Este trabalho propõe uma nova abordagem conexionista, chamada de IGMN (do inglês Incremental Gaussian Mixture Network), para aproximação incremental de funções e tarefas de tempo real. Ela é inspirada em recentes teorias do cérebro, especialmente o MPF (do inglês Memory-Prediction Framework) e a Inteligência Artificial Construtivista, que fazem com que o modelo proposto possua características especiais que não estão presentes na maioria dos modelos de redes neurais existentes. Além disso, IGMN é baseado em sólidos princípios estatísticos (modelos de mistura gaussianos) e assintoticamente converge para a superfície de regressão ótima a medida que os dados de treinamento chegam. As principais vantagens do IGMN em relação a outros modelos de redes neurais são: (i) IGMN aprende instantaneamente analisando cada padrão de treinamento apenas uma vez (cada dado pode ser imediatamente utilizado e descartado); (ii) o modelo proposto produz estimativas razoáveis baseado em poucos dados de treinamento; (iii) IGMN aprende de forma contínua e perpétua a medida que novos dados de treinamento chegam (não existem fases separadas de treinamento e utilização); (iv) o modelo proposto resolve o dilema da estabilidade-plasticidade e não sofre de interferência catastrófica; (v) a topologia da rede neural é definida automaticamente e de forma incremental (novas unidades são adicionadas sempre que necessário); (vi) IGMN não é sensível às condições de inicialização (de fato IGMN não utiliza nenhuma decisão e/ou inicialização aleatória); (vii) a mesma rede neural IGMN pode ser utilizada em problemas diretos e inversos (o fluxo de informações é bidirecional) mesmo em regiões onde a função alvo tem múltiplas soluções; e (viii) IGMN fornece o nível de confiança de suas estimativas. Outra contribuição relevante desta tese é o uso do IGMN em importantes tarefas nas áreas de robótica e aprendizado de máquina, como por exemplo a identificação de modelos, a formação incremental de conceitos, o aprendizado por reforço, o mapeamento robótico e previsão de séries temporais. De fato, o poder de representação e a eficiência e do modelo proposto permitem expandir o conjunto de tarefas nas quais as redes neurais podem ser utilizadas, abrindo assim novas direções nos quais importantes contribuições do estado da arte podem ser feitas. Através de diversos experimentos, realizados utilizando o modelo proposto, é demonstrado que o IGMN é bastante robusto ao problema de overfitting, não requer um ajuste fino dos parâmetros de configuração e possui uma boa performance computacional que permite o seu uso em aplicações de controle em tempo real. Portanto pode-se afirmar que o IGMN é uma ferramenta de aprendizado de máquina bastante útil em tarefas de aprendizado incremental de funções e predição em tempo real. / This work proposes IGMN (standing for Incremental Gaussian Mixture Network), a new connectionist approach for incremental function approximation and real time tasks. It is inspired on recent theories about the brain, specially the Memory-Prediction Framework and the Constructivist Artificial Intelligence, which endows it with some unique features that are not present in most ANN models such as MLP, RBF and GRNN. Moreover, IGMN is based on strong statistical principles (Gaussian mixture models) and asymptotically converges to the optimal regression surface as more training data arrive. The main advantages of IGMN over other ANN models are: (i) IGMN learns incrementally using a single scan over the training data (each training pattern can be immediately used and discarded); (ii) it can produce reasonable estimates based on few training data; (iii) the learning process can proceed perpetually as new training data arrive (there is no separate phases for leaning and recalling); (iv) IGMN can handle the stability-plasticity dilemma and does not suffer from catastrophic interference; (v) the neural network topology is defined automatically and incrementally (new units added whenever is necessary); (vi) IGMN is not sensible to initialization conditions (in fact there is no random initialization/ decision in IGMN); (vii) the same neural network can be used to solve both forward and inverse problems (the information flow is bidirectional) even in regions where the target data are multi-valued; and (viii) IGMN can provide the confidence levels of its estimates. Another relevant contribution of this thesis is the use of IGMN in some important state-of-the-art machine learning and robotic tasks such as model identification, incremental concept formation, reinforcement learning, robotic mapping and time series prediction. In fact, the efficiency of IGMN and its representational power expand the set of potential tasks in which the neural networks can be applied, thus opening new research directions in which important contributions can be made. Through several experiments using the proposed model it is demonstrated that IGMN is also robust to overfitting, does not require fine-tunning of its configuration parameters and has a very good computational performance, thus allowing its use in real time control applications. Therefore, IGMN is a very useful machine learning tool for incremental function approximation and on-line prediction.

