The Experts below are selected from a list of 5577 Experts worldwide ranked by ideXlab platform

Robert Sabourin - One of the best experts on this subject based on the ideXlab platform.

  • a classifier fusion system for bearing fault diagnosis
    Expert Systems With Applications, 2013
    Co-Authors: Luana Batista, Bechir Badri, Robert Sabourin, Marc Thomas
    Abstract:

    In this paper, a new strategy based on the fusion of different Support Vector Machines (SVM) is proposed in order to reduce noise effect in bearing fault diagnosis systems. Each SVM classifier is designed to deal with a specific noise configuration and, when combined together - by means of the Iterative Boolean Combination (IBC) technique - they provide high robustness to different noise-to-signal ratio. In order to produce a high amount of vibration signals, considering different defect dimensions and noise levels, the BEAring Toolbox (BEAT) is employed in this work. The experiments indicate that the proposed strategy can significantly reduce the error rates, even in the presence of very noisy signals.

  • a classifier fusion system for bearing fault diagnosis
    Expert Systems With Applications, 2013
    Co-Authors: Luana Batista, Bechir Badri, Robert Sabourin, Marc Thomas
    Abstract:

    In this paper, a new strategy based on the fusion of different Support Vector Machines (SVM) is proposed in order to reduce noise effect in bearing fault diagnosis systems. Each SVM classifier is designed to deal with a specific noise configuration and, when combined together - by means of the Iterative Boolean Combination (IBC) technique - they provide high robustness to different noise-to-signal ratio. In order to produce a high amount of vibration signals, considering different defect dimensions and noise levels, the BEAring Toolbox (BEAT) is employed in this work. The experiments indicate that the proposed strategy can significantly reduce the error rates, even in the presence of very noisy signals.

  • iterative Boolean Combination of classifiers in the roc space an application to anomaly detection with hmms
    Pattern Recognition, 2010
    Co-Authors: Wael Khreich, Eric Granger, Ali Miri, Robert Sabourin
    Abstract:

    Hidden Markov models (HMMs) have been shown to provide a high level performance for detecting anomalies in sequences of system calls to the operating system kernel. Using Boolean conjunction and disjunction functions to combine the responses of multiple HMMs in the ROC space may significantly improve performance over a ''single best'' HMM. However, these techniques assume that the classifiers are conditional independent, and their of ROC curves are convex. These assumptions are violated in most real-world applications, especially when classifiers are designed using limited and imbalanced training data. In this paper, the iterative Boolean Combination (IBC) technique is proposed for efficient fusion of the responses from multiple classifiers in the ROC space. It applies all Boolean functions to combine the ROC curves corresponding to multiple classifiers, requires no prior assumptions, and its time complexity is linear with the number of classifiers. The results of computer simulations conducted on both synthetic and real-world host-based intrusion detection data indicate that the IBC of responses from multiple HMMs can achieve a significantly higher level of performance than the Boolean conjunction and disjunction Combinations, especially when training data are limited and imbalanced. The proposed IBC is general in that it can be employed to combine diverse responses of any crisp or soft one- or two-class classifiers, and for wide range of application domains.

Marc Thomas - One of the best experts on this subject based on the ideXlab platform.

  • a classifier fusion system for bearing fault diagnosis
    Expert Systems With Applications, 2013
    Co-Authors: Luana Batista, Bechir Badri, Robert Sabourin, Marc Thomas
    Abstract:

    In this paper, a new strategy based on the fusion of different Support Vector Machines (SVM) is proposed in order to reduce noise effect in bearing fault diagnosis systems. Each SVM classifier is designed to deal with a specific noise configuration and, when combined together - by means of the Iterative Boolean Combination (IBC) technique - they provide high robustness to different noise-to-signal ratio. In order to produce a high amount of vibration signals, considering different defect dimensions and noise levels, the BEAring Toolbox (BEAT) is employed in this work. The experiments indicate that the proposed strategy can significantly reduce the error rates, even in the presence of very noisy signals.

  • a classifier fusion system for bearing fault diagnosis
    Expert Systems With Applications, 2013
    Co-Authors: Luana Batista, Bechir Badri, Robert Sabourin, Marc Thomas
    Abstract:

    In this paper, a new strategy based on the fusion of different Support Vector Machines (SVM) is proposed in order to reduce noise effect in bearing fault diagnosis systems. Each SVM classifier is designed to deal with a specific noise configuration and, when combined together - by means of the Iterative Boolean Combination (IBC) technique - they provide high robustness to different noise-to-signal ratio. In order to produce a high amount of vibration signals, considering different defect dimensions and noise levels, the BEAring Toolbox (BEAT) is employed in this work. The experiments indicate that the proposed strategy can significantly reduce the error rates, even in the presence of very noisy signals.

Luana Batista - One of the best experts on this subject based on the ideXlab platform.

