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ABSTRACT: This paper deals with the improvements of the linear artificial neural network unfolding approach aimed at accurately determining the incident neutron spectrum. The effects of the transfer functions and pre-processing of the simulated pulse height distributions from liquid scintillation detectors on the artificial neural networks performance have been studied. A better energy resolution and higher reliability of the linear artificial neural network technique have been achieved after implementation of the results of this study. The optimized structure of the network was used to unfold both monoenergetic and continuous neutron energy spectra, such as the spectra of 252Cf and 241Am-Be sources, traditionally used in the nuclear safeguards experiments. We have demonstrated that the artificial neural network energy resolution of 0.1 MeV is comparable with the one obtained by the reference maximum likelihood expectation-maximization method which was implemented by using the one step late algorithm. Although the maximum likelihood algorithm provides the unfolded results of higher accuracy, especially for continuous neutron sources, the artificial neural network approach with the improved performances is more suitable for fast and robust determination of the neutron spectra with sufficient accuracy.
Nuclear Technology and Radiation Protection. 01/2010;
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Senada Avdić
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ABSTRACT: This paper is focused on the space and temporal variability of soil moisture experimental data acquired at a few locations near landmine fields in the Tuzla Canton, as well as on the quantification of the statistical nature of soil moisture data on a small spatial scale. Measurements of soil water content at the surface were performed by an electro-magnetic sensor over 1 25, and 100 m2 grids, at intervals of 0.2, 0.5, and 1 m, respectively. The sampling of soil moisture at different spatial resolutions and over different grid sizes has been investigated in order to achieve the quantification of the statistical nature of soil moisture distribution. The statistical characterization of spatial variability was performed through variogram and correlogram analysis of measurement results. The temporal variability of the said samples was examined over a two-season period. For both sampling periods, the spatial correlation length is about 1 to 2 m, respectively, or less. Thus, sampling should be done on a larger spatial scale, in order to capture the variability of the investigated areas. Since the characteristics of many landmine sensors depend on soil moisture, the results of this study could form a useful data base for multisensor landmine detection systems with a promising performance.
Nuclear Technology and Radiation Protection. 01/2007;
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ABSTRACT: This paper deals with the analysis of experimental positron lifetime spectra in polymer materials by using various algorithms of neural networks. A method based on the use of artificial neural networks for unfolding the mean lifetime and intensity of the spectral components of simulated positron lifetime spectra was previously suggested and tested on simulated data [Pžzsitetal, Applied Surface Science, 149 (1998), 97]. In this work, the applicability of the method to the analysis of experimental positron spectra has been verified in the case of spectra from polymer materials with three components. It has been demonstrated that the backpropagation neural network can determine the spectral parameters with a high accuracy and perform the decomposi-tion of lifetimes which differ by 10% or more. The backpropagation network has not been suitable for the identification of both the parameters and the number of spectral components. Therefore, a separate artificial neural network module has been designed to solve the classification problem. Module types based on self-organizing map and learning vector quantization algorithms have been tested. The learning vector quantization algorithm was found to have better performance and reliability. A complete artificial neural network analysis tool of positron lifetime spectra has been constructed to include a spectra classification module and parameter evaluation modules for spectra with a different number of components. In this way, both flexibility and high resolution can be achieved.
Nuclear Technology and Radiation Protection. 01/2003;
