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Estimation of radiation doses for transition from emergency to existing exposure situation

Hamed A. A., Salem E. F., Abdien A. K.

Nukleonika

2015

Vol. 60, No. 4, part 2

943--950

The transition from emergency to existing exposure situation is an important stage in the nuclear or radiological emergency plan. It requires arrangements to be put in place and to be implemented so as to ensure that the transition is made in a coordinated and orderly manner of guidelines for adjusting the undertaken protective actions and other response actions. The delivering radiation doses to public and environmental pollution are monitoring that measurements should be implemented according to certain plan of intervention and protective actions in the different stages of the N/R accident. In this study, a computer model (RASCAL) is used to calculate the effect of severe accident could have detected on an operating nuclear power reactor PWR and the possible impact on the public. Features for the decision to implement the transition from emergency to existing exposure situation are proposed depending on the estimation of the collection of deliver exposure doses to the public and environment due to monitoring the different radiation sources resulting from the N/R accident.

A real-valued genetic algorithm to optimize the parameters of support vector machine for classification of multiple faults in NPP

Amer F. Z., El-Garhy A. M., Awadalla M. H., Rashad S. M., Abdien A. K.

Nukleonika

2011

Vol. 56, No. 4

323-332

Two parameters, regularization parameter c, which determines the trade off cost between minimizing the training error and minimizing the complexity of the model and parameter sigma (σ) of the kernel function which defines the non-linear mapping from the input space to some high-dimensional feature space, which constructs a non-linear decision hyper surface in an input space, must be carefully predetermined in establishing an efficient support vector machine (SVM) model. Therefore, the purpose of this study is to develop a genetic-based SVM (GASVM) model that can automatically determine the optimal parameters, c and sigma, of SVM with the highest predictive accuracy and generalization ability simultaneously. The GASVM scheme is applied on observed monitored data of a pressurized water reactor nuclear power plant (PWRNPP) to classify its associated faults. Compared to the standard SVM model, simulation of GASVM indicates its superiority when applied on the dataset with unbalanced classes. GASVM scheme can gain higher classification with accurate and faster learning speed.