• Journal of the Korean Institute of Telematics and Electronics
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• v.21 no.4
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• pp.17-23
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• 1984

A model analysis of pattern shift in the epitaxial growth of silicon on (111) substrates was performed. The growth rate anisotropy was considered as the most important affecting factor of pattern shift, and for the model establishment the off angle of the substrate and the process temperature were taken as the variables. We derived a theoretical equation of pattern shift by assuming the growth rate anisotropy as the trigonometric sine function of the off angle of the substrate and defining the growth rate anisotropy factor related to the process temperature. The pattern shift ratio calculated by this model had the same tendency with the experimental ones, which, however, were about twice greater than those. It was supposed that this discrepailcy was due to the second order affecting factor such as facetting and step broadening which had been exluded in model establishment.

• International Journal of Contents
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• v.1 no.2
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• pp.26-30
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• 2005

According to the fast growth of information on the Internet, it is becoming increasingly difficult to find and organize useful information. To reduce information overload, it needs to exploit automatic text classification for handling enormous documents. Support Vector Machine (SVM) is a model that is calculated as a weighted sum of kernel function outputs. This paper describes a document classifier for web documents in the fields of Information Technology and uses SVM to learn a model, which is constructed from the training sets and its representative terms. The basic idea is to exploit the representative terms meaning distribution in coherent thematic texts of each category by simple statistics methods. Vector-space model is applied to represent documents in the categories by using feature selection scheme based on TFiDF. We apply a category factor which represents effects in category of any term to the feature selection. Experiments show the results of categorization and the correlation of vector length.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.265-271
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• 2022

Social media is a form of communication based on the internet to share information through content and images. Their choice of profile images and type of image they post can be closely connected to their personality. The user posted images are designated as personality traits. The objective of this study is to predict five factor model personality dimensions from profile images by using deep learning and neural networks. Developed a deep learning framework-based neural network for personality prediction. The personality types of the Big Five Factor model can be quantified from user profile images. To measure the effectiveness, proposed two models using convolution Neural Networks to classify each personality of the user. Done performance analysis among two different models for efficiently predict personality traits from profile image. It was found that VGG-69 CNN models are best performing models for producing the classification accuracy of 91% to predict user personality traits.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.1
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• pp.244-254
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• 2002

To enhance the acceleration performance and fuel consumption rate of a vehicle, the torque converter is modified or newly-developed with reliable analysis model. Up to recently, the one dimensional performance model has been used for the analysis and design of torque converter. The model is described with constant parameters based on the concept of mean flow path. When it is used in practice, some experiential correction factors are needed to minimize tole estimated error. These factors have poor physical meaning and cannot be applied confidently to the other specification of torque converter. In this study, the detail dynamic model of torque converter is presented to establish the physical meaning of correction factors. To verify the validity of model, performance test was carried out with various input speed and oil temperature. The effect of oil temperature on the performance is analysed, and it is applied to the dynamic model. And, to obtain the internal flow pattern of torque converter, CFD(Computational Fluid Dyanmics) analysis is carried out on three-dimensional turbulent flow. Correction factors are determined from the internal flow pattern, and their variation is presented with the speed ratio of torque converter. Finally, the sensitivity of correction factors to the speed ratio is studied for the case of changing capacity factor with maintaining torque ratio.

• International Journal of Aeronautical and Space Sciences
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• v.15 no.1
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• pp.102-111
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• 2014

An analytical model was developed to understand the physics and predict the functional performance of a pin puller. The formulated model is based on one-dimensional gas dynamics for an ideal gas. Resistive forces against pin shaft movement were measured in quasi-static mechanical tests, the results of which were incorporated into the model. The expansion chamber pressure and the pin shaft displacement were measured from an actual firing test and compared to the model prediction. The gas generation rate was adjusted by a correction factor, and the heat transfer rate was obtained through parametric analysis. The validity of the model is assessed for additional firing tests with different amounts of pyrotechnic charge. This model can provide knowledge on how the pin puller functions, and on which design parameters contribute the most to the actuation of the pin puller. Using this model, we estimate the functional safety factor by comparing the energy generated by the pyrotechnic charge to the energy required to accomplish the function.

