Дисертації з теми "Intelligence artificielle hybride"
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Kriaa, Hassen. "Analyse et conception révisable par interaction entre agents : Une approche hybride." Pau, 2000. http://www.theses.fr/2000PAUU3011.
Roy, Patrice. "Un modèle hybride de reconnaissance de plans pour les patients Alzheimer : dilemme entrecroisé/erroné /." Thèse, Chicoutimi : Montréal : Université du Québec à Chicoutimi ; Université du Québec à Montréal, 2007. http://theses.uqac.ca.
La p. de t. porte en outre: Mémoire présenté à l'Université du Québec à Chicoutimi comme exigence partielle de la maîtrise en informatique. CaQQUQ Bibliogr.: f. 133-138. Document électronique également accessible en format PDF. CaQQUQ
Ortiz, Paul. "Conception d’un système hybride de stockage de l’énergie pour la réduction des émissions carbone dans l’habitat individuel." Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0208.
Private homes are increasingly fitted with PhotoVoltaics (PV) for increasing the renewable energy use. Typical issues of this type of installation are : (1) the production of green energy is not necessary in line with the inhabitants' energy consumption (2) a peak of energy demand on smart grid increases the carbon emission. Mitigating carbon emission in using efficiently PV is the main objective of this research, which is part of the INTERREG NW Europe RED WOLF project (H2020 programme). This project is led led by Leeds Beckett University and involves 21 partners across Europe, including University of Lorraine. The RED WOLF project aims at installing PV solar panels and home batteries across UK, France, Netherlands, and Ireland, with the overall goal to design a Smart Storage Driver (SDS) system capables at storing solar energy produced at home, and using it when a peak of energy demand is detected on smart grid. To this end, the research is divided into three steps: (i) first, it is necessary to understand how inhabitants behave in order to obtain an energy consumption pattern for each home. This pattern will be used to size the batteries and to smartly manage the switching between the use of local and smart grid energy. Specific instrumentation will be deployed to achieve this step ; (ii) second, it is necessary to design a SDS system aiming at analysing data coming from multiple sources (e.g., smart grid, weather, indoor temperature, inhabitant behaviour, battery charge level) with one major target : the limitation of carbon emission ; (iii) finally, a global analysis ofdata management (Cloud and Fog) and network management (SDN, IoT) must be carried out in order to find the best trade off between Quality of Service offered for inhabitants and environmental impact
Bultey, Alexis. "Représentation hybride des heuristiques et métaconnaissances utilisées pour la conception innovante." Strasbourg, 2009. http://www.theses.fr/2009STRA6106.
This PhD thesis is about a theoretical and practical analysis of the conceptual phases of the design process. It aims at extracting its recurrent elements in order to be reusable in different contexts and it also aims at making that process easily computer implemented. With this goal, we have studied the russian theory TRIZ, which pretends to formalize the inventive process. We conclude that this theory has a great interest for design process and we prove that this theory might be implemented in a computer-aided design system. The results of this PhD thesis can be decomposed in three parts: formalization, operationalisation and implementation. Formalization suggests an ontological modelling of one of the TRIZ methodologies, named Substance-Field analysis. Operationalisation proposes a translation of the formalization to be supported by a computer. The implementation is a software tool based on a hybrid system combining description logics and first order logic rules
Liu, Ziming. "Méthodes hybrides d'intelligence artificielle pour les applications de navigation autonome." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4004.
Autonomous driving is a challenging task that has a wide range of applications in the real world. The autonomous driving system can be used in different platforms, such as cars, drones, and robots. These autonomous systems will reduce a lot of human labor and improve the efficiency of the current transportation system. Some autonomous systems have been used in real scenarios, such as delivery robots, and service robots. In the real world, autonomous systems need to build environment representations and localize themselves to interact with the environment. There are different sensors can be used for these objectives. Among them, the camera sensor is the best choice between cost and reliability. Currently, visual autonomous driving has achieved significant improvement with deep learning. Deep learning methods have advantages for environment perception. However, they are not robust for visual localization where model-based methods have more reliable results. To utilize the advantages of both data-based and model-based methods, a hybrid visual odometry method is explored in this thesis. Firstly, efficient optimization methods are critical for both model-based and data-based methods which share the same optimization theory. Currently, most deep learning networks are still trained with inefficient first-order optimizers. Therefore, this thesis proposes to extend efficient model-based optimization methods to train deep learning networks. The Gaussian-Newton and the efficient second-order methods are applied for deep learning optimization. Secondly, the model-based visual odometry method is based on the prior depth information, the robust and accurate depth estimation is critical for the performance of visual odometry module. Based on traditional computer vision theory, stereo vision can compute the depth with the correct scale, which is more reliable than monocular solutions. However, the current two-stage 2D-3D stereo networks have the problems of depth annotations and disparity domain gap. Correspondingly, a pose-supervised stereo network and an adaptive stereo network are investigated. However, the performance of two-stage networks is limited by the quality of 2D features that build stereo-matching cost volume. Instead, a new one-stage 3D stereo network is proposed to learn features and stereo-matching implicitly in a single stage. Thirdly, to keep robust, the stereo network and the dense direct visual odometry module are combined to build a stereo hybrid dense direct visual odometry (HDVO). Dense direct visual odometry is more reliable than the feature-based method because it is optimized with global image information. The HDVO is optimized with the photometric minimization loss. However, this loss suffers noises from the occlusion area, homogeneous texture area, and dynamic objects. This thesis explores removing noisy loss values with binary masks. Moreover, to reduce the effects of dynamic objects, semantic segmentation results are used to improve these masks. Finally, to be generalized for a new data domain, a test-time training method for visual odometry is explored. These proposed methods have been evaluated on public autonomous driving benchmarks, and show state-of-the-art performances
Ali, Sadaqat. "Energy management of multi-source DC microgrid systems for residential applications." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0159.
