Made available in DSpace on 2016-06-02T19:03:58Z (GMT). No. of bitstreams: 1 5694.pdf: 2329501 bytes, checksum: 90a80d78f180e25fc719ec410704ff8f (MD5) Previous issue date: 2013-12-13 / Financiadora de Estudos e Projetos / In Computer Science, ontologies are used for knowledge representation in a number of applications, aiming to structure and handle domain semantics through models shared by humans and computational systems. Although traditional ontologies model semantic information and support reasoning tasks, they are based on a formalism which is less suitable to express the vagueness inherent in real-world phenomena and human language. To address this issue, many proposals investigate how traditional ontologies can be extended by incorporating concepts from fuzzy sets and fuzzy logic, resulting in fuzzy ontologies. In special, combining the formalism from fuzzy ontologies with fuzzy rule-based reasoning, which has been successfully applied in the context of fuzzy inference systems, can lead to more expressive inferences involving imprecision. In this sense, this doctoral thesis aims at exploring the integration of fuzzy ontology reasoning with fuzzy inference systems, resulting in the definition and the development of two approaches: HyFOM (Hybrid integration of Fuzzy Ontology and Mamdani reasoning) and FT-FIS (Fuzzy Tableau and Fuzzy Inference System). HyFOM is based on a hybrid architecture combining reasoners for ontologies, fuzzy ontologies and fuzzy inference systems, focusing on the interaction among its independent components. FT-FIS defines an interface between a fuzzy tableau-based algorithm and a fuzzy inference system, including the fuzzyRuleReasoning predicate that allows fuzzy rule-based reasoning to be invoked whenever necessary for fuzzy ontology reasoning tasks. The main contribution of HyFOM and FT-FIS comes from their reasoning architectures, which combine flexibility in terms of fuzzy rule semantics with the collaboration between inferences from both types of reasoning. Experiments regarding the recommendation of touristic attractions, based on synthetic data, revealed that HyFOM and FT-FIS provide integrated inferences, in addition to a more expressive approximation of the relation defined by fuzzy rules than the results from the fuzzyDL reasoner. In experiments involving the evaluation of chemical risk in food samples, based on real data, results obtained by HyFOM and FT-FIS are also more precise than fuzzyDL results, in comparison with reference values available in this domain. / No contexto da Ciência da Computação, ontologias são utilizadas para representação de conhecimento em diversas aplicações, com o intuito de estruturar e tratar a semântica de domínios específicos. Embora representem e permitam inferir conhecimento implícito, as ontologias convencionais baseiam-se em um formalismo que não é capaz de expressar a imprecisão presente em fenômenos do mundo real e na linguagem humana. Para abordar esta limitação, há diversas pesquisas que investigam a incorporação de conceitos da teoria de conjuntos fuzzy e da lógica fuzzy em ontologias, resultando em ontologias fuzzy. Em especial, combinar o formalismo das ontologias fuzzy com o raciocínio baseado em regras fuzzy, utilizado com sucesso no contexto de sistemas de inferência fuzzy, pode proporcionar uma maior expressividade com relação às inferências envolvendo imprecisão. Neste sentido, o objetivo deste projeto de doutorado é explorar a integração do raciocínio de ontologias fuzzy e de sistemas de inferência fuzzy, resultando na definição e no desenvolvimento das abordagens HyFOM (Hybrid integration of Fuzzy Ontology and Mamdani reasoning) e FT-FIS (Fuzzy Tableau and Fuzzy Inference System). HyFOM baseia-se em uma arquitetura híbrida que combina motores de inferência existentes na literatura para ontologias, ontologias fuzzy e sistemas de inferência fuzzy, com foco na interação entre seus componentes independentes. FT-FIS define uma interface entre um algoritmo baseado em tableau fuzzy e um sistema de inferência fuzzy, incluindo o predicado fuzzyRuleReasoning