• Title/Summary/Keyword: double truncation method

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Modal transformation tools in structural dynamics and wind engineering

  • Solari, Giovanni;Carassale, Luigi
    • Wind and Structures
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    • v.3 no.4
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    • pp.221-241
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    • 2000
  • Structural dynamics usually applies modal transformation rules aimed at de-coupling and/or minimizing the equations of motion. Proper orthogonal decomposition provides mathematical and conceptual tools to define suitable transformed spaces where a multi-variate and/or multi-dimensional random process is represented as a linear combination of one-variate and one-dimensional uncorrelated processes. Double modal transformation is the joint application of modal analysis and proper orthogonal decomposition applied to the loading process. By adopting this method the structural response is expressed as a double series expansion in which structural and loading mode contributions are superimposed. The simultaneous use of the structural modal truncation, the loading modal truncation and the cross-modal orthogonality property leads to efficient solutions that take into account only a few structural and loading modes. In addition the physical mechanisms of the dynamic response are clarified and interpreted.

Estimation of Conditional Kendall's Tau for Bivariate Interval Censored Data

  • Kim, Yang-Jin
    • Communications for Statistical Applications and Methods
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    • v.22 no.6
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    • pp.599-604
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    • 2015
  • Kendall's tau statistic has been applied to test an association of bivariate random variables. However, incomplete bivariate data with a truncation and a censoring results in incomparable or unorderable pairs. With such a partial information, Tsai (1990) suggested a conditional tau statistic and a test procedure for a quasi independence that was extended to more diverse cases such as double truncation and a semi-competing risk data. In this paper, we also employed a conditional tau statistic to estimate an association of bivariate interval censored data. The suggested method shows a better result in simulation studies than Betensky and Finkelstein's multiple imputation method except a case in cases with strong associations. The association of incubation time and infection time from an AIDS cohort study is estimated as a real data example.

Frequency Weighted Model Reduction Using Structurally Balanced Realization

  • Oh, Do-Chang;Kim, Jong-Hae
    • 제어로봇시스템학회:학술대회논문집
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    • 2003.10a
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    • pp.366-370
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    • 2003
  • This paper is on weighted model reduction using structurally balanced truncation. For a given weighted(single or double-sided) transfer function, a state space realization with the linear fractional transformation form is obtained. Then we prove that two block diagonal LMI(linear matrix inequality) solutions always exist, and it is possible to get a reduced order model with guaranteed stability and a priori error bound. Finally, two examples are used to show the validity of proposed weighted reduction method, and the method is compared with other existing methods.

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A PARAMETRIC SCHEME FOR THE NUMERICAL SOLUTION OF THE BOUSSINESQ EQUATION

  • Bratsos, A.G.
    • Journal of applied mathematics & informatics
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    • v.8 no.1
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    • pp.45-57
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    • 2001
  • A parametric scheme is proposed for the numerical solution of the nonlinear Boussinesq equation. The numerical method is developed by approximating the time and the space partical derivatives by finite-difference re placements and the nonlinear term by an appropriate linearized scheme. The resulting finite-difference method is analyzed for local truncation error and stability. The results of a number of numerical experiments are given for both the single and the double-soliton wave. AMS Mathematics Subject Classification : 65J15, 47H17, 49D15.

A PREDICTOR-CORRECTOR SCHEME FOR THE NUMERICAL SOLUTION OF THE BOUSSINESQ EQUATION

  • Ismail, M.S.;Bratsos, A.G.
    • Journal of applied mathematics & informatics
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    • v.13 no.1_2
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    • pp.11-27
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    • 2003
  • A fourth order in time and second order in space scheme using a finite-difference method is developed for the non-linear Boussinesq equation. For the solution of the resulting non-linear system a predictor-corrector pair is proposed. The method is analyzed for local truncation error and stability. The results of a number of numerical experiments for both the single and the double-soliton waves are given.

Estimation of Probability Density Function of Tidal Elevation Data using the Double Truncation Method (이중 절단 기법을 이용한 조위자료의 확률밀도함수 추정)

  • Jeong, Shin-Taek;Cho, Hong-Yeon;Kim, Jeong-Dae;Hui, Ko-Dong
    • Journal of Korean Society of Coastal and Ocean Engineers
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    • v.20 no.3
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    • pp.247-254
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    • 2008
  • The double-peak normal distribution function (DPDF) suggested by Cho et al.(2004) has the problems that the extremely high and low tidal elevations are frequently generated in the Monte-Carlo simulation processes because the upper and lower limits of the DPDF are unbounded in spite of the excellent goodness-offit results. In this study, the modified DPDF is suggested by introducing the upper and lower value parameters and re-scale parameters in order to remove these problems. These new parameters of the DPDF are optimally estimated by the non-linear optimization problem solver using the Levenberg-Marquardt scheme. This modified DPDF can remove completely the unrealistically generated tidal levations and give a slightly better fit than the existing DRDF. Based on the DPDF's characteristic power, the over- and under estimation problems of the design factors are also automatically intercepted, too.

A Method of Masking Based on Multiplicative Noise (잡음을 이용한 가계조사자료의 정보노출제한방법)

  • Jeong, Dong-Myeong;Kim, Jay-J.;Kim, Kyung-Mi
    • The Korean Journal of Applied Statistics
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    • v.22 no.1
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    • pp.141-151
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    • 2009
  • According to the type of microdata, the various methods have been in use for masking microdata. Multiplicative noise is the one of popular schemes for masking continuous variables. In this paper, we introduce the method of masking based on multiplicative noise and show some results of the application on the 2006 Householder Income and Expenditure Survey (HIES) data. To create the multiplicative noise factor, we used the triangular distribution. truncated triangular distribution, trapezoidal distribution, and double triangular distribution. Also, formulas for the domain estimation for the data masked by the multiplicative noise are developed.