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Pohang, South Korea

Dongyang University is a private university located in Yeongju, South Korea. The graduating class of 2012 numbered 672. The current president is Sung-Hae Choi . Wikipedia.

Kim S.,Dongyang University | Shiri J.,Islamic Azad University at Tabriz | Kisi O.,Canik Basari University
Water Resources Management | Year: 2012

The purpose of this study was to develop and apply the neural networks models to estimate daily pan evaporation (PE) for different climatic zones such as temperate and arid climatic zones, Republic of Korea and Iran. Three kinds of the neural networks models, namely multilayer perceptron-neural networks model (MLP-NNM), generalized regression neural networks model (GRNNM), and support vector machine-neural networks model (SVM-NNM), were used to estimate daily PE. The available climatic variables, consisted of mean air temperature (T mean), mean wind speed (U mean), sunshine duration (SD), mean relative humidity (RH mean), and extraterrestrial radiation (R a) were used to estimate daily PE using the various input combinations of climate variables. The measurements for the period of January 1985-December 1990 (Republic of Korea) and January 2002-December 2008 (Iran) were used for training and testing the employed neural networks models. The results obtained by SVM-NNM indicated that it performs better than MLP-NNM and GRNNM for estimating daily PE. A comparison was also made among the employed models, which demonstrated the superiority of MLP-NNM, GRNNM, and SVM-NNM over Linacre model and multiple linear regression model (MLRM). © 2012 Springer Science+Business Media B.V. Source

Cho G.-S.,Dongyang University
Computers and Security | Year: 2013

In this paper, we present a computer forensic method for detecting timestamp forgeries in the Windows NTFS file system. It is difficult to know precisely that the timestamps have been changed by only examining the timestamps of the file itself. If we can find the past timestamps before any changes to the file are made, this can act as evidence of file time forgery. The log records operate on files and leave large amounts of information in the $LogFile that can be used to reconstruct operations on the files and also used as forensic evidence. Log record with 0x07/0x07 opcode in the data part of Redo/Undo attribute has timestamps which contain past-and-present timestamps. The past-and-present time-stamps can be decisive evidence to indicate timestamp forgery, as they contain when and how the timestamps were changed. We used file time change tools that can easily be found on Internet sites. The patterns of the timestamp change created by the tools are different compared to those of normal file operations. Seven file operations have ten timestamp change patterns in total by features of timestamp changes in the $STANDARD-INFORMATION attribute and the $FILE-NAME attribute. We made rule sets for detecting timestamp forgery based on using difference comparison between changes in timestamp patterns by the file time change tool and normal file operations. We apply the forensic rule sets for ".txt", ".docx" and ".pdf" file types, and we show the effectiveness and validity of the proposed method. The importance of this research lies in the fact that we can find the past time in $LogFile, which gives decisive evidence of timestamp forgery. This makes the timestamp active evidence as opposed to simply being passive evidence. © 2012 Elsevier Ltd. All rights reserved. Source

Kim S.,Dongyang University
Disaster Advances | Year: 2011

The goal of this research is to develop and apply the integrational operation method (IOM) for modeling the relationship of the pan evaporation (PE) and the alfalfa reference evapotranspiration (ETr). Since the observed data of the alfalfa ETr using lysimeter have not been measured for a long time, the Penman-Monteith (PM) method is used to estimate the observed alfalfa ETr The IOM consists of the combination/ application of the stochastic and neural networks models respectively. The stochastic model of Periodic Auto Regressive Moving Average (PARMA) is applied to generate the training dataset for the monthly PE and the alfalfa ET r and the neural networks models are applied to calculate the observed test dataset reasonably. Among the six training patterns, 1,000/PARMA(1,1) /GRNNM-GA training pattern is used which can evaluate the suggested climatic variables very well and construct the reliable data for the monthly PE and the alfalfa ETr. Uncertainty analysis is also used to eliminate the climatic variables of input nodes from the 1,000/PARMA(1, 1)ZGRNNM-GA training pattern. The sensitive and insensitive climatic variables are chosen from the uncertainty analysis of the input nodes. Source

Bae J.K.,Dongyang University
Journal of Convergence Information Technology | Year: 2010

In this study, performance of classification techniques is compared in order to predict dividend policy decisions. We first analyzed the feasibility of all available companies listed in the Korea Exchange (KRX) market as dividend data sets by using classification techniques. Then we developed a prediction model based on support vector machines (SVM). We compare the classification accuracy performance between our SVM model and artificial intelligence techniques, and suggest a better dividend policy forecasting model to help a chief executive officer (CEO) or a board of directors (BOD) make better decision in a corporate dividend policy. The experiments demonstrate that the SVM model always outperforms other models in the performance of dividend policy forecasting, and hence we can predict future dividend policy more correctly than any other models. This enhancement in predictability of future dividend policy can significantly contribute to the correct valuation of a company, and hence those people from investors to financial managers to any decision makers of a company can make use of the SVM model for the better financing and investing decision making which can lead to higher profits and firm values eventually. Moreover, this is particularly important for people who want to obtain a high level of accuracy in advanced areas such as financial decision makings. Source

Bae S.-K.,Dongyang University
Journal of Information Processing Systems | Year: 2014

Various Time Synchronization protocols for a Wireless Sensor Network (WSN) have been developed since time synchronization is important in many timecritical WSN applications. Aside from synchronization accuracy, energy constraint should also be considered seriously for time synchronization protocols in WSNs, which typically have limited power environments. This paper performs analysis of prominent WSN time synchronization protocols in terms of power consumption and test by simulation. In the analysis and simulation tests, each protocol shows different performance in terms of power consumption. This result is helpful in choosing or developing an appropriate time synchronization protocol that meets the requirements of synchronization accuracy and power consumption (or network lifetime) for a specific WSN application. © 2014 KIPS. Source

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