Artificial Frame Filling Using Adaptive Neural Fuzzy Inference System for Particle Image Velocimetry Dataset

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dc.contributor.authorAkdemir, Bayram
dc.contributor.authorDogan, Sercan
dc.contributor.authorAksoy, Muharrem Hilmi
dc.contributor.authorCanli, Eyup
dc.contributor.authorOzgoren, Muammer
dc.date.accessioned2020-03-26T19:00:58Z
dc.date.available2020-03-26T19:00:58Z
dc.date.issued2015
dc.departmentSelçuk Üniversitesien_US
dc.description6th International Conference on Graphic and Image Processing (ICGIP) -- OCT 24-26, 2014 -- Beijing, PEOPLES R CHINAen_US
dc.description.abstractLiquid behaviors are very important for many areas especially for Mechanical Engineering. Fast camera is a way to observe and search the liquid behaviors. Camera traces the dust or colored markers travelling in the liquid and takes many pictures in a second as possible as. Every image has large data structure due to resolution. For fast liquid velocity, there is not easy to evaluate or make a fluent frame after the taken images. Artificial intelligence has much popularity in science to solve the nonlinear problems. Adaptive neural fuzzy inference system is a common artificial intelligence in literature. Any particle velocity in a liquid has two dimension speed and its derivatives. Adaptive Neural Fuzzy Inference System has been used to create an artificial frame between previous and post frames as offline. Adaptive neural fuzzy inference system uses velocities and vorticities to create a crossing point vector between previous and post points. In this study, Adaptive Neural Fuzzy Inference System has been used to fill virtual frames among the real frames in order to improve image continuity. So this evaluation makes the images much understandable at chaotic or vorticity points. After executed adaptive neural fuzzy inference system, the image dataset increase two times and has a sequence as virtual and real, respectively. The obtained success is evaluated using R-2 testing and mean squared error. R2 testing has a statistical importance about similarity and 0.82, 0.81, 0.85 and 0.8 were obtained for velocities and derivatives, respectively.en_US
dc.description.sponsorshipInt Assoc Comp Sci & Informat Technol, Wuhan Univen_US
dc.identifier.doi10.1117/12.2179689en_US
dc.identifier.isbn978-1-62841-558-2
dc.identifier.issn0277-786Xen_US
dc.identifier.issn1996-756Xen_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://dx.doi.org/10.1117/12.2179689
dc.identifier.urihttps://hdl.handle.net/20.500.12395/31867
dc.identifier.volume9443en_US
dc.identifier.wosWOS:000354613300062en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPIE-INT SOC OPTICAL ENGINEERINGen_US
dc.relation.ispartofSIXTH INTERNATIONAL CONFERENCE ON GRAPHIC AND IMAGE PROCESSING (ICGIP 2014)en_US
dc.relation.ispartofseriesProceedings of SPIE
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectParticle image velocimetryen_US
dc.subjectArtificial neural networken_US
dc.subjectvectoren_US
dc.subjectsphereen_US
dc.subjectframeen_US
dc.titleArtificial Frame Filling Using Adaptive Neural Fuzzy Inference System for Particle Image Velocimetry Dataseten_US
dc.typeConference Objecten_US

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