Yapay sinir ağlarının gama spektrometrik ölçümlere uygulanması
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Dosyalar
Tarih
2019
Yazarlar
Erzin, Selin
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ege Üniversitesi, Fen Bilimleri Enstitüsü
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Son yılların en yaygın konularından biri olan yapay sinir ağları (YSA), öğrenme kabiliyeti, hızlı çalışması, tanımlama kolaylığı, az bilgi ile çalışabilme, doğrusal olmayan sistemleri modelleyebilme, bilgiyi paralel olarak işleyebilme ve yeni durumlara uyum sağlayabilme gibi özelliklere sahiptir Bu özelliklerinden dolayı, YSA, nükleer spektroskopi alanında örneğin, gama spektroskopisi, alfa spektroskopisi, pik-background oranı, puls tipi dedektörlerin enerji çözünürlük iyileştirmesi, proton kaynaklı X-ışını emisyonları, X-ışını flüoresan ve Rutherford geri saçılma vb. konularında kullanılmaktadır. YSA'nın avantajlarını ve nükleer spektroskopide kullanımını göz önüne alarak, bu tezde, ilk olarak Nükleer Bilimler Enstitüsü sayım laboratuarında bulunan HPGe dedektörü kullanılarak, gama spektral analizi için YSA uygulanmıştır. Geliştirilen YSA modelinin performansı, Çandarlı kıyı sedimenti örneklerinin gama spektral analizi ile test edilmiştir. YSA ile örnekler içerisindeki gama verici radionüklitlerin varlıkları başarıyla tespit edilmiştir. Bu çalışmanın ikinci adımında, YSA, NaI(Tl) Sintilasyon Gama Spektrometre sisteminin değişik sayım sürelerinde ölçebileceği minimum dedekte edilebilir aktivite (MDA)'leri bulmak için kullanılmıştır. Bu amaçla, Enstitüde kullanılan sayım süreleri dikkate alınarak, 5000- 87000s arası, 226Ra, 232Th ve 40K radyonüklitleri için, minimum dedekte edilebilen aktivite (MDA) değerleri, Curie kriterlerine dayalı olarak hesaplanmıştır. YSA'dan elde edilen MDA değerleri ile hesaplanan MDA değerleri karşılaştırıldığında, birbirleriyle örtüştüğü görülmüştür. Bu çalışmada, ilk kez, YSA, NaI(Tl) sintilasyon gama spektroskopisi ile doğal radyonüklit analizleri için uygulanmıştır. Bu amaçla, 126 adet değişik orijinli çevresel materyal; kayaç, toprak, granit, mermer ve gübre örneklerinin içerisindeki radyum, toryum ve potasyum konsantrasyonları hesaplanmıştır. Hesaplanan aktivite değerleri, YSA modelinden tahmin edilen değerlerle karşılaştırıldığında, YSA'nın örneklerdeki 226Ra, 232Th, ve 40K aktivitelerini doğru bir şeklide tahmin edebildiği görülmüştür.
Artificial neural networks (ANN), which is one of the most common subjects of recent years, has features such as learning ability, fast work, ease of identification, ability to work with less information, modeling nonlinear systems, processing information in parallel and adapting to new situations. In the field of nuclear spectroscopy, for example, gamma spectroscopy, alpha spectroscopy, peak-background ratio, energy resolution improvement of pulse type detectors, proton-induced X-ray emissions, X-ray fluorescence and Rutherford backscatter, and the like subjects. Considering the advantages of ANN and its use in nuclear spectroscopy, in this thesis, ANN was firstly applied for gamma spectral analysis using the HPGe detector found in the census laboratory of the Institute of Nuclear Sciences. The performance of the ANN model was tested by gamma spectral analysis of Çandarlı coastal sediment samples. With ANN, the presence of gamma emitting radionuclides in the samples has been successfully determined. In the second step of this study, ANN was used to find the minimum detectable activity (MDA) that the NaI (T1) Scintillation Gamma Spectrometer system could measure at different counting times. For this purpose, minimum detectable activity (MDA) values for 226Ra, 232Th and 40K radionuclides between 5000- 87000s were calculated based on Curie criteria, taking into account the counting times used in the Institute. When the MDA values obtained from ANN were compared with the calculated MDA values, it was seen that they overlap with each other. In this study, for the first time, ANN was applied for natural radionuclide analysis by NaI (T1) scintillation gamma spectroscopy. For this purpose, 126 different environmental materials; Radium, thorium and potassium concentrations in rock, soil, granite, marble and fertilizer samples were calculated. When the calculated activity values were compared with the values estimated from the ANN model, it was found that the ANN could accurately predict 226Ra, 232Th, and 40K activities in the samples.
Artificial neural networks (ANN), which is one of the most common subjects of recent years, has features such as learning ability, fast work, ease of identification, ability to work with less information, modeling nonlinear systems, processing information in parallel and adapting to new situations. In the field of nuclear spectroscopy, for example, gamma spectroscopy, alpha spectroscopy, peak-background ratio, energy resolution improvement of pulse type detectors, proton-induced X-ray emissions, X-ray fluorescence and Rutherford backscatter, and the like subjects. Considering the advantages of ANN and its use in nuclear spectroscopy, in this thesis, ANN was firstly applied for gamma spectral analysis using the HPGe detector found in the census laboratory of the Institute of Nuclear Sciences. The performance of the ANN model was tested by gamma spectral analysis of Çandarlı coastal sediment samples. With ANN, the presence of gamma emitting radionuclides in the samples has been successfully determined. In the second step of this study, ANN was used to find the minimum detectable activity (MDA) that the NaI (T1) Scintillation Gamma Spectrometer system could measure at different counting times. For this purpose, minimum detectable activity (MDA) values for 226Ra, 232Th and 40K radionuclides between 5000- 87000s were calculated based on Curie criteria, taking into account the counting times used in the Institute. When the MDA values obtained from ANN were compared with the calculated MDA values, it was seen that they overlap with each other. In this study, for the first time, ANN was applied for natural radionuclide analysis by NaI (T1) scintillation gamma spectroscopy. For this purpose, 126 different environmental materials; Radium, thorium and potassium concentrations in rock, soil, granite, marble and fertilizer samples were calculated. When the calculated activity values were compared with the values estimated from the ANN model, it was found that the ANN could accurately predict 226Ra, 232Th, and 40K activities in the samples.
Açıklama
Anahtar Kelimeler
Gama Spektrometresi, Kantitatif Radyonüklit Analizi, Minimum Detekte Edilebilen Aktivite, Yapay Sinir Ağları, Artificial Neural Networks, Gamma Spectrometry, Quantitative Radionuclide Analysis, Minimum Detectable Activity
