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ISSN 打印: 2152-5080

ISSN 在线: 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

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SURROGATE MODELING OF STOCHASTIC FUNCTIONS−APPLICATION TO COMPUTATIONAL ELECTROMAGNETIC DOSIMETRY

卷 9, 册 4, 2019, pp. 351-363
DOI: 10.1615/Int.J.UncertaintyQuantification.2019029103
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摘要

This paper is dedicated to the surrogate modeling of a particular type of computational model called stochastic simulators, which inherently contain some source of randomness. In this particular case the output of the simulator in a given point is a probability density function. In this paper, the stochastic simulator is represented as a stochastic process and the surrogate model is built using the Karhunen-Loeve expansion. In a first approach, the stochastic process covariance was surrogated using polynomial chaos expansion; meanwhile in a second approach the eigenvectors were interpolated. The performance of the method is illustrated on a toy example and then on an electromagnetic dosimetry example. We then provide metrics to measure the accuracy of the surrogate.

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对本文的引用
  1. Al Hajj Maarouf, Wang Shanshan, Thanh Tu Lam, Azzi Soumaya, Wiart Joe, A Statistical Estimation of 5G Massive MIMO Networks’ Exposure Using Stochastic Geometry in mmWave Bands, Applied Sciences, 10, 23, 2020. Crossref

  2. Chiaramello Emma, Plets David, Fiocchi Serena, Bonato Marta, Tognola Gabriella, Parazzini Marta, Brusquet Laurent Le, Martens Luc, Joseph Wout, Ravazzani Paolo, Innovative Stochastic Modeling of Residential Exposure to Radio Frequency Electromagnetic Field Sources, IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, 5, 1, 2021. Crossref

  3. Zhu Xujia, Sudret Bruno, Emulation of Stochastic Simulators Using Generalized Lambda Models, SIAM/ASA Journal on Uncertainty Quantification, 9, 4, 2021. Crossref

  4. Wang Shanshan, Mazloum Taghrid, Wiart Joe, Prediction of RF-EMF Exposure by Outdoor Drive Test Measurements, Telecom, 3, 3, 2022. Crossref

  5. Zhu Xujia, Sudret Bruno, Global sensitivity analysis for stochastic simulators based on generalized lambda surrogate models, Reliability Engineering & System Safety, 214, 2021. Crossref

  6. Mayr Christina Maria, Schuhbäck Stefan, Wischhof Lars, Köster Gerta, Analysis of information dissemination through direct communication in a moving crowd, Safety Science, 142, 2021. Crossref

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