Visibility graph analysis of fluid flow signals

Visibility graphs have established themselves in recent years as being particularly suitable and flexible for analyzing signals measured from complex natural and artificial systems. Oil-water two-phase flow as a complicated fluid flow is one of the most complex systems. In this paper, we employ visi...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Zhongke Gao, Lingchao Ji
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 47
container_issue
container_start_page 44
container_title
container_volume
creator Zhongke Gao
Lingchao Ji
description Visibility graphs have established themselves in recent years as being particularly suitable and flexible for analyzing signals measured from complex natural and artificial systems. Oil-water two-phase flow as a complicated fluid flow is one of the most complex systems. In this paper, we employ visibility graph to analyze the signals measured from experiment two-phase flow. We first introduce the inclined oil-water two-phase flow experiment and data acquisition. Then we present the algorithm for constructing visibility graphs from signals. Finally, we infer and analyze visibility graphs from signals measured under different fluid flow conditions. The results indicate that the combination parameters of network degree are sensitive to the transition among different flow patterns, which can be used to distinguish and characterize different oil-water two-phase flow patterns. In this regard, visibility graph could be a useful tool for processing experimental signals.
doi_str_mv 10.1109/ICACI.2012.6463119
format Conference Proceeding
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_6463119</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6463119</ieee_id><sourcerecordid>6463119</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-dc16b82ed6691faee16fa00115854e53e38384e7825340a338cf928749ce33833</originalsourceid><addsrcrecordid>eNo1j81KAzEUhSMiqHVeQDd5gZnm5uZ3KYPaQsGNiruSztzUyGjLpCLz9kasm_NxONyfw9g1iAZA-PmyvW2XjRQgG6MMAvgTdgnKWASr5Ospq7x1_x7NOatyfhdClGEDRl2w-UvKaZOGdJj4dgz7Nx4-wzDllPku8jh8pb7o7pvntC1BvmJnsYCqI2fs-f7uqV3Uq8eH8syqTmD1oe47MBsnqTfGQwxEYGL4PaudVqSR0KFTZJ3UqERAdF300lnlOyoGccZu_vYmIlrvx_QRxml97Ig_XG9DlA</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Visibility graph analysis of fluid flow signals</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Zhongke Gao ; Lingchao Ji</creator><creatorcontrib>Zhongke Gao ; Lingchao Ji</creatorcontrib><description>Visibility graphs have established themselves in recent years as being particularly suitable and flexible for analyzing signals measured from complex natural and artificial systems. Oil-water two-phase flow as a complicated fluid flow is one of the most complex systems. In this paper, we employ visibility graph to analyze the signals measured from experiment two-phase flow. We first introduce the inclined oil-water two-phase flow experiment and data acquisition. Then we present the algorithm for constructing visibility graphs from signals. Finally, we infer and analyze visibility graphs from signals measured under different fluid flow conditions. The results indicate that the combination parameters of network degree are sensitive to the transition among different flow patterns, which can be used to distinguish and characterize different oil-water two-phase flow patterns. In this regard, visibility graph could be a useful tool for processing experimental signals.</description><identifier>ISBN: 9781467317436</identifier><identifier>ISBN: 1467317438</identifier><identifier>EISBN: 146731742X</identifier><identifier>EISBN: 9781467317443</identifier><identifier>EISBN: 9781467317429</identifier><identifier>EISBN: 1467317446</identifier><identifier>DOI: 10.1109/ICACI.2012.6463119</identifier><language>eng</language><publisher>IEEE</publisher><subject>Complex networks ; Data acquisition ; Dispersion ; Educational institutions ; Fluid flow measurement ; Time series analysis ; Voltage measurement</subject><ispartof>2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI), 2012, p.44-47</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6463119$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6463119$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zhongke Gao</creatorcontrib><creatorcontrib>Lingchao Ji</creatorcontrib><title>Visibility graph analysis of fluid flow signals</title><title>2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI)</title><addtitle>ICACI</addtitle><description>Visibility graphs have established themselves in recent years as being particularly suitable and flexible for analyzing signals measured from complex natural and artificial systems. Oil-water two-phase flow as a complicated fluid flow is one of the most complex systems. In this paper, we employ visibility graph to analyze the signals measured from experiment two-phase flow. We first introduce the inclined oil-water two-phase flow experiment and data acquisition. Then we present the algorithm for constructing visibility graphs from signals. Finally, we infer and analyze visibility graphs from signals measured under different fluid flow conditions. The results indicate that the combination parameters of network degree are sensitive to the transition among different flow patterns, which can be used to distinguish and characterize different oil-water two-phase flow patterns. In this regard, visibility graph could be a useful tool for processing experimental signals.