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Experimental results show that the method achieves its expected effect.
The method achieves 95.16 % accuracy with neural network as a classifier.
It can be seen that the method achieves comparatively good positioning of the target area.
The method achieves the best effect of the object detection in that year.
Furthermore, the method achieves an accurate subinteger refinement for the range profile alignment.
Compared to the mixed FEM, the method achieves local mass balance with fewer degrees of freedom.
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The method achieved the highest efficiency of 100%.
The values in bold have been used to identify the method achieving the best coding performance.
Also, the method achieved more than 93.73 % of computational saving compared to the full 22-channel case.
The method achieved 60.6 and 60%% success rate in three-channel cases of ECoG and EEG datasets, respectively.
The method achieved 7.1 average channels, 0.011 average false negative, 0.48 average false positive, and 9.54 s average latency time.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com