Thèses sur le sujet « Algorithme de passage de message »
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Taftaf, Ala. « Développements du modèle adjoint de la différentiation algorithmique destinés aux applications intensives en calcul ». Thesis, Université Côte d'Azur (ComUE), 2017. http://www.theses.fr/2017AZUR4001/document.
Texte intégralThe adjoint mode of Algorithmic Differentiation (AD) is particularly attractive for computing gradients. However, this mode needs to use the intermediate values of the original simulation in reverse order at a cost that increases with the length of the simulation. AD research looks for strategies to reduce this cost, for instance by taking advantage of the structure of the given program. In this work, we consider on one hand the frequent case of Fixed-Point loops for which several authors have proposed adapted adjoint strategies. Among these strategies, we select the one introduced by B. Christianson. We specify further the selected method and we describe the way we implemented it inside the AD tool Tapenade. Experiments on a medium-size application shows a major reduction of the memory needed to store trajectories. On the other hand, we study checkpointing in the case of MPI parallel programs with point-to-point communications. We propose techniques to apply checkpointing to these programs. We provide proof of correctness of our techniques and we experiment them on representative CFD codes
Barbier, Jean. « Statistical physics and approximate message-passing algorithms for sparse linear estimation problems in signal processing and coding theory ». Sorbonne Paris Cité, 2015. http://www.theses.fr/2015USPCC130.
Texte intégralThis thesis is interested in the application of statistical physics methods and inference to signal processing and coding theory, more precisely, to sparse linear estimation problems. The main tools are essentially the graphical models and the approximate message-passing algorithm together with the cavity method (referred as the state evolution analysis in the signal processing context) for its theoretical analysis. We will also use the replica method of statistical physics of disordered systems which allows to associate to the studied problems a cost function referred as the potential of free entropy in physics. It allows to predict the different phases of typical complexity of the problem as a function of external parameters such as the noise level or the number of measurements one has about the signal: the inference can be typically easy, hard or impossible. We will see that the hard phase corresponds to a regime of coexistence of the actual solution together with another unwanted solution of the message passing equations. In this phase, it represents a metastable state which is not the true equilibrium solution. This phenomenon can be linked to supercooled water blocked in the liquid state below its freezing critical temperature. Thanks to this understanding of blocking phenomenon of the algorithm, we will use a method that allows to overcome the metastability mimicing the strategy adopted by nature itself for supercooled water: the nucleation and spatial coupling. In supercooled water, a weak localized perturbation is enough to create a crystal nucleus that will propagate in all the medium thanks to the physical couplings between closeby atoms. The same process will help the algorithm to find the signal, thanks to the introduction of a nucleus containing local information about the signal. It will then spread as a "reconstruction wave" similar to the crystal in the water. After an introduction to statistical inference and sparse linear estimation, we will introduce the necessary tools. Then we will move to applications of these notions. They will be divided into two parts. The signal processing part will focus essentially on the compressed sensing problem where we seek to infer a sparse signal from a small number of linear projections of it that can be noisy. We will study in details the influence of structured operators instead of purely random ones used originally in compressed sensing. These allow a substantial gain in computational complexity and necessary memory allocation, which are necessary conditions in order to work with very large signals. We will see that the combined use of such operators with spatial coupling allows the implementation of an highly optimized algorithm able to reach near to optimal performances. We will also study the algorithm behavior for reconstruction of approximately sparse signals, a fundamental question for the application of compressed sensing to real life problems. A direct application will be studied via the reconstruction of images measured by fluorescence microscopy. The reconstruction of "natural" images will be considered as well. In coding theory, we will look at the message-passing decoding performances for two distincts real noisy channel models. A first scheme where the signal to infer will be the noise itself will be presented. The second one, the sparse superposition codes for the additive white Gaussian noise channel is the first example of error correction scheme directly interpreted as a structured compressed sensing problem. Here we will apply all the tools developed in this thesis for finally obtaining a very promising decoder that allows to decode at very high transmission rates, very close of the fundamental channel limit
Mekhiche, Adam. « Accès non-orthogonal aux ressources et techniques de réception associées pour les réseaux ad-hoc mobiles ». Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSEP010.
