This paper introduces the time-frequency analyzed long short-term memory(TF-LSTM) neural network method for jamming signal recognition over the Global Navigation Satellite System(GNSS) receiver. The method introduces ...This paper introduces the time-frequency analyzed long short-term memory(TF-LSTM) neural network method for jamming signal recognition over the Global Navigation Satellite System(GNSS) receiver. The method introduces the long shortterm memory(LSTM) neural network into the recognition algorithm and combines the time-frequency(TF) analysis for signal preprocessing. Five kinds of navigation jamming signals including white Gaussian noise(WGN), pulse jamming, sweep jamming, audio jamming, and spread spectrum jamming are used as input for training and recognition. Since the signal parameters and quantity are unknown in the actual scenario, this work builds a data set containing multiple kinds and parameters jamming to train the TF-LSTM. The performance of this method is evaluated by simulations and experiments. The method has higher recognition accuracy and better robustness than the existing methods, such as LSTM and the convolutional neural network(CNN).展开更多
To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on...To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on long shortterm memory(RPP-LSTM)network is proposed,which combines the memory characteristics of recurrent neural network(RNN)and the deep reinforcement learning algorithm.LSTM networks are used in this algorithm as Q-value networks for the deep Q network(DQN)algorithm,which makes the decision of the Q-value network has some memory.Thanks to LSTM network,the Q-value network can use the previous environmental information and action information which effectively avoids the problem of single-step decision considering only the current environment.Besides,the algorithm proposes a hierarchical reward and punishment function for the specific problem of UAV real-time path planning,so that the UAV can more reasonably perform path planning.Simulation verification shows that compared with the traditional feed-forward neural network(FNN)based UAV autonomous path planning algorithm,the RPP-LSTM proposed in this paper can adapt to more complex environments and has significantly improved robustness and accuracy when performing UAV real-time path planning.展开更多
When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ...When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.展开更多
The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms...The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for correlation.In this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm targets.Precisely, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM)Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation,while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.展开更多
文章提出基于双向长短期记忆(bidirectional long short-term memory,Bi-LSTM)神经网络,考虑测井曲线相关性的测井曲线预测新方法。同一口井往往可以得到反映地层与井筒属性多种测井曲线,通过分析测井曲线之间存在的相关性,根据曲线之...文章提出基于双向长短期记忆(bidirectional long short-term memory,Bi-LSTM)神经网络,考虑测井曲线相关性的测井曲线预测新方法。同一口井往往可以得到反映地层与井筒属性多种测井曲线,通过分析测井曲线之间存在的相关性,根据曲线之间的相关性大小选择合适的训练样本,利用Bi-LSTM进行测井曲线预测。同时,测井曲线前后关联性强,Bi-LSTM可以考虑数据间的前后关联,从而提高测井曲线预测精度。实验结果表明,考虑曲线相关性的Bi-LSTM模型能减少样本数据,明显提高预测精度,均方误差相比单向长短期记忆神经网络方法能减小50%以上,具有很好的应用前景。展开更多
为提高物联网入侵检测模型的综合性能,将残差神经网络(Residual Networks,ResNet)与双向长短时记忆(Long-Short Term Memory,LSTM)网络融合,构建物联网入侵检测分类模型.针对大规模物联网流量快速批量处理问题,在对原始数据进行清洗、...为提高物联网入侵检测模型的综合性能,将残差神经网络(Residual Networks,ResNet)与双向长短时记忆(Long-Short Term Memory,LSTM)网络融合,构建物联网入侵检测分类模型.针对大规模物联网流量快速批量处理问题,在对原始数据进行清洗、转换等预处理基础上,提出将多条流量样本转换为灰度图,并利用基于ResNet和双向LSTM融合的深度学习方法构建物联网入侵检测分类模型.对分类模型的网络结构、可复用性进行综合优化实验,得到最终优化模型,分类准确率达到96.77%,综合优化后的模型构建时间为39.85 s.与其他机器学习算法结果相比,该优化方法在分类准确率和效率两个方面取得了很好的效果,综合性能优于传统的入侵检测分类模型.展开更多
为提升高复杂海洋环境下声呐探测距离预测的准确性和效率,文章提出一种基于改进Transformer的传播损失与声呐探测距离建模方法,该方法能够兼容复杂海洋环境下不同点位、不同方向声信号传播损失差异,能够基于声呐方程及声呐主被动工作模...为提升高复杂海洋环境下声呐探测距离预测的准确性和效率,文章提出一种基于改进Transformer的传播损失与声呐探测距离建模方法,该方法能够兼容复杂海洋环境下不同点位、不同方向声信号传播损失差异,能够基于声呐方程及声呐主被动工作模式,快速、有效地预测多点位多方向的声呐探测距离。以真实大区域海洋环境计算得到的传播损失数据为输入,通过将双向长短时记忆网络(bidirectional long short-term memory,Bi-LSTM)与Transformer架构中自注意力机制相结合,使得模型能够有效捕捉复杂环境变化的局部精确性和全局特征。实验结果表明,所提模型预测结果与声呐方程耦合积分方式得到的探测距离具有较好的一致性;同时计算效率提高了约1 000倍,提升了声呐性能的预报效率。展开更多
