Floods are one of the most serious natural disasters that can cause huge societal and economic losses.Extensive research has been conducted on topics like flood monitoring,prediction,and loss estimation.In these resea...Floods are one of the most serious natural disasters that can cause huge societal and economic losses.Extensive research has been conducted on topics like flood monitoring,prediction,and loss estimation.In these research fields,flood velocity plays a crucial role and is an important factor that influences the reliability of the outcomes.Traditional methods rely on physical models for flood simulation and prediction and could generate accurate results but often take a long time.Deep learning technology has recently shown significant potential in the same field,especially in terms of efficiency,helping to overcome the time-consuming associated with traditional methods.This study explores the potential of deep learning models in predicting flood velocity.More specifically,we use a Multi-Layer Perceptron(MLP)model,a specific type of Artificial Neural Networks(ANNs),to predict the velocity in the test area of the Lundesokna River in Norway with diverse terrain conditions.Geographic data and flood velocity simulated based on the physical hydraulic model are used in the study for the pre-training,optimization,and testing of the MLP model.Our experiment indicates that the MLP model has the potential to predict flood velocity in diverse terrain conditions of the river with acceptable accuracy against simulated velocity results but with a significant decrease in training time and testing time.Meanwhile,we discuss the limitations for the improvement in future work.展开更多
为实现混合动力系统在电池荷电状态(state of charge,SOC)平衡以及动力性约束下的经济性提升,提出了基于偏好强化学习的混合动力能量管理策略,该策略将能量管理问题建模为马尔科夫决策过程,采用深度神经网络建立输入状态值到最优动作控...为实现混合动力系统在电池荷电状态(state of charge,SOC)平衡以及动力性约束下的经济性提升,提出了基于偏好强化学习的混合动力能量管理策略,该策略将能量管理问题建模为马尔科夫决策过程,采用深度神经网络建立输入状态值到最优动作控制输出的函数映射关系。与传统的强化学习控制算法相比,偏好强化学习算法无需设定回报函数,只需对多动作进行偏好判断即可实现网络训练收敛,克服了传统强化学习方法中回报函数加权归一化设计难题。通过仿真试验和硬件在环验证了所提出能量管理策略的有效性和可行性。结果表明,与传统强化学习能量管理策略相比,该策略能够在满足混合动力车辆SOC平衡和动力性约束下,提升经济性4.6%~10.6%。展开更多
文摘Floods are one of the most serious natural disasters that can cause huge societal and economic losses.Extensive research has been conducted on topics like flood monitoring,prediction,and loss estimation.In these research fields,flood velocity plays a crucial role and is an important factor that influences the reliability of the outcomes.Traditional methods rely on physical models for flood simulation and prediction and could generate accurate results but often take a long time.Deep learning technology has recently shown significant potential in the same field,especially in terms of efficiency,helping to overcome the time-consuming associated with traditional methods.This study explores the potential of deep learning models in predicting flood velocity.More specifically,we use a Multi-Layer Perceptron(MLP)model,a specific type of Artificial Neural Networks(ANNs),to predict the velocity in the test area of the Lundesokna River in Norway with diverse terrain conditions.Geographic data and flood velocity simulated based on the physical hydraulic model are used in the study for the pre-training,optimization,and testing of the MLP model.Our experiment indicates that the MLP model has the potential to predict flood velocity in diverse terrain conditions of the river with acceptable accuracy against simulated velocity results but with a significant decrease in training time and testing time.Meanwhile,we discuss the limitations for the improvement in future work.
文摘为实现混合动力系统在电池荷电状态(state of charge,SOC)平衡以及动力性约束下的经济性提升,提出了基于偏好强化学习的混合动力能量管理策略,该策略将能量管理问题建模为马尔科夫决策过程,采用深度神经网络建立输入状态值到最优动作控制输出的函数映射关系。与传统的强化学习控制算法相比,偏好强化学习算法无需设定回报函数,只需对多动作进行偏好判断即可实现网络训练收敛,克服了传统强化学习方法中回报函数加权归一化设计难题。通过仿真试验和硬件在环验证了所提出能量管理策略的有效性和可行性。结果表明,与传统强化学习能量管理策略相比,该策略能够在满足混合动力车辆SOC平衡和动力性约束下,提升经济性4.6%~10.6%。
