Stéphane Lecoeuche

Ecole des Mines de Douai, Douai, Nord-Pas-de-Calais, France

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Publications (87)49.65 Total impact

  • Baya Hadid · stéphane Lecoeuche · David Gille · Cécile Labarre
    [Show abstract] [Hide abstract] ABSTRACT: Issues in Energy Efficiency of Data Centers (DC) are important, due to the cumulative effects of the increase in the DCs number and in the energy consumption per center. Developing new design recommendations to improve a cooling system efficiency, commonly quantified by the PUE metric (Power Usage Effectiveness) is one objective of the Green IT organizations. For existing DCs, without considering the optimization of the IT workload, a possible way to improve the DC’s energy efficiency is to adjust the cooling setpoints. In this paper, a methodology based on predictive models is used to optimize the PUE by improving the cooling setting. The modeling approach consists in exploiting the temperatures and energy measurements at various operating conditions to predict the PUE behavior using data-driven models commonly called black box models. The optimization procedures are based on the simulation of these models in order to estimate the best working conditions.
    No preview · Conference Paper · Apr 2016
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    Full-text · Conference Paper · Dec 2015
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    [Show abstract] [Hide abstract] ABSTRACT: This paper presents a novel descriptor based on skeleton information provided by RGB-D videos for human action recognition. These features are obtained, considering the motion as continuous trajectories of skeleton joints. With the discrete information of skeleton joints position, a cubic-spline interpolation is applied to joints position, velocity and acceleration components. The training and classification steps are done using a linear SVM. In the literature, many human motion descriptors based on RGB-D cameras had already been proposed with good accuracy, but with a high computational time. The main interest of this proposed approach is its ability to calculate human motion descriptors with a low computation cost while such a descriptor leads to an acceptable accuracy of recognition. Thus, this approach can be adapted to human computer interaction applications. For the purpose of validation, we apply our method to the challenging benchmark MSR-Action3D and introduce a new indicator which is the ratio between accuracy and execution time per descriptor. Using this criterion, we show that our algorithm outperforms the state-of-art methods in terms of the combined information of rapidity and accuracy.
    Full-text · Conference Paper · Nov 2015
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    Dataset: icinco09V3
    Moussa Traore · Eric Duviella · Stéphane Lecoeuche
    Full-text · Dataset · Jan 2015
  • Antoine Sylvain · Arnaud Doniec · Rene Mandiau · Stephane Lecoeuche
    No preview · Conference Paper · Aug 2014
  • [Show abstract] [Hide abstract] ABSTRACT: In order to enhance the performance of electromagnetic interference (EMI) �lters, it is necessary to identify high-frequency parasitic elements of their passive components, mainly those related to the coupled inductors. Motivated by this issue, in this work a realistic high-frequency model is proposed for the coupled inductors. Actually, using interval analysis in particular the forward-backward contractor, a set-membership algorithm has been developed to estimate systematically the parasitic elements linked with the magnetic components. The main advantages of this algorithm compared to the �tting methods is : the values of the estimated parameters are always positive and the corrupted data are taken into account. The comparison of the simulation results and the experimental data allows to validate the proposed method.
    No preview · Article · Aug 2014 · Control Engineering Practice
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    [Show abstract] [Hide abstract] ABSTRACT: The goal of this paper is to present a new on-line human recognition system, which is able to classify persons with adaptive abilities using an incremental classifier. The proposed incremental SVM is fast, as its training phase relies on only a few images and it uses the mathematical properties of SVM to update only the needed parts. In our system, first of all, feature extraction and selection are implemented, based on color and texture features (appearance of the person). Then the incremental SVM classifier is introduced to recognize a person from a set of 20 persons in CASIA Gait Database. The proposed incremental classifier is updated step by step when a new frame including a person is presented. With this technique, we achieved a correct classification rate of 98.46%, knowing only 5% of the dataset at the beginning of the experiment. A comparison with a non-incremental technique reaches recognition rate of 99% on the same database. Extended analyses have been carried out and showed that the proposed method can be adapted to on-line setting.
    Full-text · Article · Feb 2014 · Neurocomputing
  • Antoine Chammas · Eric Duviella · Stephane Lecoeuche