que permite invocar o raciocínio baseado em regras fuzzy quando for necessário para as tarefas de raciocínio da ontologia fuzzy. A principal contribuição das arquiteturas de raciocínio de HyFOM e FT-FIS está na combinação de flexibilidade, em termos da semântica das regras fuzzy, com a colaboração entre as inferências de ambos tipos de raciocínio. Experimentos considerando a recomendação de atrações turísticas, baseados em dados sintéticos, revelaram que HyFOM e FT-FIS são capazes de proporcionar inferências integradas, além de uma aproximação mais expressiva da relação estabelecida pelas regras fuzzy que os resultados providos pelo raciocinador fuzzyDL. Em experimentos envolvendo o domínio de risco químico em alimentos, baseado em dados reais, os resultados de HyFOM e FT-FIS também são mais precisos que os resultados de fuzzyDL, em comparação com valores de referência disponíveis nesse domínio.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-04-24T12:12:06Z No. of bitstreams: 1 eduardopestanadeaguiar.pdf: 1937921 bytes, checksum: 0472ffffb70cabf120dc5de86d6626b1 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-04-24T16:55:00Z (GMT) No. of bitstreams: 1 eduardopestanadeaguiar.pdf: 1937921 bytes, checksum: 0472ffffb70cabf120dc5de86d6626b1 (MD5) / Made available in DSpace on 2017-04-24T16:55:00Z (GMT). No. of bitstreams: 1 eduardopestanadeaguiar.pdf: 1937921 bytes, checksum: 0472ffffb70cabf120dc5de86d6626b1 (MD5) Previous issue date: 2011-08-30 / A presente dissertação tem como objetivo discutir o uso de técnicas de otimização baseadas no gradiente conjugado e de informações de segunda ordem para o treinamento de sistemas de inferência fuzzy singleton e non-singleton. Além disso, as soluções computacionais derivadas são aplicadas aos problemas de classificação de distúrbios múltiplos e isolados em sinais elétricos. Os resultados computacionais, obtidos a partir de dados sintéticos de distúrbios em sinais de tensão, indicam que os sistemas de inferência fuzzy singleton e non-singleton treinados pelos algoritmos de otimização considerados apresentam maior velocidade de convergência e melhores taxas de classificação quando comparados com aqueles treinados pelo algoritmo de otimização baseada em informações de primeira ordem e é bastante competitivo em relação à rede neural artificial perceptron multicamadas - multilayer perceptron (MLP) e ao classificador de Bayes. / This master dissertation aims to discuss the use of optimization techniques based on the conjugated gradient and on second order information for the training of singleton or non-singleton fuzzy inference systems. In addition, the computacional solutions obtained are applied to isolated a multiple disturbances classification problems in electric signals. Computational results obtained from synthetic data from disturbances in electric signals indicate that singleton or non-singleton fuzzy inference systems trained by the considered optimization algorithms present greater convergence speed and better classification rates when compared to those data trained by an optimization algorithm based on first order information and is quite competitive with multilayer perceptron neural network and Bayesian classifier.

Práce popisuje základy principu funkčnosti neuronů a vytvoření umělých neuronových sítí. Je zde důkladně popsána struktura a funkce neuronů a ukázán nejpoužívanější algoritmus pro učení neuronů. Základy fuzzy logiky, včetně jejich výhod a nevýhod, jsou rovněž prezentovány. Detailněji je popsán algoritmus zpětného šíření chyb a adaptivní neuro-fuzzy inferenční systém. Tyto techniky poskytují efektivní způsoby učení neuronových sítí.

Práce popisuje základy principu funkčnosti neuronů a vytvoření umělých neuronových sítí. Je zde důkladně popsána struktura a funkce neuronů a ukázán nejpoužívanější algoritmus pro učení neuronů. Základy fuzzy logiky, včetně jejich výhod a nevýhod, jsou rovněž prezentovány. Detailněji je popsán algoritmus zpětného šíření chyb a adaptivní neuro-fuzzy inferenční systém. Tyto techniky poskytují efektivní způsoby učení neuronových sítí.

