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Antonio Vicino - One of the best experts on this subject based on the ideXlab platform.

  • On worst-case approximation of feasible system sets via orthonormal basis functions
    IEEE Transactions on Automatic Control, 2003
    Co-Authors: Marco Casini, Andrea Garulli, Antonio Vicino
    Abstract:

    This note deals with the approximation of sets of Linear time-invariant systems via orthonormal basis functions. This problem is relevant to conditional set membership identification, where a set of feasible systems is available from observed data, and a reduced-complexity model must be estimated. The basis of the model class is made of impulse responses of Linear Filters. The objective of the note is to select the basis function poles according to a worst-case optimality criterion. Suboptimal conditional identification algorithms are introduced and tight bounds are provided on the associated identification errors.

  • On worst-case approximation of feasible system sets via orthonormal basis functions
    Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228), 2001
    Co-Authors: Marco Casini, Andrea Garulli, Antonio Vicino
    Abstract:

    This paper deals with the approximation of sets of Linear time-invariant systems via orthonormal basis functions. This problem is relevant to conditional set membership identification, where a set of feasible systems is available from observed data, and a reduced-complexity model must be estimated, within a Linearly parameterized model class. The basis of the model class is a collection of impulse responses of Linear Filters (e.g. Laguerre functions), whose poles must be chosen properly. The objective of the paper is to select the basis function pole according to a worst-case optimality criterion taking into account the uncertainty system set. This leads to complicated min-max optimization problems. Suboptimal conditional identification algorithms are introduced and tight bounds are provided on the associated identification errors.

Marco Casini - One of the best experts on this subject based on the ideXlab platform.

  • On worst-case approximation of feasible system sets via orthonormal basis functions
    IEEE Transactions on Automatic Control, 2003
    Co-Authors: Marco Casini, Andrea Garulli, Antonio Vicino
    Abstract:

    This note deals with the approximation of sets of Linear time-invariant systems via orthonormal basis functions. This problem is relevant to conditional set membership identification, where a set of feasible systems is available from observed data, and a reduced-complexity model must be estimated. The basis of the model class is made of impulse responses of Linear Filters. The objective of the note is to select the basis function poles according to a worst-case optimality criterion. Suboptimal conditional identification algorithms are introduced and tight bounds are provided on the associated identification errors.

  • On worst-case approximation of feasible system sets via orthonormal basis functions
    Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228), 2001
    Co-Authors: Marco Casini, Andrea Garulli, Antonio Vicino
    Abstract:

    This paper deals with the approximation of sets of Linear time-invariant systems via orthonormal basis functions. This problem is relevant to conditional set membership identification, where a set of feasible systems is available from observed data, and a reduced-complexity model must be estimated, within a Linearly parameterized model class. The basis of the model class is a collection of impulse responses of Linear Filters (e.g. Laguerre functions), whose poles must be chosen properly. The objective of the paper is to select the basis function pole according to a worst-case optimality criterion taking into account the uncertainty system set. This leads to complicated min-max optimization problems. Suboptimal conditional identification algorithms are introduced and tight bounds are provided on the associated identification errors.

Andrea Garulli - One of the best experts on this subject based on the ideXlab platform.

  • On worst-case approximation of feasible system sets via orthonormal basis functions
    IEEE Transactions on Automatic Control, 2003
    Co-Authors: Marco Casini, Andrea Garulli, Antonio Vicino
    Abstract:

    This note deals with the approximation of sets of Linear time-invariant systems via orthonormal basis functions. This problem is relevant to conditional set membership identification, where a set of feasible systems is available from observed data, and a reduced-complexity model must be estimated. The basis of the model class is made of impulse responses of Linear Filters. The objective of the note is to select the basis function poles according to a worst-case optimality criterion. Suboptimal conditional identification algorithms are introduced and tight bounds are provided on the associated identification errors.

  • On worst-case approximation of feasible system sets via orthonormal basis functions
    Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228), 2001
    Co-Authors: Marco Casini, Andrea Garulli, Antonio Vicino
    Abstract:

    This paper deals with the approximation of sets of Linear time-invariant systems via orthonormal basis functions. This problem is relevant to conditional set membership identification, where a set of feasible systems is available from observed data, and a reduced-complexity model must be estimated, within a Linearly parameterized model class. The basis of the model class is a collection of impulse responses of Linear Filters (e.g. Laguerre functions), whose poles must be chosen properly. The objective of the paper is to select the basis function pole according to a worst-case optimality criterion taking into account the uncertainty system set. This leads to complicated min-max optimization problems. Suboptimal conditional identification algorithms are introduced and tight bounds are provided on the associated identification errors.

Ranjan Ganguli - One of the best experts on this subject based on the ideXlab platform.