Texte intégralWireless communication networks, whether cellular or mobile ad hoc networks (MANETs), must accommodate increasingly massive data transmissions due to the growing number of users and new high-consumption applications, all while efficiently managing the available scarce radio resources. It is therefore crucial to enhance the spectral and energy efficiency of these systems to meet these growing needs.Recently, standardization bodies such as 3GPP for cellular networks proposed new schemes for accessing non-orthogonal resources (NOMA) in combination with more traditional methods, such as the use of multiple-input and multiple-output (MIMO) antennas.However, these schemes also introduce technical challenges, particularly the generation of interference between users, complicating the signal detection process at the receiver. Radio equipment manufacturers, like Thales, are exploring NOMA solutions for MANETs to be able to address their challenges and increase their technical maturity.Iterative digital receivers utilizing approximate Bayesian inference, specifically message-passing methods, demonstrate the ability to outperform conventional receivers of the literature, especially in propagating conditions/setups with high levels of interference. While belief propagation (BP) was the initial method employed, it appears that expectation propagation (EP) is capable of achieving better trade-offs between performance and complexity,both in typical scenarios with few users and low data rates and in scenarios envisioned fornext-generation networks (Beyond 5G, 6G) with dozens of users sharing the same resourcesand high data rates.In this thesis, we propose doubly iterative detectors (auto and turbo iterated) capable of maintaining the performance of state-of-the-art EP while reducing complexity across a range of configurations, from small deployments with few users to massive deployments with hundreds of users. We worked on the graphical representation of the factorization of the posterior probability of the symbols to be detected by using matrix decomposition and/or interference cancellation methods. We propose the derivation of several EP and BP-based receivers meeting the requirements of the new communication challenges. Furthermore, thanks to the thoughtful scheduling of internal messages within the detector, we are able to enhance performance without a significant increase in complexity and achieve better trade-offs between performance and complexity.We also conduct a study of the asymptotic performance of our receivers to quantify their spectral efficiency using analysis tools from the information theory. The impact of factors such as imperfect knowledge of the propagation channel is also investigated, along with methods to reinforce our receivers, ensuring their use in a variety of situations
De, Bacco Caterina. « Decentralized network control, optimization and random walks on networks ». Thesis, Paris 11, 2015. http://www.theses.fr/2015PA112164/document.
Texte intégralIn the last years several problems been studied at the interface between statistical physics and computer science. The reason being that often these problems can be reinterpreted in the language of physics of disordered systems, where a big number of variables interacts through local fields dependent on the state of the surrounding neighborhood. Among the numerous applications of combinatorial optimisation the optimal routing on communication networks is the subject of the first part of the thesis. We will exploit the cavity method to formulate efficient algorithms of type message-passing and thus solve several variants of the problem through its numerical implementation. At a second stage, we will describe a model to approximate the dynamic version of the cavity method, which allows to decrease the complexity of the problem from exponential to polynomial in time. This will be obtained by using the Matrix Product State formalism of quantum mechanics. Another topic that has attracted much interest in statistical physics of dynamic processes is the random walk on networks. The theory has been developed since many years in the case the underneath topology is a d-dimensional lattice. On the contrary the case of random networks has been tackled only in the past decade, leaving many questions still open for answers. Unravelling several aspects of this topic will be the subject of the second part of the thesis. In particular we will study the average number of distinct sites visited during a random walk and characterize its behaviour as a function of the graph topology. Finally, we will address the rare events statistics associated to random walks on networks by using the large-deviations formalism. Two types of dynamic phase transitions will arise from numerical simulations, unveiling important aspects of these problems. We will conclude outlining the main results of an independent work developed in the context of out-of-equilibrium physics. A solvable system made of two Brownian particles surrounded by a thermal bath will be studied providing details about a bath-mediated interaction arising for the presence of the bath
Aubin, Benjamin. « Mean-field methods and algorithmic perspectives for high-dimensional machine learning ». Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASP083.
Texte intégralAt a time when the use of data has reached an unprecedented level, machine learning, and more specifically deep learning based on artificial neural networks, has been responsible for very important practical advances. Their use is now ubiquitous in many fields of application, from image classification, text mining to speech recognition, including time series prediction and text analysis. However, the understanding of many algorithms used in practice is mainly empirical and their behavior remains difficult to analyze. These theoretical gaps raise many questions about their effectiveness and potential risks. Establishing theoretical foundations on which to base numerical observations has become one of the fundamental challenges of the scientific community. The main difficulty that arises in the analysis of most machine learning algorithms is to handle, analytically and numerically, a large number of interacting random variables. In this manuscript, we revisit an approach based on the tools of statistical physics of disordered systems. Developed through a rich literature, they have been precisely designed to infer the macroscopic behavior of a large number of particles from their microscopic interactions. At the heart of this work, we strongly capitalize on the deep connection between the replica method and message passing algorithms in order to shed light on the phase diagrams of various theoretical models, with an emphasis on the potential differences between statistical and algorithmic thresholds. We essentially focus on synthetic tasks and data generated in the teacher-student paradigm. In particular, we apply these mean-field methods to the Bayes-optimal analysis of committee machines, to the worst-case analysis of Rademacher generalization bounds for perceptrons, and to empirical risk minimization in the context of generalized linear models. Finally, we develop a framework to analyze estimation models with structured prior informations, produced for instance by deep neural networks based generative models with random weights
Sahin, Serdar. « Advanced receivers for distributed cooperation in mobile ad hoc networks ». Thesis, Toulouse, INPT, 2019. http://www.theses.fr/2019INPT0089.