Bakgrund: Företagens interna innovationsfunktioner minskas samtidigt som företagen i större utsträckning väljer att tillämpa öppen innovation, gränsöverskridande samarbeten. Massor av forskning visar på att öppen innovation innebär en rad fördelar och möjligheter, men även utmaningar. Få studier belyser dock sambandet mellan komplexa innovationsutvecklingsprocesser och tillämpandet av gränsöverskridande samarbeten. Påverkar antalet komplexa beståndsdelar i innovationsutvecklingsprocesser i vilken konstellation företag väljer att samarbete med externa parter? Författarna ställer sig frågande till detta. Syfte: Syftet med denna studie är att undersöka hur komplexiteten i innovationsutvecklingsprocesser påverkar i vilken utsträckning företag väljer att samarbeta med externa parter. Studien ska även undersöka i vilken konstellation företag väljer att samarbeta med externa parter baserat på hur komplexa innovationsutvecklingsprocesserna är. Även Incitament för gränsöverskridande samarbete vid utvecklandet av komplexa innovationer kommer att studeras. Genomförande: Kvalitativ data erhölls genom fem samtalsintervjuer med fyra Innovationsledare och en processansvarig för innovationsutveckling avseende komplexa innovationer inom Ikea. Kvantitativ data erhölls genom en enkätundersökning som besvarades av samtliga Innovationsledare samt deras medarbetare (28 respondenter totalt) Resultat: Studien indikerar på att ju mer komplex en innovationsutvecklingsprocess är, desto större är sannolikheten att externa parter får större inblick i fler beståndsdelar avseende utvecklingsprocessen. Graden av komplexitet i utvecklingsprocessen ökar även sannolikheten för att inifrån-ut innovation tillämpas. / Background: The companys internal innovation functions decreases while companies choose to apply open innovation, cross-border cooperation. Lots of research shows that open innovation involves several benefits and opportunities, but also challenges. Few studies, however, illustrates the connection between complex innovation processes and the application of cross-border cooperation. Does the number of complex components in innovation development processes affect in which constellation companies choose to cooperate with external parties? This is something the authors ask themselves. Purpose: The purpose of this study is to investigate how the complexity of innovation development processes affects the extent to which companies choose to cooperate with external parties. The study will also investigate what constellation companies choose to collaborate with external parties based on the complexity of innovation development processes. Incentives for cross-border cooperation in the development of complex innovations will also be studied. Implementation: Qualitative data was obtained through five interviews with four Innovation leaders and a process manager for innovation development, regarding complex innovations within Ikea. Quantitative data was obtained through a survey that was answered by all Innovation Leaders and their employees (28 respondents in total). Results: The study indicates that the more complex an innovation development process is, the greater is the likelihood that external parties will gain more insight into more components of the development process. The degree of complexity in the development process also increases the likelihood that inside-out innovation is applied.

