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Simulation of Xe Redistribution in UO2

Trujillo L.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

2011

Vol. 15, No 2

157-190

The transport of fission gases in UO2 based nuclear fuels has a significant effect on the fuel performance. They can induce swelling of the fuel by the nucleation of gas bubbles within the fuel, and increase the mechanical interaction between the UO2 pellet and the cladding; also these bubbles can escape through the grain boundaries and contribute to the gaseous atmosphere in the fuel pin. We propose a model for the redistribution of xenon in the presence of different sinks, including nucleation and growth of gas bubbles. The finite element method has been implemented for the numerical solution of the model.

Signature recognition with a hybrid approach combining modular neural networks and fuzzy logic for response integration

Beltrán M., Melin P., Trujillo L., Lopez M.

Journal of Automation Mobile Robotics and Intelligent Systems

2010

Vol. 4, No. 1

20-27

This paper describes a modular neural network (MNN) with fuzzy integration for the problem of signature recognition. Currently, biometric identification has gained a great deal of research interest within the pattern recognition community. For instance, many attempts have been made in order to automate the process of identifying a person’s handwritten signature; however this problem has proven to be a very difficult task. In this work, we propose a MNN that has three separate modules, each using different image features as input, these are: edges, wavelet coefficients, and the Hough transform matrix. Then, the outputs from each of these modules are combined using a Sugeno fuzzy integral and a fuzzy inference system. The experimental results obtained using a database of 30 individual’s shows that the modular architecture can achieve a very high 99.33% recognition accuracy with a test set of 150 images. Therefore, we conclude that the proposed architecture provides a suitable platform to build a signature recognition system. Furthermore we consider the verification of signatures as false acceptance, false rejection and error recognition of the MNN.