Graphene samples with different morphologies were fabricated on the inside of copper enclosures by low pressure chemical vapor deposition and tuning the flow rate of hydrogen. It is found that the flow rate of hydroge...Graphene samples with different morphologies were fabricated on the inside of copper enclosures by low pressure chemical vapor deposition and tuning the flow rate of hydrogen. It is found that the flow rate of hydrogen greatly influences the growth of graphene. Ther-modynamic analysis indicates that a higher flow rate of hydrogen is favorable to the formation of good quality graphene with regular mor-phology. However, the mass-transfer process of methane dominates the growth driving force. At very low pressure, mass-transfer proceeds by Knudsen diffusion, and the mass-transfer flux of methane decreases as the flow rate of hydrogen increases, leading to a decrease in the growth driving force. At a higher pressure, mass-transfer proceeds by Fick＇s diffusion, and the mass-transfer flux of methane is dominated by the gas velocity, whose variation determines the growth driving force variation of graphene.展开更多
Mobile edge computing has emerged as a new paradigm to enhance computing capabilities by offloading complicated tasks to nearby cloud server.To conserve energy as well as maintain quality of service,low time complexit...Mobile edge computing has emerged as a new paradigm to enhance computing capabilities by offloading complicated tasks to nearby cloud server.To conserve energy as well as maintain quality of service,low time complexity algorithm is proposed to complete task offloading and server allocation.In this paper,a multi-user with multiple tasks and single server scenario is considered for small network,taking full account of factors including data size,bandwidth,channel state information.Furthermore,we consider a multi-server scenario for bigger network,where the influence of task priority is taken into consideration.To jointly minimize delay and energy cost,we propose a distributed unsupervised learning-based offloading framework for task offloading and server allocation.We exploit a memory pool to store input data and corresponding decisions as key-value pairs for model to learn to solve optimization problems.To further reduce time cost and achieve near-optimal performance,we use convolutional neural networks to process mass data based on fully connected networks.Numerical results show that the proposed algorithm performs better than other offloading schemes,which can generate near-optimal offloading decision timely.展开更多
基金supported by the Postdoctoral Science Foundation Funded Project(No.2012M521743)the Natural Science Basic Research Plan in Shaanxi Province of China(Program No.2012JM8009)+3 种基金the Doctoral Program Foundation of the Institutions of Higher Education of China (No.20110203120014)the Fundamental Research Funds for the Central Universities(No.K50510250005)the Applied Materials Innovation Fund of Xi'an(XA-AM-201002)the Fundamental Research Funds for the Central Universities(No.JB141108)
文摘Graphene samples with different morphologies were fabricated on the inside of copper enclosures by low pressure chemical vapor deposition and tuning the flow rate of hydrogen. It is found that the flow rate of hydrogen greatly influences the growth of graphene. Ther-modynamic analysis indicates that a higher flow rate of hydrogen is favorable to the formation of good quality graphene with regular mor-phology. However, the mass-transfer process of methane dominates the growth driving force. At very low pressure, mass-transfer proceeds by Knudsen diffusion, and the mass-transfer flux of methane decreases as the flow rate of hydrogen increases, leading to a decrease in the growth driving force. At a higher pressure, mass-transfer proceeds by Fick＇s diffusion, and the mass-transfer flux of methane is dominated by the gas velocity, whose variation determines the growth driving force variation of graphene.
基金presented in part at the EAI CHINACOM 2020supported in part by Natural Science Foundation of Jiangxi Province (Grant No.20202BAB212003)+1 种基金Projects of Humanities and Social Sciences of universities in Jiangxi (JC18224)Science and technology project of Jiangxi Provincial Department of Education(GJJ210817, GJJ210854)
文摘Mobile edge computing has emerged as a new paradigm to enhance computing capabilities by offloading complicated tasks to nearby cloud server.To conserve energy as well as maintain quality of service,low time complexity algorithm is proposed to complete task offloading and server allocation.In this paper,a multi-user with multiple tasks and single server scenario is considered for small network,taking full account of factors including data size,bandwidth,channel state information.Furthermore,we consider a multi-server scenario for bigger network,where the influence of task priority is taken into consideration.To jointly minimize delay and energy cost,we propose a distributed unsupervised learning-based offloading framework for task offloading and server allocation.We exploit a memory pool to store input data and corresponding decisions as key-value pairs for model to learn to solve optimization problems.To further reduce time cost and achieve near-optimal performance,we use convolutional neural networks to process mass data based on fully connected networks.Numerical results show that the proposed algorithm performs better than other offloading schemes,which can generate near-optimal offloading decision timely.
