Optimisation pour les moteurs de recherche

Résumé

alt=Illustration du principe de fonctionnement du PageRank|vignette|Illustration du principe de fonctionnement du PageRank. Loptimisation pour les moteurs de recherche, aussi connue sous le sigle SEO (de l'anglais « Search Engine Optimization »), inclut l'ensemble des techniques qui visent à améliorer le positionnement d'une page, d'un site ou d'une application web dans la page de résultats d'un moteur de recherche (SERP pour « Search Engine Results Page »). Ces techniques cherchent à améliorer la compréhension et la prise en compte de la thématique et du contenu d'une ou de l'ensemble des pages d'un site web par l'algorithme de classement des moteurs de recherche. L'objectif est d'améliorer le positionnement d'une page web dans les pages de résultats de recherche. Le positionnement d'un site est considéré comme bon lorsqu'il est classé dans la première page des résultats de recherche et donc dans l'une des sept à dix premières réponses naturelles d'une recherche sur des mots-clés c

Source officielle

https://fr.wikipedia.org/wiki/Optimisation_pour_les_moteurs_de_recherche

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Publications associées (100)

Publications associées

Chargement

Personnes associées (79)

Personnes associées

Chargement

Unités associées (51)

Afficher plus

Unités associées

Chargement

Concepts associés (28)

Concepts associés

Chargement

Cours associés (25)

Cours associés

Chargement

Séances de cours associées (43)

Séances de cours associées

Chargement

Optimal Process Design for Power Production from Wet Waste Biomass by Hydrothermal Gasification coupled to a Solid Oxide Fuel Cell

This report presents a system analysis of the production of electricity from wet waste biomass. A system of Hydrothermal Gasification is coupled with a Hybrid Cycle composed by a SOFC and two Gas Turbines, driven in an inverted Brayton--‐Joule cycle. A thermodynamic optimization approach, based on the system energy integration, is used to analyze several configuration options. The influence of the process design on performances is discussed for the most representative scenario that highlights the key aspects of the system. Optimization results prove that a systematic system design may reaches efficiencies higher than 60%.

2011

Chargement

Theory of representation learning in cortical neural networks

Carlos Stein Naves de Brito

Our brain continuously self-organizes to construct and maintain an internal representation of the world based on the information arriving through sensory stimuli. Remarkably, cortical areas related to different sensory modalities appear to share the same functional unit, the neuron, and develop through the same learning mechanism, synaptic plasticity. It motivates the conjecture of a unifying theory to explain cortical representational learning across sensory modalities. In this thesis we present theories and computational models of learning and optimization in neural networks, postulating functional properties of synaptic plasticity that support the apparent universal learning capacity of cortical networks. In the past decades, a variety of theories and models have been proposed to describe receptive field formation in sensory areas. They include normative models such as sparse coding, and bottom-up models such as spike-timing dependent plasticity. We bring together candidate explanations by demonstrating that in fact a single principle is sufficient to explain receptive field development. First, we show that many representative models of sensory development are in fact implementing variations of a common principle: nonlinear Hebbian learning. Second, we reveal that nonlinear Hebbian learning is sufficient for receptive field formation through sensory inputs. A surprising result is that our findings are independent of specific details, and allow for robust predictions of the learned receptive fields. Thus nonlinear Hebbian learning and natural statistics can account for many aspects of receptive field formation across models and sensory modalities. The Hebbian learning theory substantiates that synaptic plasticity can be interpreted as an optimization procedure, implementing stochastic gradient descent. In stochastic gradient descent inputs arrive sequentially, as in sensory streams. However, individual data samples have very little information about the correct learning signal, and it becomes a fundamental problem to know how many samples are required for reliable synaptic changes. Through estimation theory, we develop a novel adaptive learning rate model, that adapts the magnitude of synaptic changes based on the statistics of the learning signal, enabling an optimal use of data samples. Our model has a simple implementation and demonstrates improved learning speed, making this a promising candidate for large artificial neural network applications. The model also makes predictions on how cortical plasticity may modulate synaptic plasticity for optimal learning. The optimal sampling size for reliable learning allows us to estimate optimal learning times for a given model. We apply this theory to derive analytical bounds on times for the optimization of synaptic connections. First, we show this optimization problem to have exponentially many saddle-nodes, which lead to small gradients and slow learning. Second, we show that the number of input synapses to a neuron modulates the magnitude of the initial gradient, determining the duration of learning. Our final result reveals that the learning duration increases supra-linearly with the number of synapses, suggesting an effective limit on synaptic connections and receptive field sizes in developing neural networks.

EPFL2016

Chargement

Active Debris Removal missions consist of sending a satellite in space and removing one or more debris from their current orbit. A key challenge is to obtain information about the uncooperative target. By gathering the velocity, position, and rotation of the desired object, the satellite is able to plan its trajectory and define the sequence of approach. It requires the use of a variety of sensors with often a high data rate. For this task, a dedicated payload computer is envisioned with the responsibility of processing the information from the various rendezvous sensors. This component has the goal to provide meaningful information to the main satellite computer about the targeted debris. The focus of this work is on the data processing, the number of elements, and the electrical energy with constraints due to internal communication.First, an avionic testbench was built at the EPFL Space Center to assess early hardware and software architectures. The goal is to enable Hardware-In-the-Loop testing while developing the payload computer. A communication data bus has been implemented between the Platform and the Payload On-Board Computer. Emphasis was placed on the reliability of the high-level protocol and multiple concepts for improvement were analyzed.In a second phase, the testbench has been extended to support other data buses. The aim was to develop a reliable and efficient backup or fall-back data bus in case of failure in the main link. Four data buses have been tested with extensive analyses on their resilience to error and the efficiency of their data exchange.In parallel, extensive work has been performed to develop a simulation and optimization tool. Its goal is to support the design of payload avionic architectures for ADR missions by providing trade-offs and analyses. In the first iteration, a simulator has been created with instances of various high-level elements modeled.The second iteration introduced the additional dimension of optimization. It is trying to determine the best set of instruments and algorithms to use. With this capability, numerous analyses have been conducted on the influence of various parameters linked to the general optimizer behavior. It allows us to better understand the strength and limitations of the tool.The third step regarding the tool was to start its verification. The task has been to develop an Hardware-In-the-Loop architecture to compare its behavior with the results of the optimizer. The implementation of various mock-up algorithms enables the verification of their models in the tool. These analyses guarantee the feasibility of the outputted solution.The last part was dedicated to the creation and analysis of a realistic payload avionic architecture. The goal was to test the capability of the tool with hardware elements inspired by actual components. This work has shown the procedure to efficiently use the tool in the design phase of a mission.In conclusion, the work on the communication between the Payload and the Platform On-Board Computer has brought valuable lessons and experiences to the projects. They can now be used for the establishment of the high-level protocol into ClearSpace-1 flight software. In addition, the optimizer created allows tackling the design of complex payload avionic architecture where mass saving and processing resources are crucial. It is an essential point to develop a highly efficient payload computer for an Active Debris Removal satellite.

EPFL2022

Chargement

Theory of representation learning in cortical neural networks

Carlos Stein Naves de Brito

EPFL2016

EPFL2022