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dc.contributor.authorTerzi, M. B.en_US
dc.contributor.authorArikan, O.en_US
dc.contributor.authorKaratay S.en_US
dc.contributor.authorArikan, F.en_US
dc.contributor.authorGulyaeva T.en_US
dc.date.accessioned2018-04-12T11:42:32Z
dc.date.available2018-04-12T11:42:32Z
dc.date.issued2016en_US
dc.identifier.isbn9789292213053
dc.identifier.issn0379-6566
dc.identifier.urihttp://hdl.handle.net/11693/37512
dc.description.abstractIn this study, Total Electron Content (TEC) estimated from GPS receivers is used to model the regional and local variability that differs from global activity along with solar and geomagnetic indices. For the automated classification of regional disturbances, a classification technique based on a robust machine learning technique that have found wide spread use, Support Vector Machine (SVM) is proposed. Performance of developed classification technique is demonstrated for midlatitude ionosphere over Anatolia using TEC estimates generated from GPS data provided by Turkish National Permanent GPS Network (TNPGN-Active) for solar maximum year of 2011. As a result of implementing developed classification technique to Global Ionospheric Map (GIM) TEC data, which is provided by the NASA Jet Propulsion Laboratory (JPL), it is shown that SVM can be a suitable learning method to detect anomalies in TEC variations.en_US
dc.language.isoEnglishen_US
dc.source.titleEuropean Space Agency, (Special Publication) ESA SPen_US
dc.subjectIonosphereen_US
dc.subjectKernel functionsen_US
dc.subjectSpace weatheren_US
dc.subjectSupport vector machines (SVM)en_US
dc.subjectArtificial intelligenceen_US
dc.subjectFighter aircraften_US
dc.subjectGeomagnetismen_US
dc.subjectGlobal positioning systemen_US
dc.subjectIonospheric measurementen_US
dc.subjectLearning algorithmsen_US
dc.subjectNASAen_US
dc.subjectSupport vector machinesen_US
dc.subjectVector spacesen_US
dc.subjectAutomated classificationen_US
dc.subjectClassification techniqueen_US
dc.subjectIonospheric disturbanceen_US
dc.subjectKernel functionen_US
dc.subjectMachine learning techniquesen_US
dc.subjectMid-latitude ionosphereen_US
dc.subjectSpace weatheren_US
dc.subjectTotal electron contenten_US
dc.subjectLearning systemsen_US
dc.titleClassification of regional ionospheric disturbance based on machine learning techniquesen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Electrical and Electronics Engineering
dc.citation.volumeNumberSP-740en_US
dc.publisherEuropean Space Agencyen_US


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