• Journal of the Korea Society for Simulation
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• v.15 no.3
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• pp.11-21
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• 2006

Importance of and need for grid resource management have accelerated in accordance with increasing development of grid computing. However, it is very complex to distribute and utilize resources efficiently in geographically dispersed environments. This is due to the different access policies and constraints of grid resource owners. Users request resources according to their needs. Operators of a grid computing system need to be able to monitor the system states for reflecting these demands. So, a grid computing system needs a resource management policy that monitors states of resources and then allocates resources. This paper proposes a user demand-based grid trade management model that provides an efficient resource management by the trade allocation based on a users' demand and providers' supply strategy. To evaluate performance, this paper measures increasing rate of resource trades, average response time of trades, and processing time utilization. Firstly, the average increasing rates of trade are 585.7% and 322.6% higher than an auction model and a double auction model. Secondly, the average response time of the user demand-based grid trade management model is maintained between 3 and 5 simulation time. Finally, it is found that the processing time utilization is an average of 145.4% and 118.0% higher than an auction model and a double auction model. These empirical results demonstrate the usefulness of the user demand-based grid trade management model.

• MALSORI
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• v.68
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• pp.33-47
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• 2008

In this paper, we propose a hybrid acoustic and pronunciation model adaptation method based on context dependency for Korean-English speech recognition. The proposed method is performed as follows. First, in order to derive pronunciation variant rules, an n-best phoneme sequence is obtained by phone recognition. Second, we decompose each rule into a context independent (CI) or a context dependent (CD) one. To this end, it is assumed that a different phoneme structure between Korean and English makes CI pronunciation variabilities while coarticulation effects are related to CD pronunciation variabilities. Finally, we perform an acoustic model adaptation and a pronunciation model adaptation for CI and CD pronunciation variabilities, respectively. It is shown from the Korean-English speech recognition experiments that the average word error rate (WER) is decreased by 36.0% when compared to the baseline that does not include any adaptation. In addition, the proposed method has a lower average WER than either the acoustic model adaptation or the pronunciation model adaptation.

• The Journal of Fisheries Business Administration
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• v.43 no.2
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• pp.41-49
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• 2012

This study aimed to estimate the economic value of the recreational fishing in the Jeonnam marine ranching area as a part of the total socioeconomic evaluation of the Jeonnam marine ranching program. A travel cost method was applied to the estimation of economic value of the recreational fishing in the Jeonnam marine ranching area and input variables included annual fishing trip days, average travel cost per trip, average catch amount, monthly income, marriage, age, and personal perception on the marine ranching program. In the analysis, due to its characteristic of count data, both poisson model and negative binomial model were used. Model results indicated that a negative binomial model was statistically more suitable than the poisson model as the overdispersion problem occurred in the poisson model. All signs of the estimated parameters were estimated as previous studies showed. Based on the results, the economic value per trip of the recreational fishing in the Jeonnam marine ranching area was estimated to be 145,000 won and the annual total economic value of the recreational fishing in the Jeonnam marine ranching area was analyzed to be 2,514,000 won. In addition, the change of total value by catch rate showed that the economic value could be increased by 180,900 won as the catch increased by one kilogram.

• Ocean and Polar Research
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• v.34 no.2
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• pp.185-195
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• 2012

This paper focused on forecasting a short-term production of oysters, which have been farmed in Korea, with distinct periodicity of production by year, and different production level by month. To forecast a short-term oyster production, this paper uses monthly data (260 observations) from January 1990 to August 2011, and also adopts several econometrics methods, such as Multiple Regression Analysis Model (MRAM), Seasonal Autoregressive Integrated Moving Average (SARIMA) Model, and Vector Error Correction Model (VECM). As a result, first, the amount of short-term oyster production forecasted by the multiple regression analysis model was 1,337 ton with prediction error of 246 ton. Secondly, the amount of oyster production of the SARIMA I and II models was forecasted as 12,423 ton and 12,442 ton with prediction error of 11,404 ton and 11,423 ton, respectively. Thirdly, the amount of oyster production based on the VECM was estimated as 10,425 ton with prediction errors of 9,406 ton. In conclusion, based on Theil inequality coefficient criterion, short-term prediction of oyster by the VECM exhibited a better fit than ones by the SARIMA I and II models and Multiple Regression Analysis Model.

