Literatura científica selecionada sobre o tema "Modélisation de Dirichlet"
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Artigos de revistas sobre o assunto "Modélisation de Dirichlet"
BARÈS, Franck, e Gabrielle ALIE. "Évolution de la revue Management international : Une modélisation thématique des articles publiés entre 2009 et 2023". Management international 28, n.º 2 (2024): 127–33. http://dx.doi.org/10.59876/a-ab4g-7647.
Texto completo da fonteTeses / dissertações sobre o assunto "Modélisation de Dirichlet"
Batoux, Philippe. "Techniques d'homogénéisation pour la modélisation de piles à combustibles". Aix-Marseille 1, 1997. http://www.theses.fr/1997AIX11089.
Texto completo da fonteChristine, Vannier. "Modélisation mathématique du poumon humain". Phd thesis, Université Paris Sud - Paris XI, 2009. http://tel.archives-ouvertes.fr/tel-00739462.
Texto completo da fonteZerkoune, Abbas. "Modélisation de l'incertitude géologique par simulation stochastique de cubes de proportions de faciès : application aux réservoirs pétroliers de type carbonaté ou silico-clastique". Phd thesis, Grenoble 1, 2009. http://www.theses.fr/2009GRE10104.
Texto completo da fonteAfter finding out a potential oil field, development decisions are based on uncertain representations of the reservoir. Indeed, its characterisation uses numerical, spatial models of the reservoir. However, if they are representative of subsoil heterogeneities, the uncertainty linked to subsoil complexity remain. Usually, uncertainty is supposed to be assessed using many equiprobable models, which represent the heterogeneities expected into the reservoir. Nevertheless, those alternative images of the underground correspond to multiple realizations of a given and a single stochastic model. Those methods ignore the uncertainty related to the choice of the underlying probabilistic model. This work aims at improving that kind of uncertainty assessment when modelling petroleum reservoir. It conveys the doubt linked with our subsoil properties understanding on probabilistic models, and proposes to integrate it on them. This thesis first defines uncertainty in the context of oil industry modelling, particularly on 3D geological models comprising several litho-types or facies. To build them, we need, before any simulations, to estimate for every point in the space the probability of occurring for each facies : this is the proportions cube. Even thought those probabilities are often poorly known, they are frozen while using current methods of uncertainty assessment. So, the impact of an uncertain geological scenario on the definition of a proportion cube is forgotten. Two methods based on stochastic simulations of alternative, equiprobable proportion cubes have been developed to sample the complete geological uncertainty space. The first one is closely linked to geology. It integrates directly uncertainty related to the parameters composing the geological scenario. Based on a multi-realisation approach, it describes its implementation on every parameters of geological scenario from information at wells to maps or global hypothesis at reservoir scale resolution. A Monte Carlo approach samples the components of the sedimentary scheme. Each drawing enables to build a proportion cube using modelling tools which integrates more or less explicitly parameters of geological scenario. That methodology is illustrated and applied to an modelling process which is used to model marine carbonate deposits. The second method appears to be more geostatistics focussing on proportion cubes. It rather aims at reconcile distinct eventual sedimentary models. In the meshed model symbolising the reservoir, it assesses the probabilistic law of facies proportion in each cells – they are supposed to follow Dirichlet's probabilistic law. That assessment is done from some models inferred from different geological scenarios. Facies proportions are sequentially simulated, cell after cell, introducing a spatial correlation model (variogram), which could be deterministic as probabilistic. Various practical cases, comprising synthetic reservoirs or real field, illustrates and specifies the different steps of the proposed method
Zerkoune, Abbas. "Modélisation de l'incertitude géologique par simulation stochastique de cubes de proportions de faciès - Application aux réservoirs pétroliers de type carbonaté ou silico-clastique". Phd thesis, Université Joseph Fourier (Grenoble), 2009. http://tel.archives-ouvertes.fr/tel-00410136.
Texto completo da fonteViandier, Nicolas. "Modélisation et utilisation des erreurs de pseudodistances GNSS en environnement transport pour l’amélioration des performances de localisation". Thesis, Ecole centrale de Lille, 2011. http://www.theses.fr/2011ECLI0006/document.
Texto completo da fonteToday, the GNSS are largely present in the transport field. Currently, the scientific community aims to develop transport applications with a high accuracy, availability and integrity. These systems offer a continuous positioning service. Performances are defined by the system parameters but also by signal environment propagation. The atmosphere propagation characteristics are well known. However, it is more difficult to anticipate and analyze the impact of the propagation environment close to the antenna which can be composed, for instance, of urban obstacles or vegetation.Since several years, the LEOST and the LAGIS research axes are driven by the understanding of the propagation environment and its use as supplementary information to help the GNSS receiver to be more pertinent. This approach aims to reduce the number of sensors in the localisation system, and consequently reduces its complexity and cost. The work performed in this thesis is devoted to provide more realistic pseudorange error models and reception channel model. After, a step of observation error characterization, several pseudorange error models have been proposed. These models are the finite gaussian mixture model and the Dirichlet process mixture. The model parameters are then estimated jointly with the state vector containing position by using adapted filtering solution like the Rao-Blackwellized particle filter. The noise model evolution allows adapting to an urban environment and consequently providing a position more accurate.Each step of this work has been tested and evaluated on simulation data and real data
Simonnet, Titouan. "Apprentissage et réseaux de neurones en tomographie par diffraction de rayons X. Application à l'identification minéralogique". Electronic Thesis or Diss., Orléans, 2024. http://www.theses.fr/2024ORLE1033.
