Thesis (M.S.)--Ohio University, November, 1996. / Title from PDF t.p.

Thesis (Ph. D.)--Mechanical Engineering, Georgia Institute of Technology, 2005. / Kenneth Cunefare, Committee Chair ; Nader Sadegh, Committee Member ; Aldo Ferri, Committee Member ; Sathyanaraya Hanagud, Committee Member ; Massimo Ruzzene, Committee Member. Includes bibliographical references.

Chemnitz, Techn. Univ., Diss., 2005.

Thesis (M.Sc.)--Memorial University of Newfoundland, 2006. / Bibliography: leaves 115-116. Also available online.

Thesis (Ph. D.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains viii, 99 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 93-99).

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Thesis (M.S.)--West Virginia University, 2007. / Title from document title page. Document formatted into pages; contains viii, 95 p. : ill. Includes abstract. Includes bibliographical references (p. 79-91).

La evaluación de yacimientos mineros tiene por objetivo estimar, con el menor error posible, la calidad y cantidad de un recurso mineral, que tiene potencial de ser explotado. Una de las principales herramientas utilizadas es la Geoestadística, la cual pone énfasis en el contexto geológico y la relación espacial entre los datos a estudiar. Los métodos de estimación tradicionales, llamados Kriging, calculan el mejor estimador lineal insesgado de una variable en base a los valores de datos vecinos. Para representar de mejor manera la variabilidad espacial de los datos, se recurre a la Simulación Convencional, que consiste en añadir un factor aleatorio a Kriging para obtener una distribución de escenarios, que sirven como medida de la respuesta ante la incerteza. El inconveniente de esta técnica es que no permite correlacionar diversos puntos al mismo tiempo, sólo se pueden correlacionar dos puntos a la vez (se basa en la covarianza de 2 puntos). Este hecho representa una potencial pérdida de información en la construcción del modelo que se utilizará para realizar estimaciones o simulaciones. Al correlacionar dos puntos a la vez, se induce una suavización sobre las estimaciones, lo cual puede entregar resultados sobreestimados o subestimados. Para solucionar el problema anterior, se propone utilizar métodos que contemplan estadísticas de múltiples puntos, representadas por patrones. Se espera que la utilización de patrones junto a un método de simulación no convencional llamado Recocido Simulado (Simulated Annealing) puedan tener un mejor desempeño al evaluar un proceso que la simulación convencional. Los principales problemas del método propuesto son el tiempo de cálculo del Recocido Simulado y el manejo de patrones grandes. Se optó por resolver el primero de ellos y dejar abierta la puerta para posibles trabajos a futuro en la optimización de las estructuras de datos para el manejo de patrones grandes. El tiempo de cálculo del Recocido Simulado aumenta exponencialmente al incrementar el tamaño de los patrones a estudiar. Para solucionar esto, se realizó una implementación utilizando múltiples procesos, configurados según una técnica llamada Computación Especulativa, la cual permite realizar varios pasos de la simulación a la vez. Se estudió el speedup teórico de la paralelización y se realizaron pruebas numéricas, las cuales entregaron resultados satisfactorios en tiempo y en calidad de la imagen simulada.

"Mapping is a way of visualizing parts of the world and maps are largely diagrammatic and two dimensional. There is usually a one-to-one correspondence between places in the world and places on the map, but while there are limitless aspects to the world, the cartographer can only select a few to map" Daniel Dorling, 1996 Map generalization is the process by which small scale maps are derived from large scale maps. This requires the application of operations such as simplification, selection, displacement and amalgamation to map features subsequent to scale reduction. The work is concerned with the problem of effective rendering of large scale datasets on small display devices by developing appropriate map generalization techniques for generating schematic maps. With the advent of high-end miniature technology and large scale digital geographic data products it is essential to devise proper methodologies and techniques for the automated generation of schematic maps specifically tailored for mobile GIS applications. Schematic maps are diagrammatic representation based on linear abstractions of networks. Transportation networks are the key candidates for applying schematization to help ease the interpretation of information by the process of cartographic abstraction. This study looks at how simulated annealing optimisation technique can be successfully applied for automated generation of schematic maps from large scale digital geographic datasets tailored specifically for mobile GIS applications. The software developed makes use of a simulated annealing based schematic map generator algorithm to generate route maps from OSCAR® dataset corresponding to a series of user defined start and end points. The generated schematic route maps are displayed and tested on mobile handheld devices shows promising results for mobile GIS applications. This work concentrates on the automatic generation of schematic maps, which, in the context of mobile mapping, are seen as being a particularly useful means of displaying routes for way finding type and utility network applications.

Esse trabalho tem dois objetivos principais: o primeiro deles consiste em apresentar o estado da arte sobre processos de otimização combinatorial dando uma ênfase especial ao método Simulated Annealing (SA). São apresentados seu histórico, funcionalidades, algoritmo genérico e propostas de paralelização presentes na literatura. Além disso, é apresentado o algoritmo de geração de redes aleatórias contínuas, algoritmo, esse, projetado por pesquisadores do Instituto de Física da UFRGS que utiliza o método SA para gerar redes que atendam certas restrições. O segundo objetivo consiste empropor a paralelização desse algoritmo visando diminuir significativamente o tempo de geração de cada rede, que com o algoritmo seqüencial chega a demorar mais de um mês. Nessa etapa foi utilizada uma adaptação de um dos métodos propostos pela literatura juntamente com a técnica de divisão de domínio. Os resultados obtidos mostraram-se satisfatórios tanto em relação à qualidade numérica quanto à diminuição do tempo de processamento. Além disso, discute-se no trabalho a genericidade da proposta de paralelização a outros problemas baseados em SA. / This work has two main goals: the first one is to present the state of the art on combinatorial optimization processes, with a special emphasis to the Simulated Annealing (SA) method. The work presents its history, features, generic algorithm and proposed parallelization present in the literature. Moreover, the algorithm to generate random networks continued is presented. This algorithm was designed by researchers of the UFRGS Physics Institute and it uses the SA method. The second goal of this work is to propose a parallelization for this algorithm in order to decrease significantly the generation time of each network, that with the sequential algorithm reaches more than months. To do that was used an adaptation of one of the methods proposed by literature together with the domain partitioning technical. The results were satisfactory in terms of the numerical quality and in the decrease of the processing time. In addition, this work discusses the genericity of the proposed parallelization to other problems based on SA.