Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-07-03T18:32:31Z No. of bitstreams: 1 SelmoRodrigues.pdf: 1374245 bytes, checksum: 96afcfa04ba5cc18c4db55e4c92cdf23 (MD5) / Made available in DSpace on 2017-07-03T18:32:31Z (GMT). No. of bitstreams: 1 SelmoRodrigues.pdf: 1374245 bytes, checksum: 96afcfa04ba5cc18c4db55e4c92cdf23 (MD5) Previous issue date: 2017-01-26 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / This paper proposes a new methodology for modelling based on an evolving Neuro-Fuzzy Network Takagi-Sugeno (NFN-TS) for seasonal time series forecasting. The NFN-TS use the unobservable components extracted from the time series to evolve, i.e., to adapt and to adjust its structure, where the number of fuzzy rules of this network can increase or reduced according the components behavior. The method used to extract the components is a recursive version developed in this paper based on the Spectral Singular Analysis (SSA) technique. The proposed methodology has the principle divide to conquer, i.e., it divides a problem into easier subproblems, forecasting separately each component because they present dynamic behaviors that are simpler to forecast. The consequent propositions of fuzzy rules are linear state space models, where the states are the unobservable components data. When there are available observations from the time series, the training stage of NFN-TS is performed, i.e., the NFN-TS evolves its structure and adapts its parameters to carry out the mapping between the components data and the available sample of original time series. On the other hand, if this observation is not available, the network considers the forecasting stage, keeping its structure fixed and using the states of consequent fuzzy rules to feedback the components data to NFN-TS. The NFN-TS was evaluated and compared with other recent and traditional techniques for forecasting seasonal time series, obtaining competitive and advantageous results in relation to other papers. This paper also presents a case study of proposed methodology for real-time detection of anomalies based on a patient’s electrocardiogram data. / Esse trabalho propõe uma nova metodologia para modelagem baseada em uma Rede Neuro- Fuzzy Takagi-Sugeno (RNF-TS) evolutiva para a previsão de séries temporais sazonais. A RNF-TS considera as componentes não-observáveis extraídas a partir da série para evoluir, ou seja, adaptar e ajustar sua estrutura, sendo que a quantidade de regras fuzzy dessa rede pode aumentar ou ser reduzida conforme o comportamento das componentes. O método utilizado para extrair as componentes é uma versão recursiva desenvolvida nessa pesquisa baseada na técnica de Análise Espectral Singular (AES). A metodologia proposta tem como princípio dividir para conquistar, isto é, dividir um problema em subproblemas mais fáceis de lidar, realizando a previsão separadamente de cada componente já que apresentam comportamentos dinâmicos mais simples de prever. As proposições do consequente das regras fuzzy são modelos lineares no espaço de estados, sendo que os estados são os próprios dados das componentes não-observáveis. Quando há observações disponíveis da série temporal, o estágio de treinamento da RNF-TS é realizado, ou seja, a RNF-TS evolui sua estrutura e adapta seus parâmetros para realizar o mapeamento entre os dados das componentes e a amostra disponível da série temporal original. Caso contrário, se essa observação não está disponível, a rede aciona o estágio de previsão, mantendo sua estrutura fixa e usando os estados dos consequentes das regras fuzzy para realimentar os dados das componentes para a RNF-TS. A RNF-TS foi avaliada e comparada com outras técnicas recentes e tradicionais para previsão de séries temporais sazonais, obtendo resultados competitivos e vantajosos em relação a outras pesquisas. Este trabalho apresenta também um estudo de caso da metodologia proposta para detecção em tempo-real de anomalias baseada em dados de eletrocardiogramas de um paciente.

