• 제목/요약/키워드: random vector

검색결과 559건 처리시간 0.024초

ON AN ARRAY OF WEAKLY DEPENDENT RANDOM VECTORS

  • Jeon, Tae-Il
    • 대한수학회논문집
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    • 제16권1호
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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.

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블록 대각 구조를 지닌 2단계 확률계획법의 분해원리 (A Decomposition Method for Two stage Stochstic Programming with Block Diagonal Structure)

  • 김태호;박순달
    • 한국경영과학회지
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    • 제10권1호
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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.

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인버터 구동 시스템을 위한 새로운 공간벡터 Random PWM기법 (A New Space Vector Random PWM Scheme for Inverter Fed Drive Systems)

  • 나석환;정영국;임영철
    • 전력전자학회논문지
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    • 제6권6호
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    • pp.525-537
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    • 2001
  • 본 연구에서는 펄스의 위치를 랜덤하게 함으로써 효율적은 RPWN (Random PWM)을 구현할 수 있는 새로운 공간 벡터 RPPWM(Random Position PWM)방식을 제안하였다. 제안한 방식은 마이크로 컨트롤로 적용하기 쉬운 공간벡터 변조에 의하여 각 상의 듀티비를 연산한 다음, 스위칭 순서를 유지하며 각 3상 펄스들의 위치를 변조구간내에서 임의의 위치에 랜덤하게 배치함으로써 RPWM을 구현하는 방식이다. 제안된 방법의 타당성을 검증하기 위하여 MATLAB/SIMULINK를 이용하여 펄스위치의 랜덤화 공각벡터 변조, 인버터 및 3상 유도전동기를 포함하는 전체 시스템을 모델화하였다. 종전의 공각벡턱 PWM 그리고 제안된 RPPWM에 의한 인버터 출력 전압 전류의 고조파 성분을 시뮬레이션에 의하여 비교 검토하였으며 제안된 방법의 유용성을 입증할 수 있었다.

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마이크로프로세서 기능 검증을 위한 바이어스 랜덤 벡터 생성기 설계 (Design of A Biased Random Vector Generator for A Functional Verification of Microprocessor)

  • 권오현;양훈모;이문기
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2002년도 하계종합학술대회 논문집(2)
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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.

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마이크로프로세서 기능 검증을 위한 바이어스 랜덤 벡터 생성기 설계 (Design of A Biased Random Vector Generator for A Functional Verification of Microprocessor)

  • 권오현;양훈모;이문기
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2002년도 하계종합학술대회 논문집(2)
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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.

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Illumination correction via improved grey wolf optimizer for regularized random vector functional link network

  • Xiaochun Zhang;Zhiyu Zhou
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제17권3호
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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.

벡터 트리거조건에 의한 Random Decrement 함수의 모우드 해석 (Modal Analysis of the Vector Triggering Random Decrement Function)

  • 정범석;이외득
    • 한국전산구조공학회논문집
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    • 제15권2호
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    • pp.209-218
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    • 2002
  • Vector Random Decrement(VRD) 기법은 상시하중을 받는 선형의 구조물에서 동적응답의 장시간기록을 자유진동신호로 전환시키는 효과적인 알고리즘으로 발전되어 왔으며, 이에 따른 VRD함수는 실측한 자유감쇄응답과 거의 동일하게 모우드변수에 대한 정보를 갖는다. 본 연구에서는 모우드형상비의 개념을 동특성 평가과정인 Ibrahim Time Domain (ITD) 알고리즘에 적용하여 VRD 기법을 개선하였다. 제안된 기법에서는 이동시간의 보정과정에서 VRD 함수가 변환되지 않기 때문에 벡터 트리거조건에 적용된 최대 이동시간 영역의 정보가 VRD 함수에 누락 없이 포함되고 입력하중의 영향은 평균과정에서 소거된다. 제안된 기법에 의한 모우드변수의 추정결과를 일반적인 Random Decrement(RD) 기법과 비교하였으며, VRD 기법의 적용성을 모의 예제해석과 상시하중이 재하된 보의 실내실험으로 검증하였다.

Default Prediction of Automobile Credit Based on Support Vector Machine

  • Chen, Ying;Zhang, Ruirui
    • Journal of Information Processing Systems
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    • 제17권1호
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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.

새로운 공간벡터 Random Position PWM기법 (A New Space Vector Random Position PWM Scheme)

  • 김회근;임영철;나석환;정영국
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2001년도 추계학술대회 논문집 전기기기 및 에너지변환시스템부문
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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.

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Nonnegative estimates of variance components in a two-way random model

  • Choi, Jaesung
    • Communications for Statistical Applications and Methods
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    • 제26권4호
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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.