Effects of Dissipative Terms on Dissipative Soliton Resonance Curve

International audienceDissipative soliton resonance (DSR) is a phenomenon where the energy of a soliton in a dissipative system increases without limit at certain values of the system parameters. Using the method of collective variable approach, we have found an approximate relation between the parameters of the normalized complex cubic-quintic Ginzburg-Landau equation where the resonance manifests itself. Comparisons between the results obtained by collective variable approach, and those obtained by the method of moments show good qualitative agreement. This choice also helps to see the influence of the active terms on the resonance curve, so can be very useful in constructing passively mode-locked laser that generate solitons with the highest possible energies

Spatio-Temporal Pulsating Dissipative Solitons through Collective Variable Methods

International audienceA semi-analytical approach for the pulsating solutions of the 3D complex Cubic-quintic Ginzburg-Landau Equation (CGLE) is presented in this article. A collective variable approach is used to obtain a system of variational equations which give the evolution of the light pulses parameters as a function of the propagation distance. The collective coordinate approach is incomparably faster than the direct numerical simulation of the propagation equation. This allows us to obtain, efficiently, a global mapping of the 3D pulsating soliton. In addition it allows describing the influence of the parameters of the equation on the various physical parameters of the pulse and their dynamics

RoF ARCHITECTURE IN THE MILLIMETER WAVEBAND FOR WIRELESS SENSOR NETWORKS

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Contribution à la commande et à l'estimation des flux et constante de temps rotoriques de la machine asynchrone (les méthodes et techniques de l'Automatique avancée appliquées au Génie Electrique)

Le moteur asynchrone constitue un sytème multivariable non linéaire et complexe où des paramètres varient avec la température ou l'état magnétique. Cette nature non linéaire nous a donc amené, dans ce travail, à proposer une stratégie de commande non linéaire afin de découpler les courants statoriques du moteur dans un repère (d, q) orienté selon le principe de la commande vectorielle. Le contexte naturellement bruité du moteur associé à un ondulateur ainsi que les variations paramétriques nous ont conduit à une démarche robuste. Ainsi nous avons réalisé la synthèse des correcteurs H dans le but de maîtriser efficacement la dynamique des courants statoriques. La connaissance du flux et de la constante de temps rotoriques étant nécessaire pour établir les lois de commande et s'affranchir au mieux des variations paramétriques, en particulier celle de la constante de temps rotorique, nous avons mis en oeuvre des algorithmes d'observation par modes glissants discret étendu d'ordre complet et réduit réalisant une estimation en ligne des flux et constante de temps rotoriques. Dans la phase terminale la commande linéarisante et l'observateur sont validés puis confirmés par des simulations et une implantation en temps réel sur un banc moteurVILLEURBANNE-DOC'INSA LYON (692662301) / SudocSudocFranceF

Morpho-Syntactic Tagging of Text in "Baoule" Language Based on Hidden Markov Models (HMM)

Abstract The label text is a very important tool for the automatic processing of language. It is used in several applications such as morphological and syntactic text analysis, indexing, retrieval, finished networks deterministic (in which all combinations of words that are accepted by the grammar are listed) or by statistical grammars (e.g., an n-gram in which the probabilities of sequences of n words in a specific order are given), etc. In this article, we developed a morphosyntactic labeling system language "Baoule" using hidden Markov models. This will allow us to build a tagged reference corpus and represent major grammatical rules faced "Baoule" language in general. To estimate the parameters of this model, we used a training corpus manually labeled using a set of morpho-syntactic labels. We then proceed to an improvement of the system through the re-estimation procedure parameters of this model

Human Skeleton Detection, Modeling and Gesture Imitation Learning for a Social Purpose

International audienceGesture recognition is topical in computer science and aims at interpreting human gestures via mathematical algorithms. Among the numerous applications are physical rehabilitation and imitation games. In this work, we suggest performing human gesture recognition within the context of a serious imitation game, which would aim at improving social interactions with teenagers with autism spectrum disorders. We use an artificial intelligence algorithm to detect the skeleton of the participant, then model the human pose space and describe an imitation learning method using a Gaussian Mixture Model in the Riemannian manifold

Review of Anomaly Detection Systems in Industrial Control Systems Using Deep Feature Learning Approach

International audienceIndustrial Control Systems (ICS) or SCADA networks are increasingly targeted by cyber-attacks as their architectures shifted from proprietary hardware, software and protocols to standard and open sources ones. Furthermore, these systems which used to be isolated are now interconnected to corporate networks and to the Internet. Among the countermeasures to mitigate the threats, anomaly detection systems play an important role as they can help detect even unknown attacks. Deep learning which has gained a great attention in the last few years due to excellent results in image, video and natural language processing is being used for anomaly detection in information security, particularly in SCADA networks. The salient features of the data from SCADA networks are learnt as hierarchical representation using deep architectures, and those learnt features are used to classify the data into normal or anomalous ones. This article is a review of various architectures such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Stacked Autoencoder (SAE), Long Short Term Memory (LSTM), or a combination of those architectures, for anomaly detection purpose in SCADA networks

Radio over Fiber Architecture Very High-Speed Wireless Communications in the Unlicensed 60 GHz Band

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