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The redundant factor of RARKF is designed as a function of the motor current for tolerating the modeling error and load-dependent filtering mode switching.
Based on a harmonic drive model and a redundant adaptive robust Kalman filter (RARKF), the proposed approach can adapt torque estimation filtering optimality and robustness to the load variation by self-tuning the filtering gain and self-switching the filtering mode between optimal and robust.
In this method, filter noises and irrelevant features were filtered using Fisher classification and information gain in the filtering mode, respectively, to obtain overlapping feature subsets and effectively reduce modeling and detection time.
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Figure 9 Binary mask used to filter mode no.4.
Consequently, the signal becomes stationary and classical filtering tools can be used to filter modes.
In this case, one can use classical frequency filtering tools to filter modes.
In Figure 11 we present the result of the mode filtering for modes nos., (mode with very low energy), and (the mode with the highest energy).
In this work, a novel deblocking algorithm is proposed based on three filtering modes in terms of the activity across block boundaries.
Tai et al. [20] proposed a deblocking algorithm based on five filtering modes by considering the activity across block boundaries.
These five filtering modes include three frequency-related modes (the smooth mode, the intermediate mode, and the complex mode), one special mode, and a refined mode.
A filter mode flag set in code determines which algorithm to use.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com