Estimation of a semi-physical GLBE model using dual EnKF learning algorithm coupled with a sensor network design strategy: application to air field monitoring

International audienceIn this paper, we present the fusion of two complementary approaches for modeling and monitoring the spatio-temporal behavior of a fluid flow system. We also propose a mobile sensor deployment strategy to produce the most accurate estimate of the true system state. For this purpose, deterministic and statistical information was used. We adopted a filtering method based on a semi-physical model which derives from a fluid flow numerical model known as lattice Boltzmann model (LBM). The a priori physical knowledge was introduced by the Navier-Stokes equations which were discretized by the lattice Boltzmann approach. Moreover, its multiple-relaxation-time (MRT) variant not only improved the stability, but also enabled the introduction of additional degrees of freedom to be estimated like the synaptic weights of a neural network. The statistical knowledge was then introduced into the model by performing a sequential learning of these parameters and an estimation of the speed field of the fluid flow starting from measurements. The low spatial density of measurements, the large amount of data inherent to environmental issues and the nonlinearity of the generalized lattice Boltzmann equations (GLBE) enjoined us to use the ensemble Kalman filter (EnKF) for the recursive estimation procedure. A dual state-parameter estimation which results in a significantly reduced computation time was used by combining two filters consecutively activated in the same iteration. Finally, we proposed to complete the lack of spatial information of the sparse-observation network by adding a mobile sensor, which was routed to the location where the cell-by-cell output estimation error was the highest. Experimental results in the context of the standard lid-driven cavity problem revealed the presence of few zones of interest, where fixed sensors can be deployed to increase performances in terms of convergence speed and estimation quality. Finally, the study showed the feasibility of introducing some additional parameters which act as degrees of freedom, to perform large-eddy simulation of turbulent flows without numerical instabilities

Kullback-leibler NMF under linear equality constraints. Application to pollution source apportionment

International audienceNon negative matrix factorisation (NMF) coupled to divergence measure has been investigated in the frame of an application to polluant source identification. It relies on receptor modelling which considers the data matrix as the result of cumulative effects of p sources. NMF aims at finding a contribution matrix G and a profile matrix F by minimizing a specific cost function. The focus is made here on the Kullback-Leibler divergence (KL) cost function. Linear equality constraints are incorporated into parts of the decomposition and general mu-tiplicative like expressions, which take into account these constraints, are derived. This method is applied in the frame of source apportion-ment of particulate matter

Semi-physical neural modeling for linear signal restoration

International audienceThis paper deals with the design methodology of an Inverse Neural Network (INN) model. The basic idea is to carry out a semi-physical model gathering two types of information: the a priori knowledge of the deterministic rules which govern the studied system and the observation of the actual conduct of this system obtained from experimental data. This hybrid model is elaborated by being inspired by the mechanisms of a neuromimetic network whose structure is constrained by the discrete reverse-time state-space equations. In order to validate the approach, some tests are performed on two dynamic models. The first suggested model is a dynamic system characterized by an unspecified r-order Ordinary Differential Equation (ODE). The second one concerns in particular the mass balance equation for a dispersion phenomenon governed by a Partial Differential Equation (PDE) discretized on a basic mesh. The performances are numerically analyzed in terms of generalization, regularization and training effort

De l'algorithme à l'architecture : définition d'une architecture multi-FPGAs reconfigurable dynamiquement.

Cette étude est réalisée dans le cadre des réunions de travail du GDR PRC ISIS du CNRS (opération 6.3). L'objectif du groupe de travail est d'étudier l'exploitation de la reconfigurabilité des FPGAs pour le traitement d'image bas niveau en temps réel. L'architecture qui résulte de notre étude est composée de plusieurs FPGAs dédiés au calcul, à la gestion des mémoires et à la communication entre les différents modules. Nous proposons ainsi une alternative aux solutions ASICs ou aux gros systèmes multi-processeurs, toujours coûteux et lourds à mettre en oeuvre, pour le traitement d'image en temps réel

Routage dans un réseau de robots

National audienceCet article traite de la communications dans un réseau de robots. Plus précisément, nous nous intéressons à la communication en mode ad hoc dans laquelle tous les robots participent à l'acheminement des messages. La particularité de ce réseau est que ses éléments sont souvent déconnectés mais également que les déplacements des robots peuvent être contraints. Dans ce cadre, nous proposons un algorithme pour le routage des messages basé sur l'ordonnancement des déplacements des robots qui assure que n'importe quelle communication entre deux robots du réseau se fera en temps borné

Syntax tree fingerprinting: a foundation for source code similarity detection

Plagiarism detection and clone refactoring in software depend on one common concern: nding similar source chunks across large repositories. However, since code duplication in software is often the result of copy-paste behaviors, only minor modi cations are expected between shared codes. On the contrary, in a plagiarism detection context, edits are more extensive and exact matching strategies show their limits. Among the three main representations used by source code similarity detection tools, namely the linear token sequences, the Abstract Syntax Tree (AST) and the Program Depen- dency Graph (PDG), we believe that the AST could e ciently support the program analysis and transformations required for the advanced similarity detection process. In this paper we present a simple and scalable architecture based on syntax tree nger- printing. Thanks to a study of several hashing strategies reducing false-positive collisions, we propose a framework that e ciently indexes AST representations in a database, that quickly detects exact (w.r.t source code abstraction) clone clusters and that easily retrieves their corresponding ASTs. Our aim is to allow further processing of neighboring exact matches in order to identify the larger approximate matches, dealing with the common modi cation patterns seen in the intra-project copy-pastes and in the plagiarism cases

Viewing functions as token sequences to highlight similarities in source code

International audienceThe detection of similarities in source code has applications not only in software re-engineering (to eliminate redundancies) but also in software plagiarism detection. This latter can be a challenging problem since more or less extensive edits may have been performed on the original copy: insertion or removal of useless chunks of code, rewriting of expressions, transposition of code, inlining and outlining of functions, etc. In this paper, we propose a new similarity detection technique not only based on token sequence matching but also on the factorization of the function call graphs. The factorization process merges shared chunks (factors) of codes to cope, in particular, with inlining and outlining. The resulting call graph offers a view of the similarities with their nesting relations. It is useful to infer metrics quantifying similarity at a function level