Reproduction de : Thèse de doctorat : Informatique : Le Mans : 2007. / Titre provenant de l'écran-titre. Bibliogr. p. 227-238.

Thesis (M.S.)--California Polytechnic State University, 2009. / Title from PDF title page; viewed on June 11, 2009. "June 2009." "In partial fulfillment of the requirements for the degree [of] Master of Science in Electrical Engineering." "Presented to the Electrical Engineering faculty of California Polytechnic State University, San Luis Obispo." Major professor: John Saghri, Ph.D. Includes bibliographical references (p. 72-74). Also available on microfiche.

Thesis (M.S.)--West Virginia University, 2005. / Title from document title page. Document formatted into pages; contains ix, 50 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 48-50).

Techn. Hochsch., Diss., 2002--Aachen.

Made available in DSpace on 2014-06-12T16:01:27Z (GMT). No. of bitstreams: 2 arquivo8982_1.pdf: 1953145 bytes, checksum: 8fb42e5db4007fe0dbcdd631fff88bab (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2009 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / O presente trabalho apresenta o e-Lateo, um sistema desenvolvido sobre plataforma web, que virtualiza o arcabouço conceitual envolvido na Teoria de Dempster-Shafer e sua extensão, o Lateo. Dotado de vários recursos didáticos, preocupa-se em ocultar complexidade inerente à teoria, facilitando o uso através de interface amigável, ambiente seguro, integro, permitindo monitoramento e gerenciamento das informações. A aplicação prática do e-Lateo nos permite realizar análises mais rápidas e precisas, melhorando a compreensão dos resultados obtidos na combinação dos corpos de evidência e auxiliando as tomadas de decisão em situação de incerteza

This dissertation studies two-person signaling games where the players are assumed to be Choquet expected utility maximizers a la Schmeidler (1989). The sender sends an ambiguous message to the receiver who updates his non-additive belief according to a f-Bayesian updating rule of Gilboa and Schmeidler (1993). When the types are unambiguous in the sense of Nehring (1999), the receiver's conditional preferences after updating on an ambiguous message are always of the subjective expected utility form. This property may serious limit the descriptive power of solution concepts under non-additive beliefs, and it is scrutinized with two extreme f-Bayesian updating rules, the Dempster-Shafer and the Bayes' rule. In chapter 3, the Dempster-Shafer equilibrium proposed by Eichberger and Kelsey (2004) is reappraised. Under the assumption of unambiguous types, it is shown that the Dempster-Shafer equilibrium may give rise to a separating behavior that is never supported by perfect Bayesian equilibrium. However, it does not support any additional pooling equilibrium outcome. Since the Dempster-Shafer equilibrium may support implausible behaviors as exemplified in Ryan (2002), a refinement based on coherent beliefs is suggested. In chapter 4, a variant of perfect Bayesian equilibrium, the quasi perfect Bayesian equilibrium, is proposed, and its descriptive power is investigated. It is shown that the quasi perfect Bayesian equilibrium does not support any additional separating behavior compared to perfect Bayesian equilibrium. It may support additional pooling behavior only if the receiver perceives a correlation between the types and messages. / Ph. D.

No description available.

