Nesta tese investiga-se uma nova abordagem visando implementar os conceitos propostos na Teoria da Evidencia para fins de classificação de imagens digitais em Sensoriamento Remoto. Propõe-se aqui a utilização de variáveis auxiliares, estruturadas na forma de Planos de Informação (P.I.s) como em um SIG para gerar dados de confiança e de plausibilidade. São então aplicados limiares aos dados de confiança e de plausibilidade, com a finalidade de detectar erros de inclusão e de omissão, respectivamente, na imagem temática. Propõe-se nesta tese que estes dois limiares sejam estimados em função das acurácias do usuário e do produtor. A metodologia proposta nesta tese foi testada em uma área teste, coberta pela classe Mata Nativa com Araucária. O experimento mostrou que a metodologia aqui proposta atinge seus objetivos. / In this thesis we investigate a new approach to implement concepts developed by the Theory of Evidence to Remote Sensing digital image classification. In the proposed approach auxiliary variables are structured as layers in a GIS-like format to produce layers of belief and plausibility. Thresholds are applied to the layers of belief and plausibility to detect errors of commission and omission, respectively on the thematic image. The thresholds are estimated as functions of the user’s and producer’s accuracy. Preliminary tests were performed over an area covered by natural forest with Araucaria, showing some promising results.

Nesta tese investiga-se uma nova abordagem visando implementar os conceitos propostos na Teoria da Evidencia para fins de classificação de imagens digitais em Sensoriamento Remoto. Propõe-se aqui a utilização de variáveis auxiliares, estruturadas na forma de Planos de Informação (P.I.s) como em um SIG para gerar dados de confiança e de plausibilidade. São então aplicados limiares aos dados de confiança e de plausibilidade, com a finalidade de detectar erros de inclusão e de omissão, respectivamente, na imagem temática. Propõe-se nesta tese que estes dois limiares sejam estimados em função das acurácias do usuário e do produtor. A metodologia proposta nesta tese foi testada em uma área teste, coberta pela classe Mata Nativa com Araucária. O experimento mostrou que a metodologia aqui proposta atinge seus objetivos. / In this thesis we investigate a new approach to implement concepts developed by the Theory of Evidence to Remote Sensing digital image classification. In the proposed approach auxiliary variables are structured as layers in a GIS-like format to produce layers of belief and plausibility. Thresholds are applied to the layers of belief and plausibility to detect errors of commission and omission, respectively on the thematic image. The thresholds are estimated as functions of the user’s and producer’s accuracy. Preliminary tests were performed over an area covered by natural forest with Araucaria, showing some promising results.

Nesta tese investiga-se uma nova abordagem visando implementar os conceitos propostos na Teoria da Evidencia para fins de classificação de imagens digitais em Sensoriamento Remoto. Propõe-se aqui a utilização de variáveis auxiliares, estruturadas na forma de Planos de Informação (P.I.s) como em um SIG para gerar dados de confiança e de plausibilidade. São então aplicados limiares aos dados de confiança e de plausibilidade, com a finalidade de detectar erros de inclusão e de omissão, respectivamente, na imagem temática. Propõe-se nesta tese que estes dois limiares sejam estimados em função das acurácias do usuário e do produtor. A metodologia proposta nesta tese foi testada em uma área teste, coberta pela classe Mata Nativa com Araucária. O experimento mostrou que a metodologia aqui proposta atinge seus objetivos. / In this thesis we investigate a new approach to implement concepts developed by the Theory of Evidence to Remote Sensing digital image classification. In the proposed approach auxiliary variables are structured as layers in a GIS-like format to produce layers of belief and plausibility. Thresholds are applied to the layers of belief and plausibility to detect errors of commission and omission, respectively on the thematic image. The thresholds are estimated as functions of the user’s and producer’s accuracy. Preliminary tests were performed over an area covered by natural forest with Araucaria, showing some promising results.

