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This is achieved by using mode shapes extracted from the beam theory as generating functions.
In the second phase, a modal feature-based neural networks (MBNN) algorithm is designed to estimate the location and severity of damage in the structure by using mode shapes and modal strain energies.
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In [5], they detect double-compressed JPEG images by using mode-based first digit features combined with Fisher linear discriminant (FLD) analysis.
By using mode-dependent Lyapunov Krasovski functionals, appropriate conditions for the stochastic stability and disturbance attenuation of mixed GRN/filter system is derived.
Sufficient conditions are derived for the filtering error system to be stochastically stable and ensure a prescribed H∞ disturbance attenuation level bound by using mode-dependent Lyapunov functional approach.
By using mode-dependent average dwell time (MDADT) techniques, we obtain stability conditions for the open-loop switched nonlinear systems and stabilization conditions with H∞ performance for the closed-loop switched nonlinear systems.
To demonstrate the superiority of our method, we first compare the accuracy of double JPEG compression detection for different block sizes with the method proposed in [5], which detects double-compressed JPEG images by using mode-based first digit features, and the method proposed in [6], which uses the statistics of low-frequency DCT coefficients.
Also, network distances are preferred because we did not want to presuppose the transport mode used to get to a physician by using mode-specific speeds for calculating travel time [ 2, 16, 33].
Compared with existing results on Markov systems which are obtained by separately using mode-dependent and mode-independent methods, our result has great flexibility in practical applications.
MS/MS runs were performed by using ESI+ mode and ESI- mode (to determine fatty acid composition of phospholipids), collision energy ramp from 15 to 30 V and mass range starting from m/z 150.
During the intervention schema modes were measured by using the Mode Observation Scale (MOS) and by a pre-test/post-test design applying the Schema Mode Inventory (SMI).
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