• 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.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.901-903
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• 1995

The ordinary differential equation (ODE) method has been widely used for the convergence analysis of stochastic recursive algorithms. The principal objective of this method is to associate to a given algorithm a differential equation with continuous righthand side. Usually some assumptions should be imposed to get such a differential equation. If any of assumptions fails, then the ODE method cannot be used. Recently a new method using differential inclusions (DIs) was introduced in [3], which is useful to deal with those cases. The DI method shares the same idea with the ODE method, but it is different in that a differential inclusion is identified instead of a differential equation with continuous righthand side. In this paper, we briefly review the DI method and then analyze a Robbins and Monro (RM)-type algorithm. Our focus is placed on the projected algorithm.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.81-88
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• 2023

In the present scenario, enormous amounts of data are produced every second. These data also contain private information from sources including media platforms, the banking sector, finance, healthcare, and criminal histories. Data mining is a method for looking through and analyzing massive volumes of data to find usable information. Preserving personal data during data mining has become difficult, thus privacy-preserving data mining (PPDM) is used to do so. Data perturbation is one of the several tactics used by the PPDM data privacy protection mechanism. In Perturbation, datasets are perturbed in order to preserve personal information. Both data accuracy and data privacy are addressed by it. This paper will explore and compare several perturbation strategies that may be used to protect data privacy. For this experiment, two perturbation techniques based on random projection and principal component analysis were used. These techniques include Improved Random Projection Perturbation (IRPP) and Enhanced Principal Component Analysis based Technique (EPCAT). The Naive Bayes classification algorithm is used for data mining approaches. These methods are employed to assess the precision, run time, and accuracy of the experimental results. The best perturbation method in the Nave-Bayes classification is determined to be a random projection-based technique (IRPP) for both the cardiovascular and hypothyroid datasets.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.52 no.4
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• pp.179-188
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• 2003

Percentage differential characteristic scheme has been recognized as the principal basis for large transformer protection. Nowadays, relaying signals can contain second harmonic component to a large extent even in a normal state, and second harmonic ratio indicates a tendency of relative reduction because of the advancement of transformer's core material. And then, conventional second harmonic restraint differential relaying exposes some doubt in reliability. It is, therefore, necessary to develop a new algorithm for the effective and accurate discrimination. This paper deals with advanced fuzzy logic based relaying by using flux differential, and a new fault detection criterion logic scheme by using wavelet transform. To comparative analysis of proposed techniques, the paper constructs power system model including power transformer, utilizing the EMTP, and collects data through simulation of various internal faults and magnetizing inrush. The proposed fuzzy relaying and a new fault detection scheme were tested. The former, fuzzy relaying, was proven to be faster and more reliable than the latter.

• Proceedings of the KIEE Conference
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• 2007.11b
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• pp.75-77
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• 2007

Current percentage differential relaying has been recognized as the principal basis of main protection for stator windings of AC generator. The DWT has merit of obtaining frequency characteristics in time domain. In order to compensate for DFT's defects, we proposed fault detection algorithm using DWT. This paper describes a comparative analysis about conventional DFT-based DFR and advanced DWT-based relaying.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.744-752
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• 2012

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as an one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problems. In data preprocessing part, Principal Component Analysis(PCA) which is generally used in face recognition, which is useful to express some classes using reduction, since it is effective to maintain the rate of recognition and to reduce the amount of data at the same time. However, because of there of the whole face image, it can not guarantee the detection rate about the change of viewpoint and whole image. Thus, to compensate for the defects, Linear Discriminant Analysis(LDA) is used to enhance the separation of different classes. In this paper, we combine the PCA&LDA algorithm and design the optimized pRBFNNs for recognition module. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as two kinds of polynomials such as constant, and linear. The coefficients of connection weight identified with back-propagation using gradient descent method. The output of the pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to face image(ex Yale, AT&T) datasets and then demonstrated from the viewpoint of the output performance and recognition rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.4
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• pp.756-765
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• 2001

An effective dexterous motion control method of redundant robot manipulators based on neural optimization network is proposed to satisfy multi-criteria such as singularity avoidance, minimizing energy consumption, and avoiding physical limits of actuator, while performing a given task. The method employs a neural optimization network with parallel processing capability, where only a simple geometric analysis for resolved motion of each joint is required instead of computing of the Jacobian and its pseudo inverse matrix. For dexterous motion, a joint geometric manipulability measure(JGMM) is proposed. JGMM evaluates a contribution of each joint differential motion in enlarging the length of the shortest axis among principal axes of the manipulability ellipsoid volume approximately obtained by a geometric analysis. Redundant robot manipulators is then controlled by neural optimization networks in such a way that 1) linear combination of the resolved motion by each joint differential motion should be equal to the desired velocity, 2) physical limits of joints are not violated, and 3) weighted sum of the square of each differential joint motion is minimized where weightings are adjusted by JGMM. To show the validity of the proposed method, several numerical examples are illustrated.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.703-708
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• 2009

The present paper describes the application of an improved impression transfer vector method (Sakurai et al., 2007) to transform the three basic dimensions (Evaluation, Activity, and Potency) of higher-order impression. First, a set of shapes and surface textures of faces was represented by multi-dimensional vectors. Second, the variation among faces was coded in reduced parameters derived by applying principal component analysis. Third, a facial attribute along a given impression dimension was analyzed to select discriminative parameters from among principal components with higher sensitivity to impressions, and obtain an impression transfer vector. Finally, the parametric coordinates were changed by adding or subtracting the impression transfer vector and the image was manipulated so that its facial appearance clearly exhibits the transformed impression. A psychological rating experiment confirmed that the impression transfer vector modulated three dimensions of higher-order impression. We discussed the versatility of the impression transfer vector method.

• Proceedings of the Korean Society of Plant Pathology Conference
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• 2005.04a
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• pp.48.2-49
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• 2005

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.689-694
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• 2009

We developed a facial image generating technique that can manipulate facial impressions. The present study applied this impression transferring method to higher-order impressions such as "elegance" or "attractiveness" and confirmed the psychological validity of this method using the semantic differential method. Subsequently, we applied this method to two types of cognitive experiments. First, we examined the contributions of texture and shape on the facial impressions by using those face images for which the impressions have already been quantitatively manipulated based on this method. Second, we used such stimuli to examine the effect of facial impressions and attractiveness on the "mere exposure effect." Thus, we concluded that the impression transfer vector method is an effective tool to quantitatively manipulate the facial impressions in various cognitive studies.