Repository logo

Browsing by Author "Alqudah, Amin, author"

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Measuring the robustness of resource allocations for distributed domputer systems in a stochastic dynamic environment
    (Colorado State University. Libraries, 2006) Dewri, Rinku, author; Alqudah, Amin, author; Govindasamy, Sudha, author; Janovy, David, author; Sutton, Andrew, author; Ladd, Joshua, author; Prakash, Puneet, author; Renner, Timothy, author; Siegel, Howard Jay, author; Maciejewski, Anthony A., author; BriceƱo, Luis D., author; Smith, Jay, author; Shestak, Vladimir, author
    Heterogeneous distributed computing systems often must function in an environment where system parameters are subject to variations during operation. Robustness can be defined as the degree to which a system can function correctly in the presence of parameter values different from those assumed. We present a methodology for quantifying the robustness of resource allocations in a dynamic environment where task execution times vary within predictable ranges and tasks arrive randomly. The methodology is evaluated through measuring the robustness of three different resource allocation heuristics within the context of the stochastically modeled dynamic environment. A Bayesian regression model is fit to the combined results of the three heuristics to demonstrate the correlation between the stochastic robustness metric and the presented performance metric. The correlation results demonstrated the significant potential of the stochastic robustness metric to predict the relative performance of the three heuristics given a common objective function.
  • Thumbnail Image
    ItemOpen Access
    Rainfall estimation from spaceborne and ground based radars using neural networks
    (Colorado State University. Libraries, 2009) Alqudah, Amin, author; Chandra, Chandrasekar V., advisor
    Rainfall observed on the ground is dependent on the four dimensional radar observations. However it is difficult to express this in a simple form. A simple Z-R relation is not sufficient and has large uncertainty and it needs to be adaptively adjusted. Prior research has shown that neural networks can be used to estimate ground rainfall from radar measurements. Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) is the first space borne observation platform for mapping precipitation over the tropics. TRMM measured rainfall is important in order to study the precipitation distribution all over the globe in the tropics. TRMM ground validation is a critical important component to ensure the measurement accuracy. However, this ground validation has quite different characteristics from TRMM in terms of resolution, scale, viewing aspect, and uncertainties. This makes the use of ground radar rainfall information to correct TRMM rainfall estimates a very challenging task. In this dissertation, rainfall estimation using neural networks is investigated in order to improve rainfall estimation based on measurements taken by ground radars and TRMM-PR. Ground Radar measurements will be used to estimate rainfall using adaptive neural networks. Improvements are also suggested and performed including the use of Principal Components Analysis, ensemble average neural network, and the use of Bayesian Neural Networks. For TRMM-PR purposes a single neural network is not efficient to extract the relation between TRMM-PR measurements and the rain gauges; this is because of the resolution differences between TRMM-PR profile and the rain gauges and the low number of TRMM overpasses over these gauges which will make the training data set to have less number of profiles and not be able to generalize. Therefore, a novel hybrid Neural Network model is presented to train ground radars for rainfall estimate using rain gauge data and subsequently the trained ground radar rain estimates to train TRMM-PR based Neural Networks for rainfall estimation. This hybrid neural network model will derive the relation between rain gauges and ground radar measurements, and transfer this relation to adaptive rainfall estimation for TRMM-PR in order to estimate rainfall and generate global rainfall maps.