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Conference Paper: Self-organized feature map of particle image for flow measurement

TitleSelf-organized feature map of particle image for flow measurement
Authors
KeywordsParticle image velocimetry (PIV)
Particle tracking
Self-organized feature map (SOFM)
Neural network
Pattern recognition
Issue Date1997
PublisherS P I E - International Society for Optical Engineering. The Journal's web site is located at http://www.spie.org/app/Publications/index.cfm?fuseaction=proceedings
Citation
Optical Technology in Fluid, Thermal, and Combustion Flow III, San Diego, California, USA, 28-31 July 1997, v. 3172, p. 142-152 How to Cite?
AbstractSelf-organized feature map algorithm and the classical particle tracking technique have been adopted together to analyze the single-exposure double-frame particle images for flow measurement. Similar to the normal correlation technique in PIV, the whole region is divided into many small interrogation spots. Instead of applying the correlation algorithm to each of these spots to get their rigid translation, the self-organized feature map algorithm is used to compress the information such that every spot is represented by three coded equivalent particles.After tracking these three particle, a linear distributed velocity function can be obtained at every spot. The spot can contain ont only translation,but also rotation, shear and expansion while there is only rigid translation in the spot assumed in the commonly used correlation method. In addition to the theoretical explanation, the suggested method has been verified by a number of digital flow fields which have randomly distributed synthetic particles.
Persistent Identifierhttp://hdl.handle.net/10722/46645
ISSN

 

DC FieldValueLanguage
dc.contributor.authorChen, Yen_HK
dc.contributor.authorChwang, ATYen_HK
dc.date.accessioned2007-10-30T06:54:57Z-
dc.date.available2007-10-30T06:54:57Z-
dc.date.issued1997en_HK
dc.identifier.citationOptical Technology in Fluid, Thermal, and Combustion Flow III, San Diego, California, USA, 28-31 July 1997, v. 3172, p. 142-152en_HK
dc.identifier.issn0277-786Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/46645-
dc.description.abstractSelf-organized feature map algorithm and the classical particle tracking technique have been adopted together to analyze the single-exposure double-frame particle images for flow measurement. Similar to the normal correlation technique in PIV, the whole region is divided into many small interrogation spots. Instead of applying the correlation algorithm to each of these spots to get their rigid translation, the self-organized feature map algorithm is used to compress the information such that every spot is represented by three coded equivalent particles.After tracking these three particle, a linear distributed velocity function can be obtained at every spot. The spot can contain ont only translation,but also rotation, shear and expansion while there is only rigid translation in the spot assumed in the commonly used correlation method. In addition to the theoretical explanation, the suggested method has been verified by a number of digital flow fields which have randomly distributed synthetic particles.en_HK
dc.format.extent330176 bytes-
dc.format.extent2173 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherS P I E - International Society for Optical Engineering. The Journal's web site is located at http://www.spie.org/app/Publications/index.cfm?fuseaction=proceedingsen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsS P I E - the International Society for Optical Proceedings. Copyright © S P I E - International Society for Optical Engineering.en_HK
dc.rightsCopyright 1997 Society of Photo-Optical Instrumentation Engineers. This paper was published in Optical Technology in Fluid, Thermal, and Combustion Flow III, San Diego, California, USA, 28-31 July 1997, v. 3172, p. 142-152 and is made available as an electronic reprint with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.en_HK
dc.subjectParticle image velocimetry (PIV)en_HK
dc.subjectParticle trackingen_HK
dc.subjectSelf-organized feature map (SOFM)en_HK
dc.subjectNeural networken_HK
dc.subjectPattern recognitionen_HK
dc.titleSelf-organized feature map of particle image for flow measurementen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0277-786X&volume=3172&spage=142&epage=152&date=1997&atitle=Self-organized+feature+map+of+particle+image+for+flow+measurementen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1117/12.279734en_HK
dc.identifier.scopuseid_2-s2.0-57649098652-
dc.identifier.hkuros32521-

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