By applying the rapid solidification technique of deep undercooling,Cu65Ni35 and Cu60Ni40 alloys achieved maximum undercoolings of 284 and 222 K,respectively.Microstructural images captured reveal grain refinement in ...By applying the rapid solidification technique of deep undercooling,Cu65Ni35 and Cu60Ni40 alloys achieved maximum undercoolings of 284 and 222 K,respectively.Microstructural images captured reveal grain refinement in both alloys across both large and small undercooling ranges.High-speed photography was used to analyze the relationship between solidification front morphology and undercooling,showing that dendrite remelting and fragmentation caused grain refinement under small undercooling,while stress-induced recrystallization is responsible under large undercooling.Microhardness testing further demonstrates a sudden drop in microhardness near the critical undercooling point,providing evidence for grain refinement due to recrystallization in large undercooling tissues.展开更多
In recent times,various power control and clustering approaches have been proposed to enhance overall performance for cell-free massive multipleinput multiple-output(CF-mMIMO)networks.With the emergence of deep reinfo...In recent times,various power control and clustering approaches have been proposed to enhance overall performance for cell-free massive multipleinput multiple-output(CF-mMIMO)networks.With the emergence of deep reinforcement learning(DRL),significant progress has been made in the field of network optimization as DRL holds great promise for improving network performance and efficiency.In this work,our focus delves into the intricate challenge of joint cooperation clustering and downlink power control within CF-mMIMO networks.Leveraging the potent deep deterministic policy gradient(DDPG)algorithm,our objective is to maximize the proportional fairness(PF)for user rates,thereby aiming to achieve optimal network performance and resource utilization.Moreover,we harness the concept of“divide and conquer”strategy,introducing two innovative methods termed alternating DDPG(A-DDPG)and hierarchical DDPG(H-DDPG).These approaches aim to decompose the intricate joint optimization problem into more manageable sub-problems,thereby facilitating a more efficient resolution process.Our findings unequivo-cally showcase the superior efficacy of our proposed DDPG approach over the baseline schemes in both clustering and downlink power control.Furthermore,the A-DDPG and H-DDPG obtain higher performance gain than DDPG with lower computational complexity.展开更多
基金Funded by the Basic Research Project in Shanxi Province(No.202103021224183)the 2024 Science and Technology Plan Project of Jiaozuo City,Henan Province(No.2024410001)。
文摘By applying the rapid solidification technique of deep undercooling,Cu65Ni35 and Cu60Ni40 alloys achieved maximum undercoolings of 284 and 222 K,respectively.Microstructural images captured reveal grain refinement in both alloys across both large and small undercooling ranges.High-speed photography was used to analyze the relationship between solidification front morphology and undercooling,showing that dendrite remelting and fragmentation caused grain refinement under small undercooling,while stress-induced recrystallization is responsible under large undercooling.Microhardness testing further demonstrates a sudden drop in microhardness near the critical undercooling point,providing evidence for grain refinement due to recrystallization in large undercooling tissues.
基金supported by Guangdong Basic and Applied Basic Research Foundation under Grant 2024A1515012015supported in part by the National Natural Science Foundation of China under Grant 62201336+4 种基金in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2024A1515011541supported in part by the National Natural Science Foundation of China under Grant 62371344in part by the Fundamental Research Funds for the Central Universitiessupported in part by Knowledge Innovation Program of Wuhan-Shuguang Project under Grant 2023010201020316in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2024A1515010247。
文摘In recent times,various power control and clustering approaches have been proposed to enhance overall performance for cell-free massive multipleinput multiple-output(CF-mMIMO)networks.With the emergence of deep reinforcement learning(DRL),significant progress has been made in the field of network optimization as DRL holds great promise for improving network performance and efficiency.In this work,our focus delves into the intricate challenge of joint cooperation clustering and downlink power control within CF-mMIMO networks.Leveraging the potent deep deterministic policy gradient(DDPG)algorithm,our objective is to maximize the proportional fairness(PF)for user rates,thereby aiming to achieve optimal network performance and resource utilization.Moreover,we harness the concept of“divide and conquer”strategy,introducing two innovative methods termed alternating DDPG(A-DDPG)and hierarchical DDPG(H-DDPG).These approaches aim to decompose the intricate joint optimization problem into more manageable sub-problems,thereby facilitating a more efficient resolution process.Our findings unequivo-cally showcase the superior efficacy of our proposed DDPG approach over the baseline schemes in both clustering and downlink power control.Furthermore,the A-DDPG and H-DDPG obtain higher performance gain than DDPG with lower computational complexity.
