• Communications for Statistical Applications and Methods
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• 제28권2호
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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.

• 정보저장시스템학회논문집
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• 제10권2호
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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.

• 한국지반공학회논문집
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• 제31권6호
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• pp.59-69
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• 2015

최근 들어 집중호우로 인한 산사태 및 토석류 피해가 종종 발생하고 있다. 이에 따라 산사태 재해 예측에 관한 연구 중 산사태 민감도 분석과 토석류 위험도 분석 관련 연구는 활발하게 진행되어 왔지만, 사면 지역에 적용하기 적합한 전이 분석 관련 연구는 부족한 실정이다. 본 연구에서는 판별분석과 로지스틱 회귀 분석과 같은 통계적 방법을 이용하여 실제 토석류가 발생했던 지역에서 추출한 지형학적 인자, 지질학적 인자 등을 토대로 토석류 전이규준을 제시하였다. 10개의 지형학적 및 지질학적 인자가 독립변수로 사용되었으며 실제 466개소(비전이: 228개소, 전이: 238개소)의 토석류 비전이 및 전이 데이터가 수집되었다. 우선, Fisher의 판별 분석이 수행되었으며, 수행 결과 실제경우와 91.6%의 분류 정확도를 보였다. 하지만 전이와 비전이 두 그룹간의 공분산 동질성이 만족되지 않았으며 또한 독립변수들이 정규분포를 보이지도 않았다. 두 번째로 이항 로지스틱 회귀분석이 수행되었으며, 분석 결과 92.3%의 분류 정확도를 나타냈으며 모든 통계적 조건들도 유의하게 나타났다. 따라서 이항 로지스틱 회귀 분석을 이용한 전이 규준은 토석류 재해 발생 여부를 예측하는데 효과적으로 사용될 수 있을 것으로 판단된다.

• Journal of Information Technology Applications and Management
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• 제11권2호
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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.

• 대한전기학회논문지
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• 제39권8호
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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.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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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.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2012년도 춘계 학술논문 발표대회
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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.

• 한국경영과학회지
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• 제30권1호
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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.

• 한국연초학회지
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• 제3권2호
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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.

• 대한산업공학회지
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• 제13권1호
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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.