Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties ...Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit(SAGRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform(FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced features.Subsequently, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.展开更多
High complexity and uncertainty of air combat pose significant challenges to target intention prediction.Current interpolation methods for data pre-processing and wrangling have limitations in capturing interrelations...High complexity and uncertainty of air combat pose significant challenges to target intention prediction.Current interpolation methods for data pre-processing and wrangling have limitations in capturing interrelationships among intricate variable patterns.Accordingly,this study proposes a Mogrifier gate recurrent unit-D(Mog-GRU-D)model to address the com-bat target intention prediction issue under the incomplete infor-mation condition.The proposed model directly processes miss-ing data while reducing the independence between inputs and output states.A total of 1200 samples from twelve continuous moments are captured through the combat simulation system,each of which consists of seven dimensional features.To bench-mark the experiment,a missing valued dataset has been gener-ated by randomly removing 20%of the original data.Extensive experiments demonstrate that the proposed model obtains the state-of-the-art performance with an accuracy of 73.25%when dealing with incomplete information.This study provides possi-ble interpretations for the principle of target interactive mecha-nism,highlighting the model’s effectiveness in potential air war-fare implementation.展开更多
长尾现象在序列推荐系统中长期存在,包括长尾用户和长尾项目两个方面。虽然现有许多研究缓解了序列推荐系统中的长尾问题,但大部分只是单方面地关注长尾用户或长尾项目。然而,长尾用户和长尾项目问题常常同时存在,只考虑其中一方会导致...长尾现象在序列推荐系统中长期存在,包括长尾用户和长尾项目两个方面。虽然现有许多研究缓解了序列推荐系统中的长尾问题,但大部分只是单方面地关注长尾用户或长尾项目。然而,长尾用户和长尾项目问题常常同时存在,只考虑其中一方会导致另一方性能不佳,且未关注到长尾用户、长尾项目各自的信息匮乏问题。提出一种利用GRU双分支信息协同增强的长尾推荐模型(long-tail recommendation model utilizing gated recurrent unit dualbranch information collaboration enhancement,LT-GRU),从用户与项目两个方面共同缓解长尾问题,并通过协同增强的方式丰富长尾信息。该模型由长尾用户和长尾项目双分支组成,每个分支分别负责各自的信息处理,并相互训练以充实另一方的信息。同时,引入一种偏好机制,通过演算用户与项目的影响因子,以动态调整用户偏好与项目热度,进一步缓解长尾推荐中信息不足问题。在Amazon系列的6个真实数据集上与6种经典模型进行实验对比,相较于长尾推荐模型中最优的结果,所提模型LT-GRU在HR与NDCG两个指标上分别平均提高2.49%、3.80%。这表明,在不牺牲头部用户和热门项目推荐性能的情况下,有效地缓解了长尾用户和长尾项目问题。展开更多
基金supported by the National Natural Science Foundation of China (6202201562088101)+1 种基金Shanghai Municipal Science and Technology Major Project (2021SHZDZX0100)Shanghai Municip al Commission of Science and Technology Project (19511132101)。
文摘Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit(SAGRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform(FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced features.Subsequently, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.
基金supported by the Aeronautical Science Foundation of China(2020Z023053002).
文摘High complexity and uncertainty of air combat pose significant challenges to target intention prediction.Current interpolation methods for data pre-processing and wrangling have limitations in capturing interrelationships among intricate variable patterns.Accordingly,this study proposes a Mogrifier gate recurrent unit-D(Mog-GRU-D)model to address the com-bat target intention prediction issue under the incomplete infor-mation condition.The proposed model directly processes miss-ing data while reducing the independence between inputs and output states.A total of 1200 samples from twelve continuous moments are captured through the combat simulation system,each of which consists of seven dimensional features.To bench-mark the experiment,a missing valued dataset has been gener-ated by randomly removing 20%of the original data.Extensive experiments demonstrate that the proposed model obtains the state-of-the-art performance with an accuracy of 73.25%when dealing with incomplete information.This study provides possi-ble interpretations for the principle of target interactive mecha-nism,highlighting the model’s effectiveness in potential air war-fare implementation.
文摘长尾现象在序列推荐系统中长期存在,包括长尾用户和长尾项目两个方面。虽然现有许多研究缓解了序列推荐系统中的长尾问题,但大部分只是单方面地关注长尾用户或长尾项目。然而,长尾用户和长尾项目问题常常同时存在,只考虑其中一方会导致另一方性能不佳,且未关注到长尾用户、长尾项目各自的信息匮乏问题。提出一种利用GRU双分支信息协同增强的长尾推荐模型(long-tail recommendation model utilizing gated recurrent unit dualbranch information collaboration enhancement,LT-GRU),从用户与项目两个方面共同缓解长尾问题,并通过协同增强的方式丰富长尾信息。该模型由长尾用户和长尾项目双分支组成,每个分支分别负责各自的信息处理,并相互训练以充实另一方的信息。同时,引入一种偏好机制,通过演算用户与项目的影响因子,以动态调整用户偏好与项目热度,进一步缓解长尾推荐中信息不足问题。在Amazon系列的6个真实数据集上与6种经典模型进行实验对比,相较于长尾推荐模型中最优的结果,所提模型LT-GRU在HR与NDCG两个指标上分别平均提高2.49%、3.80%。这表明,在不牺牲头部用户和热门项目推荐性能的情况下,有效地缓解了长尾用户和长尾项目问题。
