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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Software Architecture Design for Supporting Optimization Algorithm Designs

Zhong, Da-jun 05 September 2008 (has links)
In this research, we designed and implemented optimization search algorithms to facilitate implementation of optimization search software. We provided the design of module interaction graph including modules, ports, and channels. We can map solving algorithms of sub-problems onto behavioral designs incorresponding modules. Finally, they can integrate module¡¦s with channels. Since optimization search algorithms may evolve one to several solutions at the same time, we planned a solution set organization to support designer-planned search strategy. During the optimization process, solutions or sub-solutions should be evaluated and analyzed. Because excessive executive time as commonly spent in replicated evaluation, we planned dynamic programming for reusing evaluation results to reduce replicated evaluation time. Lastly, when evolving new solutions, usually only a small number of decisions are changed. We planed a hierarchical decision representation and maintenance operations to reduce replication of common parts among solutions to further enhance its execution speed.
2

A Bayesian framework for object localisation in visual images

Sullivan, Josephine Jean January 2000 (has links)
No description available.
3

Design Optimization Procedure for Monocoque Composite Cylinder Structures Using Response Surface Techniques

Rich, Jonathan E. 03 December 1997 (has links)
An optimization strategy for the design of composite shells is investigated. This study differs from previous work in that an advanced analysis package is utilized to provide buckling information on potential designs. The Structural Analysis of General Shells (STAGS) finite element code is used to provide linear buckling calculations for a minimum buckling load constraint. A response surface, spanning the design space, is generated from a set of design points and corresponding buckling load data. This response surface is incorporated into a genetic algorithm for optimization of composite cylinders. Laminate designs are limited to those that are balanced and symmetric. Three load cases and four different variable formulations are examined. In the first approach, designs are limited to those whose normalized in-plane and out-of-plane stiffness parameters would be feasible with laminates consisting of two independent fiber orientation angles. The second approach increases the design space to include those that are bordered by those in the first approach. The third and fourth approaches utilize stacking sequence designs for optimization, with continuous and discrete fiber orientation angle variation, respectively. For each load case and different variable formulation, additional runs are made to account for inaccuracies inherent in the response surface model. This study concluded that this strategy was effective at reducing the computational cost of optimizing the composite cylinders. / Master of Science
4

Quasi 3D Multi-stage Turbomachinery Pre-optimizer

Burdyshaw, Chad Eric 04 August 2001 (has links)
A pre-optimizer has been developed which modifies existing turbomachinery blades to create new geometries with improved selected aerodynamic coefficients calculated using a linear panel method. These blade rows can then be further refined using a Navier-Stokes method for evaluation. This pre-optimizer was developed in hopes of reducing the overall CPU time required for optimization when using only Navier-Stokes evaluations. The primary method chosen to effect this optimization is a parallel evolutionary algorithm. Variations of this method have been analyzed and compared for convergence and degree of improvement. Test cases involved both single and multiple row turbomachinery. For each case, both single and multiple criteria fitness evaluations were used.
5

Fast Hardware Algorithm for Division in GF(2m) Based on the Extended Euclid's Algorithm With Parallelization of Modular Reductions

Kobayashi, Katsuki, Takagi, Naofumi 08 1900 (has links)
No description available.
6

Multilevel multidimensional scaling on the GPU

Ingram, Stephen F. 05 1900 (has links)
We present Glimmer, a new multilevel visualization algorithm for multidimensional scaling designed to exploit modern graphics processing unit (GPU) hard-ware. We also present GPU-SF, a parallel, force-based subsystem used by Glimmer. Glimmer organizes input into a hierarchy of levels and recursively applies GPU-SF to combine and refine the levels. The multilevel nature of the algorithm helps avoid local minima while the GPU parallelism improves speed of computation. We propose a robust termination condition for GPU-SF based on a filtered approximation of the normalized stress function. We demonstrate the benefits of Glimmer in terms of speed, normalized stress, and visual quality against several previous algorithms for a range of synthetic and real benchmark datasets. We show that the performance of Glimmer on GPUs is substantially faster than a CPU implementation of the same algorithm. We also propose a novel texture paging strategy called distance paging for working with precomputed distance matrices too large to fit in texture memory.
7