This research explores the theory and practice of risk assessment and project evaluationand proposes novel alternatives. Reviewing literature revealed a continuous endeavourfor better project risk modelling and analysis. A number of proposals for improving theprevailing Probability-Impact (P-I) risk model can be found in literature. Moreover,researchers have investigated the feasibility of different theories in analysing projectrisk. Furthermore, various decision support systems (DSSs) are available for aidingpractitioners in risk assessment and decision making. Unfortunately, they are sufferingfrom a low take-up. Instead, personal judgment and past experience are mainly used foranalysing risk and making decisions.In this research, a new risk model is proposed through extending the P-I risk model toinclude a third dimension: probability of impact materialisation. Such an extensionreflects the characteristics of a risk, its surrounding environment and the ability ofmitigating its impact. A new assessment methodology is devised. Dempster-ShaferTheory of Evidence (DST) is researched and presented as a novel alternative toProbability Theory (PT) and Fuzzy Sets Theory (FST) which dominate the literature ofproject risks analysis. A DST-based assessment methodology was developed forstructuring the personal experience and professional judgment of risk analysts andutilising them for risk analysis. Benefiting from the unique features of the EvidentialReasoning (ER) approach, the proposed methodology enables analysts to express theirevaluations in distributed forms, so that they can provide degrees of belief in apredefined set of assessment grades based on available information. This is a veryeffective way for tackling the problem of lack of information which is an inherentfeature of most projects during the tendering stage. It is the first time that such anapproach is ever used for handling construction risk assessment. Monetary equivalent isused as a common scale for measuring risk impact on various project success objectives,and the evidential reasoning (ER) algorithm is used as an assessment aggregation toolinstead of the simple averaging procedure which might not be appropriate in allsituations. A DST-based project evaluation framework was developed using projectrisks and benefits as evaluation attributes. Monetary equivalent was used also as acommon scale for measuring project risks and benefits and the ER algorithm as anaggregation tool.The viability of the proposed risk model, assessment methodology and projectevaluation framework was investigated through conducting interviews with constructionprofessionals and administering postal and online questionnaires. A decision supportsystem (DSS) was devised to facilitate the proposed approaches and to perform therequired calculations. The DSS was developed in light of the research findingsregarding the reasons of low take-up of the existing tools. Four validation case studieswere conducted. Senior managers in separate British construction companies tested thetool and found it useful, helpful and easy to use.It is concluded that the proposed risk model, risk assessment methodology and projectevaluation framework could be viable alternatives to the existing ones. Professionalexperience was modelled and utilised systematically for risk and benefit analysis. Thismay help closing the gap between theory and practice of risk analysis and decisionmaking in construction. The research findings recommend further exploration of thepotential applications of DST and ER in construction management domain.

Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgico / O termo Teoria da Prova foi introduzido por Hilbert para identificar o estudo sobre provas formais. Pesquisas nessa Ãrea podem ser classificadas em: a) Teoria da Prova Redutiva ou Interpretacional, cujo objetivo à demonstrar, entre outras coisas, a consistÃncia da matemÃtica utilizando somente mÃtodos finitistas, e b) Teoria da Prova Estrutural, onde caracterÃsticas estruturais das provas formais sÃo investigadas por meio de sistemas dedutivos como DeduÃÃo Natural e CÃlculo de Sequentes. Prawitz, por meio da Teoria da Prova, definiu uma Teoria dos Significados para constantes logicas e propÃs regras esquemÃticas de introduÃÃo e de eliminaÃÃo para caracterizar os conectivos proposicionais. Schroeder-Heister estendeu as definiÃÃes de Prawitz e formalizou o uso de regras como hipÃteses, tornando possÃvel a utilizaÃÃo de cÃlculos para suposiÃÃes separados de cÃlculos para constantes lÃgicas. NÃo estamos interessados na investigaÃÃo de regras esquemÃticas para dar significado a constantes lÃgicas. Pretendemos, na verdade, definir procedimentos de normalizaÃÃo esquemÃticos, baseados em tais regras esquematicas, com objetivo de identificar condiÃÃes suficientes para um sistema ser normalizÃvel. Tais resultados sÃo pertinentes à Teoria Abstrata da Prova, termo usado para identificar o estudo das condiÃÃes abstratas e gerais para a anÃlise prova-teÃrica de sistemas formais. Teoria Abstrata da Prova nÃo estuda cÃlculos lÃgicos especÃficos, mas famÃlias de cÃlculos instÃncias de regras esquemÃticas. A nossa proposta, portanto, baseia-se em regras esquemÃticas que podem ser instanciadas por regras concretas, em particular, por regras que introduzem operadores modais. Provamos, tambÃm, Teoremas de NormalizaÃÃoo Fraca e Forte para sistemas esquemÃticos definidos em funÃÃoo de nossas regras esquemÃticas, obtemos condiÃÃes suficientes para que um sistema