Evaluation of the suitability of neural network method for prediction of uranium activity ratio in environmental alpha spectra.
Applying Artificial Neural Network to an alpha spectrometry system is a good idea to discriminate the composition of environmental and non-environmental materials by the estimation of the (234)U/(238)U activity ratio. Because it eliminates limitations of classical approaches by the extraction the desired information from the average of a partial uranium raw spectrum. The network was trained by an alpha spectrum library which was developed in this work. The results indicated that there was a small difference between the target values and the predictions. These results were acceptable, because the thickness of samples and the inferring elements were different in the real library.
Department of Radiation Medicine, Shahid Beheshti University, P.O. Box 1983963113, Tehran, Iran. Electronic address: M_einian@sbu.ac.ir.
Mohammad Reza
MR
Einian
Department of Radiation Medicine, Shahid Beheshti University, P.O. Box 1983963113, Tehran, Iran. Electronic address: smr-aghamiri@sbu.ac.ir.
Seyed Mahmood Reza
SMR
Aghamiri
Department of Radiation Medicine, Shahid Beheshti University, P.O. Box 1983963113, Tehran, Iran. Electronic address: R_ghaderi@sbu.ac.ir.
Reza
R
Ghaderi
D000512:Alpha Particles; D004785:Environmental Pollutants / Q000032:analysis*; D006801:Humans; D009010:Monte Carlo Method; D016571:Neural Networks, Computer; D011848:Radioactive Pollutants / Q000032:analysis*; D013057:Spectrum Analysis / Q000379:methods / Q000706:statistics & numerical data; D014501:Uranium / Q000032:analysis*
D004785:Environmental Pollutants; D011848:Radioactive Pollutants; D014501:Uranium
Activity ratio; Advanced Alpha Spectrometric Simulation (AASI); Alpha spectrometry; Depleted uranium; Radial Basis Function neural network