Compared to the alternating current (AC) electrical grid, the direct current (DC) electrical grid has demonstrated numerous advantages, such as its natural interface with renewable energy sources (RES), energy storage systems, and DC loads. It offers superior efficiency with fewer conversion steps, simpler control without skin effect or reactive power considerations. DC microgrids remain a relatively new technology, and their network architectures, control strategies, and stabilization techniques require significant research efforts. In this context, this thesis focuses on energy management issues in a multi-source DC electrical grid dedicated to residential applications. The DC electrical grid consists of distributed generators (solar panels), a hybrid energy storage system (HESS) with batteries and a supercapacitor (SC), and DC loads interconnected via DC/DC power converters. The primary objective of this research is to develop an advanced energy management strategy (EMS) to enhance the operational efficiency of the system while improving its reliability and sustainability. A hierarchical simulation platform of the DC electrical grid has been developed using MATLAB/Simulink. It comprises two layers with different time scales: a local control layer (time scale of a few seconds to minutes due to converter switching behavior) for controlling local components, and a system-level control layer (time scale of a few days to months with accelerated testing) for long-term validation and performance evaluation of the EMS. In the local control layer, solar panels, batteries, and the supercapacitor have been modeled and controlled separately. Various control modes, such as current control, voltage control, and maximum power point tracking (MPPT), have been implemented. A low-pass filter (LPF) has been applied to divide the total HESS power into low and high frequencies for the batteries and supercapacitor. Different LPF cutoff frequencies for power sharing have also been studied. A combined hybrid bi-level EMS and automatic sizing have been proposed and validated. It mainly covers five operational scenarios, including solar panel production reduction, load reduction, and three scenarios involving HESS control combined with supercapacitor state of charge (SOC) control retention. An objective function that considers both capital expenditure (CAPEX) and operating costs (OPEX) has been designed for EMS performance evaluation. The interaction between the HESS and EMS has been jointly studied based on an open dataset of residential electrical consumption profiles covering both summer and winter seasons. Finally, an experimental platform of a multi-source DC electrical grid has been developed to validate the EMS in real-time. It comprises four lithium-ion batteries, a supercapacitor, a programmable DC power supply, a programmable DC load, corresponding DC/DC converters, and a real-time controller (dSPACE/Microlabbox). Accelerated tests have been conducted to verify the proposed EMS in different operational scenarios by integrating real solar panels and load consumption profiles. The hierarchical simulation and experimental DC electrical grid platforms can be generally used to verify and evaluate various EMS
Guitton, Julien. "Architecture hybride pour la planification d'actions et de déplacements." Phd thesis, Université Paul Sabatier - Toulouse III, 2010. http://tel.archives-ouvertes.fr/tel-00648244.
Lucien, Laurent. "Contribution à une modélisation globale de la collaboration dans les systèmes multi-agents : Application aux entités mobiles intelligentes." Thesis, Bourgogne Franche-Comté, 2018. http://www.theses.fr/2018UBFCD039/document.
We live today in an increasingly complex and interconnected world where many entities, increasingly intelligent, generate a multitude of interactions that can contribute to enrich their capabilities.We are interested in collaboration that will enable complex tasks to be performed by these machines of today and tomorrow by stimulating these structured interactions and integrating intelligent decision-making processes. In this way, it will contribute to improve their functioning and will be able to participate in their improvement (better knowledge of their environment, speed of action and decision-making, provision of new skills).The main objective of the thesis is therefore to contribute to the understanding of what collaboration is, from its definition to its implementation, by highlighting its underlying concepts. We propose a method of analysis (needs and constraints) and a collaborative agent architecture model (HACCA) to integrate all the characteristics of the collaborative processes that we present. We are also showing a first implementation in the GAMA multi-agent platform.As part of this study, we are interested in two cases of application of mobile entities: drones and connected vehicles.Thus we also contribute more to the autonomy needs and decision-making process of drones, connected and autonomous vehicles of the future, in a constrained temporal context where the quality of interactions is essential to optimize the process of achieving objectives
Osório, Fernando Santos. "Inss : un système hybride neuro-symbolique pour l'apprentissage automatique constructif." Grenoble INPG, 1998. https://tel.archives-ouvertes.fr/tel-00004899.
Various Artificial Intelligence methods have been developed to reproduce intelligent human behaviour. These methods allow to reproduce some human reasoning process using the available knowledge. Each method has its advantages, but also some drawbacks. Hybrid systems combine different approaches in order to take advantage of their respective strengths. These hybrid intelligent systems also present the ability to acquire new knowledge from different sources and so to improve their application performance. This thesis presents our research in the field of hybrid neuro-symbolic systems, and in particular the study of machine learning tools used for constructive knowledge acquisition. We are interested in the automatic acquisition of theoretical knowledge (rules) and empirical knowledge (examples). We present a new hybrid system we implemented: INSS - Incremental Neuro-Symbolic System. This system allows knowledge transfer from the symbolic module to the connectionist module (Artificial Neural Network - ANN), through symbolic rule compilation into an ANN. We can refine the initial ANN knowledge through neural learning using a set of examples. The incremental ANN learning method used, the Cascade-Correlation algorithm, allows us to change or to add new knowledge to the network. Then, the system can also extract modified (or new) symbolic rules from the ANN and validate them. INSS is a hybrid machine learning system that implements a constructive knowledge acquisition method. We conclude by showing the results we obtained with this system in different application domains: ANN artificial problems(The Monk's Problems), computer aided medical diagnosis (Toxic Comas), a cognitive modelling task (The Balance Scale Problem) and autonomous robot control. The results we obtained show the improved performance of INSS and its advantages over others hybrid neuro-symbolic systems
Singer, Benjamin. "L'intelligence artificielle au service du rugby : acquisition et modélisationd'une expertise visuelle de prise de décision tactique : construction d'un système expert hybride d'aide à l'intervention pour la formation des joueurs et des cadres techniques." Paris 10, 1995. http://www.theses.fr/1995PA100048.
We focus on our contribution at the visual knowledge acquisition and modelling levels. After justifying its necessity we propose a visual knowledge elicitation language whose formal definition is given via its static and dynamic component s. The second part of our contribution is related to the design and realization of a visual knowledge acquisition software tool driven by the previous language. The third part of our contribution deals with visual knowledge modelling at the methodological and tool levels by extending the initial method and software. The fourth part describes the whole case study carried out for modelling the visual expertise considered for tactical decision-making in rugby the complete conceptual model is built. Then we describe the design, implementation and validation of the final expert system on the target architecture. The conclusion points out the interests of our contribution both at the theoretical and practical levels, and the generality of the results achieved for team games study
Wacquant, Sylvie. "Contribution à l'étude d'un modèle de réseaux d'automates corticaux : principes & outils logiciels." Rouen, 1993. http://www.theses.fr/1993ROUES063.
Di, Marco Lionel. "Récit d'ingénierie pédagogique en santé à l'usage de l'enseignant connecté Does the acceptance of hybrid learning affect learning approaches in France? Blended Learning for French Health Students: Does Acceptance of a Learning Management System Influence Students’ Self-Efficacy?" Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALS024.