instÃncia destas regras seja normalizÃvel, definimos um procedimento que normaliza deduÃÃes concretas e comparamos nossas provas de normalizaÃÃo esquemÃtica com provas de normalizaÃÃo para sistemas definidos na literatura. / The term Theory Test was introduced by Hilbert to identify the study of formal proofs. Research in this area can be classified into: a) Proof Theory of reductive or interpretational, whose goal is to demonstrate, among other things, the consistency of mathematics using only methods finitistas, b) Structural Proof Theory, where the structural characteristics of the formal proofs are investigated by means of deductive systems as Natural Deduction and Sequent Calculus. Prawitz through Theory Proof set a Theory of Meaning for constants logics and proposed schematic introduction rules and elimination to characterize the propositional connectives. Schroeder-Heister settings Prawitz extended and formalized the use of rules as hypotheses, making possible the use of separate calculations for assumptions of calculations for logical constants. We are not interested in the investigation of schematic rules to give meaning to the logical constants. We intend to actually set schematic standardization procedures, based on such schematic rules? Attic, in order to identify sufficient conditions for a system to be normalizÃvel. These results are relevant to the Abstract Theory of Evidence, a term used to identify the study of the conditions abstract and general to the proof-theoretical analysis of formal systems. Abstract Theory of Evidence do not study specific logical calculations, but families of calculations instances of rules schematic. Our proposal is therefore based on rules schematic rules can be instantiated for concrete, in particular, by introducing rules modal operators. We prove also theorems NormalizaÃÃoo Weak and Strong systems defined in schematic funÃÃoo schematic of our rules, we obtain sufficient conditions for a system instance is normalizÃvel these rules, we define a procedure that normalizes deductions concrete evidence and compare our standards with evidence schematic standards for systems defined in the literature.

Cette thèse présente l'utilisation conjointe de la théorie de l'évidente et du suivi multi-hypothèses pour la modélisation et la gestion d'un système de suivi multi-caméras dans un environnement autoroutier. Le suivi est basé sur la ré-identification des objets (véhicules) sur la base d'information visio-temporelles. Une concrétisation de ces concepts se traduit par la conception et la mise en oeuvre d'une architecture logicielle multi-agents de gestion du suivi multi-caméras. Après une présentation de l'état de l'art sur les cadres de gestion de l'incertain et celui relatif à fusion de l'information pour la mise en correspondance, et sur les systèmes multi-agents, notre apport dans ce travail se situe à trois niveaux. Le premier a été une adaptation de la phase de décision du modèle de croyance transférable pour y intégrer l'utilisation du suivi multi-hypothèses comme outil de levée d'ambigüité rn cas d'indécision face à une situation de mise en correspondance. Le second apport a été celui de proposer une architecture logicielle à base d'agents pour la gestion du système du suivi multi-caméras. Nous en avons proposé la modélisation globale ainsi que celle des agents et de leurs interactions en utilisant une démarche personnelle d'analyse mais toutefois inspirée de langages et outils de modélisation tels que Agent UML et MaSE pour ne citer que ceux-là, du fait qu'il n'existe pas réellement un standard normalisé à ce jour dans ce domaine. Notre troisième apport a été de faire un début d'implémentation de notre architecture logicielle à base d'agent en nous basant sur la plateforme JADE (Java Agent DEvelopment Framework). Quelques expérimentations et discussions des résultats sont présentées à la fin pour déboucher sur nos conclusions et perspectives. / This thesis presents the joint use of the theory of evidence and multiple hypothesis tracking for modeling and managing a system for monitoring multiple cameras in a motorway. The tracking is based on the re-identification of objects (vehicles) on the basis of visuals and times informations. A realization of these concepts results in the design and implementation of a software architecture for multiple agents management of multiple camera tracking system. After presenting the state of the art on the frameworks of uncertainty management and that on information fusion for the matching, and the multi-agent systems, our contribution in this work is on two or three levels. The first was an adaptation of the decision phase of the transferable belief model to incorporate the use of multi-hypotheses tracking as a tool of ambiguity survey in case of indecision in matching situation. The second contribution was a proposition of agent-based software architecture for management of a multiple cameras tracking system. We have proposed the global system modeling as well as agents and their interactions modeling using a personal analysis method but nevertheless inspired by modelisation languages and tolls such as Agent UML, MaSE and others, because there is not yet a standard and normalized tool on the subject. Our third contribution was to begin an implementation of our agent-based software architecture using JADE (Java Agent Development Framework). Some experiment and discussions are presented at the end to lead to our conclusions and perspectives.

