The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,th...The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.展开更多
Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devi...Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.展开更多
开展公路隧道结构状态精准预测是掌握隧道结构状态变化、识别潜在安全风险和保障安全运营的重要技术手段。针对隧道监控量测测点的空间分布与时序特性,提出了一种基于河马优化(Hippopotamus Optimization, HO)算法和卷积神经网络(Convol...开展公路隧道结构状态精准预测是掌握隧道结构状态变化、识别潜在安全风险和保障安全运营的重要技术手段。针对隧道监控量测测点的空间分布与时序特性,提出了一种基于河马优化(Hippopotamus Optimization, HO)算法和卷积神经网络(Convolutional Neural Network, CNN)的双向长短期记忆(Bidirectional Long Short Term Memory, BiLSTM)网络公路隧道结构状态预测方法。量化分析测点间关联性,结合温度特征构建模型输入矩阵;利用CNN挖掘各测点的空间关联性,采用BiLSTM提取时间序列特征,引入HO算法优化模型参数;将预测结果映射为隧道结构状态等级,展示隧道整体受力状态。结果表明,建立的HO-CNN-BiLSTM模型能够有效提取空间和温度特征,在预测精度和稳定性方面均优于对比模型,可实现隧道结构状态精确评估,为公路隧道的安全运营及分级管控措施制定提供技术支撑。展开更多
基金supported by the National Natural Science Foundation of China(Grant No.62403486)。
文摘The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.
文摘Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.
文摘针对不同磁密幅值、频率、谐波组合等复杂激励工况下磁致伸缩建模面临的精准性问题,该文利用空间注意力机制(spatial attention mechanism,SAM)对传统的卷积神经网络(convolutional neural network,CNN)进行改进,将SAM嵌套入CNN网络中,建立SAMCNN改进型网络。再结合双向长短期记忆(bidirectional long short-term memory,BiLSTM)网络,提出电工钢片SAMCNN-BiLSTM磁致伸缩模型。首先,利用灰狼优化算法(grey wolf optimization,GWO)寻优神经网络结构的参数,实现复杂工况下磁致伸缩效应的准确表征;然后,建立中低频范围单频与叠加谐波激励等复杂工况下的磁致伸缩应变数据库,开展数据预处理与特征分析;最后,对SAMCNN-BiLSTM模型开展对比验证。对比叠加3次谐波激励下的磁致伸缩应变频谱主要分量,SAMCNN-BiLSTM模型计算值最大相对误差为3.70%,其比Jiles-Atherton-Sablik(J-A-S)、二次畴转等模型能更精确地表征电工钢片的磁致伸缩效应。
文摘开展公路隧道结构状态精准预测是掌握隧道结构状态变化、识别潜在安全风险和保障安全运营的重要技术手段。针对隧道监控量测测点的空间分布与时序特性,提出了一种基于河马优化(Hippopotamus Optimization, HO)算法和卷积神经网络(Convolutional Neural Network, CNN)的双向长短期记忆(Bidirectional Long Short Term Memory, BiLSTM)网络公路隧道结构状态预测方法。量化分析测点间关联性,结合温度特征构建模型输入矩阵;利用CNN挖掘各测点的空间关联性,采用BiLSTM提取时间序列特征,引入HO算法优化模型参数;将预测结果映射为隧道结构状态等级,展示隧道整体受力状态。结果表明,建立的HO-CNN-BiLSTM模型能够有效提取空间和温度特征,在预测精度和稳定性方面均优于对比模型,可实现隧道结构状态精确评估,为公路隧道的安全运营及分级管控措施制定提供技术支撑。
文摘为提高高超声速滑翔飞行器(HGV)轨迹预测的精度,提出一种基于时域卷积网络(temporal convolutional network,TCN)和双向长短时记忆网络(bidirectional long short-term memory network,BiLSTM)结合的HGV轨迹预测方法.该方法利用TCN的因果膨胀卷积提取HGV轨迹多尺度动态特征,融合BiLSTM的双向循环机制挖掘轨迹长时依赖与上下文关联,通过全连接层将预测结果映射到样本空间.引入贝叶斯优化(Bayesian optimization,BO)与灰狼优化(grey wolf optimization,GWO)组合优化模式,实现了网络超参数的全局优化,据此建立了深度学习框架下的HGV轨迹预测模型.数值仿真结果表明,在训练完备条件下,建立的预测模型能够有效预测HGV未来时刻的位置状态,相较于4种对比模型,该预测模型的均方根误差平均降低62.10%,平均绝对误差平均降低61.66%.
