1. Açık Erişim@BUU
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Please use this identifier to cite or link to this item: http://hdl.handle.net/11452/33158
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dc.contributor.authorGünoğlu, Kadir-
dc.contributor.authorAkkurt, İskender-
dc.contributor.authorDemirci, Zehra Nur-
dc.date.accessioned2023-06-23T12:19:46Z-
dc.date.available2023-06-23T12:19:46Z-
dc.date.issued2013-11-
dc.identifier.citationGünoğlu, K. vd. (2013). "ANN modeling of the bremsstrahlung photon flux in tantalum target". Neural Computing and Applications, 23(6), 1591-1595.en_US
dc.identifier.issn0941-0643-
dc.identifier.issn1433-3058-
dc.identifier.urihttps://doi.org/10.1007/s00521-012-1111-2-
dc.identifier.urihttp://hdl.handle.net/11452/33158-
dc.description.abstractBremsstrahlung photons produced by 15 MeV electron beam are simulated using the Monte Carlo code of FLUKA. Tantalum foils have been chosen as a target material in the simulation, and the obtained photon spectrum has been analyzed with artificial neural network (ANN) technique. In the training ANN model, the thicknesses and energy values of bremsstrahlung photons for the Ta target have been used as input. In this study, we observed that the trained ANN model is consistent with simulation results.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectComputer scienceen_US
dc.subjectBremsstrahlung photonen_US
dc.subjectMonte Carloen_US
dc.subjectTantalumen_US
dc.subjectArtificial neural networken_US
dc.subjectMonte Carlo methodsen_US
dc.subjectNeural networksen_US
dc.subject15-MeV electronsen_US
dc.subjectANN modelingen_US
dc.subjectEnergy valueen_US
dc.subjectMonte Carlo codesen_US
dc.subjectPhoton spectraen_US
dc.subjectTantalum foilsen_US
dc.subjectTantalum targeten_US
dc.subjectTarget materialsen_US
dc.subjectPhotonsen_US
dc.titleANN modeling of the bremsstrahlung photon flux in tantalum targeten_US
dc.typeArticleen_US
dc.identifier.wos000325809300008tr_TR
dc.identifier.scopus2-s2.0-84885908607tr_TR
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergitr_TR
dc.contributor.departmentUludağ Üniversitesi/Fen-Edebiyat Fakültesi/Fizik Bölümü.tr_TR
dc.contributor.orcid0000-0003-2245-8461tr_TR
dc.identifier.startpage1591tr_TR
dc.identifier.endpage1595tr_TR
dc.identifier.volume23tr_TR
dc.identifier.issue6tr_TR
dc.relation.journalNeural Computing and Applicationsen_US
dc.contributor.buuauthorDemir, Nilgün-
dc.contributor.researcheridAAH-3156-2021tr_TR
dc.relation.collaborationYurt içitr_TR
dc.subject.wosComputer science, artificial Iintelligenceen_US
dc.indexed.wosSCIEen_US
dc.indexed.scopusScopusen_US
dc.wos.quartileQ2en_US
dc.contributor.scopusid7006874016tr_TR
dc.subject.scopusSolar Heaters; Artificial Neural Network; Coefficient of Performanceen_US
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