针对未来战争对无人系统的需求不断增加,提出一种适用于狭小空间和复杂环境的新型无人系统——两栖无人作战车系统,同时具备地面行驶、墙面爬行、空中飞行和协同作战能力。采用基于Agent的建模与仿真(agent-based modeling and simulati...针对未来战争对无人系统的需求不断增加,提出一种适用于狭小空间和复杂环境的新型无人系统——两栖无人作战车系统,同时具备地面行驶、墙面爬行、空中飞行和协同作战能力。采用基于Agent的建模与仿真(agent-based modeling and simulation,ABMS)方法,在分析其应用任务场景的基础上,通过构造任务场景、桌面模型、仿真验证,深入分析两栖无人作战车系统应具备的功能,并根据分析结果提出所需解决的关键技术。结果表明:该系统对扩展无人系统任务环境、提升自主作战能力有着重要意义。展开更多
Ship motions induced by waves have a significant impact on the efficiency and safety of offshore operations.Real-time prediction of ship motions in the next few seconds plays a crucial role in performing sensitive act...Ship motions induced by waves have a significant impact on the efficiency and safety of offshore operations.Real-time prediction of ship motions in the next few seconds plays a crucial role in performing sensitive activities.However,the obvious memory effect of ship motion time series brings certain difficulty to rapid and accurate prediction.Therefore,a real-time framework based on the Long-Short Term Memory(LSTM)neural network model is proposed to predict ship motions in regular and irregular head waves.A 15000 TEU container ship model is employed to illustrate the proposed framework.The numerical implementation and the real-time ship motion prediction in irregular head waves corresponding to the different time scales are carried out based on the container ship model.The related experimental data were employed to verify the numerical simulation results.The results show that the proposed method is more robust than the classical extreme short-term prediction method based on potential flow theory in the prediction of nonlinear ship motions.展开更多
文摘针对未来战争对无人系统的需求不断增加,提出一种适用于狭小空间和复杂环境的新型无人系统——两栖无人作战车系统,同时具备地面行驶、墙面爬行、空中飞行和协同作战能力。采用基于Agent的建模与仿真(agent-based modeling and simulation,ABMS)方法,在分析其应用任务场景的基础上,通过构造任务场景、桌面模型、仿真验证,深入分析两栖无人作战车系统应具备的功能,并根据分析结果提出所需解决的关键技术。结果表明:该系统对扩展无人系统任务环境、提升自主作战能力有着重要意义。
文摘Ship motions induced by waves have a significant impact on the efficiency and safety of offshore operations.Real-time prediction of ship motions in the next few seconds plays a crucial role in performing sensitive activities.However,the obvious memory effect of ship motion time series brings certain difficulty to rapid and accurate prediction.Therefore,a real-time framework based on the Long-Short Term Memory(LSTM)neural network model is proposed to predict ship motions in regular and irregular head waves.A 15000 TEU container ship model is employed to illustrate the proposed framework.The numerical implementation and the real-time ship motion prediction in irregular head waves corresponding to the different time scales are carried out based on the container ship model.The related experimental data were employed to verify the numerical simulation results.The results show that the proposed method is more robust than the classical extreme short-term prediction method based on potential flow theory in the prediction of nonlinear ship motions.