Schema therapy is a leading contemporary approach to treating mental illness. The therapy integrally uses self-report measures of negative schemas (“long lasting patterns of emotions, cognitions and memories”), and the negative parenting patterns that are linked to the development of these schemas. However, the negative parenting measures are insufficient, and there are no corresponding measures of positive schemas or positive parenting patterns. Study 1 focused on the development of a measure for positive schemas, the Young Positive Schema Questionnaire (YPSQ). Study 2 focused on the development of a measure for positive parenting patterns, the Positive Parenting Schema Inventory (PPSI). Finally, Study 3 empirically showed that the subscales of the Young Parenting Inventory (YPI) were not robust, and it provided a revised alternative (YPI-R2). For all three studies combined, community samples (n = 204 to 628) were collected from five countries in Asia (India, Indonesia, Malaysia, Singapore, and the Philippines) as well as the United States. The factor structure of the three instruments (the YPSQ, PPSI and YPI-R2) was stable in both Eastern and Western samples (in multigroup confirmatory factor analysis). All three scales showed prediction of mental health over and above what was possible with previous measures (incremental validity). The scales were not simply proxies for previously measured constructs (divergent validity). These scales also demonstrated significant associations with other established measures of parenting (construct validity). They also showed associations with negative schemas, well-being and ill-being (convergent validity). This thesis provides the tools needed to include a focus on positive as well as negative schemas and parenting patterns in both research and clinical practice. It also shows the benefits of so doing.

Este trabalho propõe uma nova abordagem conexionista, chamada de IGMN (do inglês Incremental Gaussian Mixture Network), para aproximação incremental de funções e tarefas de tempo real. Ela é inspirada em recentes teorias do cérebro, especialmente o MPF (do inglês Memory-Prediction Framework) e a Inteligência Artificial Construtivista, que fazem com que o modelo proposto possua características especiais que não estão presentes na maioria dos modelos de redes neurais existentes. Além disso, IGMN é baseado em sólidos princípios estatísticos (modelos de mistura gaussianos) e assintoticamente converge para a superfície de regressão ótima a medida que os dados de treinamento chegam. As principais vantagens do IGMN em relação a outros modelos de redes neurais são: (i) IGMN aprende instantaneamente analisando cada padrão de treinamento apenas uma vez (cada dado pode ser imediatamente utilizado e descartado); (ii) o modelo proposto produz estimativas razoáveis baseado em poucos dados de treinamento; (iii) IGMN aprende de forma contínua e perpétua a medida que novos dados de treinamento chegam (não existem fases separadas de treinamento e utilização); (iv) o modelo proposto resolve o dilema da estabilidade-plasticidade e não sofre de interferência catastrófica; (v) a topologia da rede neural é definida automaticamente e de forma incremental (novas unidades são adicionadas sempre que necessário); (vi) IGMN não é sensível às condições de inicialização (de fato IGMN não utiliza nenhuma decisão e/ou inicialização aleatória); (vii) a mesma rede neural IGMN pode ser utilizada em problemas diretos e inversos (o fluxo de informações é bidirecional) mesmo em regiões onde a função alvo tem múltiplas soluções; e (viii) IGMN fornece o nível de confiança de suas estimativas. Outra contribuição relevante desta tese é o uso do IGMN em importantes tarefas nas áreas de robótica e aprendizado de máquina, como por exemplo a identificação de modelos, a formação incremental de conceitos, o aprendizado por reforço, o mapeamento robótico e previsão de séries temporais. De fato, o poder de representação e a eficiência e do modelo proposto permitem expandir o conjunto de tarefas nas quais as redes neurais podem ser utilizadas, abrindo assim novas direções nos quais importantes contribuições do estado da arte podem ser feitas. Através de diversos experimentos, realizados utilizando o modelo proposto, é demonstrado que o IGMN é bastante robusto ao problema de overfitting, não requer um ajuste fino dos parâmetros de configuração e possui uma boa performance computacional que permite o seu uso em aplicações de controle em tempo real. Portanto pode-se afirmar que o IGMN é uma ferramenta de aprendizado de máquina bastante útil em tarefas de aprendizado incremental de funções e predição em tempo real. / This work proposes IGMN (standing for Incremental Gaussian Mixture Network), a new connectionist approach for incremental function approximation and real time tasks. It is inspired on recent theories about the brain, specially the Memory-Prediction Framework and the Constructivist Artificial Intelligence, which endows it with some unique features that are not present in most ANN models such as MLP, RBF and GRNN. Moreover, IGMN is based on strong statistical principles (Gaussian mixture models) and asymptotically converges to the optimal regression