which is originated from United Kingdom (UK) Meteorological Office (MO), an algorithm to calculate adjoint sensitivities is developed by modifying the adjoint perturbation forecast model in the KMA UM. Then the new algorithm is used to calculate adjoint sensitivity distributions for typhoon DIANMU (201004). Major initial adjoint sensitivities calculated for the 48 h forecast error are located horizontally in the rear right quadrant relative to the typhoon motion, which is related with the inflow regions of the environmental flow into the typhoon, similar to the sensitive structures in the previous studies. Because of the upward wave energy propagation, the major sensitivities at the initial time located in the low to mid- troposphere propagate upward to the upper troposphere where the maximum of the forecast error is located. The kinetic energy is dominant for both the initial adjoint sensitivity and forecast error of the typhoon DIANMU. The horizontal and vertical energy distributions of the adjoint sensitivity for the typhoon DIANMU are consistent with those for other typhoons using other models, indicating that the tools for calculating the adjoint sensitivity in the KMA UM is credible.

• Atmosphere
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• v.22 no.4
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• pp.415-427
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• 2012

$CO_2$ is the most important trace gas related to climate change. Therefore, understanding surface carbon sources and sinks is important when seeking to estimate the impact of $CO_2$ on the environment and climate. CarbonTracker, developed by NOAA, is an inverse modeling system that estimates surface carbon fluxes using an ensemble Kalman filter with atmospheric $CO_2$ measurements as a constraint. In this study, to investigate the capability of CarbonTracker as an analysis tool for estimating surface carbon fluxes in Asia, an experiment with a nesting domain centered in Asia is performed. In general, the results show that setting a nesting domain centered in Asia region enables detailed estimations of surface carbon fluxes in Asia. From a rank histogram, the prior ensemble spread verified at observational sites located in Asia is well represented with a relatively flat rank histogram. The posterior flux in the Eurasian Boreal and Eurasian Temperate regions is well analyzed with proper seasonal cycles and amplitudes. On the other hand, in tropical regions of Asia, the posterior flux does not differ greatly from the prior flux due to fewer $CO_2$ observations. The root mean square error of the model $CO_2$ calculated by the posterior flux is less than the model $CO_2$ calculated by the prior flux, implying that CarbonTracker based on the ensemble Kalman filter works appropriately for the Asia region.

• Clinical and Experimental Pediatrics
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• v.53 no.9
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• pp.840-844
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• 2010

Purpose: Microalbuminuria is defined as increased urinary albumin excretion (30-300 mg/day) or microalbumin/creatinine ratio (30-300 mg/g) in a spot urine sample. Although microalbuminuria is a predictor of clinical nephropathy and cardiomyopathy, few studies have investigated microalbuminuria in children with urinary tract infection (UTI). Therefore, we compared the spot urine microalbumin/creatinine ratio in pediatric UTI patients with that of control subjects. Methods: We investigated the correlation between the ratio in children with UTI and age, height, weight, blood pressure, glomerular filtration rate (GFR), hematuria, vesicoureteral reflux, renal parenchymal defect, and renal scar, and its predictability for UTI complications. Results: We studied 66 patients (42 boys, 24 girls) and 52 healthy children (24 boys, 28 girls). The mean microalbumin/creatinine ratio in UTI patients was statistically significantly increased compared to the control group ($340.04{\pm}321.36mg/g$ vs. $225.68{\pm}154.61mg/g$, $P$=0.0141). The mean value of spot urine microalbumin/creatinine ratio ($384.70{\pm}342.22mg/g$ vs. $264.92{\pm}158.13mg/g$, $P$=0.0341) in 1-23 months age patient group showed statistically significant increase compared to control group. Microalbumin/creatinine ratio showed negative correlation to age (r=-0.29, $P$=0.0167), body surface area (BSA) (r=-0.29, $P$=0.0173) and GFR (r=-0.26, $P$=0.0343). The presence of hematuria ($P$=0.0169) was found to be correlated. Conclusion: The spot urine microalbumin/creatinine ratio in children with UTI was significantly greater than that in normal children, and it was positively correlated with GFR. This ratio is a potential prescreening and prognostic marker in UTI patients. Further studies are required to validate the predictability of microalbuminuria in pediatric UTI patients.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.191-199
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• 2015

The purposes of this study are to examine the predictability of variables related to SNS intention to use in collaborative learning and provide some new implications. Based on Technology Readiness and Acceptance Model (TRAM), we hypothesized that optimism, innovativeness, discomfort, insecurity as personal disposition variables, subjective norm as a social variable, and perceived usefulness and perceived ease of use as cognitive variables would predict SNS intention to use. For this study, 274 'Share Leadership' students in E university completed surveys and it was analyzed by multiple regression analysis. The results of this study showed as follows. First, optimism, innovativeness, discomfort, and subjective norm predicted perceived ease of use. Second, optimism, insecurity, subjective norm and perceived ease of use predicted perceived usefulness. Third, subjective norm, perceived ease of use and perceived usefulness predicted SNS intention to use. From this, it is revealed that positive technology readiness predict much more than negative technology readiness do and the role of teacher and peers is very important.

• Journal of Digital Convergence
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• v.18 no.8
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• pp.231-242
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• 2020

The thesis presents a system that continuously collects the human body's physiological vital information at rest with sensors and ICT information technology and predicts diabetes using the collected information. it shows the artificial neural network machine learning method and essential basic variable values. The study method analyzed the correlation between heart rate measurements of BCG and ECG sensors in 20 DM- and 15 DM+ subjects. Artificial Neural Network (ANN) machine learning program was used to predictability of diabetes. The input variables are time domain information of HRV, heart rate, heart rate variability, respiration rate, stroke volume, minimum blood pressure, highest blood pressure, age, and sex. ANN machine learning prediction accuracy is 99.53%. Thesis needs continuous research such as diabetic prediction model by BMI information, predicting cardiac dysfunction, and sleep disorder analysis model using ANN machine learning.

• Journal of the Korean earth science society
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• v.38 no.1
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• pp.11-23
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• 2017

The short term predictability of wind resources is an important factor in evaluating the economic feasibility of a wind power plant. As a method of improving the predictability, a Bayesian Kalman filter is applied as the model data postprocessing. At this time, a statistical training period is needed to evaluate the correlation between estimated model and observation data for several Kalman training periods. This study was quantitatively analyzes for the prediction characteristics according to different training periods. The prediction of the temperature and wind speed with 3-day short term Bayesian Kalman training at Taebaek area is more reasonable than that in applying the other training periods. In contrast, it may produce a good prediction result in Ieodo when applying the training period for more than six days. The prediction performance of a Bayesian Kalman filter is clearly improved in the case in which the Weather Research Forecast (WRF) model prediction performance is poor. On the other hand, the performance improvement of the WRF prediction is weak at the accurate point.

• Journal of Korea Game Society
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• v.18 no.1
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• pp.27-38
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• 2018

Today, the field that actively utilizes HMD, which is a representative implementation device of virtual reality, is game. We have frequently used interface design using metaphor to user interface of HMD based virtual reality game. The purpose of this study is to find out the metaphor types that appear in the game interface of the virtual reality FPS genre of HMD devices, which is a new medium. As a result of research, the metaphor types appearing on multiple interfaces have navigation, predictability-based, familiarizing, and physical world metaphor in terms of information perception and predictability-based and familiarizing metaphor in term of control action. It is considered possible to construct a correct mental model. It is expected that the stability-based metaphor to prevent user mistakes and the presentation metaphor to identify the identity of information space will be needed in the future.