A city is considered as a complex system. It consists of numerous interactivesub-systems and is affected by diverse factors including governmental landpolicies, population growth, transportation infrastructure, and market behavior.Land use and transportation systems are considered as the two most importantsubsystems determining urban form and structure in the long term. Meanwhile,urban growth is one of the most important topics in urban studies, and its maindriving forces are population growth and transportation development. Modelingand simulation are believed to be powerful tools to explore the mechanisms ofurban evolution and provide planning support in growth management. The overall objective of the thesis is to analyze and model urban growth basedon the simulation of land-use changes and the modeling of road networkexpansion. Since most previous urban growth models apply fixed transportnetworks, the evolution of road networks was particularly modeled. Besides,urban growth modeling is an interdisciplinary field, so this thesis made bigefforts to integrate knowledge and methods from other scientific and technicalareas to advance geographical information science, especially the aspects ofnetwork analysis and modeling. A multi-agent system was applied to model urban growth in Toronto whenpopulation growth is considered as being the main driving factor of urbangrowth. Agents were adopted to simulate different types of interactiveindividuals who promote urban expansion. The multi-agent model with spatiotemporalallocation criterions was shown effectiveness in simulation. Then, anurban growth model for long-term simulation was developed by integratingland-use development with procedural road network modeling. The dynamicidealized traffic flow estimated by the space syntax metric was not only used forselecting major roads, but also for calculating accessibility in land-usesimulation. The model was applied in the city centre of Stockholm andconfirmed the reciprocal influence between land use and street network duringthe long-term growth. To further study network growth modeling, a novel weighted network model,involving nonlinear growth and neighboring connections, was built from theperspective of promising complex networks. Both mathematical analysis andnumerical simulation were examined in the evolution process, and the effects ofneighboring connections were particular investigated to study the preferentialattachment mechanisms in the evolution. Since road network is a weightedplanar graph, the growth model for urban street networks was subsequentlymodeled. It succeeded in reproducing diverse patterns and each pattern wasexamined by a series of measures. The similarity between the properties of derived patterns and empirical studies implies that there is a universal growthmechanism in the evolution of urban morphology. To better understand the complicated relationship between land use and roadnetwork, centrality indices from different aspects were fully analyzed in a casestudy over Stockholm. The correlation coefficients between different land-usetypes and road network centralities suggest that various centrality indices,reflecting human activities in different ways, can capture land development andconsequently influence urban structure. The strength of this thesis lies in its interdisciplinary approaches to analyze andmodel urban growth. The integration of ‘bottom-up’ land-use simulation androad network growth model in urban growth simulation is the major contribution.The road network growth model in terms of complex network science is anothercontribution to advance spatial network modeling within the field of GIScience.The works in this thesis vary from a novel theoretical weighted network modelto the particular models of land use, urban street network and hybrid urbangrowth, and to the specific applications and statistical analysis in real cases.These models help to improve our understanding of urban growth phenomenaand urban morphological evolution through long-term simulations. Thesimulation results can further support urban planning and growth management.The study of hybrid models integrating methods and techniques frommultidisciplinary fields has attracted a lot attention and still needs constantefforts in near future. / <p>QC 20130514</p>

A paradigm shift is emerging in system reliability and maintainability. The military and industrial sectors are moving away from the traditional breakdown and scheduled maintenance to adopt concepts referred to as Condition Based Maintenance (CBM) and Prognostic Health Management (PHM). In addition to signal processing and subsequent diagnostic and prognostic algorithms these new technologies involve storage of large volumes of both quantitative and qualitative information to carry out maintenance tasks effectively. This not only requires research and development in advanced technologies but also the means to store, organize and access this knowledge in a timely and efficient fashion. Knowledge-based expert systems have been shown to possess capabilities to manage vast amounts of knowledge, but an intelligent systems approach calls for attributes like learning and adaptation in building autonomous decision support systems. This research presents an integrated knowledge-based approach to diagnostic reasoning for CBM of engineering systems. A two level diagnosis scheme has been conceptualized in which first a fault is hypothesized using the observational symptoms from the system and then a more specific diagnostic test is carried out using only the relevant sensor measurements to confirm the hypothesis. Utilizing the qualitative (textual) information obtained from these systems in combination with quantitative (sensory) information reduces the computational burden by carrying out a more informed testing. An Industrial Language Processing (ILP) technique has been developed for processing textual information from industrial systems. Compared to other automated methods that are computationally expensive, this technique manipulates standardized language messages by taking advantage of their semi-structured nature and domain limited vocabulary in a tractable manner. A Dynamic Case-based reasoning (DCBR) framework provides a hybrid platform for diagnostic reasoning and an integration mechanism for the operational infrastructure of an autonomous Decision Support System (DSS) for CBM. This integration involves data gathering, information extraction procedures, and real-time reasoning frameworks to facilitate the strategies and maintenance of critical systems. As a step further towards autonomy, DCBR builds on a self-evolving knowledgebase that learns from its performance feedback and reorganizes itself to deal with non-stationary environments. A unique Human-in-the-Loop Learning (HITLL) approach has been adopted to incorporate human feedback in the traditional Reinforcement Learning (RL) algorithm.

