Stéphane Lecoeuche

University of Lille Nord de France, Lille, Nord-Pas-de-Calais, France

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Publications (72)40.37 Total impact

  • [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.
    Control Engineering Practice 08/2014; · 1.91 Impact Factor
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    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.
    Neurocomputing 02/2014; 126:132–140. · 2.01 Impact Factor
  • A. Akhenak, E. Duviella, L. Bako, S. Lecoeuche
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    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.
    Control Engineering Practice 06/2013; 21(6):797–806. · 1.91 Impact Factor
  • Laurent Bako, Stéphane Lecoeuche
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    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.
    Systems & Control Letters 02/2013; 62(2):143–151. · 1.89 Impact Factor
  • A. chammas, E.Duviella, S.Lecoeuche
    International Conference on Machine Learning and Applications, Boca Raton, Florida, USA; 12/2012
  • Dulin Chen, Laurent Backo, Stéphane Lecoeuche
    Journal Européen des Systèmes Automatisés 11/2012; 45(6-7):763-777.
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    IFAC Workshop on Advanced Maintenance Engineering, Services and Technology, Seville, Espagne; 11/2012
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    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.
    International Conference on Scalable Uncertainty Management, Marburg, Allemagne; 09/2012
  • L Bako, K Boukharouba, S Lecoeuche
    Proceedings of the 16th IFAC Symposium on System Identification; 07/2012
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    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.
    01/2012; Hermès Science.
  • Dulin Chen, L. Bako, S. Lecoeuche
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    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.
    Control Applications (CCA), 2012 IEEE International Conference on; 01/2012
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    ESREL, Troyes, France; 09/2011
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    Laurent Bako, Dulin Chen, Stéphane Lecoeuche
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    ABSTRACT: The minimum-time control problem consists in finding a control policy that will drive a given dynamic system from a given initial state to a given target state (or a set of states) as quickly as possible. This is a well-known challenging problem in optimal control theory for which closed-form solutions exist only for a few systems of small dimensions. This paper presents a very generic solution to the minimum-time problem for arbitrary discrete-time linear systems. It is a numerical solution based on sparse optimization, that is the minimization of the number of nonzero elements in the state sequence over a fixed control horizon. We consider both single input and multiple inputs systems. An important observation is that, contrary to the continuous-time case, the minimum-time control for discrete-time systems is not necessarily entirely bang-bang.
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    ABSTRACT: In this work, a recursive procedure is derived for the identification of switched linear models from input–output data. Starting from some initial values of the parameter vectors that represent the different submodels, the proposed algorithm alternates between data assignment to submodels and parameter update. At each time instant, the discrete state is determined as the index of the submodel that, in terms of the prediction error (or the posterior error), appears to have most likely generated the regressor vector observed at that instant. Given the estimated discrete state, the associated parameter vector is updated based on recursive least squares or any fast adaptive linear identifier. Convergence of the whole procedure although not theoretically proved, seems to be easily achieved when enough rich data are available. It has been also observed that by appropriately choosing the data assignment criterion, the proposed on-line method can be extended to deal also with the identification of piecewise affine models. Finally, performance is tested through some computer simulations and the modeling of an open channel system.
    Nonlinear Analysis Hybrid Systems 05/2011; 5(2):242-253. · 1.79 Impact Factor
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    L. Bako, K. Boukharouba, S. Lecoeuche
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    ABSTRACT: We consider the problem of identifying a switched nonlinear system from a finite collection of input-output data. The constituent subsystems of such a switched system are all nonlinear systems. We model each individual subsystem as a sparse expansion over a dictionary of elementary nonlinear smooth functions shaped by the whole available dataset. Estimating the switched model from data is a doubly challenging problem. First one needs, without any knowledge of the parameters, to decide which subsystem is active at which time instant. Second, the representation of each nonlinear subsystem over the considered basis shall be performed in a high dimensional space. We tackle both tasks simultaneously by sparse optimization. More specifically, we view the switched nonlinear system identification problem as the problem of minimizing the ℓ<sub>0</sub> norm of an error vector. We subsequently relax it into an ℓ<sub>1</sub> convex minimization problem for which powerful numerical tools exist.