Background. The general objective of this thesis was to evaluate a hybrid pedagogical method using an integrated learning environment (ILE) in the training of health professionals. Three research questions followed one after the other. Does the acceptability of blended learning affect students' learning strategies and learning approaches? Does the acceptability of an ILE affect students' self-efficacy? What characteristics of a dematerialised course make students' attention variable?Materials & Methods. We carried out 2 quantitative observational studies, as well as a single-blind observational experiment coupled with a qualitative analysis, with different classes of midwifery students of Grenoble-Alpes University Faculty of Medicine.Results. Students have suited learning approaches and strategies despite the use of a hybrid teaching method which they reject; there is no correlation between poor acceptability of the ILE and different spheres of students' self-efficacy; and the variability of attention declared by students varies according to certain factors common to those detected by artificial intelligence (type of language, slide duration…).Discussion. The internal and external validities of this work highlight the close links between interest, motivation, engagement by identification, and attention. It is thus possible to put forward principles of pedagogical engineering in health within the framework of dematerialized courses focusing on the content, form and type of knowledge capsule. Finally, the health teacher must above all be “connected to” the students, so that technical developments can be adapted to their needs
Nou, Julien. "Gestion optimale de l'énergie thermique dans un procédé hybride : solaire/géothermie pour le chauffage de bâtiments." Phd thesis, Université de Perpignan, 2011. http://tel.archives-ouvertes.fr/tel-00756810.
Lambert, Tony. "Hybridation de méthodes complètes et incomplètes pour la résolution de CSP." Phd thesis, Université de Nantes, 2006. http://tel.archives-ouvertes.fr/tel-00130790.
de prendre en compte une hybridation entre les méthodes incomplètes et les méthodes complètes. Dans ce contexte, la résolution s'apparente à un calcul de point fixe d'un ensemble de fonctions de réductions spécifiques. Notre cadre générique permet alors d'envisager des stratégies de combinaisons et d'hybridation de manière plus fine et d'étudier leurs propriétés. Nous avons employé un cadre général approprié pour modéliser la résolution des problèmes d'optimisation et nous présentons des résultats
expérimentaux qui mettent en avant les atouts de telles
combinaisons en regard d'une utilisation indépendante des techniques de résolution.
Xu, Jin. "Un modèle multi-agent distribué et hybride pour la planification du transport à la demande temps réel." Phd thesis, INSA de Rouen, 2008. http://tel.archives-ouvertes.fr/tel-00558769.
Correa, e. Silva Fernandes Kelly Christine. "Systèmes multi-agents hybrides : une approche pour la conception de systèmes complexes." Université Joseph Fourier (Grenoble ; 1971-2015), 2001. http://www.theses.fr/2001GRE10121.
Gandibleux, Xavier. "Système d'aide à la décision pour la conduite de processus perturbés ; une approche hybride fondée sur l'intelligence artificielle, la programmation linéaire et l'aide multicritère à la décision : application à la mobilisation de réserve tertiaire d'électricité de France." Valenciennes, 1995. https://ged.uphf.fr/nuxeo/site/esupversions/0a927862-e635-4e55-8aa3-955a2086752f.
Père, Valentin. "Contributions au contrôle et au dimensionnement des micro-réseaux par apprentissage par renforcement : application aux systèmes avec production renouvelable et stockage hybride batterie-hydrogène." Electronic Thesis or Diss., Ecole nationale des Mines d'Albi-Carmaux, 2023. http://www.theses.fr/2023EMAC0018.
Combining photovoltaic panels with an electrochemical battery reduces the daily phase difference between electricity production and demand in a microgrid. For long-term electricity storage, the combined use of an electrolyzer, hydrogen storage and a fuel cell offers the possibility of conserving electricity produced in summer to meet increased winter demand. Optimal real-time control of microgrid storage units is hampered by random data and the non-linear dynamic behavior of the units over long time horizons. This work presents a methodology for sizing and controlling a microgrid comprising photovoltaic electricity production, a lithium-ion battery and hydrogen storage, based on economic, environmental and technical objectives. The sizing of the units in a microgrid establishes their constraints of use, while the criteria to be optimized for its sizing (such as the cost of energy, the rate of self-consumption, the probability of breakdowns) depend on the management of these units. This interdependence justifies the development of a sizing methodology coupled with long-term energy management algorithm. The management of a microgrid is influenced by random variables such as demand and the energy produced at any given time. Reinforcement learning is a sequential decision-making methodology based on a dynamic model of the system that can adapt its strategy to random data. As a first step, a reinforcement learning control methodology is adopted by integrating non-linearities such as the aging of the storage system. Reinforcement learning enabled the energy management system to maintain an effective unit control policy with respect to the targeted criteria. This effectiveness is maintained despite different data and a longer time horizon than those on which the model was built. The control strategies developed suggest that the advantages of long-term electricity storage depend on the characteristics of the microgrid, and in particular on the amplitude of demand and the capacity of the battery. The study shows that a compromise must be found between the economic profitability of the microgrid and the guarantee of its autonomy. A bi-level optimization method is developed to achieve optimal unit sizing and energy management. The control of the microgrid by reinforcement learning forms the inner loop, while unit sizing is carried out using a simulated-annealing algorithm in the main loop. Particular attention is paid to minimizing computing time, by developing a method for transferring control policy from one iteration of the main loop to another. Offline reinforcement learning has been used to learn unit control strategies without random interaction with the microgrid simulation. The strategies are learned by observing the control decisions made by a model trained on other sizing in previous iterations. The calculation time is reduces by over 50% and the quality of the control policy learned is not affected. The results are analyzed in regard to the objectives considered, the control strategy and the data incorporated into the microgrid simulation
Benkirane, Fatima Ezzahra. "Integration of contextual knowledge in deep Learning modeling for vision-based scene analysis." Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://www.theses.fr/2024UBFCA002.