surface as more training data arrive. The main advantages of IGMN over other ANN models are: (i) IGMN learns incrementally using a single scan over the training data (each training pattern can be immediately used and discarded); (ii) it can produce reasonable estimates based on few training data; (iii) the learning process can proceed perpetually as new training data arrive (there is no separate phases for leaning and recalling); (iv) IGMN can handle the stability-plasticity dilemma and does not suffer from catastrophic interference; (v) the neural network topology is defined automatically and incrementally (new units added whenever is necessary); (vi) IGMN is not sensible to initialization conditions (in fact there is no random initialization/ decision in IGMN); (vii) the same neural network can be used to solve both forward and inverse problems (the information flow is bidirectional) even in regions where the target data are multi-valued; and (viii) IGMN can provide the confidence levels of its estimates. Another relevant contribution of this thesis is the use of IGMN in some important state-of-the-art machine learning and robotic tasks such as model identification, incremental concept formation, reinforcement learning, robotic mapping and time series prediction. In fact, the efficiency of IGMN and its representational power expand the set of potential tasks in which the neural networks can be applied, thus opening new research directions in which important contributions can be made. Through several experiments using the proposed model it is demonstrated that IGMN is also robust to overfitting, does not require fine-tunning of its configuration parameters and has a very good computational performance, thus allowing its use in real time control applications. Therefore, IGMN is a very useful machine learning tool for incremental function approximation and on-line prediction.

Made available in DSpace on 2014-06-11T19:22:31Z (GMT). No. of bitstreams: 0 Previous issue date: 2011-05-24Bitstream added on 2014-06-13T19:27:59Z : No. of bitstreams: 1 raniel_t_me_ilha.pdf: 1815527 bytes, checksum: 2b9558bcbd56601c8cbb627feda891cf (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / A automação de esteiras rolantes é algo comum e importante em sistemas industriais, mas problemas práticos ainda representam desafios. Um dos desses desafios é manter a precisão em sistemas que exigem paradas sistemáticas, pois folgas mecânicas tendem a provocar variações nas posições de paradas ao longo do tempo. A aplicação de motores de indução têm se tornado comum e soluções eficientes e de baixo custo têm sido pesquisadas. Neste trabalho foi desenvolvido e implementado um sistema de controle de posição e velocidade aplicado em esteiras transportadoras utilizando inversor de frequência, microcontrolador, encoder óptico incremental e sensor indutivo. O movimento da esteira transportadora é efetuado por um motor de indução trifásico, que é acionado pelo conjunto microcontrolador – inversor de frequência. Este conjunto impõe uma frequência no estator do motor através de uma troca de mensagens entre microcontrolador e inversor de frequência (Sistema Mestre-Escravo). Para o envio e recebimento das mensagens, utilizou-se o protocolo de comunicação serial USS® (Universal Serial Interface Protocol) através do padrão RS-485. Os controles de posição e velocidade de rotação do eixo do motor fundamentam-se no sinal gerado pelo encoder óptico incremental, responsável por informar a posição do eixo do motor ao longo da trajetória, e no sensor indutivo que determina uma referência externa importante para a esteira transportadora. Para o funcionamento automático da esteira, elaborou-se um software em linguagem de programação C. Como resultado obteve-se um sistema de controle de posição e velocidade do eixo do motor de indução trifásico que apresenta bons resultados / Automated conveyors system have been largely used in industrial applications. However, there are still practical issues to be overcome. One of them is due to the system mechanical limitation which can lead to low accuracy for applications based on “stop-and-go” movements. Induction motors have been largely used in such applications and low costs solutions have been searched. In this work it was developed and implemented a system of positioning and velocity control applied to conveyors which is based on frequency inverter, microcontroller, optical incremental encoder and inductive sensor. The conveyor’s movement is made by means of a three-phase induction motor, which is driven by the couple microcontroller–frequency inverter. There are messages exchange between the microcontroller and the frequency inverter (Master – Slave configuration) which is based on the communication serial protocol USS through the RS-485 standard. The position and velocity of the motor spindle are controlled using an optical incremental encoder, which is responsible to provide the position of the trajectory, and an inductive sensor which determines the initial reference to the conveyor. The software used to control the system was developed in C language. The results show a low cost system with good results

