Yazar "Malidarre, Roya Boodaghi" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • [ X ]
    Öğe
    Deep learning prediction of gamma-ray-attenuation behavior of KNN-LMN ceramics
    (Ice Publishing, 2022) Malidarre, Roya Boodaghi; Arslankaya, Seher; Nar, Melek; Kirelli, Yasin; Erdamar, Işık Yeşim Dicle; Karpuz, Nurdan; Dogan, Serap Ozhan
    The significance and novelty of the present work is the preparation of non-lead ceramics with the general formula of (1 - x)K0.5Na0.5NbO3-xLaMn(0.5)Ni(0.5)O(3) (KNN-LMN) with different values of x (0 < x < 20) (mol%) to examine the shielding qualities of the KNN-LMN ceramics. This is done by carrying out Phy-X/PSD calculation and predicting the attenuation behavior of the samples by utilizing the deep learning (DL) algorithm. From the attained results, it is seen that the higher the x (concentration of LMN in the KNN-LMN lead-free ceramics), the better the shielding proficiency observed in terms of gamma-shielding performance for the chosen KNN-LMN-based lead-free ceramics. In all sections, good agreement is observed between Phy-X/PSD results and DL predictions.
  • [ X ]
    Öğe
    Prediction of radiation shielding properties for concrete by artificial neural networks
    (Springer Heidelberg, 2022) Imamoglu, Meltem Y.; Akkurt, Iskender; Arslankaya, Seher; Malidarre, Roya Boodaghi; Erdamar, Isik Yesim Dicle
    With discovering of radioactivity in the last century, the radiation started to be used in different fields, and due to its hazardous effect for human cell, radiation shielding became one of the most important topics for researcher. Besides using conventional materials such as concrete- and lead-based materials, improvement of their radiation shielding properties has also been popular. Theoretical calculation and setting up model to obtain linear attenuation coefficients to predict radiation shielding properties are the main ways for this purpose. In this study, the linear attenuation coefficients of concrete produced by adding ulexite in different rates have been predicted using the artificial neural network (ANN) model for 1 keV to 100 GeV photon energies. The main input for the ANN model was photon energy, density and ulexite rate in concrete, and the results were obtained by XCOM. The obtained ANN results were compared with the results obtained by XCOM calculations, and %99 linear correlations have been observed.