TY - GEN
T1 - Research on Deep Spatio-Temporal Model and Its Application in Situation Prediction
AU - Feng, Qi
AU - Zhang, Jinhui
AU - Gao, Xiaoguang
AU - Li, Maoqing
AU - Ning, Chenxi
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Situation prediction refers to predicting the state information of things in the future on the basis of existing information, and situational information contains complex laws of time and space. Traditional methods only consider a single factor or separate time and space. At the same time, due to the limitations of traditional algorithms, it is not possible to accurately predict air combat events with long interval dependencies. In order to solve these problems, we propose a deep spatio-Temporal model based on the dynamic graph convolution and attention mechanisms. The model extracts and analyzes the features of space and time respectively. Experimental results show that the model proposed in this paper has more stable training process and higher prediction accuracy.
AB - Situation prediction refers to predicting the state information of things in the future on the basis of existing information, and situational information contains complex laws of time and space. Traditional methods only consider a single factor or separate time and space. At the same time, due to the limitations of traditional algorithms, it is not possible to accurately predict air combat events with long interval dependencies. In order to solve these problems, we propose a deep spatio-Temporal model based on the dynamic graph convolution and attention mechanisms. The model extracts and analyzes the features of space and time respectively. Experimental results show that the model proposed in this paper has more stable training process and higher prediction accuracy.
KW - attention mechanism
KW - deep learning
KW - situation prediction
KW - spatio-Temporal model
UR - https://www.scopus.com/pages/publications/85166181233
U2 - 10.1109/ICCRE57112.2023.10155597
DO - 10.1109/ICCRE57112.2023.10155597
M3 - 会议稿件
AN - SCOPUS:85166181233
T3 - 2023 8th International Conference on Control and Robotics Engineering, ICCRE 2023
SP - 21
EP - 27
BT - 2023 8th International Conference on Control and Robotics Engineering, ICCRE 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Control and Robotics Engineering, ICCRE 2023
Y2 - 21 April 2023 through 23 April 2023
ER -