Policy-makers of today's public transportation investment projects engage in debates in which the reasonableness and clarity of their judgment are tested many times. How to recommend the transportation system that achieves project's goals and different stakeholders' needs in a most logical and justifiable manner is the main question of this dissertation. This study develops a new decision-making approach, Belief Reasoning method, for evaluating public transportation systems in the planning process. The proposed approach applies a reasoning map to model how experts perceive and reason transportation alternatives to lead to the project's goals. It applies the belief measures in the Dempster-Shafer theory of evidence as the mathematical mechanism to represent knowledge under uncertainty and ambiguity and to analyze the degree of achievement of stated goals. Three phases are involved in implementing the Belief Reasoning method. First, a set of goals, a set of characteristics of the alternatives, a set of performances and impacts are identified and the reasoning map, which connects the alternatives to the goals through a series of causal relations, is constructed. Second, a knowledge base is developed through interviewing the experts their degree of belief associated with individual premises and relations, and then aggregating the expert opinions. Third, the model is executed and the results are evaluated in three ways: (i) the transportation alternatives are evaluated based on the degree of belief for achieving individual goals; (ii) the integrity of the reasoning process is evaluated based on the measures of uncertainty associated with information used; and (iii) the critical reasoning chains that significantly influence the outcome are determined based on the sensitivity analysis. The Belief Reasoning method is compared with the Bayesian reasoning, which uses the probability measures as the measure of uncertainty. Also it is compared with the Analytical Hierarchy Process method, which uses a hierarchical tree structure and a weighting scheme. The numerical examples in transit planning are developed for comparison. The proposed Belief Reasoning method has advantages over these traditional evaluation and reasoning methods in several ways. • Use of a reasoning map structure together with an inference process, instead of a tree structure together with a weighting scheme, allows modeling interdependency, redundancy and interactions among variables, usually found in transportation systems. • Use of belief measures in Dempster-Shafer theory can preserve non-deterministic nature of inputs and performances as well as handle incomplete or partial knowledge of experts or citizens, i.e. "I don't know" type opinion. The "degrees of belief" measures allow experts to express their strength of opinions in the conservative and optimistic terms. Such operation is not possible by the probability-based approach. • Dempster-Shafer theory can avoid the scalability issue encountered in Bayesian reasoning. It can also measure uncertainty in the reasoning chains, and identify information needed for improving the reasoning process. • Use of Dempster's rule of combination, instead of the average operator in probability theory, to merge expert opinions about inputs or relations is a better way for combining conflicting and incomplete opinions. In the dissertation, the Belief Reasoning method is applied in real-world Alternatives Analysis of a transit investment project. The results show its potential to analyze and evaluate the alternatives and to provide reasons for recommending a preferred alternative and to measure the uncertainty in the reasoning process. In spite of some shortcomings, discussed in the dissertation, the Belief Reasoning method is an effective method for transportation planning compared with the existing methods. It provides means for the planners and citizens to present their own reasons and allows review and analysis of reasoning and judgments of all participating stakeholders. The proposed method can promote focused discourse among different groups of stakeholders, and enriches the quality of the planning process. / Ph. D.

The purpose of this dissertation is to develop and demonstrate methods appropriate for the quantification and propagation of uncertainty in large, high-consequence engineering projects. The term "rigorous uncertainty quantification" refers to methods equal to the proposed task. The motivating practical example is uncertainty in a Mars atmosphere model due to the incompletely characterized presence of dust. The contributions made in this dissertation, though primarily mathematical and philosophical, are driven by the immediate needs of engineers applying uncertainty quantification in the field. Arguments are provided to explain how the practical needs of engineering projects like Mars lander missions motivate the use of the objective probability bounds approach, as opposed to the subjectivist theories which dominate uncertainty quantification in many research communities. An expanded formalism for Dempster-Shafer structures is introduced, allowing for the representation of continuous random variables and fuzzy variables as Dempster-Shafer structures. Then, the correctness and incorrectness of probability bounds analysis and the Cartesian product propagation method for Dempster-Shafer structures under certain dependency conditions are proven. It is also conclusively demonstrated that there exist some probability bounds problems in which the best-possible bounds on probability can not be represented using Dempster-Shafer structures. Nevertheless, Dempster-Shafer theory is shown to provide a useful mathematical framework for a wide range of probability bounds problems. The dissertation concludes with the application of these new methods to the problem of propagating uncertainty from the dust parameters in a Mars atmosphere model to uncertainty in that model's prediction of atmospheric density. A thirty-day simulation of the weather at Holden Crater on Mars is conducted using a meso-scale atmosphere model, MRAMS. Although this analysis only addresses one component of Mars atmosphere uncertainty, it demonstrates the applicability of probability bounds methods in practical engineering work. More importantly, the Mars atmosphere uncertainty analysis provides a framework in which to conclusively establish the practical importance of epistemology in rigorous uncertainty quantification. / Ph. D.

