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Based on the former, a quality assessment for the classifier performance is proposed.
Then, a novel characterization for stability and ℓ1-induced performance is proposed.
And the possible mechanism of the enhanced photocatalytic performance is proposed.
A trade-off framework between energy consumption and network performance is proposed.
A method based on concerned performance is proposed for regularization of the reliability index constraints.
In this article, a numerical model for prediction of turbine flowmeter performance is proposed.
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Finally, some solutions to further improve its performance are proposed.
Most importantly, the preeminent performance was proposed and validated based on the direct Z-scheme.
In this paper, adaptive learning algorithms to obtain better generalization performance are proposed.
Finally, some general design guidelines that guarantee and maximize the fault isolation performance are proposed.
Further evaluations of its long-term performance are proposed for the subsequent study.
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