Unmanned aerial vehicle(UAV)-enabled edge computing is emerging as a potential enabler for Artificial Intelligence of Things(AIoT)in the forthcoming sixth-generation(6G)communication networks.With the use of flexible ...Unmanned aerial vehicle(UAV)-enabled edge computing is emerging as a potential enabler for Artificial Intelligence of Things(AIoT)in the forthcoming sixth-generation(6G)communication networks.With the use of flexible UAVs,massive sensing data is gathered and processed promptly without considering geographical locations.Deep neural networks(DNNs)are becoming a driving force to extract valuable information from sensing data.However,the lightweight servers installed on UAVs are not able to meet the extremely high requirements of inference tasks due to the limited battery capacities of UAVs.In this work,we investigate a DNN model placement problem for AIoT applications,where the trained DNN models are selected and placed on UAVs to execute inference tasks locally.It is impractical to obtain future DNN model request profiles and system operation states in UAV-enabled edge computing.The Lyapunov optimization technique is leveraged for the proposed DNN model placement problem.Based on the observed system overview,an advanced online placement(AOP)algorithm is developed to solve the transformed problem in each time slot,which can reduce DNN model transmission delay and disk I/O energy cost simultaneously while keeping the input data queues stable.Finally,extensive simulations are provided to depict the effectiveness of the AOP algorithm.The numerical results demonstrate that the AOP algorithm can reduce 18.14%of the model placement cost and 29.89%of the input data queue backlog on average by comparing it with benchmark algorithms.展开更多
【目的】研究福建省农业净碳汇的时序特征、影响因素,并对其变化趋势进行预测,为促进福建省农业低碳发展提供依据。【方法】采用排放因子法测算2002-2021年福建省农业净碳汇,运用拓展随机性环境影响评估STIRPAT(stochastic impacts by r...【目的】研究福建省农业净碳汇的时序特征、影响因素,并对其变化趋势进行预测,为促进福建省农业低碳发展提供依据。【方法】采用排放因子法测算2002-2021年福建省农业净碳汇,运用拓展随机性环境影响评估STIRPAT(stochastic impacts by regression on population,affluence and technology)模型分析福建省农业净碳汇的影响因素,并采用深度神经网络(deep neural networks,DNN)模型预测2025年全省农业净碳汇。【结果】2002-2021年福建省农业净碳汇整体呈“波动下降-平稳上升”的变化趋势;种植业碳排放占比较大,以水稻和化肥碳排放为主;水稻和蔬菜对碳汇贡献较大;城镇化水平、能源消耗水平、农业净碳汇强度、农村经济发展水平和农村居民人均可支配收入均可提升农业净碳汇,其中,能源消耗水平的提升效果最为显著;2025年农业净碳汇预计比2021年上升36.30%。【结论】近年来福建省农业净碳汇量呈逐年上升的趋势,预测2025年农业净碳汇量比2021年提高36.30%。展开更多
基金supported by the National Science Foundation of China(Grant No.62202118)the Top-Technology Talent Project from Guizhou Education Department(Qianjiao Ji[2022]073)+1 种基金the Natural Science Foundation of Hebei Province(Grant No.F2022203045 and F2022203026))the Central Government Guided Local Science and Technology Development Fund Project(Grant No.226Z0701G).
文摘Unmanned aerial vehicle(UAV)-enabled edge computing is emerging as a potential enabler for Artificial Intelligence of Things(AIoT)in the forthcoming sixth-generation(6G)communication networks.With the use of flexible UAVs,massive sensing data is gathered and processed promptly without considering geographical locations.Deep neural networks(DNNs)are becoming a driving force to extract valuable information from sensing data.However,the lightweight servers installed on UAVs are not able to meet the extremely high requirements of inference tasks due to the limited battery capacities of UAVs.In this work,we investigate a DNN model placement problem for AIoT applications,where the trained DNN models are selected and placed on UAVs to execute inference tasks locally.It is impractical to obtain future DNN model request profiles and system operation states in UAV-enabled edge computing.The Lyapunov optimization technique is leveraged for the proposed DNN model placement problem.Based on the observed system overview,an advanced online placement(AOP)algorithm is developed to solve the transformed problem in each time slot,which can reduce DNN model transmission delay and disk I/O energy cost simultaneously while keeping the input data queues stable.Finally,extensive simulations are provided to depict the effectiveness of the AOP algorithm.The numerical results demonstrate that the AOP algorithm can reduce 18.14%of the model placement cost and 29.89%of the input data queue backlog on average by comparing it with benchmark algorithms.
文摘【目的】研究福建省农业净碳汇的时序特征、影响因素,并对其变化趋势进行预测,为促进福建省农业低碳发展提供依据。【方法】采用排放因子法测算2002-2021年福建省农业净碳汇,运用拓展随机性环境影响评估STIRPAT(stochastic impacts by regression on population,affluence and technology)模型分析福建省农业净碳汇的影响因素,并采用深度神经网络(deep neural networks,DNN)模型预测2025年全省农业净碳汇。【结果】2002-2021年福建省农业净碳汇整体呈“波动下降-平稳上升”的变化趋势;种植业碳排放占比较大,以水稻和化肥碳排放为主;水稻和蔬菜对碳汇贡献较大;城镇化水平、能源消耗水平、农业净碳汇强度、农村经济发展水平和农村居民人均可支配收入均可提升农业净碳汇,其中,能源消耗水平的提升效果最为显著;2025年农业净碳汇预计比2021年上升36.30%。【结论】近年来福建省农业净碳汇量呈逐年上升的趋势,预测2025年农业净碳汇量比2021年提高36.30%。