Texte intégralMobile ad hoc networks (MANETs) are rapidly deployable wireless communications systems, operating with minimal coordination in order to avoid spectral efficiency losses caused by overhead. Cooperative transmission schemes are attractive for MANETs, but the distributed nature of such protocols comes with an increased level of interference, whose impact is further amplified by the need to push the limits of energy and spectral efficiency. Hence, the impact of interference has to be mitigated through with the use PHY layer signal processing algorithms with reasonable computational complexity. Recent advances in iterative digital receiver design techniques exploit approximate Bayesian inference and derivative message passing techniques to improve the capabilities of well-established turbo detectors. In particular, expectation propagation (EP) is a flexible technique which offers attractive complexity-performance trade-offs in situations where conventional belief propagation is limited by computational complexity. Moreover, thanks to emerging techniques in deep learning, such iterative structures are cast into deep detection networks, where learning the algorithmic hyper-parameters further improves receiver performance. In this thesis, EP-based finite-impulse response decision feedback equalizers are designed, and they achieve significant improvements, especially in high spectral efficiency applications, over more conventional turbo-equalization techniques, while having the advantage of being asymptotically predictable. A framework for designing frequency-domain EP-based receivers is proposed, in order to obtain detection architectures with low computational complexity. This framework is theoretically and numerically analysed with a focus on channel equalization, and then it is also extended to handle detection for time-varying channels and multiple-antenna systems. The design of multiple-user detectors and the impact of channel estimation are also explored to understand the capabilities and limits of this framework. Finally, a finite-length performance prediction method is presented for carrying out link abstraction for the EP-based frequency domain equalizer. The impact of accurate physical layer modelling is evaluated in the context of cooperative broadcasting in tactical MANETs, thanks to a flexible MAC-level simulator
Saade, Alaa. « Spectral inference methods on sparse graphs : theory and applications ». Thesis, Paris Sciences et Lettres (ComUE), 2016. http://www.theses.fr/2016PSLEE024/document.
Texte intégralIn an era of unprecedented deluge of (mostly unstructured) data, graphs are proving more and more useful, across the sciences, as a flexible abstraction to capture complex relationships between complex objects. One of the main challenges arising in the study of such networks is the inference of macroscopic, large-scale properties affecting a large number of objects, based solely on he microscopic interactions between their elementary constituents. Statistical physics, precisely created to recover the macroscopic laws of thermodynamics from an idealized model of interacting particles, provides significant insight to tackle such complex networks.In this dissertation, we use methods derived from the statistical physics of disordered systems to design and study new algorithms for inference on graphs. Our focus is on spectral methods, based on certain eigenvectors of carefully chosen matrices, and sparse graphs, containing only a small amount of information. We develop an original theory of spectral inference based on a relaxation of various meanfield free energy optimizations. Our approach is therefore fully probabilistic, and contrasts with more traditional motivations based on the optimization of a cost function. We illustrate the efficiency of our approach on various problems, including community detection, randomized similarity-based clustering, and matrix completion
Genaud, Stéphane. « Exécutions de programmes parallèles à passage de messages sur grille de calcul ». Habilitation à diriger des recherches, Université Henri Poincaré - Nancy I, 2009. http://tel.archives-ouvertes.fr/tel-00440503.
Texte intégralKurisummoottil, Thomas Christo. « Sparse Bayesian learning, beamforming techniques and asymptotic analysis for massive MIMO ». Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS231.
