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Literatura académica sobre el tema "Prédiction de ressources"
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Artículos de revistas sobre el tema "Prédiction de ressources"
Cairns, Robert D. "Les ressources non renouvelables : le côté offre". Articles 66, n.º 4 (28 de enero de 2009): 444–60. http://dx.doi.org/10.7202/601548ar.
Texto completoMassou, Luc, Jean-Baptiste Lanfranchi, Brian Chauvel y Stéphanie Fleck. "Travail collectif et usages du numérique : Quelle catégorisation de pratiques chez des enseignants français à l’école et au collège?" Médiations et médiatisations, n.º 5 (29 de enero de 2021): 134–61. http://dx.doi.org/10.52358/mm.vi5.171.
Texto completoBarrette, Jacques y Jules Carrière. "La performance organisationnelle et la complémentarité des pratiques de gestion des ressources humaines". Articles 58, n.º 3 (22 de enero de 2004): 427–53. http://dx.doi.org/10.7202/007494ar.
Texto completoPHOCAS, F., C. BELLOC, J. BIDANEL, L. DELABY, J. Y. DOURMAD, B. DUMONT, P. EZANNO et al. "Quels programmes d’amélioration génétique des animaux pour des systèmes d’élevage agro-écologiques ?" INRA Productions Animales 30, n.º 1 (14 de junio de 2018): 31–46. http://dx.doi.org/10.20870/productions-animales.2017.30.1.2232.
Texto completoOlivier, Jean-Marc. "Prédiction et prévision en histoire économique : les succès suisses et scandinaves étaient-ils prévisibles?" Nouvelles perspectives en sciences sociales 6, n.º 2 (13 de septiembre de 2011): 87–96. http://dx.doi.org/10.7202/1005771ar.
Texto completoFaye, R. M., S. Sawadogo y F. Mora-Camino. "Logique floue Appliquée à la gestion à long terme des ressources en eau". Revue des sciences de l'eau 15, n.º 3 (12 de abril de 2005): 579–96. http://dx.doi.org/10.7202/705470ar.
Texto completoCruz García, Lirios. "Teoría del comportamiento sustentable para el desarrollo local". Clivajes. Revista de Ciencias Sociales, n.º 9 (24 de abril de 2018): 71–94. http://dx.doi.org/10.25009/clivajes-rcs.v0i9.2546.
Texto completoMunoz, Alain. "EVALUATION PAR VALIDATION INDEPENDANTE DES PREDICTIONS DES PARAMETRES FORESTIERS REALISEES A PARTIR DE DONNEES LIDAR AEROPORTE". Revue Française de Photogrammétrie et de Télédétection 1, n.º 211-212 (6 de diciembre de 2015): 81–92. http://dx.doi.org/10.52638/rfpt.2015.547.
Texto completoROGUET, C., B. DUMONT y S. PRACHE. "Sélection et utilisation des ressources fourragères par les herbivores : théories et expérimentations à l’échelle du site et de la station alimentaires". INRAE Productions Animales 11, n.º 4 (3 de octubre de 1998): 273–84. http://dx.doi.org/10.20870/productions-animales.1998.11.4.3954.
Texto completoLevang, Patrice, Nicolas Buyse, Soaduon Sitorus y Edmond Dounias. "Impact de la décentralisation sur la gestion des ressources forestières en Indonésie". Anthropologie et Sociétés 29, n.º 1 (18 de noviembre de 2005): 81–102. http://dx.doi.org/10.7202/011741ar.
Texto completoTesis sobre el tema "Prédiction de ressources"
Santi, Nina. "Prédiction des besoins pour la gestion de serveurs mobiles en périphérie". Electronic Thesis or Diss., Université de Lille (2022-....), 2023. http://www.theses.fr/2023ULILB050.