  • A Field Programmable Gate Array (FPGA) Based Non-Linear Filters for Gas Turbine Prognostics
    'PHM Society', 2021
    Co-Authors: Jayant Kumar Nayak, Vatsala Prasad, Ranjan Ganguli
    Abstract:

    The removal of noise from signals obtained through the health monitoring systems in gas turbines is an important consideration for accurate prognostics. Several Filters have been designed and tested for this purpose, and their performance analysis has been conducted. Linear Filters are inefficient in the removal of outliers and noise because they cause smoothening of the sharp features in the signal which can indicate the onset of a fault event. On the other hand, non-Linear Filters based on image processing methods can provide more precise results for gas turbine health signals. Among others, the weighted recursive median (WRM) filter has been shown to provide greater accuracy due to its weight adaptability depending on the signal type. However, sampling data at high rates is possible which needs hardware implementation of the filter. In this paper, the design, simulation and implementation of WRM Filters on the FPGA (Field Programmable Gate Arrays) platforms Vivado Design Suite by Xilinx and Quartus Pro Lite Edition 19.3 has been performed. The architectural detail and performance result with the FPGA Filters when subjected to abrupt and gradual fault signal is presented

  • jet engine gas path measurement filtering using center weighted idempotent median Filters
    Journal of Propulsion and Power, 2003
    Co-Authors: Ranjan Ganguli
    Abstract:

    Key indicators of jet engine health are deviations in gas-path sensor measurements from a "good" baseline engine. These measurement deviations or deltas are used to detect and estimate engine deterioration and faults. Typical measurements are exhaust gas temperature, low rotor speed, high rotor speed; and fuel flow. The measurement deltas are displayed to powerplant engineers through computer visualization tools such as trend plots, which are then used for diagnostic and prognostic decisions. The measurement deltas are also used in pattern recognition and state estimation algorithms to detect and isolate faults. Both the fault detection and the visualization process are hindered by the presence of noise in the data. Traditional Linear Filters used by the gas turbine industry for smoothing gas-path measurement deltas tend to smooth out the trend shifts in the signal that can signify a fault or repair event. The Linear Filters also perform poorly when high-amplitude impulsive noise and outliers are present in the signal. However, nonLinear Filters can be designed to suppress noise while preserving the final detail in the gas turbine measurements. Results with simulated signals show noise reduction of about 60% can be obtained with a nonLinear center weighted idempotent median filter.

  • noise and outlier removal from jet engine health signals using weighted fir median hybrid Filters
    Mechanical Systems and Signal Processing, 2002
    Co-Authors: Ranjan Ganguli
    Abstract:

    The removal of noise and outliers from measurement signals is a major problem in jet engine health monitoring. Topical measurement signals found in most jet engines include low rotor speed, high rotor speed. fuel flow and exhaust gas temperature. Deviations in these measurements from a baseline 'good' engine are often called measurement deltas and the health signals used for fault detection, isolation, trending and data mining. Linear Filters such as the FIR moving average filter and IIR exponential average filter are used in the industry to remove noise and outliers from the jet engine measurement deltas. However, the use of Linear Filters can lead to loss of critical features in the signal that can contain information about maintenance and repair events that could be used by fault isolation algorithms to determine engine condition or by data mining algorithms to learn valuable patterns in the data, Non-Linear Filters such as the median and weighted median hybrid Filters offer the opportunity to remove noise and gross outliers from signals while preserving features. In this study. a comparison of traditional Linear Filters popular in the jet engine industry is made with the median filter and the subfilter weighted FIR median hybrid (SWFMH) filter. Results using simulated data with implanted faults shows that the SWFMH filter results in a noise reduction of over 60 per cent compared to only 20 per cent for FIR Filters and 30 per cent for IIR Filters. Preprocessing jet engine health signals using the SWFMH filter would greatly improve the accuracy of diagnostic systems. (C) 2002 Published by Elsevier Science Ltd.

Kiyoharu Aizawa - One of the best experts on this subject based on the ideXlab platform.

  • reconstructing arbitrarily focused images from two differently focused images using Linear Filters
    IEEE Transactions on Image Processing, 2005
    Co-Authors: Akira Kubota, Kiyoharu Aizawa
    Abstract:

    We present a novel filtering method for reconstructing an all-in-focus image or an arbitrarily focused image from two images that are focused differently. The method can arbitrarily manipulate the degree of blur of the objects using Linear Filters without segmentation. The Filters are uniquely determined from a Linear imaging model in the Fourier domain. An effective and accurate blur estimation method is developed. The simulation results show that the accuracy and computational time of the proposed method are improved compared with the previous iterative method and that the effects of blur estimation error on the quality of the reconstructed image are very small. The method performs well for real images acquired without visible artifacts.