La présente étude s’inscrit à l’intérieur du programme de recherche mené par le Groupe DÉFI Apprentissage (GDA) de l’Université de Montréal. Notre projet avait pour buts de réaliser les deux premières phases précédant la conception d’une ressource pédagogique qui offrira à l’enfant (6 à 12 ans qui a des incapacités intellectuelles) et à son entourage un dispositif temporel évolutif pour l’initier à la lecture de l’heure et à la gestion des activités de son horaire quotidien à l’âge approprié. Depuis quelques années, maints organismes tels que l’American Association on Mental Retardation (AAMR) , la Classification internationale des déficiences, incapacités et handicaps (CIDIH) de l'Organisation Mondiale de la Santé (OMS), le Ministère de l’éducation, des loisirs et des sports du Québec (MELS) ainsi que le Réseau international sur le processus de production du handicap (RIPPH) soutiennent que les chercheurs doivent s’attarder aux composantes impliquées dans l’interaction Personne-Milieu pour concevoir des stratégies d’intervention auprès de diverses populations qui éprouvent des limitations. Notre recherche adoptera cette démarche en s’appuyant sur les assises suivantes : cadre méthodologique (analyse de la valeur pédagogique), cadre conceptuel (écologie de l’éducation et processus de production du handicap) et cadre technologique (ergonomie). / The present study is part of an ongoing research program which has been undertaken by the « Groupe DÉFI Apprentissage (GDA) » at « l’Université de Montréal ». Our precise mandate was to complete the two initial phases preceding the conception of a pedagogical ressource that will offer to a child (6 to 12 years old with intellectual disabilities) and to his immediate surrounding an evolutionary temporal device that will initiate him, at the appropriate age, to time telling and to daily schedule management. In recent years, a number of organizations such as the American Association on Mental Retardation (AAMR), the World Health Organization (WHO) the Ministère de l’éducation, du loisir et du sport du Québec (MEL) and the Réseau International sur le Processus de Production du Handicap (RIPPH) have recommended that researchers should take into account the components involved in the Person-Environment interaction when they are studying intervention strategies that could be used by people with various limitations. Our research has adopted this ecological approach and it will be supported by the following foundations: methodological approach (pedagogical value analysis), conceptual approach (educational ecology) and technological approach (ergonomics).

The focus of this research is on the analysis of relations between Israel and India from 1948 to 2005. The State of Israel was established in 1948 but only on 18 September 1950 did India recognise Israel. Eventually, the two countries finally established full diplomatic relations on 29 January 1992. The research covers three specific timeframes and aims to clarify the factors that have affected and effected the relations between the two countries in terms of levels of analysis. The first timeframe (from 1948 to 1991) pertains to bilateral relations between the two countries before the establishment of diplomatic relations, including preindependence relations. India's foreign policy towards Israel reflected its selfinterest in the Middle East as well as its traditional sympathy with the Arabs and had been influenced by India's commitment to the Non-aligned Movement and the sentiments of the Indian Muslims. Eventually it was transformed into an anti- Israeli foreign policy. In the second timeframe, the change in bilateral relations between Israel and India in 1992 and the establishment of diplomatic relations between the two countries are analysed by the Aggregative Model of Bilateral Foreign Relations Strategic Change. This analysis deals with the operational environment within which the Indian systemic foreign policy changed towards Israel. In the third timeframe, the evolving bilateral relations between India and Israel from 1992 to 2005 are analysed in terms of the Oscillated Diplomacy Model. Consecutive Indian governments in power had an influence on the volume of Indian diplomacy towards Israel as well as the direction of the relations between the two countries. Furthermore, three types of mutual national strategic interests, namely, joint strategic interests, common strategic interests and discrepant strategic interests, influenced the operational diplomacy of both countries. In essence, Israeli-Indian relations from 1948 to 1991 were characterised by partial and consistent pro-Arab and anti-Israeli foreign policy. In 1992, a significant diplomatic change occurred when India and Israel established full diplomatic relations. Since then bilateral relations have evolved continually in a positive manner concentrating on the convergence of strategic interests of the two countries. / International Politics / D.Litt. et Phil. (International Politics))

