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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

AN UNCERTAINTY VISUALIZATION TECHNIQUE USING POSSIBILITY THEORY: POSSIBILISTIC MARCHING CUBES

Volume 5, Edição 5, 2015, pp. 433-451
DOI: 10.1615/Int.J.UncertaintyQuantification.2015013730
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RESUMO

This paper opens the discussion about using fuzzy measure theory for isocontour/isosurface extraction in the field of uncertainty visualization. Specifically, we propose an uncertain marching cubes algorithm in the framework of possibility theory, called possibilistic marching cubes. The proposed algorithm uses the dual measures−possibility and necessity−to represent the uncertainty in the spatial location of isocontour/isosurface, which is propagated from the uncertainty in ensemble data. In addition, a novel parametric way of constructing marginal possibility distribution is proposed so that the epistemic uncertainty due to the limited size of the ensemble is considered. The effectiveness of the proposed possibilistic marching cubes algorithm is demonstrated using 2D and 3D examples.

CITADO POR
  1. Fullér Robert, Harmati István Á., On Possibilistic Dependencies: A Short Survey of Recent Developments, in Soft Computing Based Optimization and Decision Models, 360, 2018. Crossref

  2. Gillmann Christina, Wischgoll Thomas, Hamann Bernd, Hagen Hans, Accurate and reliable extraction of surfaces from image data using a multi-dimensional uncertainty model, Graphical Models, 99, 2018. Crossref

  3. Harmati István Á., Fullér Robert, On the Lower Limit for Possibilistic Correlation Coefficient with Identical Marginal Possibility Distributions, in Interactions Between Computational Intelligence and Mathematics Part 2, 794, 2019. Crossref

  4. Gillmann Christina, Wischgoll Thomas, Hamann Bernd, Ahrens James, Modeling and Visualization of Uncertainty-Aware Geometry Using Multi-variate Normal Distributions, 2018 IEEE Pacific Visualization Symposium (PacificVis), 2018. Crossref

  5. Dutta Soumya, Shen Han-Wei, Chen Jen-Ping, In Situ Prediction Driven Feature Analysis in Jet Engine Simulations, 2018 IEEE Pacific Visualization Symposium (PacificVis), 2018. Crossref

  6. Gillmann Christina, Saur Dorothee, Wischgoll Thomas, Scheuermann Gerik, Uncertainty‐aware Visualization in Medical Imaging ‐ A Survey, Computer Graphics Forum, 40, 3, 2021. Crossref

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