  • a classifier fusion system for bearing fault diagnosis
    Expert Systems With Applications, 2013
    Co-Authors: Luana Batista, Bechir Badri, Robert Sabourin, Marc Thomas
    Abstract:

    In this paper, a new strategy based on the fusion of different Support Vector Machines (SVM) is proposed in order to reduce noise effect in bearing fault diagnosis systems. Each SVM classifier is designed to deal with a specific noise configuration and, when combined together - by means of the Iterative Boolean Combination (IBC) technique - they provide high robustness to different noise-to-signal ratio. In order to produce a high amount of vibration signals, considering different defect dimensions and noise levels, the BEAring Toolbox (BEAT) is employed in this work. The experiments indicate that the proposed strategy can significantly reduce the error rates, even in the presence of very noisy signals.

  • a classifier fusion system for bearing fault diagnosis
    Expert Systems With Applications, 2013
    Co-Authors: Luana Batista, Bechir Badri, Robert Sabourin, Marc Thomas
    Abstract:

    In this paper, a new strategy based on the fusion of different Support Vector Machines (SVM) is proposed in order to reduce noise effect in bearing fault diagnosis systems. Each SVM classifier is designed to deal with a specific noise configuration and, when combined together - by means of the Iterative Boolean Combination (IBC) technique - they provide high robustness to different noise-to-signal ratio. In order to produce a high amount of vibration signals, considering different defect dimensions and noise levels, the BEAring Toolbox (BEAT) is employed in this work. The experiments indicate that the proposed strategy can significantly reduce the error rates, even in the presence of very noisy signals.

Bechir Badri - One of the best experts on this subject based on the ideXlab platform.

  • a classifier fusion system for bearing fault diagnosis
    Expert Systems With Applications, 2013
    Co-Authors: Luana Batista, Bechir Badri, Robert Sabourin, Marc Thomas
    Abstract:

    In this paper, a new strategy based on the fusion of different Support Vector Machines (SVM) is proposed in order to reduce noise effect in bearing fault diagnosis systems. Each SVM classifier is designed to deal with a specific noise configuration and, when combined together - by means of the Iterative Boolean Combination (IBC) technique - they provide high robustness to different noise-to-signal ratio. In order to produce a high amount of vibration signals, considering different defect dimensions and noise levels, the BEAring Toolbox (BEAT) is employed in this work. The experiments indicate that the proposed strategy can significantly reduce the error rates, even in the presence of very noisy signals.

  • a classifier fusion system for bearing fault diagnosis
    Expert Systems With Applications, 2013
    Co-Authors: Luana Batista, Bechir Badri, Robert Sabourin, Marc Thomas
    Abstract:

    In this paper, a new strategy based on the fusion of different Support Vector Machines (SVM) is proposed in order to reduce noise effect in bearing fault diagnosis systems. Each SVM classifier is designed to deal with a specific noise configuration and, when combined together - by means of the Iterative Boolean Combination (IBC) technique - they provide high robustness to different noise-to-signal ratio. In order to produce a high amount of vibration signals, considering different defect dimensions and noise levels, the BEAring Toolbox (BEAT) is employed in this work. The experiments indicate that the proposed strategy can significantly reduce the error rates, even in the presence of very noisy signals.

Volker Weispfenning - One of the best experts on this subject based on the ideXlab platform.

  • simulation and optimization by quantifier elimination
    Journal of Symbolic Computation, 1997
    Co-Authors: Volker Weispfenning
    Abstract:

    We present a highly optimized method for the elimination of linear variables from a Boolean Combination of polynomial equations and inequalities. In contrast to the basic method described earlier, the practical applicability of the present method goes far beyond academic examples. The optimization is achieved by various strategies to prune superfluous branches in the elimination tree constructed by the method.The main application concerns the simulation of large technical networks of (electric, mechanical or hydraulic) components, whose characteristic curves are piecewise linear (or quadratic) in the variables to be eliminated. Typical goals are the computation of admissible ranges for certain variables and the detection of a malfunction of a network component. The algorithms are currently used in a commercial software system for industrial applications.Moreover, we extend the author's elimination method for parametric linear programming to the non-convex case by allowing arbitraryand?orCombinations of parametric linear inequalities as constraints. We present a new strategy for finding smaller elimination sets and thus smaller elimination trees for parametric linear programming. Some benchmark examples from thenetliblibrary oflpproblems show the significance of this strategy even for convex linear programming problems.

  • quantifier elimination for real algebra the cubic case
    International Symposium on Symbolic and Algebraic Computation, 1994
    Co-Authors: Volker Weispfenning
    Abstract:

    We present a special purpose quantifier elimination method that eliminates a quantifier ∃x in formulas ∃x(4) where 4 is a Boolean Combination of polynomial inequalities of degree ≤3 with respect to x. The method extends the virtual substitution of parametrized test points developed in [Weispfenning 1, Loos &. We ispf.] for the linear case and in [Weispfenning2] for the quadratic case. It has similar upper complexity bounds and offers similar advantages (relatively large preprocessing part, explicit parametric solutions). small examples suggest that the method will be of practical significance.