The Experts below are selected from a list of 43401 Experts worldwide ranked by ideXlab platform

A H Tewfik - One of the best experts on this subject based on the ideXlab platform.

  • bounded subset selection with noninteger coefficients
    European Signal Processing Conference, 2004
    Co-Authors: Masoud Alghoniemy, A H Tewfik
    Abstract:

    The subset selection problem is known to be NP hard. It was recently shown that by relaxing the requirement that the Reconstructed Signal be equal to the original, one ends with a bounded error subset selection that admits a solution in polynomial time. In the bounded error subset selection problem, the Reconstructed Signal is allowed to differ from the original Signal by a bounded error. This bounded error formulation is natural in many applications, such as coding. In this paper, we improve the accuracy and reduce the complexity of the previously proposed approach for solving the bounded error subset selection problem. In particular, unlike the previously proposed approach for solving the bounded error subset selection problem, our new algorithm accommodates cases where the coefficients of the closest sparse approximation to the underlying Signal in the dictionary are not necessarily one. Our new algorithm is based on weighting the dictionary vectors by the minimum l 2 norm solution and relaxing the integer constraint on the coefficients of the dictionary vectors. It is shown to guarantee high Signal accuracy and sparsity. Compared with the Basis Pursuit and the Method of Frames (MoF) algorithms, the proposed algorithm has a better rate-distortion behavior.

  • a sparse solution to the bounded subset selection problem a network flow model approach
    International Conference on Acoustics Speech and Signal Processing, 2004
    Co-Authors: Masoud Alghoniemy, A H Tewfik
    Abstract:

    We reformulate the problem of finding the sparsest representation of a given Signal using an overcomplete dictionary as a bounded error subset selection problem. Specifically, the Reconstructed Signal is allowed to differ from the original Signal by a bounded error. We argue that this bounded error formulation is natural in many applications, such as coding. Our novel formulation guarantees the sparsest solution to the bounded error subset selection problem by minimizing the number of nonzero coefficients in the solution vector. We show that this solution can be computed by finding the minimum cost flow path of an equivalent network. Integer programming is adopted to find the solution.

Christopher Edwards - One of the best experts on this subject based on the ideXlab platform.

  • robust sliding mode observer based actuator fault detection and isolation for a class of nonlinear systems
    International Journal of Systems Science, 2008
    Co-Authors: Xinggang Yan, Christopher Edwards
    Abstract:

    In this article, an actuator fault detection and isolation scheme for a class of nonlinear systems with uncertainty is considered. The uncertainty is allowed to have a nonlinear bound which is a general function of the state variables. A sliding mode observer is first established based on a constrained Lyapunov equation. Then, the equivalent output error injection is employed to reconstruct the fault Signal using the characteristics of the sliding mode observer and the structure of the uncertainty. The Reconstructed Signal can approximate the system fault Signal to any accuracy even in the presence of a class of uncertainty. Finally, a simulation study on a nonlinear aircraft system is presented to show the effectiveness of the scheme.

  • brief paper nonlinear robust fault reconstruction and estimation using a sliding mode observer
    Automatica, 2007
    Co-Authors: Xinggang Yan, Christopher Edwards
    Abstract:

    This paper considers fault detection and estimation issues for a class of nonlinear systems with uncertainty, using an equivalent output error injection approach. A particular design of sliding mode observer is presented for which the parameters can be obtained using LMI techniques. A fault estimation approach is presented to estimate the fault and the estimation error is dependent on the bounds on the uncertainty. For a special class of uncertainty, a fault reconstruction scheme is presented where the Reconstructed Signal can approximate the fault Signal to any accuracy. The proposed fault estimation/reconstruction Signals are only based on the available plant input/ouput information and can be calculated on-line. Finally, a simulation study on a robotic arm system is presented to show the effectiveness of the scheme.

Qing Chen - One of the best experts on this subject based on the ideXlab platform.

  • research on bearing fault feature extraction based on singular value decomposition and optimized frequency band entropy
    Mechanical Systems and Signal Processing, 2019
    Co-Authors: Tao Liu, Qing Chen
    Abstract:

