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    Analytical dual mesh method for two-phase flow through highly heterogeneous porous media

    , Article Journal of Hydrology ; Volume 400, Issue 1-2 , 2011 , Pages 195-205 ; 00221694 (ISSN) Khoozan, D ; Firoozabadi, B ; Rashtchian, D ; Ashjari, M. A ; Sharif University of Technology
    2011
    Abstract
    Detailed geological models of a reservoir may contain many more cells that can be handled by reservoir simulators due to computer hardware limitations. Upscaling is introduced as an effective way to overcome this problem. However, recovery predictions performed on a coarser upscaled mesh are inevitably less accurate than those performed on the initial fine mesh. Dual mesh method is an approach that uses both coarse and fine grid information during simulation. In the reconstruction step of this method, the equations should be solved numerically within each coarse block, which is a time consuming process. Recently, a new coarse-grid generation technique based on the vorticity preservation... 

    User adaptive clustering for large image databases

    , Article Proceedings - International Conference on Pattern Recognition, 23 August 2010 through 26 August 2010, Istanbul ; 2010 , Pages 4271-4274 ; 10514651 (ISSN) ; 9780769541099 (ISBN) Saboorian, M. M ; Jamzad, M ; Rabiee, H. R ; Sharif University of Technology
    2010
    Abstract
    Searching large image databases is a time consuming process when done manually. Current CBIR methods mostly rely on training data in specific domains. When source and domain of images are unknown, unsupervised methods provide better solutions. In this work, we use a hierarchical clustering scheme to group images in an unknown and large image database. In addition, the user should provide the current class assignment of a small number of images as a feedback to the system. The proposed method uses this feedback to guess the number of required clusters, and optimizes the weight vector in an iterative manner. In each step, after modification of the weight vector, the images are reclustered. We...