• Journal of Advanced Marine Engineering and Technology
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• v.20 no.5
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• pp.39-44
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• 1996

To overcome weak poinks of the standard k-${\varepsilon}$ turbulence model when applied to complex turbulent flows, various modified models were proposed. But their effects are confined to special flow fields. They have still some problems. Recently, an anisotropic k-${\varepsilon}$ turbulence model was also proposed to solve the drawback of the standard k-${\varepsilon}$ turbulence model. This study is concentrated on the evaluation of the anisotropic k-${\varepsilon}$ turbulence model by the analysis of axisymmetric swirling turbulent flow. Results show that the anisotropic k-${\varepsilon}$ turbulence model has scarecely the fundamentally physical mechanism of predicting the swirling structure of flow.

• Journal of Information Technology Services
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• v.15 no.1
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• pp.153-166
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• 2016

Ensemble classification is an approach that combines individually trained classifiers in order to improve prediction accuracy over individual classifiers. Ensemble techniques have been shown to be very effective in improving the generalization ability of the classifier. But base classifiers need to be as accurate and diverse as possible in order to enhance the generalization abilities of an ensemble model. Bagging is one of the most popular ensemble methods. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. In this study we proposed a new bagging variant ensemble model, Randomized Bagging (RBagging) for improving the standard bagging ensemble model. The proposed model was applied to the bankruptcy prediction problem using a real data set and the results were compared with those of the other models. The experimental results showed that the proposed model outperformed the standard bagging model.

• Transactions of the Korean Society of Automotive Engineers
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• v.4 no.3
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• pp.139-146
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• 1996

An extended $k-\varepsilon$ tuebulence model modified by considering the streamline curvature effect and standard $k-\varepsilon$ turbulence model have been applied for three dimensional analysis of turbulece flow in a $90^{\circ}$ curved duct. By comparision of the results with the experimental data, the modified extended $k-\varepsilon$ model gave closer agreement with experimental data than the results from standard $k-\varepsilon$ model owing to an extra time scale of the production rate and parameter describing effects of streamline curvature included in the dissipation rate equation.

• Smart Structures and Systems
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• v.18 no.1
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• pp.1-16
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• 2016

Model Order Reduction (MOR) denotes the theory by which one tries to catch a model of order lower than that of the real model. This is conveniently pursued in view of the design of an efficient structural control scheme, just passive within this paper. When the nonlinear response of the reference structural system affects the nature of the reduced model, making it dependent on the visited subset of the input-output space, standard MOR techniques do not apply. The mathematical theory offers some specific alternatives, which however involve a degree of sophistication unjustified in the presence of a few localized nonlinearities. This paper suggests applying standard MOR to the linear parts of the structural system, the interface remaining the original unreduced nonlinear components. A case study focused on the effects of a helicopter land crash is used to exemplify the proposal.

• Magazine of the Korean Society of Agricultural Engineers
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• v.18 no.4
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• pp.4232-4241
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• 1976

This study was carried out to clarify the stochastic characteristics of monthly rainfalls and to select a proper model for generating the sequential monthly rainfall amounts. The results abtained are as follows: 1. Log-Normal distribution function is the best fit theoretical distribution function to the empirical distribution of monthly rainfall amounts. 2. Seasonal and random components are found to exist in the time series of monthly rainfall amounts and non-stationarity is shown from the correlograms. 3. The Monte Carlo model shows a tendency to underestimate the mean values and standard deviations of monthly rainfall amounts. 4. The 1st order Markov model reproduces means, standard deviations, and coefficient of skewness with an error of ten percent or less. 5. A correlogram derived from the data generated by 1st order Markov model shows the charaterstics of historical data exactly. 6. It is concluded that the 1st order Markov model is superior to the Monte Carlo model in their reproducing ability of stochastic properties of monthly rainfall amounts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1722-1737
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• 2019