Les travaux de cette thèse visent à apporter des éléments de solutions au problème de la durée de vie des systèmes pile à combustible (FCS – Fuel Cell System) de type à « membrane échangeuse de protons » (PEM – Proton Exchange Membrane) et se décline sur deux champs disciplinaires complémentaires :Une première approche vise à augmenter la durée de vie de celle-ci par la conception et la mise en œuvre d'une architecture de pronostic et de gestion de l'état de santé (PHM – Prognostics & Health Management). Les PEM-FCS, de par leur technologie, sont par essence des systèmes multi-physiques (électriques, fluidiques, électrochimiques, thermiques, mécaniques, etc.) et multi-échelles (de temps et d'espace) dont les comportements sont difficilement appréhendables. La nature non linéaire des phénomènes, le caractère réversible ou non des dégradations, et les interactions entre composants rendent effectivement difficile une étape de modélisation des défaillances. De plus, le manque d'homogénéité (actuel) dans le processus de fabrication rend difficile la caractérisation statistique de leur comportement. Le déploiement d'une solution PHM permettrait en effet d'anticiper et d'éviter les défaillances, d'évaluer l'état de santé, d'estimer le temps de vie résiduel du système, et finalement, d'envisager des actions de maîtrise (contrôle et/ou maintenance) pour assurer la continuité de fonctionnement. Une deuxième approche propose d'avoir recours à une hybridation passive de la PEMFC avec des super-condensateurs (UC – Ultra Capacitor) de façon à faire fonctionner la pile au plus proche de ses conditions opératoires optimales et ainsi, à minimiser l'impact du vieillissement. Les UCs apparaissent comme une source complémentaire à la PEMFC en raison de leur forte densité de puissance, de leur capacité de charge/décharge rapide, de leur réversibilité et de leur grande durée de vie. Si l'on prend l'exemple des véhicules à pile à combustible, l'association entre une PEMFC et des UCs peut être réalisée en utilisant un système hybride de type actif ou passif. Le comportement global du système dépend à la fois du choix de l'architecture et du positionnement de ces éléments en lien avec la charge électrique. Aujourd'hui, les recherches dans ce domaine se focalisent essentiellement sur la gestion d'énergie entre les sources et stockeurs embarqués ; et sur la définition et l'optimisation d'une interface électronique de puissance destinée à conditionner le flux d'énergie entre eux. Cependant, la présence de convertisseurs statiques augmente les sources de défaillances et pannes (défaillance des interrupteurs du convertisseur statique lui-même, impact des oscillations de courant haute fréquence sur le vieillissement de la pile), et augmente également les pertes énergétiques du système complet (même si le rendement du convertisseur statique est élevé, il dégrade néanmoins le bilan global). / This thesis work aims to provide solutions for the limited lifetime of Proton Exchange Membrane Fuel Cell Systems (PEM-FCS) based on two complementary disciplines:A first approach consists in increasing the lifetime of the PEM-FCS by designing and implementing a Prognostics & Health Management (PHM) architecture. The PEM-FCS are essentially multi-physical systems (electrical, fluid, electrochemical, thermal, mechanical, etc.) and multi-scale (time and space), thus its behaviors are hardly understandable. The nonlinear nature of phenomena, the reversibility or not of degradations and the interactions between components makes it quite difficult to have a failure modeling stage. Moreover, the lack of homogeneity (actual) in the manufacturing process makes it difficult for statistical characterization of their behavior. The deployment of a PHM solution would indeed anticipate and avoid failures, assess the state of health, estimate the Remaining Useful Lifetime (RUL) of the system and finally consider control actions (control and/or maintenance) to ensure operation continuity.A second approach proposes to use a passive hybridization of the PEMFC with Ultra Capacitors (UC) to operate the fuel cell closer to its optimum operating conditions and thereby minimize the impact of aging. The UC appear as an additional source to the PEMFC due to their high power density, their capacity to charge/discharge rapidly, their reversibility and their long life. If we take the example of fuel cell hybrid electrical vehicles, the association between a PEMFC and UC can be performed using a hybrid of active or passive type system. The overall behavior of the system depends on both, the choice of the architecture and the positioning of these elements in connection with the electric charge. Today, research in this area focuses mainly on energy management between the sources and embedded storage and the definition and optimization of a power electronic interface designated to adjust the flow of energy between them. However, the presence of power converters increases the source of faults and failures (failure of the switches of the power converter and the impact of high frequency current oscillations on the aging of the PEMFC), and also increases the energy losses of the entire system (even if the performance of the power converter is high, it nevertheless degrades the overall system).