The firework algorithm(FWA) is a novel swarm intelligence-based method recently proposed for the optimization of multi-parameter, nonlinear functions. Numerical waveform inversion experiments using a synthetic model...The firework algorithm(FWA) is a novel swarm intelligence-based method recently proposed for the optimization of multi-parameter, nonlinear functions. Numerical waveform inversion experiments using a synthetic model show that the FWA performs well in both solution quality and efficiency. We apply the FWA in this study to crustal velocity structure inversion using regional seismic waveform data of central Gansu on the northeastern margin of the Qinghai-Tibet plateau. Seismograms recorded from the moment magnitude(MW) 5.4 Minxian earthquake enable obtaining an average crustal velocity model for this region. We initially carried out a series of FWA robustness tests in regional waveform inversion at the same earthquake and station positions across the study region,inverting two velocity structure models, with and without a low-velocity crustal layer; the accuracy of our average inversion results and their standard deviations reveal the advantages of the FWA for the inversion of regional seismic waveforms. We applied the FWA across our study area using three component waveform data recorded by nine broadband permanent seismic stations with epicentral distances ranging between 146 and 437 km. These inversion results show that the average thickness of the crust in this region is 46.75 km, while thicknesses of the sedimentary layer, and the upper, middle, and lower crust are 3.15,15.69, 13.08, and 14.83 km, respectively. Results also show that the P-wave velocities of these layers and the upper mantle are 4.47, 6.07, 6.12, 6.87, and 8.18 km/s,respectively.展开更多
在车联网(Internet of Vehicles,IoV)移动边缘计算卸载系统中,多跳卸载可将任务卸载到路侧单元(road site unit,RSU)范围外的车辆上,可以有效地利用空闲车辆的计算资源,然而由于车辆的高速移动,无法保证节点之间的稳定性。本文提出了一...在车联网(Internet of Vehicles,IoV)移动边缘计算卸载系统中,多跳卸载可将任务卸载到路侧单元(road site unit,RSU)范围外的车辆上,可以有效地利用空闲车辆的计算资源,然而由于车辆的高速移动,无法保证节点之间的稳定性。本文提出了一种基于A*算法的集中式动态多跳卸载策略,该策略以最小化任务时延为目标,将问题建模为马尔可夫决策过程(Markov decision process,MDP),采用A*算法来确定车辆任务卸载的最优队列,并利用近端策略优化算法(proximal policy optimization,PPO)进行求解。仿真结果表明,该方法与深度Q学习和贪婪策略相比,任务完成率明显提高,并且平均时延降低了30%左右。展开更多
基金supported by the National Natural Science Foundation of China (No. 41174034)
摘要The firework algorithm(FWA) is a novel swarm intelligence-based method recently proposed for the optimization of multi-parameter, nonlinear functions. Numerical waveform inversion experiments using a synthetic model show that the FWA performs well in both solution quality and efficiency. We apply the FWA in this study to crustal velocity structure inversion using regional seismic waveform data of central Gansu on the northeastern margin of the Qinghai-Tibet plateau. Seismograms recorded from the moment magnitude(MW) 5.4 Minxian earthquake enable obtaining an average crustal velocity model for this region. We initially carried out a series of FWA robustness tests in regional waveform inversion at the same earthquake and station positions across the study region,inverting two velocity structure models, with and without a low-velocity crustal layer; the accuracy of our average inversion results and their standard deviations reveal the advantages of the FWA for the inversion of regional seismic waveforms. We applied the FWA across our study area using three component waveform data recorded by nine broadband permanent seismic stations with epicentral distances ranging between 146 and 437 km. These inversion results show that the average thickness of the crust in this region is 46.75 km, while thicknesses of the sedimentary layer, and the upper, middle, and lower crust are 3.15,15.69, 13.08, and 14.83 km, respectively. Results also show that the P-wave velocities of these layers and the upper mantle are 4.47, 6.07, 6.12, 6.87, and 8.18 km/s,respectively.
摘要在车联网(Internet of Vehicles,IoV)移动边缘计算卸载系统中,多跳卸载可将任务卸载到路侧单元(road site unit,RSU)范围外的车辆上,可以有效地利用空闲车辆的计算资源,然而由于车辆的高速移动,无法保证节点之间的稳定性。本文提出了一种基于A*算法的集中式动态多跳卸载策略,该策略以最小化任务时延为目标,将问题建模为马尔可夫决策过程(Markov decision process,MDP),采用A*算法来确定车辆任务卸载的最优队列,并利用近端策略优化算法(proximal policy optimization,PPO)进行求解。仿真结果表明,该方法与深度Q学习和贪婪策略相比,任务完成率明显提高,并且平均时延降低了30%左右。