Este trabalho propõe uma nova abordagem conexionista, chamada de IGMN (do inglês Incremental Gaussian Mixture Network), para aproximação incremental de funções e tarefas de tempo real. Ela é inspirada em recentes teorias do cérebro, especialmente o MPF (do inglês Memory-Prediction Framework) e a Inteligência Artificial Construtivista, que fazem com que o modelo proposto possua características especiais que não estão presentes na maioria dos modelos de redes neurais existentes. Além disso, IGMN é baseado em sólidos princípios estatísticos (modelos de mistura gaussianos) e assintoticamente converge para a superfície de regressão ótima a medida que os dados de treinamento chegam. As principais vantagens do IGMN em relação a outros modelos de redes neurais são: (i) IGMN aprende instantaneamente analisando cada padrão de treinamento apenas uma vez (cada dado pode ser imediatamente utilizado e descartado); (ii) o modelo proposto produz estimativas razoáveis baseado em poucos dados de treinamento; (iii) IGMN aprende de forma contínua e perpétua a medida que novos dados de treinamento chegam (não existem fases separadas de treinamento e utilização); (iv) o modelo proposto resolve o dilema da estabilidade-plasticidade e não sofre de interferência catastrófica; (v) a topologia da rede neural é definida automaticamente e de forma incremental (novas unidades são adicionadas sempre que necessário); (vi) IGMN não é sensível às condições de inicialização (de fato IGMN não utiliza nenhuma decisão e/ou inicialização aleatória); (vii) a mesma rede neural IGMN pode ser utilizada em problemas diretos e inversos (o fluxo de informações é bidirecional) mesmo em regiões onde a função alvo tem múltiplas soluções; e (viii) IGMN fornece o nível de confiança de suas estimativas. Outra contribuição relevante desta tese é o uso do IGMN em importantes tarefas nas áreas de robótica e aprendizado de máquina, como por exemplo a identificação de modelos, a formação incremental de conceitos, o aprendizado por reforço, o mapeamento robótico e previsão de séries temporais. De fato, o poder de representação e a eficiência e do modelo proposto permitem expandir o conjunto de tarefas nas quais as redes neurais podem ser utilizadas, abrindo assim novas direções nos quais importantes contribuições do estado da arte podem ser feitas. Através de diversos experimentos, realizados utilizando o modelo proposto, é demonstrado que o IGMN é bastante robusto ao problema de overfitting, não requer um ajuste fino dos parâmetros de configuração e possui uma boa performance computacional que permite o seu uso em aplicações de controle em tempo real. Portanto pode-se afirmar que o IGMN é uma ferramenta de aprendizado de máquina bastante útil em tarefas de aprendizado incremental de funções e predição em tempo real. / This work proposes IGMN (standing for Incremental Gaussian Mixture Network), a new connectionist approach for incremental function approximation and real time tasks. It is inspired on recent theories about the brain, specially the Memory-Prediction Framework and the Constructivist Artificial Intelligence, which endows it with some unique features that are not present in most ANN models such as MLP, RBF and GRNN. Moreover, IGMN is based on strong statistical principles (Gaussian mixture models) and asymptotically converges to the optimal regression surface as more training data arrive. The main advantages of IGMN over other ANN models are: (i) IGMN learns incrementally using a single scan over the training data (each training pattern can be immediately used and discarded); (ii) it can produce reasonable estimates based on few training data; (iii) the learning process can proceed perpetually as new training data arrive (there is no separate phases for leaning and recalling); (iv) IGMN can handle the stability-plasticity dilemma and does not suffer from catastrophic interference; (v) the neural network topology is defined automatically and incrementally (new units added whenever is necessary); (vi) IGMN is not sensible to initialization conditions (in fact there is no random initialization/ decision in IGMN); (vii) the same neural network can be used to solve both forward and inverse problems (the information flow is bidirectional) even in regions where the target data are multi-valued; and (viii) IGMN can provide the confidence levels of its estimates. Another relevant contribution of this thesis is the use of IGMN in some important state-of-the-art machine learning and robotic tasks such as model identification, incremental concept formation, reinforcement learning, robotic mapping and time series prediction. In fact, the efficiency of IGMN and its representational power expand the set of potential tasks in which the neural networks can be applied, thus opening new research directions in which important contributions can be made. Through several experiments using the proposed model it is demonstrated that IGMN is also robust to overfitting, does not require fine-tunning of its configuration parameters and has a very good computational performance, thus allowing its use in real time control applications. Therefore, IGMN is a very useful machine learning tool for incremental function approximation and on-line prediction.

