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
目的探索性分析爆炸伤延时现场救护(prolonged field care,PFC)的技术需求,旨在为无法快速实施转运的爆炸伤患者提供“延长黄金救治时间”的医疗技术参考。方法以中国知网中的文本资料为数据源,采用文本关联度挖掘的方法获取“爆炸伤”...目的探索性分析爆炸伤延时现场救护(prolonged field care,PFC)的技术需求,旨在为无法快速实施转运的爆炸伤患者提供“延长黄金救治时间”的医疗技术参考。方法以中国知网中的文本资料为数据源,采用文本关联度挖掘的方法获取“爆炸伤”与“PFC”之间的关联强度,并按照层次分析法原理,探寻不同“建设内容”在“医院智慧化后勤管理”中的需求程度。结果一级指标的建设内容度排序为:持续通气/氧合(28.78)、医疗后送(28.78)、现场复苏(18.54)、伤情评估与监测(11.95)和伤员护理(11.95);二级指标的建设内容排序前三的指标为:持续通气(25.18)、途中救治(18.68)和控制出血(15.45)。结论持续通气、控制出血和途中救治等技术在爆炸伤PFC指标体系中的需求度较高,这可能是未来爆炸伤PFC质量提升的重要发展方向。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.
文摘目的探索性分析爆炸伤延时现场救护(prolonged field care,PFC)的技术需求,旨在为无法快速实施转运的爆炸伤患者提供“延长黄金救治时间”的医疗技术参考。方法以中国知网中的文本资料为数据源,采用文本关联度挖掘的方法获取“爆炸伤”与“PFC”之间的关联强度,并按照层次分析法原理,探寻不同“建设内容”在“医院智慧化后勤管理”中的需求程度。结果一级指标的建设内容度排序为:持续通气/氧合(28.78)、医疗后送(28.78)、现场复苏(18.54)、伤情评估与监测(11.95)和伤员护理(11.95);二级指标的建设内容排序前三的指标为:持续通气(25.18)、途中救治(18.68)和控制出血(15.45)。结论持续通气、控制出血和途中救治等技术在爆炸伤PFC指标体系中的需求度较高,这可能是未来爆炸伤PFC质量提升的重要发展方向。