Computer vision has made an important evolution starting from traditional methods to advanced Deep Learning (DL) models. One of the goals of computer vision tasks is to effectively emulate human perception. The classical process of DL models is completely dependent on visual features, which only reflects how humans visually perceive their surroundings. However, for humans to comprehensively understand their environment, their reasoning not only depends on what they see but also on their pre-acquired knowledge. Addressing this gap is essential as achieving human-like reasoning requires a seamless combination of data-driven and knowledge-driven methods. In this thesis, we propose new approaches to improve the performance of DL models by integrating Knowledge-Based Systems (KBS) within Deep Neural Networks (DNNs). The goal is to empower these networks to make informed decisions by leveraging both visual features and knowledge to emulate human-like visual analysis. These methodologies involve two main axes. First, define the representation of KBS to incorporate useful information for a specific computer vision task. Second, investigate how to integrate this knowledge into DNNs to enhance their performance. To do so, we worked on two main contributions. The first work focuses on monocular depth estimation. Considering humans as an example, they can estimate their distance with respect to seen objects, even using just one eye, based on what is called monocular cues. Our contribution involves integrating these monocular cues as human-like reasoning for monocular depth estimation within DNNs. For this purpose, we investigate the possibility of directly integrating geometric and semantic information into the monocular depth estimation process. We suggest using an ontology model in a DL context to represent the environment as a structured set of concepts linked with semantic relationships. Monocular cues information is extracted through reasoning performed on the proposed ontology and is fed together with the RGB image in a multi-stream way into the DNNs. Our approach is validated and evaluated on widespread benchmark datasets. The second work focuses on panoptic segmentation task that aims to identify and analyze all objects captured in an image. More precisely, we propose a new informed deep learning approach that combines the strengths of DNNs with some additional knowledge about spatial relationships between objects. We have chosen spatial relationships knowledge for this task because it can provide useful cues for resolving ambiguities, distinguishing between overlapping or similar object instances, and capturing the holistic structure of the scene. More precisely, we propose a novel training methodology that integrates knowledge directly into the DNNs optimization process. Our approach includes a process for extracting and representing spatial relationships knowledge, which is incorporated into the training using a specially designed loss function. The performance of the proposed method was also evaluated on various challenging datasets. To validate the effectiveness of the proposed approaches for combining KBS and DNNs regarding different methodologies, we have chosen the urban environment and autonomous vehicles as our main use case application. This domain is particularly interesting because it is a challenging and novel field in continuous development, with significant implications for the safety, comfort and mobility of humans. As a conclusion, the proposed approaches validate that the integration of knowledge-driven and data-driven methods consistently leads to improved results. Integration improves the learning process for DNNs and enhances results of computer vision tasks, providing more accurate predictions. The challenge always lies in choosing the relevant knowledge for each task, representing it in the best structure to leverage meaningful information, and integrating it most optimally into the DNN architecture
Mokhtari, Aimed. "Diagnostic des systèmes hybrides : développement d'une méthode associant la détection par classification et la simulation dynamique." Phd thesis, INSA de Toulouse, 2007. http://tel.archives-ouvertes.fr/tel-00200034.
Sauvage-Vincent, Julie. "Un langage contrôlé pour les instructions nautiques du Service Hydographique et Océanographique de la Marine." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2017. http://www.theses.fr/2017IMTA0001/document.
Controlled Natural Languages (CNL) are artificial languages that use a subset of the vocabulary, morphological forms and syntactical constructions of a natural language while eliminating its polysemy. In a way, they constitute the bridge between formal languages and natural languages. Therefore, they perform the communicative function of the textual mode while being precise and computable by the machine without any ambiguity. In particular, they can be used to facilitate the population or update of knowledge bases within the framework of a human-machine interface.Since 1971, the French Marine Hydrographic and Oceanographic Service (SHOM) issues the French Coast Pilot Books Instructions nautiques , collections of general, nautical and statutory information, intended for use by sailors. These publications aim to supplement charts, in the sense that they provide the mariner with supplemental information not in the chart. They are mandatory for fishing and commercial ships. On the other hand, the International Hydrographic Organization (IHO) issued standards providing information about navigational data exchange. Among these standards, one of a particular interest is the universal model of hydrographic data (S-100 standard, January, 2010).This thesis analyses the use of a CNL to represent knowledge contained in the Instructions nautiques. This CNL purpose is to act as a pivot between the writing of the text by the dedicated operator, the production of the printed or online publication, and the interaction with knowledge bases and navigational aid tools. We will focus especially on the interaction between the Instructions nautiques Controlled Natural Language and the corresponding Electronic Navigational Charts (ENC).More generally, this thesis asks the question of the evolution of a CNL and the underlying ontologies involved in the Instructions nautiques project. Instructions nautiques have the particularity of combining both strictness (numerical data, electronic charts, legislation) and a certain amount of flexibility (text writing by human operators, unpredictability of the knowledge to be included due to the evolution of sailors¿ practices and needs). We define in this thesis a dynamic CNL in the same way that dynamic ontologies are defined in particular domains. The language described in this thesis is intended as an interesting contribution for the community involved in CNL. Indeed, it addresses the creation of a CNL for the unexploited domain of maritime navigation, but its hybrid aspects as well through the exploration of the multiple modalities (textual and visual) coexisting in a corpus comprising ENC and their companion texts. The mechanisms of the CNL presented in this thesis, although developed for the domain of the maritime navigation, have the potential to be adapted to other domains using multimodal corpuses. Finally, the benefits in the future of a controlled hybrid language are undeniable: the use of the different modalities in their full potential can be used in many different applications (for example, the exploitation of the visual modality for a 3D extension)
Szczepanski, Nicolas. "SAT en Parallèle." Thesis, Artois, 2017. http://www.theses.fr/2017ARTO0403/document.
This thesis deals with propositional satisfiability (SAT) in a massively parallel setting. The SAT problem is widely used for solving several combinatorial problems (e.g. formal verification of hardware and software, bioinformatics, cryptography, planning, scheduling, etc.). The first contribution of this thesis concerns the design of efficient algorithms based on the approaches « portfolio » and « divide and conquer ». Secondly, an adaptation of several parallel programming models including hybrid (parallel and distributed computing) to SAT is proposed. This work has led to several contributions to international conferences and highly competitive distributed SAT solvers
Hessami, Mohammad Hessam. "Modélisation multi-échelle et hybride des maladies contagieuses : vers le développement de nouveaux outils de simulation pour contrôler les épidémies." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAS036/document.
Theoretical studies in epidemiology mainly use differential equations, often under unrealistic assumptions (e.g. spatially homogeneous populations), to study the development and spreading of contagious diseases. Such models are not, however, well adapted understanding epidemiological processes at different scales, nor are they efficient for correctly predicting epidemics. Yet, such models should be closely related to the social and spatial structure of populations. In the present thesis, we propose a series of new models in which different levels of spatiality (e.g. local structure of population, in particular group dynamics, spatial distribution of individuals in the environment, role of resistant people, etc) are taken into account, to explain and predict how communicable diseases develop and spread at different scales, even at the scale of large populations. Furthermore, the manner in which our models are parametrised allow them to be connected together so as to describe the epidemiological process at a large scale (population of a big town, country ...) and with accuracy in limited areas (office buildings, schools) at the same time.We first succeed in including the notion of groups in SIR (Susceptible, Infected, Recovered) differential equation systems by a rewriting of the SIR dynamics in the form of an enzymatic reaction in which group-complexes of different composition in S, I and R individuals form and where R people behave as non-competitive inhibitors. Then, global group dynamics simulated by stochastic algorithms in a homogeneous space, as well emerging ones obtained in multi-agent systems, are coupled to such SIR epidemic models. As our group-based models provide fine-grain information (i.e. microscopical resolution of time, space and population) we propose an analysis of criticality of epidemiological processes. We think that diseases in a given social and spatial environment present characteristic signatures and that such measurements could allow the identification of the factors that modify their dynamics.We aim here to extract the essence of real epidemiological systems by using various methods based on different computer-oriented approaches. As our models can take into account individual behaviours and group dynamics, they are able to use big-data information yielded from smart-phone technologies and social networks. As a long term objective derived from the present work, one can expect good predictions in the development of epidemics, but also a tool to reduce epidemics by guiding new environmental architectures and by changing human health-related behaviours
Ben, Saad Seifallah. "Conception d'un algorithme de coordination hybride de groupes de robots sous-marins communicants. Application : acquisition optique systématique et détaillée des fonds marins." Thesis, Brest, 2016. http://www.theses.fr/2016BRES0052/document.