Le doctorat s'inscrit dans le cadre d'une bourse CIFRE et d'un partenariat entre l'ENSTA Bretagne, l'IRISA et Thales Air Systems. Les préoccupations de ce dernier, et plus particulièrement de l'équipe de rattachement, sont de réaliser des systèmes à logiciels prépondérants embarqués. La complexité de ces systèmes et les besoins de compétitivité associés font émerger la notion de "Model-Based Product Lines(MBPLs)". Celles-ci tendent à réaliser une synergie de l'abstraction de l'Ingénierie Dirigée par les Modèles (IDM) et de la capacité de gestion de la capitalisation et réutilisation des Lignes de Produits (LdPs). La nature irrévocablement dynamique des systèmes réels induit une évolution permanente des LdPs afin de répondre aux nouvelles exigences des clients et pour refléter les changements des artefacts internes de la LdP. L'objectif de cette thèse est unique, maîtriser des incréments d'évolution d'une ligne de produits de systèmes complexes, les contributions pour y parvenir sont duales. La thèse est que 1) une variabilité multidimensionnelle ainsi qu'une modélisation relationnelle est requise dans le cadre de lignes de produits de systèmes complexes pour en améliorer la compréhension et en faciliter l'évolution (proposition d'un cadre générique de décomposition de la modélisation et d'un langage (DSML) nommé PLiMoS, dédié à l'expression relationnelle et intentionnelle dans les MBPLs), et que 2) les efforts de spécialisation lors de la dérivation d'un produit ainsi que l'évolution de la LdP doivent être guidé par une architecture conceptuelle (introduction de motifs architecturaux autour de PLiMoS et du patron ABCDE) et capitalisés dans un processus outillé semi-automatisé d'évolution incrémentale des lignes de produits par extension. / The PhD (CIFRE fundings) was supported by a partnership between three actors: ENSTA Bretagne, IRISA and Thales Air Systems. The latter's concerns, and more precisely the ones from the affiliation team, are to build embedded software-intensive systems. The complexity of these systems, combined to the need of competitivity, reveal the notion of Model-Based Product Lines (MBPLs). They make a synergy of the capabilities of modeling and product line approaches, and enable more efficient solutions for modularization with the distinction of abstraction levels and separation of concerns. Besides, the dynamic nature of real-world systems induces that product line models need to evolve continually to meet new customer requirements and to reflect changes in product line artifacts. The aim of the thesis is to handle the increments of evolution of complex systems product lines, the contributions to achieve it are twofolds. The thesis claims that i) a multidimensional variability and a relational modeling are required within a complex system product line in order to enhance comprehension and ease the PL evolution (Conceptual model modularization framework and PliMoS Domain Specific Modeling Language proposition; the language is dedicated to relational and intentional expressions in MBPLs), and that ii) specialization efforts during product derivation have to be guided by a conceptual architecture (architectural patterns on top of PLiMoS, e.g.~ABCDE) and capitalized within a semi-automatic tooled process allowing the incremental PL evolution by extension.