</description><subject>Complex networks</subject><subject>Data acquisition</subject><subject>Dispersion</subject><subject>Educational institutions</subject><subject>Fluid flow measurement</subject><subject>Time series analysis</subject><subject>Voltage measurement</subject><isbn>9781467317436</isbn><isbn>1467317438</isbn><isbn>146731742X</isbn><isbn>9781467317443</isbn><isbn>9781467317429</isbn><isbn>1467317446</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1j81KAzEUhSMiqHVeQDd5gZnm5uZ3KYPaQsGNiruSztzUyGjLpCLz9kasm_NxONyfw9g1iAZA-PmyvW2XjRQgG6MMAvgTdgnKWASr5Ospq7x1_x7NOatyfhdClGEDRl2w-UvKaZOGdJj4dgz7Nx4-wzDllPku8jh8pb7o7pvntC1BvmJnsYCqI2fs-f7uqV3Uq8eH8syqTmD1oe47MBsnqTfGQwxEYGL4PaudVqSR0KFTZJ3UqERAdF300lnlOyoGccZu_vYmIlrvx_QRxml97Ig_XG9DlA</recordid><startdate>201210</startdate><enddate>201210</enddate><creator>Zhongke Gao</creator><creator>Lingchao Ji</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201210</creationdate><title>Visibility graph analysis of fluid flow signals</title><author>Zhongke Gao ; Lingchao Ji</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-dc16b82ed6691faee16fa00115854e53e38384e7825340a338cf928749ce33833</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Complex networks</topic><topic>Data acquisition</topic><topic>Dispersion</topic><topic>Educational institutions</topic><topic>Fluid flow measurement</topic><topic>Time series analysis</topic><topic>Voltage measurement</topic><toplevel>online_resources</toplevel><creatorcontrib>Zhongke Gao</creatorcontrib><creatorcontrib>Lingchao Ji</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zhongke Gao</au><au>Lingchao Ji</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Visibility graph analysis of fluid flow signals</atitle><btitle>2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI)</btitle><stitle>ICACI</stitle><date>2012-10</date><risdate>2012</risdate><spage>44</spage><epage>47</epage><pages>44-47</pages><isbn>9781467317436</isbn><isbn>1467317438</isbn><eisbn>146731742X</eisbn><eisbn>9781467317443</eisbn><eisbn>9781467317429</eisbn><eisbn>1467317446</eisbn><abstract>Visibility graphs have established themselves in recent years as being particularly suitable and flexible for analyzing signals measured from complex natural and artificial systems. Oil-water two-phase flow as a complicated fluid flow is one of the most complex systems. In this paper, we employ visibility graph to analyze the signals measured from experiment two-phase flow. We first introduce the inclined oil-water two-phase flow experiment and data acquisition. Then we present the algorithm for constructing visibility graphs from signals. Finally, we infer and analyze visibility graphs from signals measured under different fluid flow conditions. The results indicate that the combination parameters of network degree are sensitive to the transition among different flow patterns, which can be used to distinguish and characterize different oil-water two-phase flow patterns. In this regard, visibility graph could be a useful tool for processing experimental signals.</abstract><pub>IEEE</pub><doi>10.1109/ICACI.2012.6463119</doi><tpages>4</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISBN: 9781467317436
ispartof 2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI), 2012, p.44-47
issn
language eng
recordid cdi_ieee_primary_6463119
source IEEE Electronic Library (IEL) Conference Proceedings
subjects Complex networks
Data acquisition
Dispersion
Educational institutions
Fluid flow measurement
Time series analysis
Voltage measurement
title Visibility graph analysis of fluid flow signals
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-30T01%3A42%3A10IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Visibility%20graph%20analysis%20of%20fluid%20flow%20signals&rft.btitle=2012%20IEEE%20Fifth%20International%20Conference%20on%20Advanced%20Computational%20Intelligence%20(ICACI)&rft.au=Zhongke%20Gao&rft.date=2012-10&rft.spage=44&rft.epage=47&rft.pages=44-47&rft.isbn=9781467317436&rft.isbn_list=1467317438&rft_id=info:doi/10.1109/ICACI.2012.6463119&rft_dat=%3Cieee_6IE%3E6463119%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=146731742X&rft.eisbn_list=9781467317443&rft.eisbn_list=9781467317429&rft.eisbn_list=1467317446&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6463119&rfr_iscdi=true