In the underwater environment, the needs of data acquisition have significantly increased over the last decades. As electromagnetic waves show poor propagation in sea water, acoustical sensing is generally preferred. However, the emergence of small and low cost autonomous underwater vehicles (AUV) allow for rethinking the underwater use of optical sensors as their small coverage can be significantly improved by using a fleet of coordinated underwater robots.This paper presents a strategy to coordinate the group of robots in order to systematically survey the seabed to detect small objects or singularities. The proposed hybrid coordination strategy is defined by two main modes. The first mode relies on a swarm algorithm to organize the team in geometrical formation. In the second mode, the robot formation is maintained using a hierarchical coordination. A finite state machine controls the high level hybrid strategy by defining the appropriate coordination mode according to the evolution of the mission. Before sea validation, the behavior and the performance of the hybrid coordination strategy are first assessed in simulation. The control of individual robots relies on visual servoing, implemented with the OpenCV library, and the simulation tool is based on Blender software.The dynamics of the robots has been implemented in a realistic way in Blender by using the Bullet solver and the hydrodynamic coeficcients estimated on the actual robot. First results of the hybrid coordination strategy applied on a fleet of 3 AUV’s, show execution of a video acquisition task by a group of autonomous robots controlled by vision and coordinated by a hybrid strategy
Jiménez, Jose-Fernando. "Architecture dynamique et hybride pour la reconfiguration optimale des systèmes de contrôle : application au contrôle de fabrication." Thesis, Valenciennes, 2017. http://www.theses.fr/2017VALE0031/document.
Discrete-event control systems have the opportunity to resolve significant challenges of modern society. In particular, these represent a fundamental solution to manage and control the new technological advances in compliance to the increased consciousness of sustainable development. The parameterization, configuration and decision-making of these control systems are critical aspects that impact the performance and productivity required. Dynamic control architecture approaches, such as reconfigurable control systems, have been proposed for modelling such systems. However, such approaches have failed to address the recovery of the reconfiguration process as these focus on the continuity of execution rather than on the optimisation of the reconfiguration. This dissertation proposes a reference architecture for a reconfigurable control system, named Pollux, designed to manage and adjust optimally and in real time the architecture of a control system, either to guide operational execution or to respond to a system perturbation. Considering a proposed framework of an optimal configuration of control architectures based on shared governance, this proposed approach aims to orchestrate a flexible and customizable decisional entity, a representation that characterize the unique configuration and control solution of the control architecture, and a three-module reconfiguration mechanism that integrates the optimality-based principles into the reconfiguration process, to ensure a recovery of global performance and/or minimise the degradation caused by perturbations. Our approach is applied in the manufacturing domain and is validated in a simulation and a real flexible manufacturing system cell located at the University of Valenciennes, France. The validation conducted in three experimental scenarios verified the benefits of our approach and encourage us to continue research in this direction
Orsier, Bruno. "Etude et application de systèmes hybrides neurosymboliques." Phd thesis, Université Joseph Fourier (Grenoble), 1995. http://tel.archives-ouvertes.fr/tel-00005057.
Massucci, Louis. "Théorie de l’apprentissage et identification des systèmes dynamiques hybrides." Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0174.
This thesis deals with the application of statistical learning theory to the identification of hybrid dynamical systems. Learning theory allows one to obtain statistical guarantees on the accuracy of models for a finite number of data. Here, by extending the framework of this theory to that of dynamic systems, we propose new solutions for the identification of switched systems. Indeed, a new generalization error bound valid for the identification of switched systems is obtained, and its use gives rise to a new model selection method for estimating the number of submodels of hybrid systems. The bound is adapted for different regularization scenarios, and new optimization algorithms taking into account these different scenarios are proposed. This new method for estimating the number of modes is compared with existing ones, and the advantages and disadvantages of each of these methods are studied
Dinu, Razvan. "Web Agents : towards online hybrid multi-agent systems." Thesis, Montpellier 2, 2012. http://www.theses.fr/2012MON20126/document.
Multi-agent systems have been used in a wide range of applications from computer-based simulations and mobile robots to agent-oriented programming and intelligent systems in real environments. However, the largest environment in which software agents can interact is, without any doubt, the World Wide Web and ever since its birth agents have been used in various applications such as search engines, e-commerce, and most recently the semantic web. However, agents have yet to be used on the Web in a way that leverages the full power of artificial intelligence and multi-agent systems, which have the potential of making life much easier for humans. This thesis investigates how this can be changed, and how agents can be brought to the core of the online experience in the sense that we want people to talk and interact with agents instead of "just using yet another application or website". We analyze what makes it hard to develop intelligent agents on the web and we propose a web agent model (WAM) inspired by recent results in multi-agent systems. Nowadays, a simple conceptual model is the key for widespread adoption of new technologies and this is why we have chosen the MASQ meta-model as the basis for our approach, which provides the best compromise in terms of simplicity of concepts, generality and applicability to the web. Since until now the model was introduced only in an informal way, we also provide a clear formalization of the MASQ meta-model.Next, we identify the three main challenges that need to be addressed when building web agents: integration of bodies, web semantics and user friendliness. We focus our attention on the first two and we propose a set of principles to guide the development of what we call strong web agents. Finally, we validate our proposal through the implementation of an award winning platform called Kleenk. Our work is just a step towards fulfilling the vision of having intelligent web agents mediate the interaction with the increasingly complex World Wide Web
Siboni, Didier. "La gestion de service sur les réseaux hétérogènes interconnectes : utilisation des techniques d'intelligence artificielle et architectures hybrides." Versailles-St Quentin en Yvelines, 1997. http://www.theses.fr/1997VERS0005.
Zhao, Zhou. "Heart Segmentation and Evaluation of Fibrosis." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS003.
Atrial fibrillation is the most common heart rhythm disease. Due to a lack of understanding in the matter of underlying atrial structures, current treatments are still not satisfying. Recently, with the popularity of deep learning, many segmentation methods based on deep learning have been proposed to analyze atrial structures, especially from late gadolinium-enhanced magnetic resonance imaging. However, two problems still occur: 1) segmentation results include the atrial-like background; 2) boundaries are very hard to segment. Most segmentation approaches design a specific network that mainly focuses on the regions, to the detriment of the boundaries. Therefore, in this dissertation, we propose two different methods to segment the heart, one two-stage and one end-to-end trainable method. And then, for evaluating the fibrosis degree, we also proposed two methods, one is to combine deep learning with morphology, and the other is to use deep learning directly. Finally, the efficiency of the proposed approach is verified on some public datasets
Rybnik, Mariusz. "Contribution to the modelling and the exploitation of hybrid multiple neural networks systems : application to intelligent processing of information." Paris 12, 2004. https://athena.u-pec.fr/primo-explore/search?query=any,exact,990003948290204611&vid=upec.