No description available.

Les modélisations par chaînes de Markov cachées permettent de résoudre un grand nombre de problèmes inverses se posant en traitement d’images ou de signaux. En particulier, le problème de segmentation figure parmi les problèmes où ces modèles ont été le plus sollicités. Selon ces modèles, la donnée observable est considérée comme une version bruitée de la segmentation recherchée qui peut être modélisée à travers une chaîne de Markov à états finis. Des techniques bayésiennes permettent ensuite d’estimer cette segmentation même dans le contexte non-supervisé grâce à des algorithmes qui permettent d’estimer les paramètres du modèle à partir de l’observation seule. Les chaînes de Markov cachées ont été ultérieurement généralisées aux chaînes de Markov couples et triplets, lesquelles offrent plus de possibilités de modélisation tout en présentant des complexités de calcul comparables, permettant ainsi de relever certains défis que les modélisations classiques ne supportent pas. Un lien intéressant a également été établi entre les modèles de Markov triplets et la théorie de l’évidence de Dempster-Shafer, ce qui confère à ces modèles la possibilité de mieux modéliser les données multi-senseurs. Ainsi, dans cette thèse, nous abordons trois difficultés qui posent problèmes aux modèles classiques : la non-stationnarité du processus caché et/ou du bruit, la corrélation du bruit et la multitude de sources de données. Dans ce cadre, nous proposons des modélisations originales fondées sur la très riche théorie des chaînes de Markov triplets. Dans un premier temps, nous introduisons les chaînes de Markov à bruit M-stationnaires qui tiennent compte de l’aspect hétérogène des distributions de bruit s’inspirant des chaînes de Markov cachées M-stationnaires. Les chaînes de Markov cachée ML-stationnaires, quant à elles, considèrent à la fois la loi a priori et les densités de bruit non-stationnaires. Dans un second temps, nous définissons deux types de chaînes de Markov couples non-stationnaires. Dans le cadre bayésien, nous introduisons les chaînes de Markov couples M-stationnaires puis les chaînes de Markov couples MM-stationnaires qui considèrent la donnée stationnaire par morceau. Dans le cadre évidentiel, nous définissons les chaînes de Markov couples évidentielles modélisant l’hétérogénéité du processus caché par une fonction de masse. Enfin, nous présentons les chaînes de Markov multi-senseurs non-stationnaires où la fusion de Dempster-Shafer est employée à la fois pour modéliser la non-stationnarité des données (à l’instar des chaînes de Markov évidentielles cachées) et pour fusionner les informations provenant des différents senseurs (comme dans les champs de Markov multi-senseurs). Pour chacune des modélisations proposées, nous décrivons les techniques de segmentation et d’estimation des paramètres associées. L’intérêt de chacune des modélisations par rapport aux modélisations classiques est ensuite démontré à travers des expériences menées sur des données synthétiques et réelles / Hidden Markov chains (HMCs) based approaches have been shown to be efficient to resolve a wide range of inverse problems occurring in image and signal processing. In particular, unsupervised segmentation of data is one of these problems where HMCs have been extensively applied. According to such models, the observed data are considered as a noised version of the requested segmentation that can be modeled through a finite Markov chain. Then, Bayesian techniques such as MPM can be applied to estimate this segmentation even in unsupervised way thanks to some algorithms that make it possible to estimate the model parameters from the only observed data. HMCs have then been generalized to pairwise Markov chains (PMCs) and triplet Markov chains (TMCs), which offer more modeling possibilities while showing comparable computational complexities, and thus, allow to consider some challenging situations that the conventional HMCs cannot support. An interesting link has also been established between the Dempster-Shafer theory of evidence and TMCs, which give to these latter the ability to handle multisensor data. Hence, in this thesis, we deal with three challenging difficulties that conventional HMCs cannot handle: nonstationarity of the a priori and/or noise distributions, noise correlation, multisensor information fusion. For this purpose, we propose some original models in accordance with the rich theory of TMCs. First, we introduce the M-stationary noise- HMC (also called jumping noise- HMC) that