    Abstract Singular value decomposition (SVD) is widely used in condition monitoring of modern machine for its unique advantages. A novel relative change rate of singular value kurtosis (SVK) is proposed in order to determine the Reconstructed order of singular values effectively. Since the bandwidth parameter of the band-pass filter designed by FBE need to be determined based on experience, obviously, there are significant deficiencies. Then, a optimized frequency band entropy (OFBE) method based on the principle of maximum kurtosis is proposed to optimize the bandwidth parameters. In addition, because the fault Signal of the rolling bearing at the initial stage is very weak and submerged by ambient noise, SVD cannot extract fault features clearly, a new method for fault feature extraction of rolling bearing based on SVD and OFBE, named SVD-SVK-OFBE, is proposed. Firstly, the Hankel matrix is Reconstructed from the original vibration Signal in the phase space and the noise reduction is performed using SVD. Here, the relative change rate of singular value kurtosis is performed on the Hankel matrix to determine the Reconstructed order. Secondly, the OFBE analysis is performed on the Reconstructed Signal to determine the center frequency and the bandwidth of the band-pass filter adaptively. The bandwidth of the designed band-pass filter is optimized by the kurtosis maximum principle. Thirdly, the Reconstructed Signal of SVD is filtered by the optimized filter, and the envelope demodulation analysis is performed on the filtered Signal. Finally, the fault feature frequency is extracted and compared with the theoretical fault feature frequency to identify the fault type of the rolling bearing. The effectiveness and advantages of the method described in this paper are verified by the simulation analysis and experimental data analysis of the rolling bearing.

Masoud Alghoniemy - One of the best experts on this subject based on the ideXlab platform.

  • bounded subset selection with noninteger coefficients
    European Signal Processing Conference, 2004
    Co-Authors: Masoud Alghoniemy, A H Tewfik
    Abstract:

    The subset selection problem is known to be NP hard. It was recently shown that by relaxing the requirement that the Reconstructed Signal be equal to the original, one ends with a bounded error subset selection that admits a solution in polynomial time. In the bounded error subset selection problem, the Reconstructed Signal is allowed to differ from the original Signal by a bounded error. This bounded error formulation is natural in many applications, such as coding. In this paper, we improve the accuracy and reduce the complexity of the previously proposed approach for solving the bounded error subset selection problem. In particular, unlike the previously proposed approach for solving the bounded error subset selection problem, our new algorithm accommodates cases where the coefficients of the closest sparse approximation to the underlying Signal in the dictionary are not necessarily one. Our new algorithm is based on weighting the dictionary vectors by the minimum l 2 norm solution and relaxing the integer constraint on the coefficients of the dictionary vectors. It is shown to guarantee high Signal accuracy and sparsity. Compared with the Basis Pursuit and the Method of Frames (MoF) algorithms, the proposed algorithm has a better rate-distortion behavior.

  • a sparse solution to the bounded subset selection problem a network flow model approach
    International Conference on Acoustics Speech and Signal Processing, 2004
    Co-Authors: Masoud Alghoniemy, A H Tewfik
    Abstract:

    We reformulate the problem of finding the sparsest representation of a given Signal using an overcomplete dictionary as a bounded error subset selection problem. Specifically, the Reconstructed Signal is allowed to differ from the original Signal by a bounded error. We argue that this bounded error formulation is natural in many applications, such as coding. Our novel formulation guarantees the sparsest solution to the bounded error subset selection problem by minimizing the number of nonzero coefficients in the solution vector. We show that this solution can be computed by finding the minimum cost flow path of an equivalent network. Integer programming is adopted to find the solution.

Xinggang Yan - One of the best experts on this subject based on the ideXlab platform.

  • robust sliding mode observer based actuator fault detection and isolation for a class of nonlinear systems
    International Journal of Systems Science, 2008
    Co-Authors: Xinggang Yan, Christopher Edwards
    Abstract:

    In this article, an actuator fault detection and isolation scheme for a class of nonlinear systems with uncertainty is considered. The uncertainty is allowed to have a nonlinear bound which is a general function of the state variables. A sliding mode observer is first established based on a constrained Lyapunov equation. Then, the equivalent output error injection is employed to reconstruct the fault Signal using the characteristics of the sliding mode observer and the structure of the uncertainty. The Reconstructed Signal can approximate the system fault Signal to any accuracy even in the presence of a class of uncertainty. Finally, a simulation study on a nonlinear aircraft system is presented to show the effectiveness of the scheme.

  • brief paper nonlinear robust fault reconstruction and estimation using a sliding mode observer
    Automatica, 2007
    Co-Authors: Xinggang Yan, Christopher Edwards
    Abstract:

    This paper considers fault detection and estimation issues for a class of nonlinear systems with uncertainty, using an equivalent output error injection approach. A particular design of sliding mode observer is presented for which the parameters can be obtained using LMI techniques. A fault estimation approach is presented to estimate the fault and the estimation error is dependent on the bounds on the uncertainty. For a special class of uncertainty, a fault reconstruction scheme is presented where the Reconstructed Signal can approximate the fault Signal to any accuracy. The proposed fault estimation/reconstruction Signals are only based on the available plant input/ouput information and can be calculated on-line. Finally, a simulation study on a robotic arm system is presented to show the effectiveness of the scheme.