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Some typical time frequency analysis methods cannot achieve ideal effects.
Because the NYFR output in our paper contains three modulations, some time-frequency transform methods cannot achieve an optimal result.
Combining with the statistical techniques such as the maximum likelihood estimation, we can solve the optimal shape parameters of the Gaussian kernels by the kernel-based probabilities even though the classical kernel-based methods cannot achieve the uncertain data.
These results demonstrate the fact that thresholding-based methods cannot achieve optimal performance since not all functional binding sites are conserved.
Experimental miRNA-recognition methods are laborious and time-consuming; hence these methods cannot achieve high throughput currently.
In comparison, other methods cannot achieve such high recall and precision based on 30% reciprocal overlap with the true SCNA.
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As a result, the MES method cannot achieve precise TOA estimations as shown in Fig. 2.
However, the proposed threshold selection method cannot achieve accurate TOA estimations in low SNR values.
The individual forecasting method cannot achieve a high accuracy due to the intrinsic characteristics of wind speed and wind power.
Conventional methods, such as the optimal-elevation threshold method, the optimal-curvature threshold method, and the D8 method, cannot achieve satisfactory performance under these conditions.
Due to the jump of the coefficient across the interface, the standard linear finite element method cannot achieve optimal convergence when the uniform mesh is used.
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means cannot achieve
methods cannot acquire
methods cannot satisfy
methods cannot address
methods cannot manage
methods cannot select
methods cannot recognize
methods cannot determine
methods cannot provide
methods cannot build
methods cannot diagnose
methods cannot be
methods cannot measure
methods cannot analyze
methods cannot estimate
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