PIR sensörleriyle alev tespiti

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buir.contributor.authorÇetin, A. Enis
buir.contributor.orcidÇetin, A. Enis|0000-0002-3449-1958
dc.citation.epage4en_US
dc.citation.spage1en_US
dc.contributor.authorTöreyin, B. Uǧuren_US
dc.contributor.authorSoyer, E. Bireyen_US
dc.contributor.authorUrfalıoǧlu, Onayen_US
dc.contributor.authorÇetin, A. Enisen_US
dc.coverage.spatialAydin, Turkey
dc.date.accessioned2016-02-08T11:37:03Z
dc.date.available2016-02-08T11:37:03Z
dc.date.issued2008-04en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 20-22 April 2008
dc.descriptionConference name: IEEE 16th Signal Processing, Communication and Applications Conference, SIU 2008
dc.description.abstractBu bildiride, pasif kızılberisi sensor (PIR) tabanlı bir alev tespit sistemi sunulmaktadır. Önerilen yangın tespit sistemi oda içlerinde kullanılabilir. Kontrolsuz büyüyen yangın alevlerindeki kırpışma, oda içi gündelik insan hareketleri olan yürüme ve koşma ile birlikte, PIR sensörü işaretlerinin dalgacık dönüşümü katsayılarıyla eğitilmiş bir dizi saklı Markov modeliyle modellenmiştir. Sensör sisteminin görüş alanı içerisinde bir hareket tespit edildiğinde, sensör sinyali dalgacık domeninde çözümlenmekte ve bir dizi saklı Markov modeline beslenmektedir. En yüksek olasılık değerini üreten saklı Markov modeline göre “ateş” veya "ateş değil" kararı verilmektedir.
dc.description.abstractIn this paper, a flame detection system based on a pyroelectric (or passive) infrared (PIR) sensor is described. The flame detection system can be used for fire detection in large rooms. The flame flicker process of an uncontrolled fire and ordinary activity of human beings are modeled using a set of Hidden Markov Models (HMM), which are trained using the wavelet transform of the PIR sensor signal. Whenever there is an activity within the viewing range of the PIR sensor system, the sensor signal is analyzed in the wavelet domain and the wavelet signals are fed to a set of HMMs. A fire or no fire decision is reached according to the HMM producing the highest probability. ©2008 IEEE.
dc.description.provenanceMade available in DSpace on 2016-02-08T11:37:03Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2008en
dc.identifier.doi10.1109/SIU.2008.4632660en_US
dc.identifier.urihttp://hdl.handle.net/11693/26828
dc.language.isoTurkishen_US
dc.publisherIEEE
dc.relation.isversionofhttp://dx.doi.org/10.1109/SIU.2008.4632660en_US
dc.source.titleIEEE 16th Signal Processing, Communication and Applications Conference, SIU 2008en_US
dc.subjectFire detectionsen_US
dc.subjectFlame detectionsen_US
dc.subjectFlame flickersen_US
dc.subjectHuman beingsen_US
dc.subjectPir sensorsen_US
dc.subjectSensor signalsen_US
dc.subjectWavelet domainsen_US
dc.subjectFiresen_US
dc.subjectFlammabilityen_US
dc.subjectMarkov processesen_US
dc.subjectSensorsen_US
dc.subjectSignal detectionen_US
dc.subjectSignal processingen_US
dc.subjectWavelet transformsen_US
dc.subjectHidden Markov modelsen_US
dc.titlePIR sensörleriyle alev tespitien_US
dc.title.alternativeFlame detection using PIR sensorsen_US
dc.typeConference Paperen_US

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