• Communications of the Korean Mathematical Society
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• v.16 no.1
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• pp.125-135
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• 2001

In this article we investigate the dependence between components of the random vector which is given as an asymptotic limit of an array of random vectors with interlaced mixing conditions. We discuss the cross covariance of the limiting vector process and give a stronger condition to have a central limit theorem for an array of random vectors with mixing conditions.

• Journal of the Korean Operations Research and Management Science Society
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• v.10 no.1
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• pp.9-13
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• 1985

This paper develops a decomposition method for stochastic programming with a block diagonal structure. Here we assume that the right-hand side random vector of each subproblem is differente each other. We first, transform this problem into a master problem, and subproblems in a similar way to Dantizig-Wolfe's Decomposition Princeple, and then solve this master problem by solving subproblems. When we solve a subproblem, we first transform this subproblem to a Deterministic Equivalent Programming (DEF). The form of DEF depends on the type of the random vector of the subproblem. We found the subproblem with finite discrete random vector can be transformed into alinear programming, that with continuous random vector into a convex quadratic programming, and that with random vector of unknown distribution and known mean and variance into a convex nonlinear programming, but the master problem is always a linear programming.

• The Transactions of the Korean Institute of Power Electronics
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• v.6 no.6
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• pp.525-537
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• 2001

In this paper a new space vector RPPWM(Random Position PWM) is proposed. In the propsed RPPWM each of three phase pulses is located randomly in each switching interval. Based on the Space vector modulation technique the duty ratio of the pulses is calculated Along with the randomization of the PWM pulses. we can obtain the effects of spread spectra of votlage, current as in the case of randomly changed switching frequency, To verify the validity of the proposed RPPWM simulation study was tried using Matlab/Simulink The main model described in Simulink block diagrams includes the space vector modulation block pulse position randomization block inverter block 3 phase induction motor block and so on By the simulation study, the harmonics of the output voltage and the current of inverter are predicted in different PWM methods- SVPWM, LLPWM proposed RPPWM.

• Proceedings of the IEEK Conference
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• 2002.06b
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• pp.273-276
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• 2002

In this paper, we propose a bias random vector generator which can verify functions of microprocessor effectively. This generator is a pre-processor of assembly program, and defines pre-processor instructions which create random vector only in the pall which the designer wants to verify. Therefore, this generator shows higher detection ration than any other generators. And, we can cut down design costs because of shortening a Period for verifying function.

• Proceedings of the IEEK Conference
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• 2002.06b
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• pp.121-124
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• 2002

In this paper, we propose a bias random vector generator which can verify functions of microprocessor effectively. This generator is a pre-processor of assembly program, and defines pre-processor instructions which create random vector only in the part which the designer wants to verify. Therefore, this generator shows higher detection ration than any other generators. And, we can cut down design costs because of shortening a period for verifying function.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.816-839
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• 2023

In a random vector functional link (RVFL) network, shortcomings such as local optimal stagnation and decreased convergence performance cause a reduction in the accuracy of illumination correction by only inputting the weights and biases of hidden neurons. In this study, we proposed an improved regularized random vector functional link (RRVFL) network algorithm with an optimized grey wolf optimizer (GWO). Herein, we first proposed the moth-flame optimization (MFO) algorithm to provide a set of excellent initial populations to improve the convergence rate of GWO. Thereafter, the MFO-GWO algorithm simultaneously optimized the input feature, input weight, hidden node and bias of RRVFL, thereby avoiding local optimal stagnation. Finally, the MFO-GWO-RRVFL algorithm was applied to ameliorate the performance of illumination correction of various test images. The experimental results revealed that the MFO-GWO-RRVFL algorithm was stable, compatible, and exhibited a fast convergence rate.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.15 no.2
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• pp.209-218
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• 2002

The Vector Random Decrement technique has been developed as an efficient method for transforming ambient responses into free decays of linear structures. It is shown that the VRD functions nay contain as much information about the modes as the really measured free decay responses. In this paper, the theory of the VRD technique is extended by applying the concept of the mode shape ratio into the Ibrahim Time Domain modal parameter identification algorithm. The VRD function is not shifted in the correction procedures for constant time shifts of the proposed VRD technique. Thus, a number of points equal to the largest of the time shifts used in the vector triggering condition are not deleted. In the VRD functions, any influence of the input to the system is averaged out. The proposed technique is compared with the traditional VRD technique by assessment of the modal parameters. The applicability of the VRD technique has been justified through a simulation study and a study of the response of a laboratory beam model subject to ambient loads.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.75-88
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• 2021

Automobile credit business has developed rapidly in recent years, and corresponding default phenomena occur frequently. Credit default will bring great losses to automobile financial institutions. Therefore, the successful prediction of automobile credit default is of great significance. Firstly, the missing values are deleted, then the random forest is used for feature selection, and then the sample data are randomly grouped. Finally, six prediction models of support vector machine (SVM), random forest and k-nearest neighbor (KNN), logistic, decision tree, and artificial neural network (ANN) are constructed. The results show that these six machine learning models can be used to predict the default of automobile credit. Among these six models, the accuracy of decision tree is 0.79, which is the highest, but the comprehensive performance of SVM is the best. And random grouping can improve the efficiency of model operation to a certain extent, especially SVM.

• Proceedings of the KIEE Conference
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• 2001.10a
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• pp.168-174
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• 2001

In this paper, a new space vector RPPWM (Random Position PWM) is proposed. In the proposed RPPWM, each of three phase pulses is located randomly in each switching interval. Based on the space vector modulation technique, the duty ratio of the pulses is calculated. Along with the randomization of the PWM pulses, we can obtain the effects of spread spectra of voltage, current as in the case of randomly changed switching frequency. To verify the validity of the proposed RPPWM, simulation study was tried using Matlab/simulink. The main model described in Simulink block diagrams includes the space vector modulation block, pulse position randomization block, inverter block, 3 phase induction motor block, and so on. By the simulation study, the harmonics of the output voltage, and the current of inverter are predicted in different PWM methods- SVPWM, LLPWM, proposed RPPWM.

• Communications for Statistical Applications and Methods
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• v.26 no.4
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• pp.337-346
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• 2019

This paper discusses a method for obtaining nonnegative estimates for variance components in a random effects model. A variance component should be positive by definition. Nevertheless, estimates of variance components are sometimes given as negative values, which is not desirable. The proposed method is based on two basic ideas. One is the identification of the orthogonal vector subspaces according to factors and the other is to ascertain the projection in each orthogonal vector subspace. Hence, an observation vector can be denoted by the sum of projections. The method suggested here always produces nonnegative estimates using projections. Hartley's synthesis is used for the calculation of expected values of quadratic forms. It also discusses how to set up a residual model for each projection.