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    Queen-bee algorithm for energy efficient clusters in wireless sensor networks

    , Article World Academy of Science, Engineering and Technology ; Volume 73 , 2011 , Pages 1080-1083 ; 2010376X (ISSN) Pooranian, Z ; Barati, A ; Movaghar, A ; Sharif University of Technology
    Abstract
    Wireless sensor networks include small nodes which have sensing ability; calculation and connection extend themselves everywhere soon. Such networks have source limitation on connection, calculation and energy consumption. So, since the nodes have limited energy in sensor networks, the optimized energy consumption in these networks is of more importance and has created many challenges. The previous works have shown that by organizing the network nodes in a number of clusters, the energy consumption could be reduced considerably. So the lifetime of the network would be increased. In this paper, we used the Queen-bee algorithm to create energy efficient clusters in wireless sensor networks.... 

    The prediction of permeability using an artificial neural network system

    , Article Petroleum Science and Technology ; Volume 30, Issue 20 , 2012 , Pages 2108-2113 ; 10916466 (ISSN) Pazuki, G. R ; Nikookar, M ; Dehnavi, M ; Al Anazi, B ; Sharif University of Technology
    2012
    Abstract
    The authors studied the efficiency and accuracy of neural network model for prediction of permeability as a key parameter in reservoir characterization. So, some multilayer perceptron (MLP) neural network models with different learning algorithms of Levenberg-Margnardt, back propagation, improved back propagation (IBP), and quick propagation with three layers and different node numbers (3, 4, 5, 6, 7) in the middle layer have been presented. These models have been obtained by 630 permeability data from one of offshore reservoirs located in Saudi Arabia. The accuracy of models was studied by comparing the obtained results of each model with experimental data. So, the neural network with IBP...