Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

Thesis (M.S.)--University of Alabama at Birmingham, 2007. / Description based on contents viewed June 25, 2007; title from title screen. Includes bibliographical references (p. 41).

No description available.

No description available.

by Robert C. Meton. / Bibliography: leaves [33]-[34].

The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed on November 18, 2009). Thesis advisor: Dr. Roger Fales. Includes bibliographical references.

This thesis investigates the application of Kriging meta-modelling techniques in the field of building design and optimisation. In conducting this research, there were two key motivational factors. The first is the need for building designers to have tools that allow low energy buildings to be designed in a fast and efficient manner. The second motivating factor is the need for optimisation tools that account, or help account, for the wide variety of uses that a building might have; so-called Robust Optimisation (RO). This thesis therefore includes an analysis of Kriging meta-modelling and first applies this to simple building problems. I then use this simple building model to determine the effect of the updated UK Test Reference Years (TRYs) on energy consumption. Second, I examine Kriging-based optimisation techniques for a single objective. I then revisit the single-building meta-model to examine the effect of uncertainty on a neighbourhood of buildings and compare the results to the output of a brute-force analysis of a full building simulator. The results show that the Kriging emulation is an effective tool for creating a meta-model of a building. The subsequent use in the analysis of the effect of TRYs on building shows that UK buildings are likely to use less heating in the future but are likely to overheat more. In the final two chapters I use the techniques developed to create a robust building optimisation algorithm as well as using Kriging to improve the optimisation efficiency of the well-known NSGA-II algorithm. I show that the Kriging-based robust optimiser effectively finds more robust solutions than traditional global optimisation. I also show that Kriging techniques can be used to augment NSGA-II so that it finds more diverse solutions to some types of multi-objective optimisation problems. The results show that Kriging has significant potential in this field and I reveal many potential areas of future research. This thesis shows how a Kriging-enhanced NSGA-II multi-objective optimisation algorithm can be used to improve the performance of NSGA-II. This new algorithm has been shown to speed up the convergence of some multi-objective optimisation algorithms significantly. Although further work is required to verify the results for a wider variety of building applications, the initial results are promising.

abstract: As the information available to lay users through autonomous data sources continues to increase, mediators become important to ensure that the wealth of information available is tapped effectively. A key challenge that these information mediators need to handle is the varying levels of incompleteness in the underlying databases in terms of missing attribute values. Existing approaches such as Query Processing over Incomplete Autonomous Databases (QPIAD) aim to mine and use Approximate Functional Dependencies (AFDs) to predict and retrieve relevant incomplete tuples. These approaches make independence assumptions about missing values--which critically hobbles their performance when there are tuples containing missing values for multiple correlated attributes. In this thesis, I present a principled probabilis- tic alternative that views an incomplete tuple as defining a distribution over the complete tuples that it stands for. I learn this distribution in terms of Bayes networks. My approach involves min- ing/"learning" Bayes networks from a sample of the database, and using it do both imputation (predict a missing value) and query rewriting (retrieve relevant results with incompleteness on the query-constrained attributes, when the data sources are autonomous). I present empirical studies to demonstrate that (i) at higher levels of incompleteness, when multiple attribute values are missing, Bayes networks do provide a significantly higher classification accuracy and (ii) the relevant possible answers retrieved by the queries reformulated using Bayes networks provide higher precision and recall than AFDs while keeping query processing costs manageable. / Dissertation/Thesis / M.S. Computer Science 2011

A presente dissertação apresenta dois ensaios sobre decisão, incerteza e escolha social no contexto eleitoral. O primeiro ensaio aborda de modo teórico o tema da ambiguidade nos discursos de campanha dos políticos, relacionando-o com a teoria da decisão sob incerteza, e trazendo os seus efeitos para a tomada de decisão individual e coletiva. O segundo ensaio trata de uma análise teórica acerca da liberdade de escolha dos indivíduos durante o processo eleitoral quando há incerteza a respeito das características dos candidatos, utilizando como base duas abordagens axiomáticas de liberdade de escolha presentes na literatura. / This dissertation presents two essays on decision, uncertainty and social choice in the electoral context. The first essay theoretically approaches the topic of ambiguous campaign discourses, relating it to the decision theory under uncertainty, and showing what effects it can have on individual and social decision-making processes. The second essay analyzes the freedom of choice aspects of the electoral process when there is uncertainty about the characteristics of candidates, using two axiomatic approaches of freedom that are present in the literature.

O mapeamento da incerteza dos teores através de métodos de simulação geoestatística é uma metodologia que está começando a ser amplamente utilizada na indústria mineira. No entanto, o uso correto desse intervalo de incerteza para os processos subsequentes à avaliação de recursos ainda carece de entendimento e aplicação no setor industrial, onde o modelo de krigagem é empregado para o planejamento de mina. O sequenciamento da produção de longo prazo, com o objetivo de maximizar o Valor Presente Líquido (VPL), é uma das etapas mais importantes no planejamento de lavra e, portanto, o acesso à informação da variabilidade dos teores torna-se de grande importância permitindo a maximização dos lucros e da extração dos recursos. O objetivo deste estudo é avaliar o impacto da incerteza geológica no planejamento de lavra de modo a definir um sequenciamento a partir das simulações. Para isso ser alcançado, propõe-se uma análise probabilística incorporando as incertezas dos teores no sequenciamento de lavra, e quantificando, através de um índice de classificação proposto no estudo, os potenciais ganhos e riscos de perda associado a cada cenário sequenciado. O método de co-simulação por bandas rotativas é utilizado para gerar cenários equiprováveis. Em seguida, o sequenciamento de lavra é definido para cada cenário de teor simulado utilizando um software de otimização. Então, cada sequência de extração dos blocos obtida foi reavaliada para cada simulação de teor. Além disso, um índice de classificação é utilizado para definir o sequenciamento de lavra que gera o maior VPL. Por fim, é realizada uma comparação entre a opção de planejamento selecionada pela metodologia proposta e o modelo utilizado tradicionalmente na indústria obtido a partir de interpolações lineares (krigagem) dos teores. / Mapping grade uncertainty through geostatistical simulation is becoming widely used in the mining industry. However, the proper use of this uncertainty interval for subsequent decision making processes still lacks of understanding and use, where a grade kriging model is mostly employed for mine planning. The long-term scheduling aiming at maximizing Net Present Value (NPV) is one of the most important stages in mine planning, and therefore the assessment of grade variability is of great importance and allowing maximizing profit and resources extraction. The aim of this study is to evaluate the impact of geological uncertainty in mine planning in order to define the best scheduling given grade uncertainty derived from the simulations. For this to be achieved, it is proposed a probabilistic analysis incorporating grade uncertainties in the mining sequencing and quantifying, through a proposed classification index, the potential gains and risks of loss associated with each sequenced scenario. Turning bands algorithm is used to generate equally probable scenarios. Next, it is defined the mining sequence for each grade simulated scenario using an optimization software. Then, each block extraction sequence obtained is reevaluated for each grade simulation. Furthermore, a classification index is used to select the schedule which leads to the highest NPV. Finally, a comparison is performed with the selected schedule and the one obtained using the model traditionally used based on kriged grades.