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Visual Analysis of Spatia-temporal Relations of Pairwise Attributes in Unsteady Flow

Marzieh Berenjkoub, Rodolfo Ostilla Monico, Bob Laramee Orcid Logo, Guoning Chen

IEEE Transactions on Visualization and Computer Graphics, Volume: 25, Issue: 1, Pages: 1246 - 1256

Swansea University Author: Bob Laramee Orcid Logo

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DOI (Published version): 10.1109/tvcg.2018.2864817

Abstract

Despite significant advances in the analysis and visualization of unsteady flow, the interpretation of it’s behavior still remains a challenge.In this work, we focus on the linear correlation and non-linear dependency of different physical attributes of unsteady flows to aid theirstudy from a new pe...

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Published in: IEEE Transactions on Visualization and Computer Graphics
ISSN: 1077-2626 2160-9306
Published: Institute of Electrical and Electronics Engineers (IEEE) 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa41185
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first_indexed 2018-07-30T19:31:20Z
last_indexed 2021-01-29T04:02:56Z
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spelling 2021-01-28T13:12:28.4076582 v2 41185 2018-07-30 Visual Analysis of Spatia-temporal Relations of Pairwise Attributes in Unsteady Flow 7737f06e2186278a925f6119c48db8b1 0000-0002-3874-6145 Bob Laramee Bob Laramee true false 2018-07-30 SCS Despite significant advances in the analysis and visualization of unsteady flow, the interpretation of it’s behavior still remains a challenge.In this work, we focus on the linear correlation and non-linear dependency of different physical attributes of unsteady flows to aid theirstudy from a new perspective. Specifically, we extend the existing spatial correlation quantification, i.e. the Local Correlation Coefficient(LCC), to the spatio-temporal domain to study the correlation of attribute-pairs from both the Eulerian and Lagrangian views. To studythe dependency among attributes, which need not be linear, we extend and compute the mutual information (MI) among attributes overtime. To help visualize and interpret the derived correlation and dependency among attributes associated with a particle, we encodethe correlation and dependency values on individual pathlines. Finally, to utilize the correlation and MI computation results to identifyregions with interesting flow behavior, we propose a segmentation strategy of the flow domain based on the ranking of the strengthof the attributes relations. We have applied our correlation and dependency metrics to a number of 2D and 3D unsteady flows withvarying spatio-temporal kernel sizes to demonstrate and assess their effectiveness. Journal Article IEEE Transactions on Visualization and Computer Graphics 25 1 1246 1256 Institute of Electrical and Electronics Engineers (IEEE) 1077-2626 2160-9306 flow visualization, scientific visualization, information theory 1 1 2019 2019-01-01 10.1109/tvcg.2018.2864817 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2021-01-28T13:12:28.4076582 2018-07-30T15:11:22.9509136 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Marzieh Berenjkoub 1 Rodolfo Ostilla Monico 2 Bob Laramee 0000-0002-3874-6145 3 Guoning Chen 4 0041185-08102018150957.pdf 41185.pdf 2018-10-08T15:09:57.7630000 Output 34820325 application/pdf Accepted Manuscript true 2018-10-08T00:00:00.0000000 true eng
title Visual Analysis of Spatia-temporal Relations of Pairwise Attributes in Unsteady Flow
spellingShingle Visual Analysis of Spatia-temporal Relations of Pairwise Attributes in Unsteady Flow
Bob Laramee
title_short Visual Analysis of Spatia-temporal Relations of Pairwise Attributes in Unsteady Flow
title_full Visual Analysis of Spatia-temporal Relations of Pairwise Attributes in Unsteady Flow
title_fullStr Visual Analysis of Spatia-temporal Relations of Pairwise Attributes in Unsteady Flow
title_full_unstemmed Visual Analysis of Spatia-temporal Relations of Pairwise Attributes in Unsteady Flow
title_sort Visual Analysis of Spatia-temporal Relations of Pairwise Attributes in Unsteady Flow
author_id_str_mv 7737f06e2186278a925f6119c48db8b1
author_id_fullname_str_mv 7737f06e2186278a925f6119c48db8b1_***_Bob Laramee
author Bob Laramee
author2 Marzieh Berenjkoub
Rodolfo Ostilla Monico
Bob Laramee
Guoning Chen
format Journal article
container_title IEEE Transactions on Visualization and Computer Graphics
container_volume 25
container_issue 1
container_start_page 1246
publishDate 2019
institution Swansea University
issn 1077-2626
2160-9306
doi_str_mv 10.1109/tvcg.2018.2864817
publisher Institute of Electrical and Electronics Engineers (IEEE)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description Despite significant advances in the analysis and visualization of unsteady flow, the interpretation of it’s behavior still remains a challenge.In this work, we focus on the linear correlation and non-linear dependency of different physical attributes of unsteady flows to aid theirstudy from a new perspective. Specifically, we extend the existing spatial correlation quantification, i.e. the Local Correlation Coefficient(LCC), to the spatio-temporal domain to study the correlation of attribute-pairs from both the Eulerian and Lagrangian views. To studythe dependency among attributes, which need not be linear, we extend and compute the mutual information (MI) among attributes overtime. To help visualize and interpret the derived correlation and dependency among attributes associated with a particle, we encodethe correlation and dependency values on individual pathlines. Finally, to utilize the correlation and MI computation results to identifyregions with interesting flow behavior, we propose a segmentation strategy of the flow domain based on the ranking of the strengthof the attributes relations. We have applied our correlation and dependency metrics to a number of 2D and 3D unsteady flows withvarying spatio-temporal kernel sizes to demonstrate and assess their effectiveness.
published_date 2019-01-01T03:52:30Z
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score 11.036116

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