针对航空发动机剩余使用寿命(RUL)预测方法空间特征提取不充分、时间特征利用不充分,导致RUL预测准确性较低的问题,提出一种融合注意力机制的时空图卷积网络模型GCNBL-A3T(Graph Convolutional Network combined with Bidirectional Lon...针对航空发动机剩余使用寿命(RUL)预测方法空间特征提取不充分、时间特征利用不充分,导致RUL预测准确性较低的问题,提出一种融合注意力机制的时空图卷积网络模型GCNBL-A3T(Graph Convolutional Network combined with Bidirectional Long short-term memory and ATTenTion mechanism)。首先,使用一维卷积神经网络(1D-CNN)提取初始特征;其次,依次使用图卷积网络(GCN)和双向长短期记忆(Bi-LSTM)网络分别提取空间特征和时间特征;再次,利用自注意力机制处理特征并重新分配权重;最后,输入全连接网络获得RUL预测结果。使用商用模块化航空推进系统仿真(C-MAPSS)数据集验证所提模型的有效性。实验结果显示,与先进模型相比,所提模型的Score分数在3个数据子集上取得最小值,在1个数据子集上取得次小值;均方根误差(RMSE)在1个数据子集上取得最小值,在3个数据子集上取得次小值。消融实验结果也验证了所提模型的各模块能有效提升预测精度。展开更多
Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a vi...Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a viewpoint in DoDAF2.0,the operational viewpoint(OV)describes operational activities,nodes,and resource flows.The OV models are important for SoS architecture development.However,as the SoS complexity increases,constructing OV models with traditional methods exposes shortcomings,such as inefficient data collection and low modeling standards.Therefore,we propose an intelligent modeling method for five OV models,including operational resource flow OV-2,organizational relationships OV-4,operational activity hierarchy OV-5a,operational activities model OV-5b,and operational activity sequences OV-6c.The main idea of the method is to extract OV architecture data from text and generate interoperable OV models.First,we construct the OV meta model based on the DoDAF2.0 meta model(DM2).Second,OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field(BiLSTM-CRF)model.And OV architecture relationships are collected with relationship extraction rules.Finally,we define the generation rules for OV models and develop an OV modeling tool.We use unmanned surface vehicles(USV)swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method.展开更多
基金supported by the National Natural Science Foundation of China (62003354)。
文摘This paper introduces the time-frequency analyzed long short-term memory(TF-LSTM) neural network method for jamming signal recognition over the Global Navigation Satellite System(GNSS) receiver. The method introduces the long shortterm memory(LSTM) neural network into the recognition algorithm and combines the time-frequency(TF) analysis for signal preprocessing. Five kinds of navigation jamming signals including white Gaussian noise(WGN), pulse jamming, sweep jamming, audio jamming, and spread spectrum jamming are used as input for training and recognition. Since the signal parameters and quantity are unknown in the actual scenario, this work builds a data set containing multiple kinds and parameters jamming to train the TF-LSTM. The performance of this method is evaluated by simulations and experiments. The method has higher recognition accuracy and better robustness than the existing methods, such as LSTM and the convolutional neural network(CNN).
基金supported by the Natural Science Basic Research Prog ram of Shaanxi(2022JQ-593)。
文摘To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on long shortterm memory(RPP-LSTM)network is proposed,which combines the memory characteristics of recurrent neural network(RNN)and the deep reinforcement learning algorithm.LSTM networks are used in this algorithm as Q-value networks for the deep Q network(DQN)algorithm,which makes the decision of the Q-value network has some memory.Thanks to LSTM network,the Q-value network can use the previous environmental information and action information which effectively avoids the problem of single-step decision considering only the current environment.Besides,the algorithm proposes a hierarchical reward and punishment function for the specific problem of UAV real-time path planning,so that the UAV can more reasonably perform path planning.Simulation verification shows that compared with the traditional feed-forward neural network(FNN)based UAV autonomous path planning algorithm,the RPP-LSTM proposed in this paper can adapt to more complex environments and has significantly improved robustness and accuracy when performing UAV real-time path planning.