Multilevel multidimensional scaling on the GPU

Ingram, Stephen F. 05 1900 (has links)
We present Glimmer, a new multilevel visualization algorithm for multidimensional scaling designed to exploit modern graphics processing unit (GPU) hard-ware. We also present GPU-SF, a parallel, force-based subsystem used by Glimmer. Glimmer organizes input into a hierarchy of levels and recursively applies GPU-SF to combine and refine the levels. The multilevel nature of the algorithm helps avoid local minima while the GPU parallelism improves speed of computation. We propose a robust termination condition for GPU-SF based on a filtered approximation of the normalized stress function. We demonstrate the benefits of Glimmer in terms of speed, normalized stress, and visual quality against several previous algorithms for a range of synthetic and real benchmark datasets. We show that the performance of Glimmer on GPUs is substantially faster than a CPU implementation of the same algorithm. We also propose a novel texture paging strategy called distance paging for working with precomputed distance matrices too large to fit in texture memory.
8

Comparing Approaches to Initializing the Expectation-Maximization Algorithm

Dicintio, Sabrina 09 October 2012 (has links)
The expectation-maximization (EM) algorithm is a widely utilized approach to max- imum likelihood estimation in the presence of missing data, this thesis focuses on its application within the model-based clustering framework. The performance of the EM algorithm can be highly dependent on how the algorithm is initialized. Several ways of initializing the EM algorithm have been proposed, however, the best method to use for initialization remains a somewhat controversial topic. From an attempt to obtain a superior method of initializing the EM algorithm, comes the concept of using multiple existing methods together in what will be called a `voting' procedure. This procedure will use several common initialization methods to cluster the data, then a nal starting ^zig matrix will be obtained in two ways. The hard `voting' method follows a majority rule, whereas the soft `voting' method takes an average of the multiple group memberships. The nal ^zig matrix obtained from both methods will dictate the starting values of ^ g; ^ g; and ^ g used to initialize the EM algorithm.
9

Multilevel multidimensional scaling on the GPU

Ingram, Stephen F. 05 1900 (has links)
We present Glimmer, a new multilevel visualization algorithm for multidimensional scaling designed to exploit modern graphics processing unit (GPU) hard-ware. We also present GPU-SF, a parallel, force-based subsystem used by Glimmer. Glimmer organizes input into a hierarchy of levels and recursively applies GPU-SF to combine and refine the levels. The multilevel nature of the algorithm helps avoid local minima while the GPU parallelism improves speed of computation. We propose a robust termination condition for GPU-SF based on a filtered approximation of the normalized stress function. We demonstrate the benefits of Glimmer in terms of speed, normalized stress, and visual quality against several previous algorithms for a range of synthetic and real benchmark datasets. We show that the performance of Glimmer on GPUs is substantially faster than a CPU implementation of the same algorithm. We also propose a novel texture paging strategy called distance paging for working with precomputed distance matrices too large to fit in texture memory. / Science, Faculty of / Computer Science, Department of / Graduate
10

Evolutionary Algorithms for Model-Based Clustering

Kampo, Regina S. January 2021 (has links)
Cluster analysis is used to detect underlying group structure in data. Model-based clustering is the process of performing cluster analysis which involves the fitting of finite mixture models. However, parameter estimation in mixture model-based approaches to clustering is notoriously difficult. To this end, this thesis focuses on the development of evolutionary computation as an alternative technique for parameter estimation in mixture models. An evolutionary algorithm is proposed and illustrated on the well-established Gaussian mixture model with missing values. Next, the family of Gaussian parsimonious clustering models is considered, and an evolutionary algorithm is developed to estimate the parameters. Next, an evolutionary algorithm is developed for latent Gaussian mixture models and to facilitate the flexible clustering of high-dimensional data. For all models and families of models considered in this thesis, the proposed algorithms used for model-fitting and parameter estimation are presented and the performance illustrated using real and simulated data sets to assess the clustering ability of all models. This thesis concludes with a discussion and suggestions for future work. / Dissertation / Doctor of Philosophy (PhD)

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