针对远洋干线中超大型集装箱船(Ultra-Large Container Ship,ULCS)的多港口Bay位优化问题(Multi-Port Master Bay Plan Problem,MP-MBPP),提出以堆垛为基本计算单元的混合整数规划(Mixed Integer Programming,MIP)模型。该模型以倒箱数...针对远洋干线中超大型集装箱船(Ultra-Large Container Ship,ULCS)的多港口Bay位优化问题(Multi-Port Master Bay Plan Problem,MP-MBPP),提出以堆垛为基本计算单元的混合整数规划(Mixed Integer Programming,MIP)模型。该模型以倒箱数最少、靠港时间最短为目标,根据航段距离动态,考虑船舶的结构强度约束,满足冷藏箱、重大件货物和45英尺集装箱的装载需求。该模型使用商用求解器CPLEX进行求解,试验结果表明:该模型可针对21000 TEU集装箱船多港口配载问题高效地给出可行解,为船舶大数据智能运维平台国产化解决方案的制订提供理论基础。展开更多
A continuous submarine depth control strategy based on multi-model and machine learning switching method under full working condition is proposed in this paper.A submarine motion model with six-degree-offreedom is fir...A continuous submarine depth control strategy based on multi-model and machine learning switching method under full working condition is proposed in this paper.A submarine motion model with six-degree-offreedom is first built and decoupled according to the force analysis.The control set with corresponding precise model set is then optimized according to different working conditions.The multi-model switching strategy is studied using machine learning algorithm.The simulation experiments indicate that a multi-model controller comprised of the proportional-integral-derivative(PID),fuzzy PID(FPID)and model predictive controllers with support vector machine(SVM)switching strategy can realize the continuous submarine depth control under full working condition,showing a good control performance compared with a single PID controller.展开更多
文摘针对远洋干线中超大型集装箱船(Ultra-Large Container Ship,ULCS)的多港口Bay位优化问题(Multi-Port Master Bay Plan Problem,MP-MBPP),提出以堆垛为基本计算单元的混合整数规划(Mixed Integer Programming,MIP)模型。该模型以倒箱数最少、靠港时间最短为目标,根据航段距离动态,考虑船舶的结构强度约束,满足冷藏箱、重大件货物和45英尺集装箱的装载需求。该模型使用商用求解器CPLEX进行求解,试验结果表明:该模型可针对21000 TEU集装箱船多港口配载问题高效地给出可行解,为船舶大数据智能运维平台国产化解决方案的制订提供理论基础。
基金the National Natural Science Foundation of China(No.51579201)。
文摘A continuous submarine depth control strategy based on multi-model and machine learning switching method under full working condition is proposed in this paper.A submarine motion model with six-degree-offreedom is first built and decoupled according to the force analysis.The control set with corresponding precise model set is then optimized according to different working conditions.The multi-model switching strategy is studied using machine learning algorithm.The simulation experiments indicate that a multi-model controller comprised of the proportional-integral-derivative(PID),fuzzy PID(FPID)and model predictive controllers with support vector machine(SVM)switching strategy can realize the continuous submarine depth control under full working condition,showing a good control performance compared with a single PID controller.
