• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.135-150
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• 2021

We propose principal differential analysis based classification methods. Computations of squared multiple correlation function (RSQ) and principal differential analysis (PDA) scores are reviewed; in addition, we combine principal differential analysis results with the logistic regression for binary classification. In the numerical study, we compare the principal differential analysis based classification methods with functional principal component analysis based classification. Various scenarios are considered in a simulation study, and principal differential analysis based classification methods classify the functional data well. Gene expression data is considered for real data analysis. We observe that the PDA score based method also performs well.

• Transactions of the Society of Information Storage Systems
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• v.10 no.2
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• pp.50-54
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• 2014

Object image using DRPE(Double Random Phase Encryption) in 4f system is encrypted by space-division method using amplitude mask. However, this method has the weakness for the case of having partial data of amplitude mask which can access the original image. To improve the security, we propose the method using the 2-dimension logistic chaos function which shuffles the encrypted data. It is shown in simulation results that the proposed method is highly sensitive to chaos function parameters. To properly decrypt from shuffled encryption data, below 1e-5 % errors of each parameter should be required. Thus compared with conventional method the proposed shows the higher security level.

• Journal of the Korean Geotechnical Society
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• v.31 no.6
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• pp.59-69
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• 2015

Recently, landslide and debris-flow disasters caused by severe rain storms have frequently occurred. Many researches related to landslide susceptibility analysis and debris-flow hazard analysis have been conducted, but there are not many researches related to mobilization analysis for landslides transforming into debris-flow in slope areas. In this study, statistical analyses such as discriminant analysis and logistic regression analysis were conducted to develop a mobilization criterion using geomorphological and geological factors. Ten parameters of geomorphological and geological factors were used as independent variables, and 466 cases (228 non-mobilization cases and 238 mobilization cases) were investigated for the statistical analyses. First of all, Fisher's discriminant function was used for the mobilization criterion. It showed 91.6 percent in the accuracy of actual mobilization cases, but homogeneity condition of variance and covariance between non-mobilization and mobilization groups was not satisfied, and independent variables did not follow normal distribution, either. Second, binomial logistic analysis was conducted for the mobilization criterion. The result showed 92.3 percent in the accuracy of actual mobilization cases, and all assumptions for the logistic analysis were satisfied. Therefore, it can be concluded that the mobilization criterion for debris-flow using binomial logistic regression analysis can be effectively applied for the prediction of debris-flow hazard analysis.

• Journal of Information Technology Applications and Management
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• v.11 no.2
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• pp.191-204
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• 2004

Many software reliability growth model(SRGM) have been proposed for past several decades. Most of these propositions assumed the S/W debugging testing efforts be constant or even did not consider them. A few papers were presented as the software reliability evaluation considering the testing effort was important afterwards. The testing effort forms which have been presented by this kind of papers were exponential, Rayleigh, Weibull, or Logistic functions, and one of these 4 types was used as a testing effort function depending on the S/W developing circumstances. We consider the methology to evaluate the SRGN using least square estimator(LSE) and maximum likelihood estimator(MLE) for those 4 functions, and then examine parameters applying actual data adopted from real field test of developing S/W.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.8
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• pp.856-867
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• 1990

In this paper, the convergence and the learning accuracy of the back-propagation (BP) method in neural network are investigated by 1) analyzing the reason for decelerating the convergence of BP method and examining the rapid deceleration of the convergence when the learning is executed on the part of sigmoid activation function with the very small first derivative and 2) proposing the modified logistic activation function by defining, the convergence factor based on the analysis. Learning on the output patterns of binary as well as analog forms are tested by the proposed method. In binary output patter, the test results show that the convergence is accelerated and the learning accuracy is improved, and the weights and thresholds are converged so that the stability of neural network can be enhanced. In analog output patter, the results show that with extensive initial transient phenomena the learning error is decreased according to the convergence factor, subsequently the learning accuracy is enhanced.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.877-878
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• 2008

We suggest a feature reduction method to classify mouse function data sets, which integrate several biological data sets represented as high dimensional vectors. To increase classification accuracy and decrease computational overhead, it is important to reduce the dimension of features. To do this, we employed Hybrid Huberized Support Vector Machine with kernels used for a kernel logistic regression method. When compared to support vector machine, this a pproach shows the better accuracy with useful features for each mouse function.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.05a
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• pp.251-252
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• 2012

The exiting studies on the strength prediction by maturity method is mainly focused on concrete using OPC, meanwhile the study on the concrete mixing blast furnace slag powder (BFSP) is insufficient. The purpose of this study is to investigate the relationships between compressive strength and equivalent age by existing Maturity functions, i.e., Nurse-saul function Arrhenius function. This study also compared and examined the strength prediction of concrete mixing BGSP using ACI model and Logistic Curve prediction equation. Therefore, it is intended that fundamental data are presented for quality management and process management of concrete mixing BFSP.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.1
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• pp.55-74
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• 2005

Bankruptcy prediction has drawn a lot of research interests in previous literature, and recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper employs a relatively new machine learning technique, support vector machines (SVMs). to bankruptcy prediction problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, we use grid search technique using 5-fold cross-validation to find out the optimal values of the parameters of kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM. we compare its performance with multiple discriminant analysis (MDA), logistic regression analysis (Logit), and three-layer fully connected back-propagation neural networks (BPNs). The experiment results show that SVM outperforms the other methods.

• Journal of the Korean Society of Tobacco Science
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• v.3 no.2
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• pp.109-114
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• 1981

This experiment was conducted with three varieties (Hicks, Burley 21, Sohyang) and cropping systems (Improved mulching, Mulching, Non mulching) of NC 2326 to analyze growth kinetics by means of growth function involving its velocity and accelerated velocity. The basic growth data were obtained by harvest method at interval of ten days from transplanting to hundred days and analyzed by , regression equation, determinant of matrix, and differentiation. The plot of total dry weight of leaves, stalk and roots per a plant vs. time forms a sigmoid curve and its function fitted logistic satisfactorily. Tobacco plant grows at an accelerated velocity. And growth velocity, symmetric about an inflection point, is proportional to biomass attained and to the difference between biomass attained and the maximum, and to the decrease according to the biomass. Of varieties and cropping systems, the most maximum velocity was 9.58g per day per plant in mulching cultivation of NC 2326 and maximum accelerated velocity was 264mg per $day^2$ per plant in Burley 21.

• Journal of Korean Institute of Industrial Engineers
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• v.13 no.1
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• pp.25-29
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• 1987

When the historical data are available, the diffusion model, which describes the time pattern of the adoption process of a new product or technology or service, has been used as a reasonable predictor in the telecommunication demand forecasting area. This paper shows that the diffusion model is applicable when the historical data are not available. The model used is in the form of a "logistic" function. The parameters of the function are estimated using the questionnaire and the historical data of reference products. From the questionnaire, an initial and an upper limit long run value of the market share are estimated, and the diffusion time to the upper limit value is determined by the relation between the investment and the utility.