• Advances in Energy Research
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• v.6 no.2
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• pp.131-143
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• 2019

Global Solar Radiation (GSR) is the key element for performance estimation of any Solar Power Plant (SPP). Its forecasting may help in estimation of power production from a SPP well in advance, and may also render help in optimal use of this power. Seasonal Auto-Regressive Moving Average (SARMA) and Artificial Neural Network (ANN) models are combined in order to develop a hybrid model (SARMA-ANN) conceiving the characteristics of both linear and non-linear prediction models. This developed model has been used for prediction of GSR at Gorakhpur, situated in the northern region of India. The proposed model is beneficial for the univariate forecasting. Along with this model, we have also used Auto-Regressive Moving Average (ARMA), SARMA, ANN based models for 1 - 6 day-ahead forecasting of GSR on hourly basis. It has been found that the proposed model presents least RMSE (Root Mean Square Error) and produces best forecasting results among all the models considered in the present study. As an application, the comparison between the forecasted one and the energy produced by the grid connected PV plant installed on the parking stands of the University shows the superiority of the proposed model.

• Journal of the Earthquake Engineering Society of Korea
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• v.23 no.4
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• pp.231-238
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• 2019

A conventional lumped-mass stick model is based on the tributary area method to determine the masses lumped at each node and used in earthquake engineering due to its simplicity in the modeling of structures. However the natural frequencies of the conventional model are normally not identical to those of the actual structure. To solve this problem, recently an updated lumped-mass stick model is developed to provide the natural frequencies identical to actual structure. The present study is to investigate the seismic response accuracy of the updated lumped-mass stick model, comparing with the response results of the shaking table test. For the test, a small size four-story steel frame structure is prepared and tested on shaking table applying five earthquake ground motions. From the comparison with shaking table test results, the updated model shows an average error of 3.65% in the peak displacement response and 9.68% in the peak acceleration response. On the other hand, the conventional model shows an average error of 5.15% and 27.41% for each response.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.43.1-43.1
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• 2019

In this study, we apply a deep learning model to denoising solar magnetograms. For this, we design a model based on conditional generative adversarial network, which is one of the deep learning algorithms, for the image-to-image translation from a single magnetogram to a denoised magnetogram. For the single magnetogram, we use SDO/HMI line-of-sight magnetograms at the center of solar disk. For the denoised magnetogram, we make 21-frame-stacked magnetograms at the center of solar disk considering solar rotation. We train a model using 7004 paris of the single and denoised magnetograms from 2013 January to 2013 October and test the model using 1432 pairs from 2013 November to 2013 December. Our results from this study are as follows. First, our model successfully denoise SDO/HMI magnetograms and the denoised magnetograms from our model are similar to the stacked magnetograms. Second, the average pixel-to-pixel correlation coefficient value between denoised magnetograms from our model and stacked magnetogrmas is larger than 0.93. Third, the average noise level of denoised magnetograms from our model is greatly reduced from 10.29 G to 3.89 G, and it is consistent with or smaller than that of stacked magnetograms 4.11 G. Our results can be applied to many scientific field in which the integration of many frames are used to improve the signal-to-noise ratio.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2001.10a
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• pp.181-185
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• 2001

Three-dimensional finite-element-based numerical model of turbulent flow, heat transfer, macroscopic solidification and inclusion trajectory in a continuos steel slab caster was developed Turbulence was incorporated using the Improved Low-Re turbulence model with positive preserving approach. The mushy region was modeled as the porous media with average effective viscosity. A series of simulations was carried out to investigate the effects of the casting speed, the slab size, the delivered superheat the immersion depth of the SEN on the transport phenomena. In the absence of any known experimental data related to velocity profiles, the numerical predictions of the solidified profile on a caster was compared with breakouts data and a good agreement was found.

• Journal of Korean Society for Atmospheric Environment
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• v.27 no.1
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• pp.87-96
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• 2011

In this study, the AERMOD dispersion model was used for predicting odor concentrations and back-calculating industrial area source odor emission rate. The studied area was Sihwa industrial complex in Korea. Odor samples were collected during two days over a year period in 2009. The comparison between the predicted and observed concentrations indicates that the AERMOD model could fairly well predict average downwind odor concentrations. The results show odor emission rates of Sihwa industrial complex area source were ranged from 0.204 to 2.320 $OUms^{-1}$ (average 0.476 $OUms^{-1}$). The results also show wind speed and direction are important parameters to the odor dispersion.

• Journal of Microbiology and Biotechnology
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• v.13 no.5
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• pp.789-793
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• 2003

This study focused on the optimization of the illumination method for efficient use of light energies in a photobioreactor. In order to investigate the effect of radiator position, a model simulation study was carried out using Synechococcus sp. PCC 6301 and an internally radiating photobioreactor as a model system. The efficiency of light transfer in a photobioreactor was analyzed by estimating the average light intensity in a photobioreactor. The simulation result, indicate that there exists an optimal position of internal radiators, and that the optimal position varies with radiator number and cell concentration. When light radiators are placed at the optimal position, the average light intensity is about 30% higher than that obtained by placing radiators at the circumstance or center of a photobioreactor. The method presented in this work may be useful for improving light transfer efficiency in a photobioreactor.