Texto completoMulti-access Edge computing is an emerging paradigm within the Internet of Things (IoT) that complements Cloud computing. This paradigm proposes the implementation of computing servers located close to users, reducing the pressure and costs of local network infrastructure. This proximity to users is giving rise to new use cases, such as the deployment of mobile servers mounted on drones or robots, offering a cheaper, more energy-efficient and flexible alternative to fixed infrastructures for one-off or exceptional events. However, this approach also raises new challenges for the deployment and allocation of resources in terms of time and space, which are often battery-dependent.In this thesis, we propose predictive tools and algorithms for making decisions about the allocation of fixed and mobile resources, in terms of both time and space, within dynamic environments. We provide rich and reproducible datasets that reflect the heterogeneity inherent in Internet of Things (IoT) applications, while exhibiting a high rate of contention and interference. To achieve this, we are using the FIT-IoT Lab, an open testbed dedicated to the IoT, and we are making all the code available in an open manner. In addition, we have developed a tool for generating IoT traces in an automated and reproducible way. We use these datasets to train machine learning algorithms based on regression techniques to evaluate their ability to predict the throughput of IoT applications. In a similar approach, we have also trained and analysed a neural network of the temporal transformer type to predict several Quality of Service (QoS) metrics. In order to take into account the mobility of resources, we are generating IoT traces integrating mobile access points embedded in TurtleBot robots. These traces, which incorporate mobility, are used to validate and test a federated learning framework based on parsimonious temporal transformers. Finally, we propose a decentralised algorithm for predicting human population density by region, based on the use of a particle filter. We test and validate this algorithm using the Webots simulator in the context of servers embedded in robots, and the ns-3 simulator for the network part
Galtier, Virginie. "Éléments de gestion des ressources de calcul dans les réseaux actifs hétérogènes". Nancy 1, 2002. http://docnum.univ-lorraine.fr/public/SCD_T_2002_0017_GALTIER.pdf.
Texto completoChoutri, Amira. "Gestion des ressources et de la consommation énergétique dans les réseaux mobiles hétérogènes". Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLV043/document.
Texto completoThe objective of this thesis is to develop methods for a targeted and efficient management of users mobility in heterogeneous mobile networks. This network is characterized by the deployment of different types of cells (macro, micro, pico and/or femto). The massive deployment of small cells (pico and femto) provides a supplementary coverage and capacity to mobile networks, specially in dense areas. However, the resulting real-time constraints limit the offered QoS. Furthermore, for commercial and/or environmental reasons, the needs to reduce the energy consumed by mobile networks became reality. Thus, mobile operators have to find a good compromise between, on the one hand, the users velocity and the guaranteed QoS, and on the other hand, the cost of deployment of such networks. For that, in the context of users mobility management, we propose models for resource and energy consumption management of base stations. The first model aims at controlling resource sharing between clients of the mobile operators. Based on a mobility prediction of users, this model anticipates the resource management of a base station. The second model aims at reducing energy consumption of the network by managing mobile users assignment to detected cells. This allows a continuous control of consumed energy of base stations while offered QoS is guaranteed. Based on simulation of a real mobile network topology, the performances of proposed models are evaluated while considering different possible scenarios. They are compared to the performances of different strategies as the ones proposed in literature or adopted by current mobile operators
Miegemolle, Bernard. "Prédiction de comportement d'applications parallèles et placement à l'aide de modèles économiques sur une grille de calcul". Phd thesis, INSA de Toulouse, 2008. http://tel.archives-ouvertes.fr/tel-00420473.
Texto completoDufour, Luc. "Contribution à la mise au point d'un pilotage énergétique décentralisé par prédiction". Thesis, Ecole nationale des Mines d'Albi-Carmaux, 2017. http://www.theses.fr/2017EMAC0004/document.