Made available in DSpace on 2015-03-03T15:08:47Z (GMT). No. of bitstreams: 1 BrunoSJC_TESE.pdf: 2605632 bytes, checksum: cc7fdbd9d8d7dfe3adac23f17fab1ae2 (MD5) Previous issue date: 2014-05-13 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / In this work, we propose a two-stage algorithm for real-time fault detection and identification of industrial plants. Our proposal is based on the analysis of selected features using recursive density estimation and a new evolving classifier algorithm. More specifically, the proposed approach for the detection stage is based on the concept of density in the data space, which is not the same as probability density function, but is a very useful measure for abnormality/outliers detection. This density can be expressed by a Cauchy function and can be calculated recursively, which makes it memory and computational power efficient and, therefore, suitable for on-line applications. The identification/diagnosis stage is based on a self-developing (evolving) fuzzy rule-based classifier system proposed in this work, called AutoClass. An important property of AutoClass is that it can start learning from scratch". Not only do the fuzzy rules not need to be prespecified, but neither do the number of classes for AutoClass (the number may grow, with new class labels being added by the on-line learning process), in a fully unsupervised manner. In the event that an initial rule base exists, AutoClass can evolve/develop it further based on the newly arrived faulty state data. In order to validate our proposal, we present experimental results from a level control didactic process, where control and error signals are used as features for the fault detection and identification systems, but the approach is generic and the number of features can be significant due to the computationally lean methodology, since covariance or more complex calculations, as well as storage of old data, are not required. The obtained results are significantly better than the traditional approaches used for comparison / Este trabalho prop?e um algoritmo de dois estagios para detec??o e identifica??o de falhas, em tempo real, em plantas industriais. A proposta baseia-se na analise de caracter?sticas selecionadas utilizando estimativa de densidade recursiva e um novo algoritmo evolutivo de classifica??o. Mais especificamente, a abordagem proposta para detec??o e baseada no conceito de densidade no espa?o de dados, o que difere da tradicional fun??o densidade de probabilidade, porem, sendo uma medida bastante util na detec??o de anormalidades/outliers. Tal densidade pode ser expressa por uma fun??o de Cauchy e calculada recursivamente, o que torna o algoritmo computacionalmente eficiente, em termos de processamento e memoria, e, dessa maneira, apropriado para aplica??es on-line. O estagio de identifica??o/diagnostico e realizado por um classificador baseado em regras fuzzy capaz de se auto-desenvolver (evolutivo), chamado de AutoClass, e introduzido neste trabalho. Uma propriedade importante do AutoClass e que ele e capaz de aprender a partir do zero". Tanto as regras fuzzy, quanto o numero de classes para o algoritmo n?o necessitam de pre-especifica??o (o numero de classes pode crescer, com os rotulos de classe sendo adicionados pelo processo de aprendizagem on-line), de maneira n~ao-supervisionada. Nos casos em que uma base de regras inicial existe, AutoClass pode evoluir/desenvolver-se a partir dela, baseado nos dados adquiridos posteriormente. De modo a validar a proposta, o trabalho apresenta resultados experimentais de simula??o e de aplica??es industriais reais, onde o sinal de controle e erro s?o utilizados como caracter?sticas para os estagios de detec??o e identifica??o, porem a abordagem e generica, e o numero de caracter?sticas selecionadas pode ser significativamente maior, devido ? metodologia computacionalmente eficiente adotada, uma vez que calculos mais complexos e armazenamento de dados antigos n?o s?o necess?rios. Os resultados obtidos s?o signifificativamente melhores que os gerados pelas abordagens tradicionais utilizadas para compara??o

Grâce aux avancées conjointes des systèmes microélectroniques, des technologies sans fil et de la microélectronique embarquée, les réseaux de capteurs sans fil (RCsF) ont récemment pu voir le jour. Très sophistiqués et en interaction directe avec leur environnement, ces systèmes informatiques et électroniques communiquent principalement à travers des réseaux radio qui en font des objets communicants autonomes. Ils offrent l'opportunité de prendre en compte les évolutions temporelles et spatiales du monde physique environnant. Les RCsF se retrouvent donc au cœur de nombreuses applications couvrant des domaines aussi variés que la santé, la domotique, l'intelligence ambiante, les transports, la sécurité, l'agronomie et l'environnement. Ils connaissent un véritable essor et ce dans divers domaines des STIC : hardware, système d'exploitation, conception d'antenne, système d'information, protocoles réseaux, théorie des graphes, algorithmique distribuée, sécurité, etc. L’intérêt des communautés issues de la recherche et de l’industrie pour ces RCsF s’est accru par la