For a great number of actually encountered problems (complex processes modelization, pattern recognition, medical diagnosis support, fault detection) data is presented in form of database. The data is next transformed and processed. This work is concentrated on the development of semi-automatic data processing structures. Proposed approach is based on iterative decomposition of an initial problem. The main idea is to decompose initia!ly complex problems in order to obtain simplification simultaneously on structural level and processing level. Thus, the principal idea of present work is con nected to task decomposition techniques called "Divide to Conquer". A key point of our approach is the integration of Complexity Estimation techniques
Rybnik, Mariusz Madani Kurosh. "Contribution to the modelling and the exploitation of hybrid multiple neural networks systems application to intelligent processing of information /." Créteil : Université de Paris-Val-de-Marne, 2007. http://doxa.scd.univ-paris12.fr:8080/theses-npd/th0394829.htm.
Version électronique uniquement consultable au sein de l'Université Paris 12 (Intranet). Titre provenant de l'écran-titre. Bibliogr. : 116 réf.
Andriamasinoro, Fenintsoa. "Proposition d'un modèle d'agents hybrides basé sur la motivation naturelle." Phd thesis, Université de la Réunion, 2003. http://tel.archives-ouvertes.fr/tel-00474542.
Bomme, Patricia. "Objets hybrides dans des applications scientifiques orientées objets." Compiègne, 1998. http://www.theses.fr/1998COMP1113.
Bontorin, Guilherme. "Matrice d'électrodes intelligentes : un outil pour améliorer les performances spatiotemporelles des systèmes hybrides (vivant-artificiel), en boucle fermée et en temps réel." Phd thesis, Université Sciences et Technologies - Bordeaux I, 2010. http://tel.archives-ouvertes.fr/tel-00561026.
Kanaoui, Nadia Madani Kurosh. "Contribution à l'étude et à la mise en oeuvre d'approches hybrides d'aide au diagnostic application aux domaines biomédical et industriel /." [S.l.] : [s.n.], 2008. http://doxa.scd.univ-paris12.fr:80/theses/th0405815.pdf.
Kanaoui, Nadia. "Contribution à l'étude et à la mise en oeuvre d'approches hybrides d'aide au diagnostic : application aux domaines biomédical et industriel." Paris 12, 2007. http://www.theses.fr/2007PA120066.
Research work developed in this thesis deals with decision support systems for fault diagnosis, pattern recognition and decision-making based on artificial intelligence using hybrid techniques, and soft computing implying neural networks and fuzzy logic. The aim of this work is absolutely not to replace specialized human (doctor, expert,. . . ) but to suggest efficient Diagnosis Support Systems (DSS) with a certain confidence index. Thus, the main objective is the development of hybrid modular approaches allowing the elaboration of such DSS for certain kinds od applications (biomedicine and industrial). For that, a global methodology, based on multiple knowledge representation and multiple classification has been suggested exploiting different representation and classification strategies. Potential advantages of this methodology are : the multiple knowledge representation from same source or different sources of information (exploiting rich information which can be extracted from different representations : signal, global image, subdivided image), the multiple classification (redundancy and/or complementary), the hybrid structure in classification and decision-making based on hybrid modular approaches in order to exploit the complementary aspect, and the exploitation of a confidence parameter in the decision-making to suggest a final result of diagnosis with a confidence index. More, the modular aspect in this methodology will facilitate its adaptation from one application to another
Sassatelli, Lucile. "Codes LDPC multi-binaires hybrides et méthodes de décodage itératif." Phd thesis, Université de Cergy Pontoise, 2008. http://tel.archives-ouvertes.fr/tel-00819413.
Ramachandra, Rao Sanjay Kamath. "Question Answering with Hybrid Data and Models." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASS024.
Question Answering is a discipline which lies in between natural language processing and information retrieval domains. Emergence of deep learning approaches in several fields of research such as computer vision, natural language processing, speech recognition etc. has led to the rise of end-to-end models.In the context of GoASQ project, we investigate, compare and combine different approaches for answering questions formulated in natural language over textual data on open domain and biomedical domain data. The thesis work mainly focuses on 1) Building models for small scale and large scale datasets, and 2) Leveraging structured and semantic information into question answering models. Hybrid data in our research context is fusion of knowledge from free text, ontologies, entity information etc. applied towards free text question answering.The current state-of-the-art models for question answering use deep learning based models. In order to facilitate using them on small scale datasets on closed domain data, we propose to use domain adaptation. We model the BIOASQ biomedical question answering task dataset into two different QA task models and show how the Open Domain Question Answering task suits better than the Reading Comprehension task by comparing experimental results. We pre-train the Reading Comprehension model with different datasets to show the variability in performance when these models are adapted to biomedical domain. We find that using one particular dataset (SQUAD v2.0 dataset) for pre-training performs the best on single dataset pre-training and a combination of four Reading Comprehension datasets performed the best towards the biomedical domain adaptation. We perform some of the above experiments using large scale pre-trained language models like BERT which are fine-tuned to the question answering task. The performance varies based on the type of data used to pre-train BERT. For BERT pre-training on the language modelling task, we find the biomedical data trained BIOBERT to be the best choice for biomedical QA.Since deep learning models tend to function in an end-to-end fashion, semantic and structured information coming from expert annotated information sources are not explicitly used. We highlight the necessity for using Lexical and Expected Answer Types in open domain and biomedical domain question answering by performing several verification experiments. These types are used to highlight entities in two QA tasks which shows improvements while using entity embeddings based on the answer type annotations. We manually annotated an answer variant dataset for BIOASQ and show the importance of learning a QA model with answer variants present in the paragraphs.Our hypothesis is that the results obtained from deep learning models can further be improved using semantic features and collective features from different paragraphs for a question. We propose to use ranking models based on binary classification methods to better rank Top-1 prediction among Top-K predictions using these features, leading to an hybrid model that outperforms state-of-art-results on several datasets. We experiment with several overall Open Domain Question Answering models on QA sub-task datasets built for Reading Comprehension and Answer Sentence Selection tasks. We show the difference in performance when these are modelled as overall QA task and highlight the wide gap in building end-to-end models for overall question answering task
Vion, Julien. "Contributions à la résolution générique des problèmes de satisfaction de contraintes." Phd thesis, Université d'Artois, 2007. http://tel.archives-ouvertes.fr/tel-00262080.