Cette thèse s'intéresse à l'étude des problèmes de diagnostic des fautes sur les systèmes à événements discrets en utilisant les modèles réseau de Petri. Des techniques d'exploration incrémentale et à-la-volée sont développées pour combattre le problème de l'explosion de l'état lors de l'analyse de la diagnosticabilité. Dans le contexte atemporel, la diagnosticabilité de modèles RdP-L est abordée par l'analyse d'une série de problèmes K-diagnosticabilité. L'analyse de la diagnosticabilité est effectuée sur la base de deux modèles nommés respectivement FM-graph et FM-set tree qui sont développés à-la-volée. Un diagnostiqueur peut être dérivé à partir du FM-set tree pour le diagnostic en ligne. Dans le contexte temporel, les techniques de fractionnement des intervalles de temps sont élaborées pour développer représentation de l'espace d'état des RdP-LT pour laquelle des techniques d'analyse de la diagnosticabilité peuvent être utilisées. Sur cette base, les conditions nécessaires et suffisantes pour la diagnosticabilité de RdP-LT ont été déterminées. En pratique, l'analyse de la diagnosticabilité est effectuée sur la base de la construction à-la-volée d'une structure nommée ASG et qui contient des informations relatives à l'occurrence de fautes. D'une manière générale, l'analyse effectuée sur la base des techniques à-la-volée et incrémentale permet de construire et explorer seulement une partie de l'espace d'état, même lorsque le système est diagnosticable. Les résultats des simulations effectuées sur certains benchmarks montrent l'efficacité de ces techniques en termes de temps et de mémoire par rapport aux approches traditionnelles basées sur l'énumération des états / This PhD thesis deals with fault diagnosis of discrete event systems using Petri net models. Some on-the-fly and incremental techniques are developed to reduce the state explosion problem while analyzing diagnosability. In the untimed context, an algebraic representation for labeled Petri nets (LPNs) is developed for featuring system behavior. The diagnosability of LPN models is tackled by analyzing a series of K-diagnosability problems. Two models called respectively FM-graph and FM-set tree are developed and built on the fly to record the necessary information for diagnosability analysis. Finally, a diagnoser is derived from the FM-set tree for online diagnosis. In the timed context, time interval splitting techniques are developed in order to make it possible to generate a state representation of labeled time Petri net (LTPN) models, for which techniques from the untimed context can be used to analyze diagnosability. Based on this, necessary and sufficient conditions for the diagnosability of LTPN models are determined. Moreover, we provide the solution for the minimum delay ∆ that ensures diagnosability. From a practical point of view, diagnosability analysis is performed on the basis of on-the-fly building of a structure that we call ASG and which holds fault information about the LTPN states. Generally, using on-the-fly analysis and incremental technique makes it possible to build and investigate only a part of the state space, even in the case when the system is diagnosable. Simulation results obtained on some chosen benchmarks show the efficiency in terms of time and memory compared with the traditional approaches using state enumeration

Le présent travail de thèse s’inscrit dans le cadre d’un projet industriel proposé par une entreprise luxembourgeoise et en collaboration avec le Centre de Recherche Public Henry Tudor du Luxembourg (Laboratoire des Technologies Industriels (LTI)). L’objectif consiste à mettre au point une méthode numérique de détermination de la forme des outils d’emboutissage et du flan de pièces de précision en tôlerie fine pour que ce dernier, une fois déformé en une ou plusieurs opérations, correspond à la définition tridimensionnelle du cahier des charges. La méthode a pour objectif de remplacer une démarche expérimentale coûteuse par essais–erreur. Deux démarches numériques sont proposées, la première est relative à la détermination de la forme du flan. Elle consiste à estimer la forme du flan par Approche Inverse en partant de la forme 3D demandée. Puis, un logiciel de simulation incrémental par éléments finis en 3D est utilisé dans une procédure d’optimisation heuristique pour déterminer la forme du flan. Dans la deuxième démarche, il s’agit de déterminer la forme des outils d’emboutissage en utilisant le logiciel de simulation incrémental couplé avec une méthode de compensation du retour élastique en 2D. La démarche numérique est validée expérimentalement dans le cas d’un emboutissage réalisé en une ou plusieurs passes, à l’aide d’une presse manuelle, sans serre flan et avec des outils de forme très complexe. / The present study deals with an industrial project proposed by a luxembourgian enterprise and in collaboration with the luxembourgian research centre Henry Tudor (Laboratory of Industrial Technologies (LTI)). The main objective is to build a numerical approaches for the determination of the initial blank shape contour and tools shape for 3D thin metallic precision parts obtained by stamping, knowing the 3D CAD geometry of the final part. The purpose of the present procedure is to replace the expensive and time consuming experimental trial and error optimization method. Two numerical approaches have been proposed, the first is regarding the determination of the blank shape. An estimation of the blank shape can be given using the Inverse Approach. Update of the blank shape will then be continued by iterations combining heuristic optimization algorithms and incremental stamping codes. The second approach is based on precise finite element models and on spring-back compensation algorithm. The numerical approaches are tested in the case of a special stamping process where the parts are pressed in one or more steps using a manual press, without blank holder and by the mean of complex shape tools.