takes into account the nonstationary aspect of the noise distributions in an analogous manner with the switching-HMCs. Afterward, ML-stationary HMC consider nonstationarity of both the a priori and/or noise distributions. Second, we tackle the problem of non-stationary PMCs in two ways. In the Bayesian context, we define the M-stationary PMC and the MM-stationary PMC (also called switching PMCs) that partition the data into M stationary segments. In the evidential context, we propose the evidential PMC in which the realization of the hidden process is modeled through a mass function. Finally, we introduce the multisensor nonstationary HMCs in which the Dempster-Shafer fusion has been used on one hand, to model the data nonstationarity (as done in the hidden evidential Markov chains) and on the other hand, to fuse the information provided by the different sensors (as in the multisensor hidden Markov fields context). For each of the proposed models, we describe the associated segmentation and parameters estimation procedures. The interest of each model is also assessed, with respect to the former ones, through experiments conducted on synthetic and real data

L’objectif de ces travaux de proposer aux autorités locales un outil d’aide à la décision pour formaliser une démarche participative tout au long de la conception d’un projet de transport urbain durable, dans un contexte multicritères et multi acteurs. La méthodologie développée repose sur l’évaluation a priori des impacts d’un projet local, en impliquant les acteurs dès la phase de diagnostic. Cette phase permet d’identifier des groupes d’acteurs selon leur perception des phénomènes urbains, des interactions et des préférences déclarées d’évolution. Elle permet d’établir une liste d’indicateurs communs qui seront évalués. Le choix des alternatives à étudier est le résultat de l’application conjointe de techniques de transférabilité - fondées sur l’étude de projets développés dans d’autres villes - et de l’étude des préférences déclarées des usagers locaux. La probabilité d’utilisation du service s’évalue à l’aide d’un modèle comportemental agrégé qui intègre la perception floue et l’indécision des usagers devant une situation nouvelle. Les changements de comportement des usagers sont pris en compte à l’aide d’un indicateur de robustesse qui teste les effets des paramètres exogènes sur l’évolution de la probabilité d’utilisation du service. Un micro-simulateur de trafic permet d’évaluer les impacts des différents scénarii sur le trafic, l’environnement et le bien-être des citoyens sous la forme monétarisée, afin d’illustrer les coûts et les bénéfices indirects attendus de l’implantation du projet. Une solution de compromis est proposée : elle vise à fournir la solution qui contenterait au mieux l’ensemble des représentants des groupes d’acteurs - et non la solution optimale en termes d’impacts. / The objective of this research is to provide to the local authorities a decision aid tool in order to formalize a participatory approach during the conception of a sustainable urban transport project, in a multi-criteria and multi-actors context. The methodology is based on the a priori evaluation of the impacts of a local project, involving stakeholders as soon as the diagnosis phase. This phase aims at identifying groups of actors according to their perception of urban phenomena, their interactions and their stated preferences of evolution. The diagnosis phase leads to the setting-up of a list of common indicators to be evaluated. The choice of alternatives to be studied is the result of the transferability techniques - based on the projects developed in other cities - and the stated preferences of local users. The probability of using the service is evaluated using an aggregated behavioral model that takes into account the fuzzy perception and the indecision of users in a new situation. Changes in the behaviour of the users are taken into account thanks to an indicator of robustness that tests the impact of exogenous parameters on the evolution of the probability of using a service. A traffic micro--simulator aims at assessing the impacts of the various scenarios on traffic, environment and the welfare of citizens– which is monetized. It illustrates the costs and indirect benefits expected with the implementation of project. A compromise solution is proposed: it aims at identifying an alternative that would best satisfy the representatives of the stakeholder groups – and not necessarily the optimal solution in terms of impacts.