Texte intégralMultiple antennas at the base station side can be used to enhance the spectral efficiency and energy efficiency of the next generation wireless technologies. Indeed, massive multi-input multi-output (MIMO) is seen as one promising technology to bring the aforementioned benefits for fifth generation wireless standard, commonly known as 5G New Radio (5G NR). In this monograph, we will explore a wide range of potential topics in multi-userMIMO (MU-MIMO) relevant to 5G NR,• Sum rate maximizing beamforming (BF) design and robustness to partial channel stateinformation at the transmitter (CSIT)• Asymptotic analysis of the various BF techniques in massive MIMO and• Bayesian channel estimation methods using sparse Bayesian learning.One of the potential techniques proposed in the literature to circumvent the hardware complexity and power consumption in massive MIMO is hybrid beamforming. We propose a globally optimal analog phasor design using the technique of deterministic annealing, which won us the best student paper award. Further, in order to analyze the large system behaviour of the massive MIMO systems, we utilized techniques from random matrix theory and obtained simplified sum rate expressions. Finally, we also looked at Bayesian sparse signal recovery problem using the technique called sparse Bayesian learning (SBL). We proposed low complexity SBL algorithms using a combination of approximate inference techniques such as belief propagation (BP), expectation propagation and mean field (MF) variational Bayes. We proposed an optimal partitioning of the different parameters (in the factor graph) into either MF or BP nodes based on Fisher information matrix analysis
RAJI, MOURAD. « Algorithme de reconnaissance de formes discretes par passage au continu. Application a la recherche de similarite moleculaire et a la mesure de chiralite geometrique ». Paris 7, 1996. http://www.theses.fr/1996PA077270.
Texte intégralGabrié, Marylou. « Towards an understanding of neural networks : mean-field incursions ». Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEE035.
Texte intégralMachine learning algorithms relying on deep new networks recently allowed a great leap forward in artificial intelligence. Despite the popularity of their applications, the efficiency of these algorithms remains largely unexplained from a theoretical point of view. The mathematical descriptions of learning problems involves very large collections of interacting random variables, difficult to handle analytically as well as numerically. This complexity is precisely the object of study of statistical physics. Its mission, originally pointed towards natural systems, is to understand how macroscopic behaviors arise from microscopic laws. In this thesis we propose to take advantage of the recent progress in mean-field methods from statistical physics to derive relevant approximations in this context. We exploit the equivalences and complementarities of message passing algorithms, high-temperature expansions and the replica method. Following this strategy we make practical contributions for the unsupervised learning of Boltzmann machines. We also make theoretical contributions considering the teacher-student paradigm to model supervised learning problems. We develop a framework to characterize the evolution of information during training in these model. Additionally, we propose a research direction to generalize the analysis of Bayesian learning in shallow neural networks to their deep counterparts
Sabah, Quentin. « Siaam : Simple Isolation for an Actor-based Abstract Machine ». Thesis, Grenoble, 2013. http://www.theses.fr/2013GRENM082/document.
Texte intégralIn this thesis we study state isolation and efficient message-passing in the context of concurrent object-oriented programming. The ’ownership’ and ’reference uniqueness’ techniques have been extensively employed to address concurrency safety in the past. Strikingly the vast majority of the previous works rely on a set of statically checkable typing rules, either requiring an annotation overhead or introducing strong restrictions on the shape and the aliasing of the exchanged messages.Our contribution with SIAAM is the demonstration of a purely runtime, actor-based, annotation-free, aliasing-proof approach to concurrent state isolation allowing efficient communication of arbitrary objects graphs. We present the formal semantic of SIAAM, along with a machine-checked proof of isolation. An implementation of the model has been realized in a state-of-the-art Java virtual-machine and a set of custom static analyses automatically reduce the runtime overhead
Diakhaté, François. « Contribution à l'élaboration de supports exécutifs exploitant la virtualisation pour le calcul hautes performances ». Phd thesis, Université Sciences et Technologies - Bordeaux I, 2010. http://tel.archives-ouvertes.fr/tel-00798832.
Texte intégralKumar, Ratnesh. « Segmentation vidéo et suivi d'objets multiples ». Thesis, Nice, 2014. http://www.theses.fr/2014NICE4135/document.
