TAILIEUCHUNG - Numerical evaluation of gamma radiation monitoring

In this study a new AGRS system empowered by ANN-BFGS has been proposed and evaluated on available empirical AGRS data. To that effect different architectures of adaptive ANN-BFGS were implemented for a sort of published experimental AGRS outputs. The selected approach among of various training methods, with its low iteration cost and nondiagonal scaling allocation is a new powerful algorithm for AGRS data due to its inherent stochastic properties. | Nuclear Engineering and Technology 51 2019 807 817 Contents lists available at ScienceDirect Nuclear Engineering and Technology journal homepage locate net Original Article Numerical evaluation of gamma radiation monitoring Mohsen Rezaei Mansour Ashoor Leila Sarkhosh Nuclear Science and Technology Research Institute AEOI Tehran 1439955931 Iran ARTICLE INFO ABSTRACT Article history Received 21 July 2018 Received in revised form 14 November 2018 Accepted 24 December 2018 Available online 25 December 2018 Keywords Artificial neural networks BFGS training algorithm Airborne gamma ray spectrometry Nuclear site surveillance Airborne Gamma Ray Spectrometry AGRS with its important applications such as gathering radiation information of ground surface geochemistry measuring of the abundance of Potassium Thorium and Uranium in outer earth layer environmental and nuclear site surveillance has a key role in the field of nuclear science and human life. The Broyden Fletcher Goldfarb Shanno BFGS with its advanced numerical unconstrained nonlinear optimization in collaboration with Artificial Neural Networks ANNs provides a noteworthy opportunity for modern AGRS. In this study a new AGRS system empowered by ANN-BFGS has been proposed and evaluated on available empirical AGRS data. To that effect different architectures of adaptive ANN-BFGS were implemented for a sort of published experimental AGRS outputs. The selected approach among of various training methods with its low iteration cost and nondiagonal scaling allocation is a new powerful algorithm for AGRS data due to its inherent stochastic properties. Experiments were performed by different architectures and trainings the selected scheme achieved the smallest number of epochs the minimum Mean Square Error MSE and the maximum performance in compare with different types of optimization strategies and algorithms. The proposed method is capable to be implemented on a cost effective and minimum electronic .

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