    [Show abstract] [Hide abstract] ABSTRACT: In this paper, a fault diagnosis architecture based on a dynamical clustering algorithm is developed to detect and isolate faults in wind turbines. The challenge is to deal with different kinds of faults. Constraints on the time of detection are also added in the sense that a fault must be detected as soon as possible. Also, limited historical data corresponding only to normal operating modes are available. Our methodology is based on a data-driven model and is therefore not dependent of the physical models in the wind turbine. It consists of extracting, from sensor measurements, features that are fed into a dynamical clustering algorithm. The latter learns process behaviors characterized by clusters with the ability to update, recursively, the parameters of these clusters. These parameters are used to create detection signals and health indicators used for diagnosis.
    No preview · Conference Paper · Dec 2013
  • A. Akhenak · E. Duviella · L. Bako · S. Lecoeuche
    [Show abstract] [Hide abstract] ABSTRACT: The paper presents an online strategy for sensor and/or actuator fault detection and isolation applied to a dam-gallery. A recursive subspace identification algorithm is used to estimate the dam-gallery model parameters. The main contribution consists in developing a specific identification scheme, insensitive to a certain type of faults. That is, the identified parameters are invariant to the faults. A fault estimation procedure is proposed to detect potential faults. The proposed approach appears to be suitable for open channel systems for which the characteristics are not easily measurable.
    No preview · Article · Jun 2013 · Control Engineering Practice
  • Laurent Bako · Stéphane Lecoeuche
    [Show abstract] [Hide abstract] ABSTRACT: A new continuous state observer is derived for discrete-time linear switched systems under the assumptions that neither the continuous state nor the discrete state are known. A specificity of the proposed observer is that, in contrast to the state of the art, it does not require an explicit prior estimation of the discrete state. The key idea of the method consists in minimizing a non-smooth ℓ2ℓ2-norm-based weighted cost functional, constructed from the matrices of all the subsystems regardless of when each of them is active. In the light of recent development in the literature of compressed sensing, the minimized cost functional has the ability to promote sparsity in a way that makes the knowledge of the discrete mode sequence unnecessary.
    No preview · Article · Feb 2013 · Systems & Control Letters
  • A. chammas · E.Duviella · S.Lecoeuche
    No preview · Conference Paper · Dec 2012
  • Dulin Chen · Laurent Backo · Stéphane Lecoeuche
    No preview · Article · Nov 2012 · Journal Européen des Systèmes Automatisés
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    Full-text · Conference Paper · Nov 2012
  • Dulin Chen · Laurent Bako · Stephane Lecoeuche
    [Show abstract] [Hide abstract] ABSTRACT: This paper addresses the problem of driving the state of a linear discrete-time system to zero in minimum time. The inputs are constrained to lie in a bounded and convex set. The solution presented in the paper is based on the observation that the state sequence induced by the minimum-time control sequence is the sparsest possible state sequence over a certain finite horizon. That is, the desired state sequence must contain as many zero vectors as possible, all those zeros corresponding to the highest values of the time index. Hence, by taking advantage of some recent developments in sparse optimization theory, we propose a numerical solution. We show in simulation that the proposed method can effectively solve the minimum-time problem even for multi-inputs linear discrete-time systems.
    No preview · Conference Paper · Oct 2012
  • [Show abstract] [Hide abstract] ABSTRACT: In this paper, we present a methodology for drift detection and characterization. Our methodology is based on extracting indicators that reflect the health state of a system. It is situated in an architecture of fault diagnosis/prognosis of dynamical system that we present in this paper. A dynamical clustering algorithm is used as a major tool. The feature vectors are clustered and then the parameters of these clusters are updated as each feature vector arrives. The cluster parameters serve to compute indicators for drift detection and characterization. Then, a prognosis block uses these drift indicators to estimate the remaining useful life. The architecture is tested on a case study of a tank system with different scenarios of single and multiple faults, and with different dynamics of drift.
    No preview · Conference Paper · Sep 2012
  • Nacim Meslem · Cécile Labarre · Stéphane Lecoeuche
    [Show abstract] [Hide abstract] ABSTRACT: Parasitic parameters in electrical networks are usually sources of intolerant electromagnetic interference in their near environment. In order to understand better the undesirable phenomenon, the values of these unknown parameters must be estimated with a good accuracy. This work shows how interval analysis can help designing set-membership algorithm that is able to solve with numerical guarantee the kind of issue. A simple example, namely second order filter, is studied and our method shows promising performances for dealing with complex circuits.