Texte intégralIn this thesis we propose novel algorithms for video analysis. The first contribution of this thesis is in the domain of video segmentation wherein the objective is to obtain a dense and coherent spatio-temporal segmentation. We propose joining both spatial and temporal aspects of a video into a single notion Fiber. A fiber is a set of trajectories which are spatially connected by a mesh. Fibers are built by jointly assessing spatial and temporal aspects of the video. Compared to the state-of-the-art, a fiber based video segmentation presents advantages such as a natural spatio-temporal neighborhood accessor by a mesh, and temporal correspondences for most pixels in the video. Furthermore, this fiber-based segmentation is of quasi-linear complexity w.r.t. the number of pixels. The second contribution is in the realm of multiple object tracking. We proposed a tracking approach which utilizes cues from point tracks, kinematics of moving objects and global appearance of detections. Unification of all these cues is performed on a Conditional Random Field. Subsequently this model is optimized by a combination of message passing and an Iterated Conditional Modes (ICM) variant to infer object-trajectories. A third, minor, contribution relates to the development of suitable feature descriptor for appearance matching of persons. All of our proposed approaches achieve competitive and better results (both qualitatively and quantitatively) than state-of-the-art on open source datasets
Glück, Olivier. « Optimisations de la bibliothèque de communication MPI pour machines parallèles de type " grappe de PCs " sur une primitive d'écriture distante ». Paris 6, 2002. http://www.theses.fr/2002PA066158.
Texte intégralRocha, barbosa Cassandra. « Coordination et ordonnancement de tâches à grains fins entre environnements d'exécution HPC ». Electronic Thesis or Diss., Reims, 2023. http://www.theses.fr/2023REIMS016.
Texte intégralSupercomputers are becoming more and more complex to use. This is why the use of so-called hybrid programming models, MPI + X, are being implemented in applications. These new types of models allow a more efficient use of a supercomputer, but also create new problems during the execution of applications. These problems are of different types.More specifically, we will study three problems related to MPI + X programming. The progression of non-blocking MPI communications within the X environment. Then two types of possible imbalance in MPI+X applications. The first being between MPI processes and the second within an MPI process, i.e., imbalance within X.A solution in the case of an X environment in recursive tasks will first be presented for the MPI communication progress problem using progress task insertion in the X environment. For the imbalance between MPI processes, a solution for resource rebalancing within a node will be presented. Finally, for the imbalance in the X environment, a solution to use the imbalance to run a second application will also be presented
Guiroux, Hugo. « Comprendre la performance des algorithmes d'exclusion mutuelle sur les machines multicoeurs modernes ». Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAM088.
Texte intégralA plethora of optimized mutual exclusion lock algorithms have been designed over the past 25 years to mitigate performance bottlenecks related to critical sections and synchronization.Unfortunately, there is currently no broad study of the behavior of these optimized lock algorithms on realistic applications that consider different performance metrics, such as energy efficiency and tail latency.In this thesis, we perform a thorough and practical analysis, with the goal of providing software developers with enough information to achieve fast, scalable and energy-efficient synchronization in their systems.First, we provide a performance study of 28 state-of-the-art mutex lock algorithms, on 40 applications, and four different multicore machines.We not only consider throughput (traditionally the main performance metric), but also energy efficiency and tail latency, which are becoming increasingly important.Second, we present an in-depth analysis in which we summarize our findings for all the studied applications.In particular, we describe nine different lock-related performance bottlenecks, and propose six guidelines helping software developers with their choice of a lock algorithm according to the different lock properties and the application characteristics.From our detailed analysis, we make a number of observations regarding locking algorithms and application behaviors, several of which have not been previously discovered:(i) applications not only stress the lock/unlock interface, but also the full locking API (e.g., trylocks, condition variables),(ii) the memory footprint of a lock can directly affect the application performance,(iii) for many applications, the interaction between locks and scheduling is an important application performance factor,(iv) lock tail latencies may or may not affect application tail latency,(v) no single lock is systematically the best,(vi) choosing the best lock is difficult (as it depends on many factors such as the workload and the machine), and(vii) energy efficiency and throughput go hand in hand in the context of lock algorithms.These findings highlight that locking involves more considerations than the simple "lock - unlock" interface and call for further research on designing low-memory footprint adaptive locks that fully and efficiently support the full lock interface, and consider all performance metrics
Colombet, Laurent. « Parallélisation d'applications pour des réseaux de processeurs homogènes ou hétérogènes ». Grenoble INPG, 1994. http://tel.archives-ouvertes.fr/tel-00005084.
