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
How to mine valuable information from massive multisource heterogeneous data and identify the intention of aerial targets is a major research focus at present. Aiming at the longterm dependence of air target intention...How to mine valuable information from massive multisource heterogeneous data and identify the intention of aerial targets is a major research focus at present. Aiming at the longterm dependence of air target intention recognition, this paper deeply explores the potential attribute features from the spatiotemporal sequence data of the target. First, we build an intelligent dynamic intention recognition framework, including a series of specific processes such as data source, data preprocessing,target space-time, convolutional neural networks-bidirectional gated recurrent unit-atteneion (CBA) model and intention recognition. Then, we analyze and reason the designed CBA model in detail. Finally, through comparison and analysis with other recognition model experiments, our proposed method can effectively improve the accuracy of air target intention recognition,and is of significance to the commanders’ operational command and situation prediction.展开更多
The bio-based epoxy nanocomposite(GAER/DOPO-POSS)was prepared from gallic epoxy resin(GAER)and polyhedral oligomeric silsesquioxane(which containing 9,10-dihydrogen-9-oxo-10-phosphorus-phenanthrene-10-oxide groups,cal...The bio-based epoxy nanocomposite(GAER/DOPO-POSS)was prepared from gallic epoxy resin(GAER)and polyhedral oligomeric silsesquioxane(which containing 9,10-dihydrogen-9-oxo-10-phosphorus-phenanthrene-10-oxide groups,called DOPO-POSS).The polyhedral oligomeric silsesquioxane containing epoxy groups(E-POSS)was grafted onto aminated graphene oxide(E-GO),then the novel POSS-E-GO was obtained.The POSS-E-GO was used as modifier for GAER/DOPO-POSS nanocomposite.The influences of POSS-E-GO content on mechanical properties,dynamic mechanical properties and thermal stability of GAER/DOPO-POSS nanocomposites were determined.The experimental results show that POSS-E-GO can significantly improve the toughness of the GAER/DOPO-POSS nanocomposite.When 0.5wt% POSS-E-GO was added in GAER/DOPO-POSS nanocomposite,the impact strength of the nanocomposite was 4.74 kJ/m^(2) higher than that in the absence of POSS-E-GO,meantime the initial thermal degradation temperature was 277℃.展开更多
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.
基金supported by the National Natural Science Foundation of China (61502523)。
文摘How to mine valuable information from massive multisource heterogeneous data and identify the intention of aerial targets is a major research focus at present. Aiming at the longterm dependence of air target intention recognition, this paper deeply explores the potential attribute features from the spatiotemporal sequence data of the target. First, we build an intelligent dynamic intention recognition framework, including a series of specific processes such as data source, data preprocessing,target space-time, convolutional neural networks-bidirectional gated recurrent unit-atteneion (CBA) model and intention recognition. Then, we analyze and reason the designed CBA model in detail. Finally, through comparison and analysis with other recognition model experiments, our proposed method can effectively improve the accuracy of air target intention recognition,and is of significance to the commanders’ operational command and situation prediction.
基金Funded by the Natural Science Foundation of Hebei Province(No.B2019210221)the Project by S&T Program of Hebei(No.206Z1202G)。
文摘The bio-based epoxy nanocomposite(GAER/DOPO-POSS)was prepared from gallic epoxy resin(GAER)and polyhedral oligomeric silsesquioxane(which containing 9,10-dihydrogen-9-oxo-10-phosphorus-phenanthrene-10-oxide groups,called DOPO-POSS).The polyhedral oligomeric silsesquioxane containing epoxy groups(E-POSS)was grafted onto aminated graphene oxide(E-GO),then the novel POSS-E-GO was obtained.The POSS-E-GO was used as modifier for GAER/DOPO-POSS nanocomposite.The influences of POSS-E-GO content on mechanical properties,dynamic mechanical properties and thermal stability of GAER/DOPO-POSS nanocomposites were determined.The experimental results show that POSS-E-GO can significantly improve the toughness of the GAER/DOPO-POSS nanocomposite.When 0.5wt% POSS-E-GO was added in GAER/DOPO-POSS nanocomposite,the impact strength of the nanocomposite was 4.74 kJ/m^(2) higher than that in the absence of POSS-E-GO,meantime the initial thermal degradation temperature was 277℃.
