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- 저자 : Moshkbar-Bakhshayesh Khalil (검색결과 2 건)
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Khalil Moshkbar-Bakhshayesh 한국원자력학회 2021 Nuclear Engineering and Technology Vol.53 No.12
Several reasons such as no free lunch theorem indicate that there is not a universal Feature selection (FS)technique that outperforms other ones. Moreover, some approaches such as using synthetic dataset, inpresence of large number of FS techniques, are very tedious and time consuming task. In this study totackle the issue of dependency of estimation accuracy on the selected FS technique, a methodology basedon the heterogeneous ensemble is proposed. The performance of the major learning algorithms of neuralnetwork (i.e. the FFNN-BR, the FFNN-LM) in combination with the diverse FS techniques (i.e. the NCA, theF-test, the Kendall's tau, the Pearson, the Spearman, and the Relief) and different combination techniquesof the heterogeneous ensemble (i.e. the Min, the Median, the Arithmetic mean, and the Geometric mean)are considered. The target parameters/transients of Bushehr nuclear power plant (BNPP) are examined asthe case study. The results show that the Min combination technique gives the more accurate estimation. Therefore, if the number of FS techniques is m and the number of learning algorithms is n, by the heterogeneous ensemble, the search space for acceptable estimation of the target parameters may bereduced from n m to n 1. The proposed methodology gives a simple and practical approach for morereliable and more accurate estimation of the target parameters compared to the methods such as the useof synthetic dataset or trial and error methods
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Moshkbar-Bakhshayesh Khalil,Mohtashami Soroush 한국원자력학회 2022 Nuclear Engineering and Technology Vol.54 No.11
Precise modelling of the interaction of ions with materials is important for many applications including material characterization, ion implantation in devices, thermonuclear fusion, hadron therapy, secondary particle production (e.g. neutron), etc. In this study, a new approach using the Geant4 toolkit in combination with the Bayesian regularization (BR) learning algorithm of the feed-forward neural network (FFNN) is developed to estimate the range of ions in materials accurately and quickly. The different incident ions at different energies are interacted with the target materials. The Geant4 is utilized to model the interactions and to calculate the range of the ions. Afterward, the appropriate architecture of the FFNN-BR with the relevant input features is utilized to learn the modelled ranges and to estimate the new ranges for the new cases. The notable achievements of the proposed approach are: 1- The range of ions in different materials is given as quickly as possible and the time required for estimating the ranges can be neglected (i.e. less than 0.01 s by a typical personal computer). 2- The proposed approach can generalize its ability for estimating the new untrained cases. 3- There is no need for a pre-made lookup table for the estimation of the range values.
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