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Academic literature on the topic 'Sorties stationnaires'
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Journal articles on the topic "Sorties stationnaires"
Ben Abdallah, Mohamed, Mounir Ayadi, Frédéric Rotella, and Mohamed Benrejeb. "Commande par platitude de systèmes multi-entrées multi-sorties non stationnaires." Journal Européen des Systèmes Automatisés 48, no. 1-3 (May 30, 2014): 79–100. http://dx.doi.org/10.3166/jesa.48.79-100.
Full textBizimana, Aimé-Jules, and Benoit Gauthier. "Le journalisme de guerre et les risques intégrés lors des opérations militaires en Afghanistan." Sur le journalisme, About journalism, Sobre jornalismo 10, no. 1 (June 12, 2021): 82. http://dx.doi.org/10.25200/slj.v10.n1.2021.440.
Full textBizimana, Aimé-Jules, and Benoit Gauthier. "Le journalisme de guerre et les risques intégrés lors des opérations militaires en Afghanistan." Sur le journalisme, About journalism, Sobre jornalismo 10, no. 1 (June 15, 2021): 82–97. http://dx.doi.org/10.25200/slj.v10.n1.2021.455.
Full textCavaco, Sandra, Jean-Yves Lesueur, and Mareva Sabatier. "Stratégies de recherche, contraintes spatiales et hétérogénéité des transitions vers l’emploi : estimation économétrique d’un modèle structurel de recherche." Articles 80, no. 2-3 (October 24, 2005): 439–64. http://dx.doi.org/10.7202/011395ar.
Full textServais, P., P. Laurent, G. Billen, and D. Gatel. "Développement d'un modèle décrivant les variations de CODB et de biomasse bactérienne dans les réseaux de distribution." Revue des sciences de l'eau 8, no. 4 (April 12, 2005): 427–62. http://dx.doi.org/10.7202/705232ar.
Full textDissertations / Theses on the topic "Sorties stationnaires"
Oudrhiri, Ali. "Performance of a Neural Network Accelerator Architecture and its Optimization Using a Pipeline-Based Approach." Electronic Thesis or Diss., Sorbonne université, 2023. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2023SORUS658.pdf.
Full textIn recent years, neural networks have gained widespread popularity for their versatility and effectiveness in solving a wide range of complex tasks. Their ability to learn and make predictions from large data-sets has revolutionized various fields. However, as neural networks continue to find applications in an ever-expanding array of domains, their significant computational requirements become a pressing challenge. This computational demand is particularly problematic when deploying neural networks in resource-constrained embedded devices, especially within the context of edge computing for inference tasks. Nowadays, neural network accelerator chips emerge as the optimal choice for supporting neural networks at the edge. These chips offer remarkable efficiency with their compact size, low power consumption, and reduced latency. Moreover, the fact that they are integrated on the same chip environment also enhances security by minimizing external data communication. In the frame of edge computing, diverse requirements have emerged, necessitating trade-offs in various performance aspects. This has led to the development of accelerator architectures that are highly configurable, allowing them to adapt to distinct performance demands. In this context, the focus lies on Gemini, a configurable inference neural network accelerator designed with imposed architecture and implemented using High-Level Synthesis techniques. The considerations for its design and implementation were driven by the need for parallelization configurability and performance optimization. Once this accelerator was designed, demonstrating the power of its configurability became essential, helping users select the most suitable architecture for their neural networks. To achieve this objective, this thesis contributed to the development of a performance prediction strategy operating at a high-level of abstraction, which considers the chosen architecture and neural network configuration. This tool assists clients in making decisions regarding the appropriate architecture for their specific neural network applications. During the research, we noticed that using one accelerator presents several limits and that increasing parallelism had limitations on performances. Consequently, we adopted a new strategy for optimizing neural network acceleration. This time, we took a high-level approach that did not require fine-grained accelerator optimizations. We organized multiple Gemini instances into a pipeline and allocated layers to different accelerators to maximize performance. We proposed solutions for two scenarios: a user scenario where the pipeline structure is predefined with a fixed number of accelerators, accelerator configurations, and RAM sizes. We proposed solutions to map the layers on the different accelerators to optimise the execution performance. We did the same for a designer scenario, where the pipeline structure is not fixed, this time it is allowed to choose the number and configuration of the accelerators to optimize the execution and also hardware performances. This pipeline strategy has proven to be effective for the Gemini accelerator. Although this thesis originated from a specific industrial need, certain solutions developed during the research can be applied or adapted to other neural network accelerators. Notably, the performance prediction strategy and high-level optimization of NN processing through pipelining multiple instances offer valuable insights for broader application
Le, Ber Catherine. "Simulation sur ordinateur d'un système linéaire continu et stationnaire." Brest, 1988. http://www.theses.fr/1988BRES2020.
Full textTugaut, Julian. "Processus auto-stabilisants dans un paysage multi-puits." Phd thesis, Université Henri Poincaré - Nancy I, 2010. http://tel.archives-ouvertes.fr/tel-00573044.
Full textBaudel, Manon. "Théorie spectrale pour des applications de Poincaré aléatoires." Thesis, Orléans, 2017. http://www.theses.fr/2017ORLE2058/document.
Full textWe consider stochastic differential equations, obtained by adding weak Gaussian white noise to ordinary differential equations admitting N asymptotically stable periodic orbits. We construct a discrete-time,continuous-space Markov chain, called a random Poincaré map, which encodes the metastable behaviour of the system. We show that this process admits exactly N eigenvalues which are exponentially close to 1,and provide expressions for these eigenvalues and their left and right eigenfunctions in terms of committorfunctions of neighbourhoods of periodic orbits. We also provide a bound for the remaining part of the spectrum. The eigenvalues that are exponentially close to 1 and the right and left eigenfunctions are well-approximated by principal eigenvalues, quasistationary distributions, and principal right eigenfunctions of processes killed upon hitting some of these neighbourhoods. Each eigenvalue that is exponentially close to 1is also related to the mean exit time from some metastable neighborhood of the periodic orbits. The proofsrely on Feynman–Kac-type representation formulas for eigenfunctions, Doob’s h-transform, spectral theory of compact operators, and a recently discovered detailed balance property satisfied by committor functions