Texte intégralThe aim of this thesis is to study and develop efficient methods for parallelization of scientific applications on parallel computers with distributed memory. In the first part we present two libraries of PVM((\it Parallel Virtual Machine)) and MPI ((\it Message Passing Interface)) communication tools. They allow implementation of programs on most parallel machines, but also on heterogeneous computer networks. This chapter illustrates the problems faced when trying to evaluate performances of networks with heterogeneous processors. To evaluate such performances we modified and adapted the concepts of speed-up and efficiency to account for heterogeneity. The second part deals with a study of parallel application libraries such as ScaLAPACK and with the development of communication masking techniques. The general concept is based on communication anticipation, in particular by pipelining message sending operations. Experimental results on Cray T3D and IBM SP1 machines validates the theoretical studies performed on basic algorithms of the libraries discussed above
Damez, Lionel. « Approche multi-processeurs homogènes sur System-on-Chip pour le traitement d'image ». Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2009. http://tel.archives-ouvertes.fr/tel-00724443.
Texte intégralGauchard, David. « Simulation hybride des réseaux IP-DiffServ-MPLS multi-services sur environnement d'exécution distribuée ». Toulouse 3, 2003. http://www.theses.fr/2003TOU30192.
Texte intégralLokhov, Andrey Y. « Dynamic cavity method and problems on graphs ». Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112331/document.
Texte intégralA large number of optimization, inverse, combinatorial and out-of-equilibrium problems, arising in the statistical physics of complex systems, allow for a convenient representation in terms of disordered interacting variables defined on a certain network. Although a universal recipe for dealing with these problems does not exist, the recent years have seen a serious progress in understanding and quantifying an important number of hard problems on graphs. A particular role has been played by the concepts borrowed from the physics of spin glasses and field theory, that appeared to be extremely successful in the description of the statistical properties of complex systems and in the development of efficient algorithms for concrete problems.In the first part of the thesis, we study the out-of-equilibrium spreading problems on networks. Using dynamic cavity method on time trajectories, we show how to derive dynamic message-passing equations for a large class of models with unidirectional dynamics -- the key property that makes the problem solvable. These equations are asymptotically exact for locally tree-like graphs and generally provide a good approximation for real-world networks. We illustrate the approach by applying the dynamic message-passing equations for susceptible-infected-recovered model to the inverse problem of inference of epidemic origin. In the second part of the manuscript, we address the optimization problem of finding optimal planar matching configurations on a line. Making use of field-theory techniques and combinatorial arguments, we characterize a topological phase transition that occurs in the simple Bernoulli model of disordered matching. As an application to the physics of the RNA secondary structures, we discuss the relation of the perfect-imperfect matching transition to the known molten-glass transition at low temperatures, and suggest generalized models that incorporate a one-to-one correspondence between the contact matrix and the nucleotide sequence, thus giving sense to the notion of effective non-integer alphabets
Hu, Ruijing. « Algorithmes de dissémination épidémiques dans les réseaux à grande échelle : comparaison et adaptation aux topologies ». Phd thesis, Université Pierre et Marie Curie - Paris VI, 2013. http://tel.archives-ouvertes.fr/tel-00931796.
Texte intégralDiallo, Alpha Boubacar. « Développement et parallélisation d'algorithmes bioinformatiques pour la reconstruction d'arbres phylogénétiques et de réseaux réticulés ». Mémoire, 2007. http://www.archipel.uqam.ca/4752/1/M10004.pdf.
Texte intégralPham, Cong Duc. « Détection et localisation de défauts dans les réseaux de distribution HTAen présence de génération d'énergie dispersée ». Phd thesis, 2005. http://tel.archives-ouvertes.fr/tel-00164643.
Texte intégralindicateurs de passage de défaut (IPD). Les études ont été effectuées dans le cadre du développement attendu et
croissant des GED (sources de Génération d'Energie Décentralisées).
La première partie du mémoire est consacrée à l'analyse du comportement des IPD. En ce qui concerne
l'influence du contexte de fonctionnement sur la réponse des IPD, une partie est destinée à vérifier le
fonctionnement des modèles IPD développés et les règles d'utilisation des IPD prévus. Une autre analyse
l'influence des GED sur l'utilisation des IPD sur la détection et localisation de défauts. Pour l'amélioration de la
robustesse du diagnostic avec IPD en présence de fausses indications, une méthode de détermination de la
section en défaut (limitée par des IPD) est proposée.
La deuxième partie du mémoire est consacrée à une méthode d'optimisation du placement des IPD dans les
réseaux HTA sur la base d'algorithmes génétiques. Nous avons défini différents critères pour l'optimisation ; ils
sont validés par un programme de calcul des indices de fiabilité. L'influence de la GED dans le départ HTA sur
le placement optimal des IPD est analysée en tenant compte du coût de l'énergie non fournie par la GED et du
fonctionnement envisageable comme un secours de la GED.