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In conclusion, the grid sensitivity analysis not only affect the thermal behaviour of the TES tank but also the computational time.
The evolution has included advancements in not only the level of process descriptions, but also the computational implementation, including numerical methods.
Simulation results show that not only the filtering performance of the PTSKF is compatible to that of the previous proposed parameterized minimum-variance filter (PMVF) but also the computational complexity of the former is less intensive than that of the latter.
The Gabor wavelet-based pyramid effectively reduces not only the computational cost of the Gabor filtering but also the computational complexity of feature representation of a model face, preserving the facial information.
This is because the multi-feature fusion location algorithm using multi-feature extraction acceleration and different dimensions of feature fusion estimation moving trajectory of the target object not only reduces the processing time but also the computational complexity and the positioning method, so that the positioning acceleration effect is very obvious.
Such a pre-enclosing technique reduces not only the overestimation of target shapes with large DAGs but also the computational cost incurred while performing interval operations with processors that are optimized for floating-point arithmetic.
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In addition, the investigation results show that reasonable and reliable lumped-pass model can not only guarantee the accuracy of the simulated results, but also improve the computational efficiency in the thermo-elastic plastic FE analysis procedure.
In particular, a compromising solution in aspect of not only reducing the conservatism but also suppressing the computational burden is obtained in the special case of m=1.
Conventional methods are more likely to generate inaccurate channel estimates when SNR is low, which not only limits the performance of the detector but also increases the computational burden.
The proposed framework cannot only dramatically improve the retrieval performance but also reduce the computational cost in dealing with the newcome models.
The implementation of dynamic refinement in Smoothed Particle Hydrodynamics (SPH) cannot only describe the physical field more minutely but also decrease the computational cost because of the finer spatial resolution and the locally self-adaptive refinement.
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