    No preview · Article · Jul 2012
  • Nacim Meslem · Cécile Labarre · Stéphane Lecoeuche
    No preview · Article · Jul 2012
  • L Bako · K Boukharouba · S Lecoeuche
    No preview · Conference Paper · Jul 2012
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    Imen Saffar · Arnaud Doniec · Jacques Boonaert · Stéphane Lecoeuche
    [Show abstract] [Hide abstract] ABSTRACT: Résumé Les simulations multi-agents consistent à utiliser un en-semble d'agents en interaction de manière à reproduire la dynamique et l'évolution des phénomènes que l'on cherche à simuler. Elles sont aujourd'hui une alternative crédible aux simulations classiques basées sur des modèles analy-tiques, mais leur mise en oeuvre reste difficile. Cette tâche est généralement réalisée par le concepteur qui possède une certaine expertise du phénomène à simuler et dispose de données d'observation de ce même phénomène. Dans ce papier, nous proposons une manière originale de traiter l'observation de comportements réels pour la modélisation d'agents simulés en s'appuyant sur des techniques de clus-tering. La faisabilité de notre approche est démontrée au travers d'un exemple de simulation d'activité humaine. Mots Clef Simulation Multi-Agents, Fouille de données, Modélisa-tion de comportements, Conception automatique d'agents, Techniques de segmentation. Abstract The multiagents simulations consist in using a set of inter-acting agents to reproduce the dynamics and the evolution of the phenomena that we seek to simulate. They are consi-dered now as an alternative to the classical simulations based on analytical models. But, their implementation re-mains difficult, particularly in terms of behaviors extrac-tion and agents modelling. This task is usually performed by the designer who has some expertises, using extracted observation data from the process. In this paper, we pro-pose an original way to deal with observations to model agent behaviors based on clustering techniques. The feasi-bility of our approach is demonstrated through a simula-tion example of human activity.
    Full-text · Article · Jan 2012
  • Marion Gilson · Laurent Bako · F Carrillo · Stéphane Lecoeuche · Guillaume Mercère
    [Show abstract] [Hide abstract] ABSTRACT: Incontournables de nombreuses disciplines scientifiques et technologiques (physique, chimie, biologie, économie...). La modélisation permet en effet de formaliser le comportement du processus étudié à l'aide d'une représentation, baptisée " modèle ", à partir de laquelle il est possible de comprendre, commander ou améliorer le fonctionnement du procédé analysé. Il est important de noter que ce champ thématique à caractère pluridisciplinaire (automatique, traitement du signal, statistique, analyse numérique, génie des procédés...) trouve ses applications dans des domaines très variés allant des processus de fabrication aux systèmes de transport, en passant par les processus environnementaux. L'objectif de ce numéro est de rendre compte des travaux récents dans le domaine de la modélisation et de l'identification des systèmes. Il est constitué de dix articles sélectionnés par le comité scientifique parmi les 43 communications présentées lors des troisièmes Journées Identification et Modélisation Expérimentale (JIME'2011) organisées sous l'égide du groupe de travail Identification de Systèmes du GdR MACS à l'École des Mines de Douai en avril 2011. Ces journées avaient pour objectifs de rassembler les acteurs francophones du domaine de l'identification des systèmes et de proposer une image de la recherche en identification et en modélisation expérimentale, grâce à des présentations orales, des sessions posters et des démonstrations logicielles. Les dix articles sélectionnés ont été retravaillés par les auteurs puis ont suivi le processus de relecture de JESA afin de constituer ce numéro. Les articles ainsi réunis permettent de mettre en lumière les derniers développements théoriques dans le domaine de l'identification des systèmes, ainsi que leurs nombreuses applications et interactions avec d'autres communautés scientifiques. Les avancées récentes concernent notamment, le choix optimal du signal d'excitation, l'identification de systèmes non linéaires, l'identification de systèmes bouclés, l'identification de modèles à temps continu pour des domaines variés tels que la robotique ou les bassins versants.
    No preview · Book · Jan 2012

Publication Stats

502 Citations
49.65 Total Impact Points

Institutions

  • 2003-2015
    • Ecole des Mines de Douai
      • Department of Computer Science and Automatic Control
      Douai, Nord-Pas-de-Calais, France
    • University of Science and Technology Liaoning
      Аньшань, Liaoning, China
  • 2010-2014
    • University of Lille Nord de France
      Lille, Nord-Pas-de-Calais, France
  • 2008-2009
    • Université des Sciences et Technologies de Lille 1
      • Laboratoire d'Automatique, Génie Informatique et Signal (LAGIS)
      Lille, Nord-Pas-de-Calais, France