• 제목/요약/키워드: weighted function

검색결과 747건 처리시간 0.024초

Structural damage identification based on transmissibility assurance criterion and weighted Schatten-p regularization

  • Zhong, Xian;Yu, Ling
    • Structural Engineering and Mechanics
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    • 제82권6호
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    • pp.771-783
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    • 2022
  • Structural damage identification (SDI) methods have been proposed to monitor the safety of structures. However, the traditional SDI methods using modal parameters, such as natural frequencies and mode shapes, are not sensitive enough to structural damage. To tackle this problem, this paper proposes a new SDI method based on transmissibility assurance criterion (TAC) and weighted Schatten-p norm regularization. Firstly, the transmissibility function (TF) has been proved a useful damage index, which can effectively detect structural damage under unknown excitations. Inspired by the modal assurance criterion (MAC), TF and MAC are combined to construct a new damage index, so called as TAC, which is introduced into the objective function together with modal parameters. In addition, the weighted Schatten-p norm regularization method is adopted to improve the ill-posedness of the SDI inverse problem. To evaluate the effectiveness of the proposed method, some numerical simulations and experimental studies in laboratory are carried out. The results show that the proposed method has a high SDI accuracy, especially for weak damages of structures, it can precisely achieve damage locations and quantifications with a good robustness.

A General Semiparametric Additive Risk Model

  • Park, Cheol-Yong
    • Journal of the Korean Data and Information Science Society
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    • 제19권2호
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    • pp.421-429
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    • 2008
  • We consider a general semiparametric additive risk model that consists of three components. They are parametric, purely and smoothly nonparametric components. In parametric component, time dependent term is known up to proportional constant. In purely nonparametric component, time dependent term is an unknown function, and time dependent term in smoothly nonparametric component is an unknown but smoothly function. As an estimation method of this model, we use the weighted least square estimation by Huffer and McKeague (1991). We provide an illustrative example as well as a simulation study that compares the performance of our method with the ordinary least square method.

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Generalized Weighted Linear Models Based on Distribution Functions

  • Yeo, In-Kwon
    • 한국통계학회:학술대회논문집
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    • 한국통계학회 2003년도 추계 학술발표회 논문집
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    • pp.161-166
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    • 2003
  • In this paper, a new form of generalized linear models is proposed. The proposed models consist of a distribution function of the mean response and a weighted linear combination of distribution functions of covariates. This form addresses a structural problem of the link function in the generalized linear models. Markov chain Monte Carlo methods are used to estimate the parameters within a Bayesian framework.

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Censored Kernel Ridge Regression

  • Shim, Joo-Yong
    • Journal of the Korean Data and Information Science Society
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    • 제16권4호
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    • pp.1045-1052
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    • 2005
  • This paper deals with the estimations of kernel ridge regression when the responses are subject to randomly right censoring. The weighted data are formed by redistributing the weights of the censored data to the uncensored data. Then kernel ridge regression can be taken up with the weighted data. The hyperparameters of model which affect the performance of the proposed procedure are selected by a generalized approximate cross validation(GACV) function. Experimental results are then presented which indicate the performance of the proposed procedure.

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ASYMPTOTIC NORMALITY OF ESTIMATOR IN NON-PARAMETRIC MODEL UNDER CENSORED SAMPLES

  • Niu, Si-Li;Li, Qlan-Ru
    • 대한수학회지
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    • 제44권3호
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    • pp.525-539
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    • 2007
  • Consider the regression model $Y_i=g(x_i)+e_i\;for\;i=1,\;2,\;{\ldots},\;n$, where: (1) $x_i$ are fixed design points, (2) $e_i$ are independent random errors with mean zero, (3) g($\cdot$) is unknown regression function defined on [0, 1]. Under $Y_i$ are censored randomly, we discuss the asymptotic normality of the weighted kernel estimators of g when the censored distribution function is known or unknown.

On a Result of N. Terglane

  • Lahiri, Indrajit;Ghosh, Goutam Kumar
    • Kyungpook Mathematical Journal
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    • 제51권2호
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    • pp.195-204
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    • 2011
  • We prove a uniqueness theorem for meromorphic functions sharing three weighted values, which improves a result given by N. Terglane in 1989 and a result given by X. M. Li and H. X. Yi in 2003. Some examples are provided to show that the result of the paper is best possible.

VALUE DISTRIBUTIONS OF L-FUNCTIONS CONCERNING POLYNOMIAL SHARING

  • Mandal, Nintu
    • 대한수학회논문집
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    • 제36권4호
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    • pp.729-741
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    • 2021
  • We mainly study the value distributions of L-functions in the extended selberg class. Concerning weighted sharing, we prove an uniqueness theorem when certain differential monomial of a meromorphic function share a polynomial with certain differential monomial of an L-function which improve and generalize some recent results due to Liu, Li and Yi [11], Hao and Chen [3] and Mandal and Datta [12].

Support vector expectile regression using IRWLS procedure

  • Choi, Kook-Lyeol;Shim, Jooyong;Seok, Kyungha
    • Journal of the Korean Data and Information Science Society
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    • 제25권4호
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    • pp.931-939
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    • 2014
  • In this paper we propose the iteratively reweighted least squares procedure to solve the quadratic programming problem of support vector expectile regression with an asymmetrically weighted squares loss function. The proposed procedure enables us to select the appropriate hyperparameters easily by using the generalized cross validation function. Through numerical studies on the artificial and the real data sets we show the effectiveness of the proposed method on the estimation performances.

Parameter Identification of Induction Motors using Variable-weighted Cost Function of Genetic Algorithms

  • Megherbi, A.C.;Megherbi, H.;Benmahamed, K.;Aissaoui, A.G.;Tahour, A.
    • Journal of Electrical Engineering and Technology
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    • 제5권4호
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    • pp.597-605
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    • 2010
  • This paper presents a contribution to parameter identification of a non-linear system using a new strategy to improve the genetic algorithm (GA) method. Since cost function plays an important role in GA-based parameter identification, we propose to improve the simple version of GA, where weights of the cost function are not taken as constant values, but varying along the procedure of parameter identification. This modified version of GA is applied to the induction motor (IM) as an example of nonlinear system. The GA cost function is the weighted sum of stator current and rotor speed errors between the plant and the model of induction motor. Simulation results show that the identification method based on improved GA is feasible and gives high precision.

WEIGHTED COMPOSITION OPERATORS FROM F(p, q, s) INTO LOGARITHMIC BLOCH SPACE

  • Ye, Shanli
    • 대한수학회지
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    • 제45권4호
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    • pp.977-991
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    • 2008
  • We characterize the boundedness and compactness of the weighted composition operator $uC_{\psi}$ from the general function space F(p, q, s) into the logarithmic Bloch space ${\beta}_L$ on the unit disk. Some necessary and sufficient conditions are given for which $uC_{\psi}$ is a bounded or a compact operator from F(p,q,s), $F_0$(p,q,s) into ${\beta}_L$, ${\beta}_L^0$ respectively.