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Littérature scientifique sur le sujet « Divergence informationnelle »
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Articles de revues sur le sujet "Divergence informationnelle"
Calvo-Schimmel, Alejandra, S. Newman, K. Sterba, M. Mueller, C. Miaskowski et S. Qanungo. « Besoins non satisfaits en soins de soutien chez les survivants du cancer de la prostate à un stade avancé : exploration par méthodes mixtes ». Canadian Oncology Nursing Journal 32, no 4 (24 octobre 2022) : 526–41. http://dx.doi.org/10.5737/23688076324526.
Texte intégralDussault, Gilles. « The Future of Professional Monopolies ». Articles 40, no 2 (12 avril 2005) : 324–39. http://dx.doi.org/10.7202/050136ar.
Texte intégralNoone, Rebecca. « The Everyday Information Experience of Street-Level Wayfinding : A Critical Look ». Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l'ACSI, 8 novembre 2020. http://dx.doi.org/10.29173/cais1153.
Texte intégralGranval, Aurore. « Bibliothèques académiques, communales et scolaires : mêmes missions pédagogiques ? Un retour d’expérience ». Revue électronique suisse de science de l'information (RESSI), no 22 (16 mai 2022). http://dx.doi.org/10.55790/journals/ressi.2022.e808.
Texte intégralOliphant, Tami. « Interpreting mediated quantitative data : Insights from information behaviour research ». Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l'ACSI, 28 juillet 2018. http://dx.doi.org/10.29173/cais987.
Texte intégralPoirier, Éric. « Towards an empirical evaluation of translated texts and translation quality ». Journal of Data Mining & ; Digital Humanities Towards robotic translation?, IV. Challenges for... (12 décembre 2022). http://dx.doi.org/10.46298/jdmdh.9123.
Texte intégralThèses sur le sujet "Divergence informationnelle"
Abci, Boussad. « Approche informationnelle pour la navigation autonome tolérante aux défauts : application aux systèmes robotiques mobiles ». Thesis, Lille 1, 2019. http://www.theses.fr/2019LIL1I073.
Texte intégralOver the last years, autonomous navigation for mobile robot systems has known an increasing interest from the scientific community. This is mainly due to the diversity of its applications and the different challenges that it represents. Without any human intervention, autonomous navigation must be safe, reliable and accurate. Nevertheless, it may be subject to various degradations that could compromise its objective. Indeed, external disturbances, as well as sensor and actuator faults, may affect the different aspects of autonomous navigation, which are localization, path planning and trajectory tracking. This is why we are devoting this thesis to the design of new algorithms that contribute to make the navigation system robust against external disturbances and tolerant to sensor and actuator fauts. We have adopted a residual generation based fault-diagnosis strategy combined with a robust sliding mode controller that is robust against a certain class of perturbations that are not necessary uniformly bounded. The proposed diagnostic layer is purely informational. It is based on the use of two information filters with different evolution models, and the divergences of Bhattacharyya and Kullback-Leibler for residual design. These residuals are evaluated using statistical methods, in order to detect, isolate then exclude sensor and actuator faults from the navigation system. The proposed approach is applied to different differential drive mobile-robot systems. Experimental results obtained by using the CRIStAL robotic platform, so-called PRETIL, are presented and discussed
Harbaoui, Nesrine. « Diagnostic adaptatif à l'environnement de navigation : apport de l'apprentissage profond pour une localisation sûre et précise ». Electronic Thesis or Diss., Université de Lille (2022-....), 2022. http://www.theses.fr/2022ULILB041.
Texte intégralFor an autonomous terrestrial transport system, the ability to determine its position is essential in order to allow other functions, such as control or perception, to be safely controlled or perceived. Thus, the criticality of these functions generates important requirements in terms of safety (integrity), availability, accuracy and precision. For land vehicles, meeting these requirements is related to various parameters such as vehicle dynamics, weather conditions, or the navigation context, which includes both the operational environment and the behavior of the host vehicle or user. All of these circumstances can be an obstacle to the reception of Global Navigation Satellite System (GNSS) signals since the environment determines the type and quality of electromagnetic signals available for positioning.Although many navigation and positioning techniques have been developed, none is capable of providing a reliable and accurate position in all contexts. Therefore, in order to deploy a localization function capable of operating in different contexts, based on low cost sensors, mainly GNSS and Inertial Navigation system (IMU), it is necessary, from the design phase, to develop strategies that solve both the antagonism of certain requirements and the adaptation to changing environment/dynamics. In this context, this thesis proposes a diagnostic layer that adapts by deep learning methods to changes in the context and adjusts the trade-off between functional requirements. This layer is integrated in a fault-tolerant data fusion framework through an informational divergence, the α-Rényi divergence, known by its generalization of other divergences such as the Kullback-Leibler divergence, the Bhattacharyya distance. In order to detect and isolate the divergence faults based on the generation of residuals, we offer the solution of selecting the appropriate residual for each situation by fixing the value of the parameter α using artificial intelligence technologies in order to increase the detectability of the defects. In order to increase the availability of the system while maintaining an acceptable level of operational safety, a context-sensitive threshold that adjusts the trade-off between the probability of false alarm and the probability of missed detection is proposed. To test and validate the proposed approaches, two types of data have been provided; real by the PRETIL platform of the CRIStAL laboratory and simulated by the Stella NGC simulator as a part of the ANR LOCSP project
Al, Hage Joelle. « Fusion de données tolérante aux défaillances : application à la surveillance de l’intégrité d’un système de localisation ». Thesis, Lille 1, 2016. http://www.theses.fr/2016LIL10074/document.
Texte intégralThe interest of research in the multi-sensor data fusion field is growing because of its various applications sectors. Particularly, in the field of robotics and localization, the use of different sensors informations is a vital step to ensure a reliable position estimation. In this context of multi-sensor data fusion, we consider the diagnosis, leading to the identification of the cause of a failure, and the sensors faults tolerance aspect, discussed in limited work in the literature. We chose to develop an approach based on a purely informational formalism: information filter on the one hand and tools of the information theory on the other. Residuals based on the Kullback-Leibler divergence are developed. These residuals allow to detect and to exclude the faulty sensors through optimized thresholding methods. This theory is tested in two applications. The first application is the fault tolerant collaborative localization of a multi-robot system. The second application is the localization in outdoor environments using a tightly coupled GNSS/odometer with a fault tolerant aspect
Daher, Mohamad. « Fusion multi-capteurs tolérante aux fautes pour un niveau d'intégrité élevé du suivi de la personne ». Thesis, Lille 1, 2017. http://www.theses.fr/2017LIL10136/document.
Texte intégralAbout one third of home-dwelling older people suffer a fall each year. The most painful falls occur when the person is alone and unable to get up, resulting in huge number of elders which are associated with institutionalization and high morbidity-mortality rate. The PAL (Personally Assisted Living) system appears to be one of the solutions of this problem. This ambient intelligence system allows elderly people to live in an intelligent and pro-active environment. This thesis describes the ongoing work of in-home elder tracking, activities daily living recognition, and automatic fall detection system using a set of non-intrusive sensors that grants privacy and comfort to the elders. In addition, a fault-tolerant fusion method is proposed using a purely informational formalism: information filter on the one hand, and information theory tools on the other hand. Residues based on the Kullback-Leibler divergence are used. Using an appropriate thresholding, these residues lead to the detection and the exclusion of sensors faults. The proposed algorithms were validated with many different scenarios containing the different activities: walking, sitting, standing, lying down, and falling. The performances of the developed methods showed a sensitivity of more than 94% for the fall detection of persons and more than 92% for the discrimination between the different ADLs (Activities of the daily life)