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International Journal for Uncertainty Quantification

Publicou 6 edições por ano

ISSN Imprimir: 2152-5080

ISSN On-line: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

A NONSTATIONARY COVARIANCE FUNCTION MODEL FOR SPATIAL UNCERTAINTIES IN ELECTROSTATICALLY ACTUATED MICROSYSTEMS

Volume 5, Edição 2, 2015, pp. 99-121
DOI: 10.1615/Int.J.UncertaintyQuantification.2015011166
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RESUMO

This paper presents a data-driven method of estimating stochastic models that describe spatial uncertainties. Relating these uncertainties to the spatial statistics literature, we describe a general framework that can handle heterogeneous random processes by providing a parameterization for the nonstationary covariance function in terms of a transformation function and then estimating the unknown hyperparameters from data using Bayesian inference. The transformation function is specified as a displacement that transforms the coordinate space to a deformed configuration in which the covariance between points can be represented by a stationary model. This approach is then used to model spatial uncertainties in microelectromechanical actuators, where the ground plate is assumed to have a spatially varying profile. We estimate the stochastic model corresponding to the random surface using synthetic profilometric data that simulate multiple experimental measurements of ground plate surface roughness. We then demonstrate the effect of the uncertainty on the displacement of the actuator as well as on other parameters, such as the pull-in voltage. We show that the nonstationarity is essential when performing uncertainty quantification in electrostatic microactuators.

CITADO POR
  1. Alwan Aravind, Aluru N R, Data-driven stochastic models for spatial uncertainties in micromechanical systems, Journal of Micromechanics and Microengineering, 25, 11, 2015. Crossref

  2. Alwan Aravind, Aluru Narayana R., Analysis of the Effect of Spatial Uncertainties on the Dynamic Behavior of Electrostatic Microactuators, Communications in Computational Physics, 20, 2, 2016. Crossref

  3. Kim Namjung, Yang Chen, Lee Howon, Aluru Narayana, Spatial Uncertainty Modeling for Surface Roughness of Additively Manufactured Microstructures via Image Segmentation, Applied Sciences, 9, 6, 2019. Crossref

  4. Fan Zhang Nien, Evaluation of measurement uncertainty from a nonstationary process, Measurement Science and Technology, 30, 6, 2019. Crossref

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