Parmi les combustibles nucléaires irradiés dans les Réacteurs à Eau Pressurisée d'Électricité de France, on trouve le combustible MOX, acronyme anglais de Mixed Oxide car il combine du dioxyde de plutonium et d'uranium. On y distingue trois phases, correspondant à des teneurs massiques en plutonium différentes. La teneur en matière fissile y étant différente, ces phases évoluent différemment sous irradiation, tant du point de vue mécanique que du point de vue chimique. Pour modéliser correctement le comportement macroscopique du combustible MOX dans un code de calcul industriel, les modèles ont besoin d'être alimentés de façon pertinente en propriétés effectives, mais il est aussi intéressant de disposer d'informations sur les champs locaux afin d'établir des couplages entre les mécanismes (couplage mécanique physico-chimie). L'objectif de la thèse fut donc de développer une modélisation par changement d'échelles, basée sur l'approche NTFA (Michel et Suquet 2003). Ces développements ont été réalisés sur des microstructures tridimensionnelles (3D) représentatives du combustible MOX et pour un comportement local visco-élastique vieillissant avec déformations libres. Dans un premier temps, pour représenter le combustible MOX en 3D nous avons utilisé des méthodes existantes pour traiter et segmenter les images expérimentales 2D, puis nous avons remonté les informations 2D indispensables (fuseau diamétral des inclusions et fractions surfaciques respectives) en 3D par la méthode stéréologique de Saltykov (Saltykov 1967) et enfin nous avons développé des outils pour représenter et discrétiser un composite multiphasé particulaire, type MOX. / Among the nuclear fuels irradiated in the Pressure Water Reactor of Électricité de France, MOX fuel is used, a Mixed OXide of plutonium and uranium. In this fuel, three phases with different plutonium content can be observed. The different fissile plutonium content in each phase leads different mechanical and physico-chemical evolutions under irradiation. To predict correctly the macroscopic behavior of MOX nuclear fuels in industrial nuclear fuel codes, models need to be fed in effective properties. But it's also interresting to obtain the local fields to establish coupling between mechanisms (mechanical and physico-chemical coupling). The aim of the PhD was to develop homogenisation method based on Non uniform Transformation Field Analysis (NTFA Michel and Suquet 2003}). These works were realised on three dimensions MOX microstructures and for local ageing visco-elastic behavior with free strains. The first work of the PhD was the numerical representation of the MOX microstructure in 3D. Three steps were realized. The first one consisted in the acquisition and the treatment of experimental pictures thanks to two soft-wares already developed. The second used the stereological model of textit{Saltykov} cite{R2S67} to go back up the two-dimensional statistical information in three-dimensional. And the last step was to develop tools which are able to build a numerical representation of the MOX microstructure. The second work of the PhD was to develop the NTFA model. Some theoretical (three dimensional, free strains and ageing hadn't ever studied) and numerical (choice and reduction of plastic modes, impact of the microstructures) studies were realised.