Digital Twins in Unmanned Aerial Vehicles for Rapid Medical Resource Delivery in Epidemics.
Digital Twins in Unmanned Aerial Vehicles for Rapid Medical Resource Delivery in Epidemics.
复制标题
DOI：
10.1109/tits.2021.3113787
复制
发表时间：
2022-12
期刊：
影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
作者：
目的探讨无人机数字孪生（DTs）在新冠肺炎疫情防控中快速、准确地提供医疗资源方面的作用。分析了无人机技术在新型冠状病毒防控中的可行性。介绍了深度学习（DL）算法。基于改进的AlexNet构建了无人机dt信息预测模型，并通过仿真实验对其性能进行了分析。随着终端用户和任务比例的增加，在传输功率降低的情况下，该模型可以提供更小的传输延迟、更小的吞吐量需求能耗、更短的任务完成时间和更高的资源利用率。在预测精度方面，该模型在信噪比、比特量化、导频数、导频污染系数、不同天线数等方面误差较小，精度较高。具体来说，其预测准确率达到95.58%，预测速度稳定在35帧/秒左右。因此，所提出的模型具有更强的鲁棒性，可以在最小化数据传输误差的同时做出更准确的预测。研究结果可为精准投入新型冠状病毒肺炎防控医疗资源提供参考。
The purposes are to explore the effect of Digital Twins (DTs) in Unmanned Aerial Vehicles (UAVs) on providing medical resources quickly and accurately during COVID-19 prevention and control. The feasibility of UAV DTs during COVID-19 prevention and control is analyzed. Deep Learning (DL) algorithms are introduced. A UAV DTs information forecasting model is constructed based on improved AlexNet, whose performance is analyzed through simulation experiments. As end-users and task proportion increase, the proposed model can provide smaller transmission delays, lesser energy consumption in throughput demand, shorter task completion time, and higher resource utilization rate under reduced transmission power than other state-of-art models. Regarding forecasting accuracy, the proposed model can provide smaller errors and better accuracy in Signal-to-Noise Ratio (SNR), bit quantizer, number of pilots, pilot pollution coefficient, and number of different antennas. Specifically, its forecasting accuracy reaches 95.58% and forecasting velocity stabilizes at about 35 Frames-Per-Second (FPS). Hence, the proposed model has stronger robustness, making more accurate forecasts while minimizing the data transmission errors. The research results can reference the precise input of medical resources for COVID-19 prevention and control.