• Journal of Communications and Networks
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• v.13 no.6
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• pp.621-624
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• 2011

With the rapid growth of internet traffic, accurate and reliable prediction of internet traffic has been a key issue in network management and planning. This paper proposes an autoregressive-generalized autoregressive conditional heteroscedasticity (AR-GARCH) error model for forecasting internet traffic and evaluates its performance by comparing it with seasonal autoregressive integrated moving average (ARIMA) models in terms of root mean square error (RMSE) criterion. The results indicated that the seasonal AR-GARCH models outperformed the seasonal ARIMA models in terms of forecasting accuracy with respect to the RMSE criterion.

• Water for future
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• v.19 no.3
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• pp.267-276
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• 1986

This study aims at the development of flood runoff model by comparing and analyzing nonlinear models with linear models in rier basins. The models which are used at the analysis are Nash model and Runoff function method as linear models, and Tank model and Storage function method as nonlinear models. The results, which are obtained from the analysis of these models by using hydrologic data of a representative basin in Nakdong river, Wi-chun basin, show that the peak time, peak flow and flood hydrogrphs by nonlinear models are better than those by linear models in comparison with observed ones, and that nonlinear models are suittable as flood runoff model.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.325-332
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• 2002

This article describes (1) 2 and 3 dimensional electromagnetic finite element models for an active heteropolar radial magnetic bearing, (2) the procedure for obtaining the bearing stiffnesses by simulating the models and (3) the reviews of the models by comparing an experimental test to the ideal closed loop analysis with the stiffnesses calculated from (2). The 3 dimensional model for the magnetic bearing may be very effectively applied to several types of magnetic bearings.

• Journal of Korean Library and Information Science Society
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• v.36 no.2
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• pp.49-68
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• 2005

The purpose of this study is to examine the research trends and their experiment results on the applications of the language models for information retrieval. We reviewed the previous studies with the following categories: (1) the first generation of language modeling information retrieval (LMIR) experiments which are mainly focused on comparing the language modeling information retrieval with the traditional retrieval models in their retrieval performance, and (2) the second generation of LMIR experiments which are focused on comparing the expanded language modeling information retrieval with the basic language models in their retrieval performance. Through the analysis of the previous experiments results, we found that (1) language models are outperformed the probabilistic model or vector space model approaches, and (2) the expended language models demonstrated better results than the basic language models in their retrieval performance.

• Journal of Korea Trade
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• v.25 no.4
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• pp.17-36
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• 2021

Purpose - This study provides useful information to stakeholders by forecasting the tramp shipping market, which is a completely competitive market and has a huge fluctuation in freight rates due to low barriers to entry. Moreover, this study provides the most effective parameters for Baltic Dry Index (BDI) prediction and an optimal model by analyzing and comparing deep learning models such as the artificial neural network (ANN), recurrent neural network (RNN), and long short-term memory (LSTM). Design/methodology - This study uses various data models based on big data. The deep learning models considered are specialized for time series models. This study includes three perspectives to verify useful models in time series data by comparing prediction accuracy according to the selection of external variables and comparison between models. Findings - The BDI research reflecting the latest trends since 2015, using weekly data from 1995 to 2019 (25 years), is employed in this study. Additionally, we tried finding the best combination of BDI forecasts through the input of external factors such as supply, demand, raw materials, and economic aspects. Moreover, the combination of various unpredictable external variables and the fundamentals of supply and demand have sought to increase BDI prediction accuracy. Originality/value - Unlike previous studies, BDI forecasts reflect the latest stabilizing trends since 2015. Additionally, we look at the variation of the model's predictive accuracy according to the input of statistically validated variables. Moreover, we want to find the optimal model that minimizes the error value according to the parameter adjustment in the ANN model. Thus, this study helps future shipping stakeholders make decisions through BDI forecasts.