• Journal of Biomedical Engineering Research
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• v.22 no.4
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• pp.369-375
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• 2001

The statistical regression model is one of the most frequently used clinical analysis methods. It has basic assumption of linearity, additivity and normal distribution of data. However, most of biological data in medical field are nonlinear and unevenly distributed. To overcome the discrepancy between the basic assumption of statistical model and actual biological data, we propose a new analytical method based on artificial neural network. The newly developed multilayer perceptron(MLP) is trained with 120 data set (60 normal, 60 patient). On applying test data, it shows the discrimination power of 0.76. The diabetic risk factors were also identified from the MLP neural network model and the logistic regression model. The signigicant risk factors identified by MLP model were post prandial glucose level(PP2), sex(male), fasting blood sugar(FBS) level, age, SBP, AC and WHR. Those from the regression model are sex(male), PP2, age and FBS. The combined risk factors can be identified using the MLP model. Those are total cholesterol and body weight, which is consistent with the result of other clinical studies. From this experiment we have learned that MLP can be applied to the combined risk factor analysis of biological data which can not be provided by the conventional statistical method.

• The Journal of Information Technology
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• v.9 no.4
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• pp.75-85
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• 2006

This study is to test the influence of stock option granting information on the firm's performance. The important issue in stock option is that agent cost is the important determinant factor for the long term performance. The agent cost arises between the manager and shareholders. So many study are concentrated in diminishing the agent cost, and develop some substitute tools to measure the agent cost. The event study about stock option analyzes returns around event date at a time. Event study provides estimation periods and cumulative returns. Announcements about stock option are generally associated with positive abnormal returns in short term period, but not showing positive effect in long term period. It is important to investigate the responses of stocks to new information contained in the announcements of stock option. Therefore it is important to study the long term performance in the case of stock option. The event time portfolio approach exists the CAR model, BHAR model and WR model. And the calendar time portfolio approach has the 3 factor model, 4 factor model, CTAR model, and RATS model. This study is forced to develop and arrange two approach method in evaluating the performance, the event time portfolio approach and calendar time portfolio approach.

• Journal of Magnetics
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• v.21 no.4
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• pp.516-523
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• 2016

In this paper, coupling factors are calculated based on numerical analysis in order to assess various non-uniform low-frequency magnetic field exposure situations. Two types of non-uniform magnetic field sources are considered; circular coil and parallel wires with balanced currents. For each magnetic field source, source current values are determined so that reference magnetic field magnitude can be measured at the specified point on the human model. Various exposure situations are investigated by changing parameters such as the distance between source and human model, radius of circular coil, and the gap between parallel wires. For equivalent human models, prolate spheroid model and simplified human model from IEC 62311 standard are used. The calculated coupling factor values are compared with those obtained by 2D uniform disk human model, and the dependence of coupling factor on the choice of equivalent human model is analyzed.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.157-164
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• 2021

The aim of this study is to provide empirical evaluation of the accuracy of data-model fit using growing levels of invariance models. Overall model accuracy of factor solutions was evaluated by the examination of the order for testing three levels of measurement invariance (MIV) starting with configural invariance (model 0). Model testing was evaluated by the Chi-square difference test (∆𝛘²) between two groups, and root mean square error of approximation (RMSEA), comparative fit index (CFI), and Tucker-Lewis index (TLI) were used to evaluate the all-model fits. Factorial invariance result revealed that stability of the models was varying over increasing levels of measurement as a function of variable-to-factor ratio (VTF), subject-to-variable ratio (STV), and their interactions. There were invariant factor loadings and invariant intercepts among the groups indicating that measurement invariance was achieved. For VTF ratio (3:1, 6:1, and 9:1), the models started to show accuracy over levels of measurement when STV ratio was 6:1. Yet, the frequency of stability models over 1000 replications increased (from 69% to 89%) as STV ratio increased. The models showed more accuracy at or above 39:1 STV.

• Journal of the Society of Naval Architects of Korea
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• v.50 no.5
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• pp.291-297
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• 2013

Generally, form factor is determined through ITTC method. Determining the form factor from ITTC method includes the assumption that the form factor of a full-scale ship is the same value as its model ship. In other words, the form factor is independent on Reynolds number. However, for the more appropriate prediction of the resistance performance of a full-scale ship, the form factor must be determined with the consideration of the variation attendant on Reynolds number. In this research, several Geosim ship models are adopted to investigate the scale effect, and correlation lines of form factor are improved to suggest the better extrapolation method for the prediction of the form factor of full-scale ship. The corrected form factors using the correlation lines are compared with those determined from the results of low-speed resistance tests. To consider the influence of hull form, the correlation lines are determined for the group of high-speed ships and the group of low-speed ships, respectively. The corrected form factors have shown good agreement among the prediction results from each Geosim ship model to the full-scale ship.