• 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.

• Journal of the Korean Statistical Society
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• v.13 no.2
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• pp.107-113
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• 1984

In a linear regression model the idependent variables are frequently subject to measurement errors. For this case, the problem of estimating unknown parameters has been extensively discussed in the literature while very few has been concerned with the effect of measurement errors on prediction. This paper investigates the behavior of the predicted values of the dependent variable in terms of the average mean square error of prediction (AMSEP). AMSEP may be used as a criterion for selecting an appropriate estimation method, for designing an estimation experiment, and for developing cost-effective future sampling schemes.

• Journal of Navigation and Port Research
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• v.28 no.6
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• pp.509-515
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• 2004

The aim of this paper is to forecast passenger numbers and freight volumes in 2005 and it is proposed optimal tonnage of passenger ship. The forecasting of passenger numbers and freight volumes is important problem in order to determine optimal tonnage of passenger ship, port plan and development. In this paper, the forecasting of passenger numbers and freight volumes are performed by the method of neural network using back-propagation learning algorithm. And this paper compares the forecasting performance of neural networks with moving average method and exponential smooth method As the result of analysis. The forecasting of passenger numbers and freight volumes is that the neural networks performed better than moving average method and exponential smoothing method on the basis of MSE(mean square error) and MAE(mean absolute error).

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.8
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• pp.63-68
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• 2004

The capacity of MIMO systems is numerically analyzed when channel estimation error exists. The analysis shows that the capacity is influenced by Mean Square Error (MSE) as well as average Signal to Noise Ratio (SNR). Furthermore, in this paper we present the standard selecting a channel estimator suitable to a system owing to get a tolerable channel estimation error in a given average SNR and channel capacity loss. The simulation results show that the tolerable MSEs for 1 bps/Hz capacity loss are about 10$^{-2}$ and 10$^{-4}$ at n dB and 40 dB average SNR, respectively.

• Journal of Korea Water Resources Association
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• v.39 no.8 s.169
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• pp.677-690
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• 2006

The LH-OAT (Latin Hypercube One factor At a Time) method for sensitivity analysis and SCE-UA (Shuffled Complex Evolution at University of Arizona) optimization method were applied for the automatic calibration of SWAT model in Bocheong-cheon watershed. The LH-OAT method which combines the advantages of global and local sensitivity analysis effectively identified the sensitivity ranking for the parameters of SWAT model over feasible parameter space. Use of this information allows us to select the calibrated parameters for the automatic calibration process. The performance of the automatic calibration of SWAT model using SCE-UA method depends on the length of calibration period, the number of calibrated parameters, and the selection of statistical error criteria. The performance of SWAT model in terms of RMSE (Root Mean Square Error), NSEF (Nash-Sutcliffe Model Efficiency), RMAE (Relative Mean Absolute Error), and NMSE (Normalized Mean Square Error) becomes better as the calibration period and the number of parameters defined in the automatic calibration process increase. However, NAE (Normalized Average Error) and SDR (Standard Deviation Ratio) were not improved although the calibration period and the number of calibrated parameters are increased. The result suggests that there are complex interactions among the calibration data, the calibrated parameters, and the model error criteria and a need for further study to understand these complex interactions at various representative watersheds.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.12
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• pp.1527-1536
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• 1999

In this paper, a triply-encoded Hadamard transform imaging spectrometer is proposed by applying the grill spectrometer to the Hadamard transform imaging spectrometer. The proposed system encodes the input radiation triply ; once through the input image mask and twice through the two masks in the grill spectrometer. We use an electro-optical mask in the grill spectrometer which is controlled by a left-cyclic simplex matrix. Then we modeled the system using $D^{-1}$ method. In this paper, the average mean square error associated with a recovered estimate is considered for performance evaluation. The relative performance is compared with those of the other conventional systems.

• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.349-363
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• 2020

Penalized spline is a suitable nonparametric approach in estimating mean model in small area. However, application of the approach in informative sampling in a published article is uncommon. We propose a semiparametric mixed-model using penalized spline under informative sampling to estimate mean of small area. The response variable is explained in terms of mean model, informative sample effect, area random effect and unit error. We approach the mean model by penalized spline and utilize a penalized spline function of the inclusion probability to account for the informative sample effect. We determine the best and unbiased estimators for coefficient model and derive the restricted maximum likelihood estimators for the variance components. A simulation study shows a decrease in the average absolute bias produced by the proposed model. A decrease in the root mean square error also occurred except in some quadratic cases. The use of linear and quadratic penalized spline to approach the function of the inclusion probability provides no significant difference distribution of root mean square error, except for few smaller samples.

• Speech Sciences
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• v.10 no.2
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• pp.77-84
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• 2003

In this research, we seek the beginning of the speech and detect the stationary speech region using lip information. Performing running average of the estimated speech signal in the stationary region, we reduce the effect of musical noise which is inherent to the conventional MlMSE (Minimum Mean Square Error) speech enhancement algorithm. In addition to it, SFM (Spectral Flatness Measure) is incorporated to reduce the speech signal estimation error due to speaking habit and some lacking lip information. The proposed algorithm with Wiener filtering shows the superior performance to the conventional methods according to MOS (Mean Opinion Score) test.

• Journal of Information Technology Applications and Management
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• v.28 no.4
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• pp.13-20
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• 2021

In this paper, I was applied the life distribution following linear failure rate distribution, Lindley distribution and Burr-Hatke exponential distribution extensively used in the arena of software reliability and were associated the reliability possessions of the software using the nonhomogeneous Poisson process with finite failure. Furthermore, the average value functions of the life distribution are non-increasing form. Case of the linear failure rate distribution (exponential distribution) than other models, the smaller the estimated value estimation error in comparison with the true value. In terms of accuracy, since Burr-Hatke exponential distribution and exponential distribution model in the linear failure rate distribution have small mean square error values, Burr-Hatke exponential distribution and exponential distribution models were stared as the well-organized model. Also, the linear failure rate distribution (exponential distribution) and Burr-Hatke exponential distribution model, which can be viewed as an effectual model in terms of goodness-of-fit because the larger assessed value of the coefficient of determination than other models. Through this study, software workers can use the design of mean square error, mean value function as a elementary recommendation for discovering software failures.

• Korean Journal of Agricultural Science
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• v.38 no.1
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• pp.129-143
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• 2011

In this study, to select the incoming solar radiation equation which is most suitable for the estimation of Penman evaporation, 12 incoming solar radiation equations were selected. The Penman evaporation rates were estimated using 12 selected incoming solar radiation equations, and the estimated Penman evaporation rates were compared with measured pan evaporation rates. The monthly average daily meteorological data measured from 17 meteorological stations (춘천, 강능, 서울, 인천, 수원, 서산, 청주, 대전, 추풍령, 포항, 대구, 전주, 광주, 부산, 목포, 제주, 진주) were used for this study. To evaluate the reliability of estimated evaporation rates, mean absolute bias error(MABE), root mean square error(RMSE), mean percentage error(MPE) and Nash-Sutcliffe equation were applied. The study results indicate that to estimate pan evaporation using Penman evaporation equation, incoming solar radiation equation using meteorological data such as precipitation, minimum air temperature, sunshine duration, possible duration of sunshine, and extraterrestrial radiation are most suitable for 11 study stations out of 17 study stations.