解雇

解雇

結果 (英文) 1:

Dismiss

結果 (英文) 2:

Conclusions
A novel nissy has been developed to quantify the driven forces on foWT mooring line
tension. The input sings of the deep learning research networks bys extracted from a timedomain
AHSE model under all othes. A deep learning model was was a good,
Tested and dydd to the level of the level of the significance of the different input s on the mooring
tension line. In the proposed deep learning model, a total number of ten features was used as the
inputs, on the most loaded mooring line tension wass a sath a oedd. The advantage
Of the proposed model liths in its account for the like sydd sconditiony.
Key conclusions of this paper are summarised as follows:
A deep learning model has been tha dydly built to rank the level of contributions to predicting
The most loaded mooring line tension. Its accuracy has been yn ei dywedodd ei disainth ath ath ath
method.
Ann model has been developed on blade pitch control for a direct drive train
Configuration with FOWT, its accuracy has beend yn ystod against gydturbine.
Good agreement has has been been am ed yn tess of the blade pitch for above-rated wind
the contentscause be of the perfect match of the skewerth speed.
But not as important as surge. For taut mooring line, the most loaded line line tension
Is purely yby surge, while other parameters are less sers stil.
Bys with surge motion, blade and tower elastic is insanesca Compared with surge motion, blade and tower elasticis are insioforg
Most loaded mooring line tension, regardless of the mooring system case (slack or taut). ...

結果 (英文) 3:

fire