A new approach to predict dynamic mooring tension using LSTM neural network based on responses of floating structure
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Wang, Ziming
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State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian,116024, ChinaState Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian,116024, China
Wang, Ziming
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]
Qiao, Dongsheng
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State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian,116024, ChinaState Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian,116024, China
Qiao, Dongsheng
[1
]
Yan, Jun
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State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian,116024, ChinaState Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian,116024, China
Yan, Jun
[1
]
Tang, Guoqiang
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State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian,116024, ChinaState Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian,116024, China
Tang, Guoqiang
[1
]
Li, Binbin
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Shenzhen International Graduate School, Tsinghua University, Shenzhen,518055, ChinaState Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian,116024, China
Li, Binbin
[2
]
Ning, Dezhi
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State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian,116024, ChinaState Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian,116024, China
Ning, Dezhi
[1
]
机构:
[1] State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian,116024, China
[2] Shenzhen International Graduate School, Tsinghua University, Shenzhen,518055, China