Texto completoThis work presents a data-intensive solution to manage energy flux after a low transformer voltage named microgrid concept. A microgrid is an aggregation of building with a decentralized energy production and or not a storage system. These microgrid can be aggregate to create an intelligent virtual power plant. However, many problems must be resolved to increase the part of these microgrid and the renewable resource in a energy mix. The physic model can not integrate and resolve in a short time the quickly variations. The intelligent district can be integrate a part of flexibility in their production with a storage system. This storage can be electrical with a battery or thermal with the heating and the hot water. For a virtual power plant, the system can be autonomous when the price electricity prediction is low and increase the production provided on the market when the price electricity is high. For a energy supplier and with a decentralized production building distant of a low transformer voltage, a regulation with a storage capacity enable a tension regulation. Finally, the auto-consumption becomes more and more interesting combined with a low electrical storage price and the result of the COP 21 in Paris engage the different country towards the energy transition. In these cases, a flexibility is crucial at the building level but this flexibility is possible if, and only if, the locally prediction are correct to manage the energy. The main novelties of our approach is to provide an easy implemented and flexible solution to predict the consumption and the production at the building level based on the machine learning technique and tested on the real use cases in a residential and tertiary sector. A new evaluation of the consumption is realized: the point of view is energy and not only electrical. The energy consumption is decomposed between the heating consumption, the hot water consumption and the electrical devices consumption. A prediction every hour is provided for the heating and the hot water consumption to estimate the thermal storage capacity. A characterization of Electrical devices consumption is realized by a non-intrusive disaggregation from the global load curve. The heating and the hot water are identify to provide a non intrusive methodology of prediction. Every day, the heating, the hot water, the household appliances, the cooling and the stand by are identified. Every 15 minutes, our software provide a hot water prediction, a heating prediction, a decentralized prediction and a characterization of the electrical consumption. A comparison with the different physic model simulated enable an error evaluation the error of our different implemented model
Leroy, Boris. "Utilisation des bases de données biodiversité pour la conservation des taxons d’invertébrés : indices de rareté des assemblages d’espèces et modèles de prédiction de répartition d’espèces". Paris, Muséum national d'histoire naturelle, 2012. http://www.theses.fr/2012MNHN0033.
Texto completoInvertebrate taxa are underrepresented in conservation biology. To improve their inclusion, we aimed at providing principles and tools for their conservation. We analysed biodiversity database —defined as databases compiling species occurrences in space and time— which are the only sources of data for most invertebrate taxa. We applied important principles of data quality, and used a metric to quantify the completeness of biodiversity databases. We first developed a new tool at the assemblage level on the basis of databases of spiders and marine invertebrates: the Index of Relative Rarity. This index integrates a flexible parameter (the rarity cutoff) which allows fitting the index with respect to the considered taxon, geographic area and spatial scale. We then improved this index by including multiple scales or multiple phyla to assess the rarity of assemblages. We then developed tools at the species level: species distribution models. Using spiders as an example, we proposed an appropriate application for conservation purposes, to (1) define conservation priorities for species and (2) identify where conservation actions are most likely to succeed. The principles and methods that we developed allow an appropriate use of available biodiversity databases for conservation, are transferable to other invertebrate taxa and are innovative tools for conservation programs across multiple spatial scales
Bassard, David. "Méthodologie de prédiction et d’optimisation du potentiel méthane de mélanges complexes en co-digestion". Thesis, Compiègne, 2015. http://www.theses.fr/2015COMP2175/document.
Texto completoThe co-digestion of agro-industrial substrates in anaerobic conditions falls within the objectives of an optimized management of agricultural resources along with reduction of anthropogenic impacts and development of renewable energies. Considering scientific and industrial bottlenecks from literature review, it could be identified that a methodological approach was the key to an enhanced understanding of anaerobic co-digestion. Ultimately, formulation of the substrate and co-substrates (digestor’s inputs) appeared to be the main actuator to optimize anaerobic co-digestion. Conciliating both scientific and industrial issues, this thesis led to the following findings : (i) an implementation of simple and cost-saving methods to characterize the inputs of digestor and biogas production, (ii) a determination of fundamental relationship between substrate blend and his biomethane potential, (iii) a development of predictive tools for biomethane potential of substrate blends as well as global and specific biodegradability of substrates, (iv) an enhanced comprehension of first, interactions between codigested substrates and the microbial consortium and second, the adaptation capacity of the microbial consortium to various organic loading (homeostatic capacity)
Monteil, Thierry. "Du cluster à la grille sous l'angle de la performance". Habilitation à diriger des recherches, Institut National Polytechnique de Toulouse - INPT, 2010. http://tel.archives-ouvertes.fr/tel-00547021.
Texto completoGbaguidi, Fréjus A. Roméo. "Approche prédictive de l'efficacité énergétique dans les Clouds Datacenters". Electronic Thesis or Diss., Paris, CNAM, 2017. http://www.theses.fr/2017CNAM1163.
