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 | A neural tensor decomposition model for high-order sparse data recovery ...
When faced with high missing ratios or sparse observed sets, the recovery results become less ideal [20]. More importantly, the nonlinear information in the data may obscure the low rankness and the model performance may be hindered by the multi-linear hypothesis in the decomposition, making it fail to capture the nonlinear features [21].
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 | Missing measurement data recovery methods in structural health ...
A data recovery method based on the OMP algorithm is used in the missing response recovery for aviation anti-rust aluminum plates [32]. Li et al. [6] studied an approach that uses convex optimization theory and OMP algorithm to achieve CS-based electromechanical admittance data recovery.
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 | False data injection attacks data recovery in smart grids: A graph ...
False data injection (FDI) attacks, one of the most classical cyber attacks, have increasingly posed a significant threat to the security and reliability of power systems [5]. Such attacks, mislead the system state estimation results, by manipulating the measurements of sensors, and thereby affecting the secure operations of the power system [6]. Research on FDI attacks and their data recovery ...
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 | Analysis of false lock in Mueller-Muller clock and data recovery system ...
Another view suggests that data correlation is the key contributor [ [9], [10]]. In this work, we provide a comprehensive analysis of MMPD false lock and introduce an enhanced mitigation strategy, validated via simulations. Section 2 investigates the false-lock mechanism and presents an improved phase detection strategy.
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 | Kernel Bayesian tensor ring decomposition for multiway data recovery
With the aid of side information, the recovery performance of the methods above is improved. However, all matrix-based methods with side information encounter difficulties when dealing with high-order data because they cannot incorporate specific side information enhancements.
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