• Journal of Navigation and Port Research
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• v.28 no.9
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• pp.809-813
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• 2004

Experiments revealed that green water phenomena resemble dam-break, in which flow over deck edge forms a vertical wall of water and suddenly falls down into deck. In this paper the dam breaking problems were formulated using Glimm's algorithm, so-rolled, Random Choice method and, several validations were presented.

• Journal of the Korean Institute of Telematics and Electronics
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• v.12 no.3
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• pp.30-36
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• 1975

Distortions are analyzed and compared for two TDM channel expansion methods- periodic sample skipping and sampling frequency reduction. Signal is assumed to be stationary random signal with zero.mean. Channel noise and interference are not considered in the analysis. For speech signal, it is shown that the periodic sample skipping method could be a better choice under practical design constraints.

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.177-183
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• 2011

Hidden Markov random eld(HMRF) is one of the most common model for image segmentation which is an important preprocessing in many imaging devices. The HMRF has unknown hyper-parameters on Markov random field to be estimated in segmenting testing images. However, in practice, due to computational complexity, it is often assumed to be a fixed constant. In this paper, we numerically show that the segmentation results very depending on the fixed hyper-parameter, and, if the parameter is misspecified, they further depend on the choice of the class-labelling algorithm. In contrast, the HMRF with estimated hyper-parameter provides consistent segmentation results regardless of the choice of class labelling and the estimation method. Thus, we recommend practitioners estimate the hyper-parameter even though it is computationally complex.

• Journal of the Korean Vacuum Society
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• v.5 no.2
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• pp.99-106
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• 1996

Two types of regularization method (singular system and HMP approaches) for generating depth-concentration profiles from angle-resolved XPS data were evaluated. Both approaches showed qualitatively similar results although they employed different numerical algorithms. The application of the regularization method to simulated data demonhstrates its excellent utility for the complex depth profile system . It includes the stable restoration of depth-concentration profiles from the data with considerable random error and the self choice of smoothing parameter that is imperative for the successful application of the regularization method. The self choice of smoothing parameter is based on generalized cross-validation method which lets the data themselves choose the optimal value of the parameter.

• Journal of Industrial Technology
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• v.20 no.A
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• pp.159-167
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• 2000

In the study rainfall frequency analysis attemped the many specific property data record duration it is differance from occur to error-term and probability ditribution of concern manifest. error-term analysis of method are fact sample data using method in other hand it is not appear to be fault that sample data of number to be small random variates. Therefore, day-rainfall data: to randomicity consider of this study sample data to the Monte Carlo method by randomize after data recode duration of form was choice method which compared an assumed maternal distribution from splitting frequency analysis consequence. In the conclusion, frequency analysis of chuncheon region rainfall appeared samll RMSE to the Gamma II distribution. In the rainfall frequency analysis estimate RMSE using random variates great transform, RMSE is appear that return period increasing little by little RMSE incresed and data number incresing to RMSE decreseing.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.741-757
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• 2000

In may experimental situations, whenever a block design is used, the block effect is usually considered to be fixed. There are, however, experimental situations in which it should be treated as random. The choice of a blocking arrangement for a response surface design can have a considerable effect on estimating the mean response and on the size of he prediction variance even if the experimental runs re the same. Therefore, care should be exercised in the selection of blocks. In this paper, in the presence of a random block effect, we propose a graphical method or evaluating the effect of blocking in response surface designs using cuboidal regions. This graphical method can be used to investigate how the blocking has influence on the prediction variance throughout all experimental regions of interest when this region is cuboidal, and compare the block effects in the cases of the orthogonal and non-orthogonal block designs, respectively.

• Analytical Science and Technology
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• v.8 no.4
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• pp.707-714
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• 1995

Two types of regularization method (singular system and HMP approaches) for generating depth-concentration profiles from angle-resolved XPS data were evaluated. Both approaches showed qualitatively similar results although they employed different numerical algorithms. The application of the regularization method to simulated data demonstrates its excellent utility for the complex depth profile system. It includes the stable restoration of the depth-concentration profiles from the data with considerable random error and the self choice of smoothing parameter that is imperative for the successful application of the regularization method. The self choice of smoothing parameter is based on generalized cross-validation method which lets the data themselves choose the optimal value of the parameter.

• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.79-89
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• 2016

In this paper, we proposed the object classification method using genetic and dynamic random forest consisting of optimal combination of unit tree. The random forest can ensure good generalization performance in combination of large amount of trees by assigning the randomization to the training samples and feature selection, etc. allocated to the decision tree as an ensemble classification model which combines with the unit decision tree based on the bagging. However, the random forest is composed of unit trees randomly, so it can show the excellent classification performance only when the sufficient amounts of trees are combined. There is no quantitative measurement method for the number of trees, and there is no choice but to repeat random tree structure continuously. The proposed algorithm is composed of random forest with a combination of optimal tree while maintaining the generalization performance of random forest. To achieve this, the problem of improving the classification performance was assigned to the optimization problem which found the optimal tree combination. For this end, the genetic algorithm methodology was applied. As a result of experiment, we had found out that the proposed algorithm could improve about 3~5% of classification performance in specific cases like common database and self infrared database compare with the existing random forest. In addition, we had shown that the optimal tree combination was decided at 55~60% level from the maximum trees.

• Journal of the Optical Society of Korea
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• v.19 no.3
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• pp.241-247
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• 2015

The double-random phase encryption (DRPE) algorithm is a robust technique for image encryption, due to its high speed and encoding a primary image to stationary white noise. Recently it was reported that DRPE in the Fresnel domain can achieve a better avalanche effect than that in Fourier domain, which means DRPE in the Fresnel domain is much safer, to some extent. Consequently, a method based on DRPE in the Fresnel domain would be a good choice. In this paper we present an image-authentication method which uses only partial phase information from a double-random-phase-encrypted image in the Fresnel domain. In this method, only part of the phase information of an image encrypted with DRPE in the Fresnel domain needs to be kept, while other information like amplitude values can be eliminated. Then, with the correct phase keys (we do not consider wavelength and distance as keys here) and a nonlinear correlation algorithm, the encrypted image can be authenticated. Experimental results demonstrate that the encrypted images can be successfully authenticated with this partial phase plus nonlinear correlation technique.

• Convergence Security Journal
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• v.16 no.6_2
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• pp.73-82
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• 2016

This study is to investigate the Effect of Career Choice Motives of the Private Security Guards on Job choice evaluation and Job Attitude. For this study visiting the 8 companies around the capital area and surveyed from May 1st to October 10th 2015 using the stratified random sampling method. A total of 240 questionnaires were distributed and among them, 220 copies were used except for analysis. I used SPSSWIN 21.0 and AMOS 21.0 to reliability analysis, factor analysis, analysis of structural equation model, path analysis. The level of statistical significance was set to .05. The following are conclusions. Job choice motivation has a positive effect on Job choice evaluation and Job attitude but Job choice evaluation doesn't play intermediary role on relationship between Job choice motivation and Job attitude.