Texto completoWith the democratization of digital technologies, the construction of a globalized cyberspace insidiously transforms our lifestyle. Connect more than 4 billion people at high speed, requires the invention of new concept of service provision and trafic management that are capable to face the challenges. For that purpose, Cloud Computing have been set up to enable Datacenters to provide part or total IT components needed by companies for timely services delivering with performance that meets the requirements of their clients. Consequently, the proliferation of Datacenters around the world has brought to light the worrying question about the amount of energy needed for their function and the resulting difficulty for the humanity, whose current reserves are not extensible indefinitely. It was therefore necessary to develop techniques that reduce the power consumption of Datacenters by minimizing the energy losses orchestrated on servers where each wasted watt results in a chain effect on a substantial increase in the overall bill of Datacenters. Our work consisted first in making a review of the literature on the subject and then testing the ability of some prediction tools to improve the anticipation of the risks of energy loss caused by the misallocation of virtual equipment on servers. This study focused particularly on the ARMA tools and neural networks which in the literature have produced interesting results in related fields. After this step, it appeared to us that ARMA tools, although having less performance than neural networks in our context, runs faster and are best suited to be implemented in cloud computing environments. Thus, we used the results of this method to improve the decision-making process, notably for the proactive re-allocation of virtual equipment before it leads to under-consumption of resources on physical servers or over-consumption inducing breaches of SLAs. Based on our simulations, this approach enabled us to reduce energy consumption on a firm of 800 servers over a period of one day by more than 5Kwh. This gain could be significant when considering the enormous size of modern data centers and projected over a relatively long period of time. It would be even more interesting to deepen this research in order to generalize the integration of this predictive approach into existing techniques in order to significantly optimize the energy consumption within Datacenters while preserving performance and quality of service which are key requirements in the concept of Cloud Computing
Gaudin, Théophile. "Développement de modèles QSPR pour la prédiction et la compréhension des propriétés amphiphiles des tensioactifs dérivés de sucre". Thesis, Compiègne, 2016. http://www.theses.fr/2016COMP2318/document.
Texto completoSugar-based surfactants are the main family of bio-based surfactants and are good candidates as substitutes for petroleum-based surfactants, since they originate from renewable resources and can show as good as, or even better, performances in various applications, such as detergent and cosmetic formulation, enhanced oil or mineral recovery, etc. Different amphiphilic properties can characterize surfactant performance in such applications, like critical micelle concentration, surface tension at critical micelle concentration, efficiency and Kraft point. Predicting such properties would be beneficial to quickly identify surfactants that exhibit desired properties. QSPR models are tools to predict such properties, but no reliable QSPR model was identified for bio-based surfactants, and in particular sugar-based surfactants. During this thesis, such QSPR models were developed. A reliable database is required to develop any QSPR model. Regarding sugar-based surfactants, no database was identified for the targeted properties. This motivated the elaboration of the first database of amphiphilic properties of sugar-based surfactants. The analysis of this database highlighted various empirical relationships between the chemical structure of these molecules and their amphiphilic properties, and enabled to isolate the most reliable datasets with the most homogeneous possible protocol, to be used for the development of the QSPR models. After the development of a robust strategy to calculate molecular descriptors that constitute QSPR models, notably relying upon conformational analysis of sugar-based surfactants and descriptors calculated only for the polar heads and for the alkyl chains, different QSPR models were developed, validated, and their applicability domain defined, for the critical micelle concentration, the surface tension at critical micelle concentration, the efficiency and the Kraft point. For the three first properties, good quantitative models were obtained. If the quantum chemical descriptors brought a significant additional predictive power for the surface tension at critical micelle concentration, and a slight improvement for the critical micelle concentration, no gain was observed for efficiency. For these three properties, simple models based on constitutional descriptors of polar heads and alkyl chains of the molecule (like atomic counts) were also obtained. For the Krafft point, two qualitative decision trees, classifying the molecule as water soluble or insoluble at room temperature, were proposed. The use of quantum chemical descriptors brought an increase in predictive power for these decision trees, even if a quite reliable model only based on constitutional descriptors of polar heads and alkyl chains was also obtained. At last, we showed how these QSPR models can be used, to predict properties of new surfactants before synthesis in a context of computational screening, or missing properties of existing surfactants, and for the in silico design of new surfactants by combining different polar heads with different alkyl chain