Cette thèse constitue une nouvelle approche pour la prédiction des maladies des plantes dans une plantation par combinaison de fusion de données et modélisation spatio-temporelle. La maladie des plantes est un problème majeur dans le monde de l'agriculture. Par exemple en Malaisie, la maladie de la pourriture de basal de la tige (BSR) causée par le champignon Ganoderma Boninense est la maladie la plus grave pour les plantations de palmiers à huile. Le champignon infecte les palmiers à huile,causant des pertes de rendement et détruisant au final les arbres. Divers facteurs ont été précédemment signalés, qui influencent l'incidence de la BSR, tels que les cultures précédentes, les techniques de replantation, les types de sols et l'âge des arbres. Une gestion efficace et durable des stratégies pour contrôler le BSR se heurte principalement à un manque de compréhension des mécanismes d'établissement de la maladie, de son développement et de sa propagation. La présente recherche est une tentative d'appliquer la technique de fusion de données et la modélisation temporelle en système d'Information géographique (SIG) pour étudier le comportement des maladies des plantes dans un domaine particulier (zone artisanale). Cette recherche portera sur comment les SIG peuvent aider à évaluer la distribution des maladies des plantes dans une plantation de petite échelle. Avec les progrès simultanés dans les systèmes de positionnement global (GPS) et l'utilisation des systèmes d'Information géographique, ces techniques ont fourni de puissants outils d'analyse pour l'agriculture de précision. Les données pour l'analyse proviennent de palmiers à huile des expériences de densité de plantation aux stations de recherche MPOB à Teluk Intan, Perak, Malaisie.Dans le cas de la maladie de la BSR, les résultats de l'émission de modélisation prédictive ont observé une corrélation entre les maladies BSR prédites avec celles visuellement données par le BSR. Il a été constaté que la modélisation prédictive proposée a bien prédit la présence de la maladie de la BSR. Même si au début d'infection des maladies BSR, le modèle n'a pas fixé exactement la distribution de la maladie, la performance du modèle sera améliorée avec la sélection de la source de données. Dans l'ensemble, le modèle a bien prédit la présence de maladies avec une précision allant jusqu'à 98,9 %. / This thesis represents a new approach for predicting plant disease in a plantation through combination of data fusion and spatio-temporal modelling. Plant disease is a major problem in the world of agriculture. Example in Malaysia, basalstem rot disease (BSR) caused by Ganoderma Boinense is the most serious disease for oil palm plantation in Malaysia. The fungus infects oil palm trees, initially causing yield loss and finally killing the trees. Various factors were previously reported to influence incidence of BSR, such as previous crops, techniques for replanting, types of soils and the age of trees. At present effective and sustainable management strategies to control BSR are hampered mainly by a lack of understanding of mechanisms of disease establishment, development and spread. The present research is an attempt to apply data fusion technique and temporal modelling in Geographical Information System (GIS) to investigate the behaviour of plant disease in a specific area (small skill area). This research will focus on how GIS can help to assess the distribution plant disease in a small scale plantation. With concurrent advances in global positioning systems (GPS) and the use of geographical Information Systems(GIS) techniques have provided powerful analysis tools for precision agriculture. Data for analysis were obtained from oil palm planting density experiments at MPOB research stations at Teluk Intan, Perak, Malaysia. In the case of BSR disease, the results of the predictive modelling show a significance correlation between predicted BSR diseases with visually observed BSR data. It found that the proposed predictive modelling has well predicted the presence of BSR disease. Although at the beginning stage of BSR diseases infection, the model has not fitted exactly the distribution of the disease, we believe that with the proper selection of the source of data, the performance of the model will be improved.Overall, the model has well predicted the presence of diseases with accuracy up to 98.9%.