Par ailleurs, nous avons également cherché à améliorer MGAC, tout d'abord en équipant celui-ci d'heuristiques de choix de valeurs. Nous nous sommes pour cela basés sur l'heuristique de Jeroslow-Wang, issue du problème SAT. En utilisant deux techniques de conversion de CSP vers SAT, nous montrons comment cette heuristique se comporterait sur un CSP. Enfin, nous avons cherché à utiliser une hybridation entre un algorithme de recherche locale basé sur la pondération des contraintes et un algorithme MGAC équipé de l'heuristique dom/wdeg, en exploitant les possibilités d'apprentissage de l'un et l'autre algorithmes.
De manière transversale, l'ensemble des techniques développées dans le cadre de cette thèse a amené à la réalisation d'une API pour le langage Java, capable de résoudre un CSP au sein d'une application Java quelconque. Cette API a été développée dans l'optique "boîte noire" : le moins de paramètres et d'expertise possibles sont demandés à l'utilisateur. Un prouveur basé sur CSP4J a concouru lors les compétitions internationales de prouveurs CSP avec des résultats encourageants.
Mellouk, Abdelhamid. "Un système neuro- prédictif pour la reconnaissance automatique de la parole continue." Paris 11, 1994. http://www.theses.fr/1994PA112476.
Bouzid, Salah Eddine. "Optimisation multicritères des performances de réseau d’objets communicants par méta-heuristiques hybrides et apprentissage par renforcement." Thesis, Le Mans, 2020. http://cyberdoc-int.univ-lemans.fr/Theses/2020/2020LEMA1026.pdf.
The deployment of Communicating Things Networks (CTNs), with continuously increasing densities, needs to be optimal in terms of quality of service, energy consumption and lifetime. Determining the optimal placement of the nodes of these networks, relative to the different quality criteria, is an NP-Hard problem. Faced to this NP-Hardness, especially for indoor environments, existing approaches focus on the optimization of one single objective while neglecting the other criteria, or adopt an expensive manual solution. Finding new approaches to solve this problem is required. Accordingly, in this thesis, we propose a new approach which automatically generates the deployment that guarantees optimality in terms of performance and robustness related to possible topological failures and instabilities. The proposed approach is based, on the first hand, on the modeling of the deployment problem as a multi-objective optimization problem under constraints, and its resolution using a hybrid algorithm combining genetic multi-objective optimization with weighted sum optimization and on the other hand, the integration of reinforcement learning to guarantee the optimization of energy consumption and the extending the network lifetime. To apply this approach, two tools are developed. A first called MOONGA (Multi-Objective Optimization of wireless Network approach based on Genetic Algorithm) which automatically generates the placement of nodes while optimizing the metrics that define the QoS of the CTN: connectivity, m-connectivity, coverage, k-coverage, coverage redundancy and cost. MOONGA tool considers constraints related to the architecture of the deployment space, the network topology, the specifies of the application and the preferences of the network designer. The second optimization tool is named R2LTO (Reinforcement Learning for Life-Time Optimization), which is a new routing protocol for CTNs, based on distributed reinforcement learning that allows to determine the optimal rooting path in order to guarantee energy-efficiency and to extend the network lifetime while maintaining the required QoS
Imbach, Rémi. "Résolution de contraintes géométriques en guidant une méthode homotopique par la géométrie." Thesis, Strasbourg, 2013. http://www.theses.fr/2013STRAD033/document.
Depending on the required application field, the solutions of a geometric constraints system (GCS) are either : – symbolic and exact such as construction plans, providing all the solutions, obtained by applying geometric rules. Many problems, mostly in a 3D context, resist to this approach ; – or numerical and approximated : they are the solutions of a system of equations built from the constraints, provided by generical numerical methods that are efficient when only one solution is sought. However, searching all the solutions leads to an exponential computation cost, due to the nature of problems. Continuation methods, also called homotopic methods, find all the solutions of a polynomial system. Using them to solve systems of equations associated to systems of constraints is nevertheless costly. Moreover, combining them with geometric reasoning is a challenge, because they act in a projective complex space and not in the realizations space. The aim of this work is to specialize a continuation method to GCS. Geometry is exploited to simplify and justify its adaptation in the space of realizations, so allowing geometric reasoning. Cases where the connected components of the solution space of a problem have heterogeneous dimensions are addressed. The method discussed here provides in a first step solutions that are similar to a sketch drawn by the user. Then a procedure is proposed to search new solutions. Its iterative nature seems to make the exponential complexity of this task bearable. A new decomposition method is proposed, that restrains the resolution cost
Jobczyk, Krystian. "Temporal planning with fuzzy constraints and preferences." Thesis, Normandie, 2017. http://www.theses.fr/2017NORMC259/document.
Temporal planning forms conceptually a part of temporal reasoning and it belongs to research area of Artificial Intelligence and it may be seen as an extension of classical planning by temporal aspects of acting. Temporal planing is usually complemented by considering preferences or different types of temporal constraints imposed on execution of actions. There exist many approaches to this issue. One one hand, there are different paradigms to temporal planning, such as: planning via search in graphs (STRIPS), planning via satisfiability or planning in terms of Markov processes. These approaches are mutually incompatible. In addition, temporal planning requires a subject-specification as it is rather defined in a methodological way. On the other hand, temporal constraints are represented and modeled in different ways dependently on their quantitative or qualitative nature. In particular, Allen’s relations between temporal intervals – an important class of temporal constraints – do not have any quantitative aspects and cannot be considered in computational contexts. According to this situation, this PhD-thesis is aimed at the proposing a depth-analysis of temporal planning with fuzzy constraints which contains some remedies on these difficulties. Namely, two approaches to the representation and modeling of these issues are put forward. In the first one (chapter 2, chapter 3) – fuzzy Allen’s relations as fuzzy temporal constraints are represented by norms of convolutions in a Banach space of Lebesgue integrable functions. It allows us immerse Allen’s relations in the computational contexts of temporal planning (based on STRIPS and on DavisPutnam procedure) and to elucidate their quantitative nature. This approach is developed in a context of Multi-Agent Problem as a subject basis of this approach. In the second one (chapter 4, chapter 5) – fuzzy temporal constrains with fuzziness introduced by preferences are represented in a logical terms of Preferential Halpern-Shoham Logic. It allows us to adopt these result in a construction of the plan controller. This approach is developed in a context of Temporal Traveling Salesman Problem as a subject basis of this approach. Finally, an attempt to reconcile these two lines of representation of fuzzy temporal constraints was also proposed
De, Wulf Martin. "From timed models to timed implementations." Doctoral thesis, Universite Libre de Bruxelles, 2006. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210797.
