• Title/Summary/Keyword: parametric function

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Failure patterns of repairable systems and a flexible intensity function model

  • Jiang, R.;Huang, C.
    • International Journal of Reliability and Applications
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    • v.13 no.2
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    • pp.81-90
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    • 2012
  • Engineering systems are usually repairable. The reliability of a repairable system can be represented by failure intensity function. A type of shape of failure intensity function is called a failure pattern. Reliability-Centred Maintenance (RCM) presents six typical failure patterns but its definition is unclear. It is an open issue how to recognize the failure pattern of repairable systems. This paper first discusses the problems of RCM with the notion of failure pattern; then presents the method for failure pattern recognition; and finally proposes a flexible failure intensity function model. The appropriateness of the model is illustrated by a real-world example.

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Robust Control via Peak Control of Sensitivity Function (민감도 함수의 최대치 제어를 통한 강인제어)

  • Suh, Sang-Min
    • Journal of Institute of Control, Robotics and Systems
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    • v.15 no.11
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    • pp.1071-1075
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    • 2009
  • This article describes a robust control method by using peak control of a sensitivity function in the state-feedback control systems. This method apparently reduces the peak, and as a result makes closed loop systems more stable. The designed closed loop systems also make the response to an external step disturbance more fast with a lower undershoot. At the conclusion, it is verified that the proposed method enhances robust stability and robust performance to parametric uncertainties through $\mu$-plot.

A Study on Goodness-of-fit Test for Density with Unknown Parameters

  • Hang, Changkon;Lee, Minyoung
    • Communications for Statistical Applications and Methods
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    • v.8 no.2
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    • pp.483-497
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    • 2001
  • When one fits a parametric density function to a data set, it is usually advisable to test the goodness of the postulated model. In this paper we study the nonparametric tests for testing the null hypothesis against general alternatives, when the null hypothesis specifies the density function up to unknown parameters. We modify the test statistic which was proposed by the first author and his colleagues. Asymptotic distribution of the modified statistic is derived and its performance is compared with some other tests through simulation.

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Hybridal Method for the Prediction of Wave Instabilities Inherent in High Energy-Density Combustors (2): Cumulative Effects of Pressure Coupled Responses on Cavity Acoustics

  • Lee, Gil-Yong;Yoon, Woong-Sup
    • International Journal of Aeronautical and Space Sciences
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    • v.7 no.2
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    • pp.33-41
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    • 2006
  • Theoretical-numerical approach of combustion instability in a specific rocket engine is conducted with parametric response functions. Fluctuating instantaneous burning rate is assumed to be functionally coupled with acoustic pressures and have a finite or time-varying amplitudes and phase lags. Only when the amplitudes and phases of combustion response function are sufficiently large and small respectively, the triggered unstable waves are amplified.

Modulation Transfer Function (MTF) Measurement for KOMPSAT EOC image data Using Edge Method

  • Song J. H.;Lee D. H.
    • Proceedings of the KSRS Conference
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    • 2004.10a
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    • pp.489-493
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    • 2004
  • The Modulation Transfer Function (MTF) is commonly used to characterize the spatial quality of imaging systems. This work is the attempt to measure the MTF for KOMPSAT EOC using the non-parametric method as ground inputs. The spatial performance of the KOMPSAT EOC was analyzed by edge method while in flight using multi-temporal image data collected over test site in Seoul. The results from this work demonstrate the potential applicability of this method to estimate MTF for high spatial resolution satellite KOMPSAT-2 that is being developed to be launched in 2005.

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Generalized Weighted Linear Models Based on Distribution Functions

  • Yeo, In-Kwon
    • Proceedings of the Korean Statistical Society Conference
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    • 2003.10a
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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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Mixed Effects Kernel Binomial Regression

  • Hwang, Chang-Ha
    • Journal of the Korean Data and Information Science Society
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    • v.19 no.4
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    • pp.1327-1334
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    • 2008
  • Mixed effect binomial regression models are widely used for analysis of correlated count data in which the response is the result of a series of one of two possible disjoint outcomes. In this paper, we consider kernel extensions with nonparametric fixed effects and parametric random effects. The estimation is through the penalized likelihood method based on kernel trick, and our focus is on the efficient computation and the effective hyperparameter selection. For the selection of hyperparameters, cross-validation techniques are employed. Examples illustrating usage and features of the proposed method are provided.

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