We developed an insider threat detection model to be used by organizations that repeat tasks at regular intervals. The model identifies the best combination of different feature selection algorithms, unsupervised learning algorithms, and standard scores. We derive a model specifically optimized for the organization by evaluating each combination in terms of accuracy, AUC (Area Under the Curve), and TPR (True Positive Rate). In order to validate this model, a four-year log was applied to the system handling sensitive information from public institutions. In the research target system, the user log was analyzed monthly based on the fact that the business process is processed at a cycle of one year, and the roles are determined for each person in charge. In order to classify the behavior of a user as abnormal, the standard scores of each organization were calculated and classified as abnormal when they exceeded certain thresholds. Using this method, we proposed an optimized model for the organization and verified it.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.17 no.2
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• pp.105-117
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• 2004

In each industry field, many engineers have tried to develop integrated environments using information technology. The core technology in building integrated environments is the database based on standardized information. To meet the requirements, this study builds a database with detailed design information as a part of integrating digital information generated from every work of steel bridges. The data model used to build the database was developed based on the international standard, namely ISO/STEP. The data model is classified into geometric and non-geometric parts to represent the design information of steel bridges. The geometric parts are represented by a three dimensional solid model so that they may be able to reuse existing information. Also, the non-geometric parts represent information requirements that are analyzed by the development method of standard data model. To verify the data model, this study validates the syntax of the model on EXPRESS Engine and verifies the validation of the model by applying the design data of Hannam bridge to the database.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.252-252
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• 2011

Rainfall-runoff models are calibrated and validated by using a same data set such as observations. The past climate change effects the present rainfall pattern and also will effect on the future. To predict rainfall-runoff more preciously we have to consider the climate change pattern in the past, present and the future time. Thus, in this study, the climate change represents changes in mean precipitation and standard deviation in different patterns. In some river basins, there is no enough length of data for the analysis. Therefore, we have to generate the synthetic data using proper distribution for calculation of precipitation based on the observed data. In this study, Kajiyama model is used to analyze the runoff in the dry and the wet period, separately. Mean and standard deviation are used for generating precipitation from the gamma distribution. Twenty hypothetical scenarios are considered to show the climate change conditions. The mean precipitation are changed by -20%, -10%, 0%, +10% and +20% for the data generation with keeping the standard deviation constant in the wet and the dry period respectively. Similarly, the standard deviations of precipitation are changed by -20%, -10%, 0%, +10% and +20% keeping the mean value of precipitation constant for the wet and the dry period sequentially. In the wet period, when the standard deviation value varies then the mean NSE ratio is more fluctuate rather than the dry period. On the other hand, the mean NSE ratio in some extent is more fluctuate in the wet period and sometimes in the dry period, if the mean value of precipitation varies while keeping the standard deviation constant.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.139-149
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• 2024

In this paper, we propose the standard architecture for the IFF interface SW in naval combat management system(CMS). The proposed standard interface architecture is a method designed to reduce modification efforts and man-month of reliability test for the existing the IFF interface SW of 11 types. We identified highly dependent CMS and GFE information, leading to the redefinition of standard requirements and functions, and proceeded with the initial design applying the Naval Shield Component Platform(NSCP). Subsequently, using the Feature Model, we derived additional common and variable elements for the interface of multiple CMS and GFE. Considering the S.O.L.I.D principles, we designed the final architecture. The proposed IFF Interface SW, based on the standard architecture, is expected to enhance management efficiency through a common architecture, increase code reusability and scalability, and reduce development costs by shortening reliability testing times.

• Journal of computational fluids engineering
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• v.15 no.4
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• pp.1-8
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• 2010

This article describes the numerical investigation of turbulent blood flow in the stenosed artery bifurcation under periodic acceleration of the human body. Numerical analyses for turbulent blood flow were performed with different magnitude of periodic accelerations using a modified turbulence model which was considering drag reduction of non-Newtonian fluid. The blood was considered to be a non-Newtonian fluid which was based on the power-law viscosity. In order to validate the modified $k-{\varepsilon}$ model, numerical simulations were compared with the standard $k-{\varepsilon}$ model and the Malin's low Reynolds number turbulence model for power-law fluid. As results, the modified $k-{\varepsilon}$ model represents intermediate characteristics between laminar and standard $k-{\varepsilon}$ model, and the modified $k-{\varepsilon}$ model showed good agreements with Malin's verified power law model. Moreover, the computing time and computer resource of the modified $k-{\varepsilon}$ model were reduced about one third than low Reynolds number model including Malin's model.