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Ships are susceptible to wind and waves causing the declines of installation accuracy and maintenance safety of offshore wind turbines. The most seriously affected case is the non-stationary ship roll motion under long-peaked random wave spectrum. To ensure the stability of offshore operations under complex sea conditions, it is necessary to improve the generalization of the prediction model. In this paper, a preferential feature federation method was proposed. Firstly, the non-stationary ship roll motion was decomposed into multi-component stationary sequences by using the variable modal decomposition method. Then, the long and short-term memory neural network with attention mechanism was used to build a local multi-dimensional multi-step prediction model with error correction. Finally, in order to improve the prediction effect of new type ship motions in complex sea conditions, a federation method was used to combine some ship motion data holders for best model parameters, which were selected with the maximum mean discrepancy method with high similarity for preferential feature federated training. The experimental results show that the federated model has higher prediction accuracy and better generalization ability, which can help the stability control of wave compensation during offshore wind turbines installation.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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