• Journal of Communications and Networks
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• 제13권6호
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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 Data and Information Science Society
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• 제15권3호
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• pp.677-687
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• 2004

We consider the comparison of two one-parameter exponential distributions with the complete data as well as the type II censored data. We adapt Bayesian test procedure for nested hypothesis based on the Bayesian reference criterion. Specifically we derive the expression for the Bayesian reference criterion to solve our problem. Also we provide numerical examples using simulated data sets to illustrate our results.

• 한국통신학회논문지
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• 제34권2A호
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• pp.85-90
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• 2009

In this paper, as an alternative to constant modulus algorithm based on MSE, maximization of the probability that equalizer output power is equal to the constant modulus of the transmitted symbols is introduced. The proposed algorithm using the gradient ascent method to the maximum probability criterion has superior convergence and steady-state MSE performance, and the error samples of the proposed algorithm exhibit more concentrated density functions in blind equalization environments. Simulation results indicate that the proposed training has a potential advantage versus MSE training for the constant modulus approach to blind equalization.

• Journal of Information Technology Applications and Management
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• 제26권3호
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• pp.19-27
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• 2019

In this study, we propose a methodology for predicting the movie audience based on movie information that can be easily acquired before opening and effectively distinguishing qualitative variables. In addition, we constructed a model to estimate the number of movie audiences at the time of data acquisition through the configured variables. Another purpose of this study is to provide a criterion for categorizing success of movies with qualitative characteristics. As an evaluation criterion, we used information gain ratio which is the node selection criterion of C4.5 algorithm. Through the procedure we have selected 416 movie data features. As a result of the multiple linear regression model, the performance of the regression model using the variables selection method based on the information gain ratio was excellent.

• 한국지구과학회지
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• 제39권6호
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• pp.593-599
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• 2018

P파와 S파의 도착시간 정보는 지진 발생위치 결정, 1차원 및 3차원 지하구조 등 지진학 연구 수행에 중요한 정보이다. 최근 지진관측소의 수가 비약적으로 증가함에 따라 관측망을 운영하면서 수동으로 지진파의 도착시간을 측정하는 것은 상당한 시간이 소요되는 일이 되었다. 본 연구에서는 진원요소에 대한 사전정보(지진 발생위치와 시간)를 확보할 수 있는 경우 Akaike Information Criterion (AIC)을 적용하여 추가의 관측소에서 국지지진의 P파와 S파의 도착시간을 자동측정하였다. 해당 방법을 경산(DAG2) 지진관측소에 기록된 자료에 적용한 후 수동 측정한 값과 자동 측정한 값을 비교한 결과 P파의 경우 95.1%, S파의 경우 93.7%가 0.1초 이하의 차이를 보이면서 결정되는 것을 확인하였다. 자동측정결과의 높은 정확성은 향후 고밀도 지진관측망 운영에 성공적으로 적용될 수 있음을 시사한다.

• Journal of Multimedia Information System
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• 제4권4호
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• pp.295-300
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• 2017

In this paper deals with the problem of modeling customs information and the criterion for controlling its reliability in the process of managing customs clearance of goods is considered. As the main object of the study, the information of the cargo customs declaration, which is submitted to the customs authorities in electronic form for customs clearance of goods, is considered. The main criteria for determining the reliability of customs information, based on the classical methods used by other fields of science, are given.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제2권1호
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• pp.41-60
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• 1998

This paper derives a consistent and bias corrected extension of Akaike's Information Criterion (AIC), $AIC_{bc}$, based on Kullback-Leibler information. This criterion has terms that penalize the overparametrization more strongly for small and large samples than that of AIC. The overfitting problem of the asymptotically efficient model selection criteria for small and large samples will be overcome. The $AIC_{bc}$ also provides a consistent model order selection. Thus, it is widely applicable to data with small and/or large sample sizes, and to cases where the number of free parameters is a relatively large fraction of the sample size. Relationships with other model selection criteria such as $AIC_c$ of Hurvich, CAICF of Bozdogan and etc. are discussed. Empirical performances of the $AIC_{bc}$ are studied and discussed in better model order choices of a linear regression model using a Monte Carlo experiment.

• Journal of the Korean Statistical Society
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• 제36권3호
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• pp.435-445
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• 2007

This paper addresses for the first time the use of complete data information criteria in unsupervised learning of the Naive-Bayes model. A Monte Carlo study sets a large experimental design to assess these criteria, unusual in the Bayesian network literature. The simulation results show that complete data information criteria underperforms the Bayesian information criterion (BIC) for these Bayesian networks.

• Journal of the Korean Data and Information Science Society
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• 제14권3호
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• pp.707-714
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• 2003

Because of complex aliasing, nonregular designs have traditionally been used for screening only main effects. However, complex aliasing actually may allow some interactions entertained and estimated without making additional runs. According to hierarchical principle, the minimum aberration has been used as an important criterion for selecting regular fractional factorial designs. The criterion is not applicable to nonregular designs. In this paper, we give a criterion for choosing fractional factorial designs based on the fan theory. The criterion is focused on the partial ordering given by set inclusion on estimable sets which is called leaves.

• 지구물리와물리탐사
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• 제2권2호
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• pp.112-121
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• 1999

This article reviews the development of geophysical inverse theory. In a series of articles published in 1967, 1968, and 1979, G. Backus and F. Gilbert a trade-off between model resolution and estimation errors in geophysical inverse problems, and gave a criterion to compromise the reciprocal relation. Although the criterion was not clear in the physical point of view, it had been extensively used in the interpretation of geophysical date in the 1970s. This was the starting point of the fruitful development of inverse theory in geophysics. A reasonable criterion to compromise the reciprocal relation was derived to solve linear problems by D. D. jackson in 1979, introducing the concept of a priori information about unknown model parameters. This Jackson's approach was extended to solve nonlinear problems on the basis o probabilistic approach to the inverse problems formulated by A. Tarantola and B. Vallete in 1982. At the end of 1980s ABIC (Akaike Bayesian Information Criterion) was introduced for selecting a more reasonable model in geophysics. Now the date inversion is regarded as the process of extracting new information from observed data, combining in with a priori information about model parameters, and constructing a more clear image of model.