Texto completo da fonteUnderstanding the chemical and mechanical behavior of compacted materials (e.g. soil, subsoil, engineered materials) requires a quantitative description of the material's structure, and in particular the nature of the various mineralogical phases and their spatial relationships. Natural materials, however, are composed of numerous small-sized minerals, frequently mixed on a small scale. Recent advances in synchrotron-based X-ray diffraction tomography (to be distinguished from phase contrast tomography) now make it possible to obtain tomographic volumes with nanometer-sized voxels, with a XRD pattern for each of these voxels (where phase contrast only gives a gray level). On the other hand, the sheer volume of data (typically on the order of 100~000 XRD patterns per sample slice), combined with the large number of phases present, makes quantitative processing virtually impossible without appropriate numerical codes. This thesis aims to fill this gap, using neural network approaches to identify and quantify minerals in a material. Training such models requires the construction of large-scale learning bases, which cannot be made up of experimental data alone.Algorithms capable of synthesizing XRD patterns to generate these bases have therefore been developed.The originality of this work also concerned the inference of proportions using neural networks. To meet this new and complex task, adapted loss functions were designed.The potential of neural networks was tested on data of increasing complexity: (i) from XRD patterns calculated from crystallographic information, (ii) using experimental powder XRD patterns measured in the laboratory, (iii) on data obtained by X-ray tomography. Different neural network architectures were also tested. While a convolutional neural network seemed to provide interesting results, the particular structure of the diffraction signal (which is not translation invariant) led to the use of models such as Transformers. The approach adopted in this thesis has demonstrated its ability to quantify mineral phases in a solid. For more complex data, such as tomography, improvements have been proposed
Viandier, Nicolas. "Modélisation et utilisation des erreurs de pseudodistances GNSS en environnement transport pour l'amélioration des performances de localisation". Phd thesis, Ecole Centrale de Lille, 2011. http://tel.archives-ouvertes.fr/tel-00664264.
Texto completo da fonteChiron, Guillaume. "Système complet d’acquisition vidéo, de suivi de trajectoires et de modélisation comportementale pour des environnements 3D naturellement encombrés : application à la surveillance apicole". Thesis, La Rochelle, 2014. http://www.theses.fr/2014LAROS030/document.
Texto completo da fonteThis manuscript provides the basis for a complete chain of videosurveillence for naturally cluttered environments. In the latter, we identify and solve the wide spectrum of methodological and technological barriers inherent to : 1) the acquisition of video sequences in natural conditions, 2) the image processing problems, 3) the multi-target tracking ambiguities, 4) the discovery and the modeling of recurring behavioral patterns, and 5) the data fusion. The application context of our work is the monitoring of honeybees, and in particular the study of the trajectories bees in flight in front of their hive. In fact, this thesis is part a feasibility and prototyping study carried by the two interdisciplinary projects EPERAS and RISQAPI (projects undertaken in collaboration with INRA institute and the French National Museum of Natural History). It is for us, computer scientists, and for biologists who accompanied us, a completely new area of investigation for which the scientific knowledge, usually essential for such applications, are still in their infancy. Unlike existing approaches for monitoring insects, we propose to tackle the problem in the three-dimensional space through the use of a high frequency stereo camera. In this context, we detail our new target detection method which we called HIDS segmentation. Concerning the computation of trajectories, we explored several tracking approaches, relying on more or less a priori, which are able to deal with the extreme conditions of the application (e.g. many targets, small in size, following chaotic movements). Once the trajectories are collected, we organize them according to a given hierarchical data structure and apply a Bayesian nonparametric approach for discovering emergent behaviors within the colony of insects. The exploratory analysis of the trajectories generated by the crowded scene is performed following an unsupervised classification method simultaneously over different levels of semantic, and where the number of clusters for each level is not defined a priori, but rather estimated from the data only. This approach is has been validated thanks to a ground truth generated by a Multi-Agent System. Then we tested it in the context of real data
Plessis, Sylvain. "Modélisation probabiliste pour l'étude de l'influence des ions sur la composition des espèces neutres dans l'atmosphère de Titan : Inversion bayésienne de spectres de masse INMS et représentation des recombinaisons dissociatives par distributions de Dirichlet imbriquées". Paris 11, 2010. http://www.theses.fr/2010PA112129.
Texto completo da fonteTitan's high atmosphere neutral composition has been infered from CASSINI INMS ionic mass spectra inversion and by coupled models. We used Bayesian Monte Carlo methods to perform ionic mass spectra inversion with uncertainty propagation. We have shown that most of the neutral species are not constrained by this method. In a second time, we studied the dissociative recombination (DR) to improve the model description of this process. Being only partially characterized by experiments, knowledge of this process adopts a probabilistic tree structure. Nested Dirichlet distributions are efficient in representing such structures. We could therefore build a comprehensive database of DR relative to Titan, integrating all pieces of information. First results showed that a complete description of DR is enough to account for the densities of some neutral species, in particular nitrogen-bearing species