The power generated by a solar panel is directly related to the level of solar radiation falling on it. The most advanced solar panels convert 20% to 25% of irradiance energy, besides, a steady panel has a decrease in its production and in certain moments of the day it doesn't produce. Therefore, it is necessary get more radiation to increase the generated power by the panel and a system able to follow the sun's movement could improve efficiency in this point. But the cost is an important variable to consider due to the enhancement of the final system. In this book, a new sun tracker system for two axes converter panels was presented. The proposed system was designed, implemented and practically tested. The performance results were compared with that of a fixed panel system. Experimental results show considerably higher output voltage and provide more electrical energy.Results show promising achievements for industrial photovoltaic systems, solar power.Using this method, during long time results in more productivity and dramatically improvement in the efficiency of solar panels and maximizing utilization of clean and affordable solar energy for domestic and industrial purposes.

In this book some applications of artificial neural network in nuclear engineering are presented. In densitometry, number of scattered and counted gamma photons highly depends on material density. Using this relation, two different multi-layer perceptron artificial neural networks are proposed to predict material density. The results of proposed ANNs show that the presented model could be employed in densitometry of materials. Furthermore, the development of an ANN model for prediction of the highest value of X-ray yield in PFs is showed. The comparison between predicted and experimental results by ANN model illustrates that there is a good adaptation between them. So, the MLP architecture can be applied as a high efficient tool to predict the highest value of X-ray yield in the PFs.