Computer Science is currently facing a grand challenge :finding good design practices for embedded systems. Embedded systems are essentially computers interacting with some physical process. You could find one in a braking systems or in a nuclear power plant for example. They present several design difficulties :first they are reactive systems, interacting indefinitely with their environment. Second,they must satisfy real-time constraints specifying when they should respond, and not only how. Finally, their environment is often deeply continuous, presenting complex dynamics. The formal models of choice for specifying such systems are timed and hybrid automata for which model checking is pretty well studied.
In a first part of this thesis, we study a complete design approach, including verification and code generation, for timed automata. We have to define a new semantics for timed automata, the AASAP semantics, that preserves the decidability properties for model checking and at the same time is implementable. Our notion of implementability is completely novel, and relies on the simulation of a semantics that is obviously implementable on a real platform. We wrote tools for the analysis and code generation and exemplify them on a case study about the well known Philips Audio Control Protocol.
In a second part of this thesis, we study the problem of controller synthesis for an environment specified as a hybrid automaton. We give a new solution for discrete controllers having only an imperfect information about the state of the system. In the process, we defined a new algorithm, based on the monotonicity of the controllable predecessors operator, for efficiently finding a controller and we show some promising applications on a classical problem :the universality test for finite automata.
Doctorat en sciences, Spécialisation Informatique
info:eu-repo/semantics/nonPublished
Vu, Duc Tan. "Commande tolérante aux défauts des entraînements de machines synchrones à aimants permanents polyphasées non-sinusoïdales sous contraintes de courant et de tension pour les applications automobiles." Electronic Thesis or Diss., Paris, HESAM, 2020. http://www.theses.fr/2020HESAE040.
Electric drives for electrified vehicles need to fulfil some specific requirements from automotive markets such as high efficiency, high volume power and torque densities, low-cost but safe-to-touch, high functional reliability, high torque quality, and flux-weakening control. In this context, multiphase permanent magnet synchronous machine (PMSM) drives have become suitable candidates to meet the above requirements. The main objective of this doctoral thesis is to propose and refine fault-tolerant control strategies for non-sinusoidal multiphase PMSM drives that require less constraints on their design. In addition, constraints on current and voltage defined by the inverter and the machine are considered to optimize the machine control under the non-sinusoidal condition without exceeding their allowable limits. Therefore, the system sizing is guaranteed, especially in flux-weakening operations. The proposed fault-tolerant control strategies, based on the mathematical model of multiphase drives, enrich the control field of multiphase drives by providing various control options. The selection of one of the proposed control options can be a trade-off between a high quality torque but a low average value and a high average torque but a relatively high ripple. The control and torque performances of the drives can be refined by using artificial intelligence with a simple type of artificial neural networks named ADALINE (ADAptive LInear NEuron). With self-learning ability, fast convergence, and simplicity, ADALINEs can be applied to industrial multiphase drives. All proposed control strategies in this doctoral thesis are validated with an experimental seven-phase PMSM drive. The non-sinusoidal back electromotive force (back-EMF) of the experimental seven-phase PMSM is complex with the presence of multi-harmonics. Experimental results verify the effectiveness of the proposed strategies, and their applicability in a multiphase machine with a complex non-sinusoidal back-EMF
Marchand, Estelle. "Analyse de sensibilité déterministe pour la simulation numérique du transfert de contaminants." Phd thesis, Université Paris Dauphine - Paris IX, 2007. http://tel.archives-ouvertes.fr/tel-00271632.
Osorio, Fernando Santos. "INSS : un système hybride neuro-symbolique pour l'apprentissage automatique constructif." Phd thesis, 1998. http://tel.archives-ouvertes.fr/tel-00004899.
Hallé, Sylvain. "Spécification, validation et satisfiabilité [i.e. satisfaisabilité] de contraintes hybrides par réduction à la logique temporelle." Thèse, 2008. http://www.archipel.uqam.ca/1680/1/D1740.pdf.
Rialle, Vincent. "Décision et Cognition en Biomédecine : modèles et Intégration." Habilitation à diriger des recherches, 1994. http://tel.archives-ouvertes.fr/tel-00201144.
L'entreprise consistant à programmer une machine afin qu'elle produise des raisonnements habituellement attendus d'un spécialiste met au premier plan quelques difficiles questions relatives au pourquoi et au comment de tels systèmes. Le constat sinon d'échec du moins d'immenses difficultés de mise en œuvre des systèmes à bases de connaissances déclaratives, ouvrent la porte à un retour en force de l'expérience et de la mémoire cumulée de l'activité décisionnelle du praticien au cours des mois et des années de pratique. Dans cette optique, l'expérience emmagasinée sous forme de bases de cas dans la mémoire de l'ordinateur prend le pas sur la connaissance figée et laborieusement élaborée dans une base de connaissances pour la construction d'un système d'IAM. Des systèmes hybrides — permettant d'associer des connaissances déclarées par le spécialiste et des connaissances apprises automatiquement — constituent en quelque sorte un idéal que l'on s'efforce d'atteindre, notamment dans notre projet ESPRIT-III : MIX.
L'introduction de ce mémoire tente de préciser ce passage de la représentation à l'émergence de connaissances qui consacre en quelque sorte l'immersion de l'IAM dans les sciences de la cognition (et qui correspond en gros à mon parcours de chercheur depuis une douzaine d'années). Divers aspects de la problématique générale de la construction de classifieurs y sont abordés et une présentation succincte des diverses approches de l'émergence est proposée (connexionnisme, algorithmique génétique, induction...) et illustrée par le projet COGNIMED.
Autour et parfois en marge de cette problématique centrale, s'ordonnent un certain nombre de travaux que j'ai pu conduire ces dernières années dans les domaines de la psychiatrie-psychologie, de l'analyse textuelle et de la "philosophie de l'esprit". Ces travaux sont également évoqués.
Le mémoire est structuré en quatre parties principales et une annexe :
- Une partie introductive présentant de manière synthétique la thématique générale de recherche ainsi qu'un bref état de l'art du domaine dans lequel se placent mes travaux. La présentation des problèmes et des orientations de ce domaine sera appuyée par une bibliographie propre à l'introduction.
- La première partie est consacrée à la description des recherches qui ont été effectuées depuis une dizaine d'années. Outre la description des thèmes de recherche, cette partie inclut la présentation de quelques articles et résumés de travaux.
- La deuxième partie donne une liste complète et structurée des publications, communications, posters, etc.
- La troisième partie, orientée vers le futur, est consacrée aux projets imminents et aux perspectives de recherche et de développement à moyen terme.
- L'annexe présente l'activité d'encadrement d'étudiants de troisième cycle, directement liée aux activités d'enseignement et de recherche.