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International Journal for Uncertainty Quantification
Главный редактор: Habib N. Najm (open in a new tab)
Ассоциированный редакторs: Dongbin Xiu (open in a new tab) Tao Zhou (open in a new tab)
Редактор-основатель: Nicholas Zabaras (open in a new tab)

Выходит 6 номеров в год

ISSN Печать: 2152-5080

ISSN Онлайн: 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

SEQUENTIAL SPARSITY ITERATIVE OPTIMAL DESIGN MODEL FOR CALIBRATION OF COMPLEX SYSTEMS WITH EPISTEMIC UNCERTAINTY

Том 6, Выпуск 2, 2016, pp. 175-193
DOI: 10.1615/Int.J.UncertaintyQuantification.2016016845
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Краткое описание

As for the experimental optimal design of some complex systems, it is difficult to obtain the accurate response model between the performance index and influence factors. But in some cases the prior information could provide a clue to construct the possible response model. An effective model calibration method is presented here based on the typical uncertainty quantification framework. In order to solve this epistemic uncertainty, some kinds of prior information about the system are utilized to obtain model-oriented basis functions, then a corresponding redundant regression model is designed to describe the internal response relationship. Through analyzing the influences of experimental costs, sampling sequences, and spatial positions of different experiment points, we define a sequential sparsity iterative optimal design model integrated with costs and spatio-temporal weights for experimental design. Based on sparse component analysis theory, calibration of a regression model with different stages is transformed into a sparse reconstruction problem. The conclusions from theoretical inferences as well as simulation results of the combined trigonometric polynomial function model and radar measurement model show that the parameter estimation error of the regression model is smaller, which demonstrates that the above-mentioned model is more efficient and comprehensive for its consideration of the weights for different influence factors and its consistence with practical experimental regulations.

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