    Decision and Control (CDC), 2010 49th IEEE Conference on; 01/2011
  • Grzegorz Dziczkowski, Arnaud Doniec, Stéphane Lecoeuche
    ICAART 2011 - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence, Volume 1 - Artificial Intelligence, Rome, Italy, January 28-30, 2011; 01/2011
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    Laurent Bako, Stephane Lecoeuche
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    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, contrary to the state-of-art, it does not require an explicit estimation of the discrete state. The key idea of the method consists in minimizing a non-smooth l2-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 some recent development in the literature of compressed sensing, the minimized cost functional has the ability to promote sparsity in a way that makes prior knowledge/estimation of the discrete mode sequence unnecessary.
  • Imen Saffar, Arnaud Doniec, Jacques Boonaert, Stéphane Lecoeuche
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    ABSTRACT: The multi-agent simulation consists in using a set of interacting agents to reproduce the dynamics and the evolution of the phenomena that we seek to simulate. It is considered now as an alternative to classical simulations based on analytical models. But, its implementation remains difficult, particularly in terms of behaviors extraction and agents modelling. This task is usually performed by the designer who has some expertise and available observation data on the process. In this paper, we propose a novel way to make use of the observations of real world agents to model simulated agents. The modelling is based on clustering techniques. Our approach is illustrated through an example in which the behaviors of agents are extracted as trajectories and destinations from video sequences analysis. This methodology is investigated with the aim to apply it, in particular, in a retail space simulation for the evaluation of marketing strategies. This paper presents experiments of our methodology in the context of a public area modelling.
    IEEE 23rd International Conference on Tools with Artificial Intelligence, ICTAI 2011, Boca Raton, FL, USA, November 7-9, 2011; 01/2011
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    Yanyun Lu, Anthony Fleury, Jacques Boonaert, Stéphane Lecoeuche
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    ABSTRACT: The goal of this paper is to contribute to the realization of a system able to recognize people in video surveillance images. The context of this study is to classify a new frame including a person into a set of already known people, using an incremental classifier. To reach this goal, we first present the feature extraction and selection that have been made on appearance based on features (from color and texture), and then we introduce the incremental classifier used to differentiate people from a set of 20 persons. This incremental classifier is then updated at each new frame with the new knowledge that has been presented. With this technique, we achieved 92% of correct classification on the used database. These results are then compared to the 99% of correct classification in the case of a nonincremental technique and these results are explained. Some future works will try to rise the performances of incremental learning the one of non-incremental ones.
    Adaptive and Intelligent Systems - Second International Conference, ICAIS 2011, Klagenfurt, Austria, September 6-8, 2011. Proceedings; 01/2011
  • K. Boukharouba, L. Bako, S. Lecoeuche
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    ABSTRACT: In this paper we propose an online multi-category support vector classifier dedicated to non-stationary environment. Our algorithm recursively discriminates between datasets of three or more classes, one sample at a time. With its incremental and decremental procedures, it can achieve an efficient update of the decision function after the incorporation/elimination of the incoming/oldest data. The key idea is to keep the KKT conditions of one single optimization problem satisfied, while adding or eliminating data. Compared to the QP approach, our classifier is able to provide accurate results. The performance of the proposed algorithm is shown on synthetic and experimental data.
    Machine Learning and Applications, 2009. ICMLA '09. International Conference on; 01/2010

Publication Stats

250 Citations
40.37 Total Impact Points


  • 2011–2014
    • University of Lille Nord de France
      Lille, Nord-Pas-de-Calais, France
  • 2003–2014
    • Ecole des Mines de Douai
      Douai, Nord-Pas-de-Calais, France
  • 2007–2009
    • École Centrale de Lille
      Lille, Nord-Pas-de-Calais, France
  • 2008
    • Université des Sciences et Technologies de Lille 1
      Lille, Nord-Pas-de-Calais, France