• Journal of Biosystems Engineering
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• v.27 no.3
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• pp.219-226
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• 2002

Desorption equilibrium moisture contents of oak mushroom were measured by the static method using salt solutions at flour temperature levels of 35$\^{C}$, 45$\^{C}$, 55$\^{C}$ and 6$\^{C}$ and five relative humidity levels in the range from 11.0% to 90.8%. EMC data were fitted to the modified Henderson, Chung-Pfost, modified Halsey and modified Oswin models using nonlinear regression analysis. Drying tests far oak mushroom were conducted in an experimental dryer equipped with air conditioning unit. The drying test were performed in triplicate at flour air temperatures of 35$\^{C}$, 45$\^{C}$, 55$\^{C}$ and 65$\^{C}$ and three relative humidities of 30%, 50% and 70% respectively. Measured moisture ratio data were fitted to the selected four drying models(Lewis, Page, simplified diffusion and Thompson models) using stepwise multiple regression analysis. The results of comparing root mean square errors for EMC models showed that modified Halsey was the best model, and modified Oswin models could be available far oak mushroom. The results of comparing coefficients of determination and root mean square errors of moisture ratio for four drying models showed that Page model were found to fit adequately to all drying test data with a coefficient of determination of 0.9990 and root mean square error of moisture ratio of 0.00739.

• ETRI Journal
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• v.21 no.3
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• pp.6-15
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• 1999

Many studies in the software reliability have attempted to develop a model for predicting the faults of a software module because the application of good prediction models provides the optimal resource allocation during the development period. In this paper, we consider the change request data collected from the field test of the software module that incorporate a functional relation between the faults and some software metrics. To this end, we discuss the general aspect if regression method, the problem of multicollinearity and the measures of model evaluation. We consider four possible regression models including two stepwise regression models and two nonlinear models. Four developed models are evaluated with respect to the predictive quality.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.789-800
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• 2008

Current credit evaluation models based on financial data make use of smoothing estimated default ratios which are transformed from each financial variable. In this work, some problems of the credit evaluation models developed by financial experts are discussed and we propose improved credit evaluation models based on the stepwise variable selection method and Box-Cox transformed data whose distribution is much skewed to the right. After comparing goodness-of-fit tests of these models, the validation of the credit evaluation models using statistical methods such as the stepwise variable selection method and Box-Cox transformation function is explained.

• Computers and Concrete
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• v.6 no.1
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• pp.19-40
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• 2009

This paper describes the development of modified heat of hydration and maturity-strength models for concrete containing fly ash and slag. The modified models are developed based on laboratory and literature test results, which include different types of cement, fly ash, and slag. The new models consider cement type, water-to-cementitious material ratio (w/cm), mineral admixture, air content, and curing conditions. The results show that the modified models well predict heat evolution and compressive strength development of concrete made with different cementitious materials. Using the newly developed models, the sensitivity analysis was also performed to study the effect of each parameter on the hydration and strength development. The results illustrate that comparing with other parameters studied, w/cm, air content, fly ash, and slag replacement level have more significantly influence on concrete strength at both early and later age.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.3
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• pp.239-247
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• 2013

The global gravitational model is essential for precision geoid model construction. Also, it would be used as basic scientific data in geophysical and oceanographic fields. In Korea, EGM2008 has been used from the late 2000s. After publishing EGM2008, new gravitational models such as GOCO02S, GOCO03S, EIGEN-6C, EIGEN-6C2 based on GOCE data were developed. Therefore, we need to verify recent models to select optimal one for geoid computation in Korea. In this study, we compared new models generated based on the GOCE data to EGM2008 and verified the precision of models by comparing with NGII(National Geographic Information Institute) GPS/Leveling data. When comparing EIGEN models to EGM2008, the difference is about 8cm. On the other h and, about 70cm of difference between GOCO models and EGM2008 has been calculated. The reason for this is because GOCO models have been developed using only satellite data while EGM2008 has been used gravity and altimeter data as well as satellite data. When comparing global gravitational model to GPS/Leveling data, EGM2008 showed the best precision of 6.1cm over whole Korean peninsula. The new global gravitational model using additional GOCE data will be published consistently, so the precision verification of new model should be continued.