potentielle fiabilité, précision, flexibilité, faible coût ainsi que la facilité de déploiement de ces systèmes. La spontanéité, l’adaptabilité du réseau et la dynamicité de sa topologie dans le déploiement des RCsF soulèvent néanmoins de nombreuses questions encore ouvertes. Dans le cadre de cette thèse, nous nous sommes intéressés aux aspects liés à la problématique du routage dans un RCsF, l’objectif étant de proposer des approches algorithmiques permettant de faire du routage adaptatif multi critères dans un RCsF. Nous nous sommes concentrés sur deux critères principaux : la consommation d’énergie dans les capteurs et le délai d’acheminement des informations collectées par les capteurs. Nous avons proposé ainsi un nouveau protocole de routage, appelé EDEAR (Energy and Delay Efficient Adaptive Routing), qui se base sur un mécanisme d’apprentissage continu et distribué permettant de prendre en compte la dynamicité du réseau. Celui-ci utilise deux types d’agents explorateurs chargés de la collecte de l’information pour la mise à jour des tables de routage. Afin de réduire la consommation d’énergie et la surcharge du réseau, nous proposons également un processus d’exploration des routes basé sur une diffusion optimisée des messages de contrôle. Le protocole EDEAR calcule les routes qui minimisent simultanément l’énergie consommée et le délai d’acheminement des informations de bout en bout permettant ainsi de maximiser la durée de vie du réseau. L’apprentissage se faisant de manière continue, le routage se fait donc de façon évolutive et permet ainsi une réactivité aux différents évènements qui peuvent intervenir sur le réseau. Le protocole proposé est validé et comparé aux approches traditionnelles, son efficacité au niveau du routage adaptatif est mise particulièrement en évidence aussi bien dans le cas de capteurs fixes que de capteurs mobiles. En effet, celui-ci permet une meilleure prise en compte de l'état du réseau contrairement aux approches classiques / Through the joint advanced microelectronic systems, wireless technologies and embedded microelectronics, wireless sensor networks have recently been possible. Given the convergence of communications and the emergence of ubiquitous networks, sensor networks can be used in several applications and have a great impact on our everyday life. There is currently a real interest of research in wireless sensor networks; however, most of the existing routing protocols propose an optimization of energy consumption without taking into account other metrics of quality of service. In this thesis, we propose an adaptive routing protocol called "EDEAR" which takes into account both necessary criteria to the context of communications in sensor networks, which are energy and delay of data delivery. We are looking the routes for optimizing a nodes’ lifetime in the network, these paths are based on joint optimization of energy consumption and delay through a multi criteria cost function. The proposed algorithm is based on the use of the dynamic state-dependent policies which is implemented with a bio-inspired approach based on iterative trial/error paradigm. Our proposal is considered as a hybrid protocol: it combines on demand searching routes concept and proactive exploration concept. It uses also a multipoint relay mechanism for energy consumption in order to reduce the overhead generated by the exploration packets. Numerical results obtained with NS simulator for different static and mobility scenario show the efficiency of the adaptive approaches compared to traditional approaches and proves that such adaptive algorithms are very useful in tracking a phenomenon that evolves over time

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Conifers are important both ecologically and socioeconomically, however, same parts of their biology are not that well researched. This includes genetics and breeding and partly even physiology. Because quantitative genetic analyzes applied in breeding necessitate an analysis of a large number of samples, and conventional methods of analysis are quite time-consuming, certain parameters describing e.g. the activity of photosynthetic electron-transport chain (ETC) are considered for such use. Several methods of the measurement of the activity of photosynthetic ETC exist, but there are some problems with their usage in conifers. I studied this issue from different points of view in three parts of this thesis. 1) I compared the photosynthetic ETC activity in 8 species of conifers using chlorophyll (Chl) fluorescence measurements on intact needles and polarographic measurements in isolated chloroplasts. Each method brought different information. 2) I measured Chl fluorescence parameters, reflectance spectra and pigment content in 536 genetically defined trees of Pinus sylvestris L. Many parameters showed relatively high genetic variability and heritability. I have also determined the suitability of various reflectance indices to estimate pigment and water content of needles. 3) I have optimized the...

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