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Neutron Buildup Factors Calculation for Support Vector Regression Application in Shielding Analysis (CROSBI ID 637553)

Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija

Dučkić, Paulina ; Matijević, Mario ; Grgić, Davor Neutron Buildup Factors Calculation for Support Vector Regression Application in Shielding Analysis // 11th International Conference of the Croatian Nuclear Society Conference Proceedings / Šimić, Zdenko ; Tomšić, Željko ; Grgić, Davor (ur.). Zagreb: Hrvatsko nuklearno društvo, 2016. str. 051-1-051-11

Podaci o odgovornosti

Dučkić, Paulina ; Matijević, Mario ; Grgić, Davor

engleski

Neutron Buildup Factors Calculation for Support Vector Regression Application in Shielding Analysis

In this paper initial set of data for neutron buildup factors determination using Support Vector Regression (SVR) method is prepared. The performance of SVR technique strongly depends on the quality of information used for model training. Thus it is very important to provide representative data to the SVR. SVR is a supervised type of learning so it demands data in the input/output form. In the case of neutron buildup factors estimation, the input parameters are the incident neutron energy, shielding thickness and shielding material and the output parameter is the neutron buildup factor value. So far initial sets of data for different shielding configurations have been obtained using SCALE4.4 sequence SAS3. However, this results were obtained using group constants, thus the incident neutron energy was determined as the average value for each energy group. Obtained in this way, the data provided to the SVR are fewer and therefore insufficient. More valuable information is obtained using SCALE6.2beta5 sequence MAVRIC which can perform calculations for the explicit incident neutron energy, which leads to greater maneuvering possibilities when active learning measures are employed, and consequently improves the quality of the developed SVR model.

neutron buildup factor; support vector regression; machine learning; point kernel

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Podaci o prilogu

051-1-051-11.

2016.

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objavljeno

978-953-55224-8-5

Podaci o matičnoj publikaciji

11th International Conference of the Croatian Nuclear Society Conference Proceedings

Šimić, Zdenko ; Tomšić, Željko ; Grgić, Davor

Zagreb: Hrvatsko nuklearno društvo

Podaci o skupu

11th International Conference of the Croatian Nuclear Society

predavanje

05.06.2016-08.06.2016

Zadar, Hrvatska

Povezanost rada

Elektrotehnika