文摘When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.
文摘The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for correlation.In this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm targets.Precisely, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM)Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation,while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.
文摘文章提出基于双向长短期记忆(bidirectional long short-term memory,Bi-LSTM)神经网络,考虑测井曲线相关性的测井曲线预测新方法。同一口井往往可以得到反映地层与井筒属性多种测井曲线,通过分析测井曲线之间存在的相关性,根据曲线之间的相关性大小选择合适的训练样本,利用Bi-LSTM进行测井曲线预测。同时,测井曲线前后关联性强,Bi-LSTM可以考虑数据间的前后关联,从而提高测井曲线预测精度。实验结果表明,考虑曲线相关性的Bi-LSTM模型能减少样本数据,明显提高预测精度,均方误差相比单向长短期记忆神经网络方法能减小50%以上,具有很好的应用前景。
文摘为提高物联网入侵检测模型的综合性能,将残差神经网络(Residual Networks,ResNet)与双向长短时记忆(Long-Short Term Memory,LSTM)网络融合,构建物联网入侵检测分类模型.针对大规模物联网流量快速批量处理问题,在对原始数据进行清洗、转换等预处理基础上,提出将多条流量样本转换为灰度图,并利用基于ResNet和双向LSTM融合的深度学习方法构建物联网入侵检测分类模型.对分类模型的网络结构、可复用性进行综合优化实验,得到最终优化模型,分类准确率达到96.77%,综合优化后的模型构建时间为39.85 s.与其他机器学习算法结果相比,该优化方法在分类准确率和效率两个方面取得了很好的效果,综合性能优于传统的入侵检测分类模型.
文摘为提升高复杂海洋环境下声呐探测距离预测的准确性和效率,文章提出一种基于改进Transformer的传播损失与声呐探测距离建模方法,该方法能够兼容复杂海洋环境下不同点位、不同方向声信号传播损失差异,能够基于声呐方程及声呐主被动工作模式,快速、有效地预测多点位多方向的声呐探测距离。以真实大区域海洋环境计算得到的传播损失数据为输入,通过将双向长短时记忆网络(bidirectional long short-term memory,Bi-LSTM)与Transformer架构中自注意力机制相结合,使得模型能够有效捕捉复杂环境变化的局部精确性和全局特征。实验结果表明,所提模型预测结果与声呐方程耦合积分方式得到的探测距离具有较好的一致性;同时计算效率提高了约1 000倍,提升了声呐性能的预报效率。
文摘针对航空发动机剩余使用寿命(RUL)预测方法空间特征提取不充分、时间特征利用不充分,导致RUL预测准确性较低的问题,提出一种融合注意力机制的时空图卷积网络模型GCNBL-A3T(Graph Convolutional Network combined with Bidirectional Long short-term memory and ATTenTion mechanism)。首先,使用一维卷积神经网络(1D-CNN)提取初始特征;其次,依次使用图卷积网络(GCN)和双向长短期记忆(Bi-LSTM)网络分别提取空间特征和时间特征;再次,利用自注意力机制处理特征并重新分配权重;最后,输入全连接网络获得RUL预测结果。使用商用模块化航空推进系统仿真(C-MAPSS)数据集验证所提模型的有效性。实验结果显示,与先进模型相比,所提模型的Score分数在3个数据子集上取得最小值,在1个数据子集上取得次小值;均方根误差(RMSE)在1个数据子集上取得最小值,在3个数据子集上取得次小值。消融实验结果也验证了所提模型的各模块能有效提升预测精度。
基金National Natural Science Foundation of China(71690233,71971213,71901214)。
文摘Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a viewpoint in DoDAF2.0,the operational viewpoint(OV)describes operational activities,nodes,and resource flows.The OV models are important for SoS architecture development.However,as the SoS complexity increases,constructing OV models with traditional methods exposes shortcomings,such as inefficient data collection and low modeling standards.Therefore,we propose an intelligent modeling method for five OV models,including operational resource flow OV-2,organizational relationships OV-4,operational activity hierarchy OV-5a,operational activities model OV-5b,and operational activity sequences OV-6c.The main idea of the method is to extract OV architecture data from text and generate interoperable OV models.First,we construct the OV meta model based on the DoDAF2.0 meta model(DM2).Second,OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field(BiLSTM-CRF)model.And OV architecture relationships are collected with relationship extraction rules.Finally,we define the generation rules for OV models and develop an OV modeling tool.We use unmanned surface vehicles(USV)swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method.
