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
轴承的早期故障信号属于微弱信号,其故障特征提取一直是旋转机械故障诊断的一大难点。笔者将掩膜法引入到局部均值分解(local mean decomposition,简称LMD)分解中,提出了一种基于LMD和掩膜法(mask signal,简称MS)的滚动轴承微弱故障提...轴承的早期故障信号属于微弱信号,其故障特征提取一直是旋转机械故障诊断的一大难点。笔者将掩膜法引入到局部均值分解(local mean decomposition,简称LMD)分解中,提出了一种基于LMD和掩膜法(mask signal,简称MS)的滚动轴承微弱故障提取方法。由于LMD在噪声背景下分解出的功能分量(product function,简称PF)存在模态混叠现象,很难辨别故障频率的真伪,所以引入了掩膜信号法对LMD分解出的与原信号相关性强的PF分量进行处理,抑制模态混叠现象,提取故障频率。文中以滚动轴承实际故障信号为对象进行分析,通过将掩膜信号法与LMD方法相结合的方式,对存在噪声的故障信号进行处理,将故障频率处的峭度值提高了8倍,同时将信噪比提高了19.1%,成功提取了故障信号,为故障特征提取提供一种新的诊断方法。展开更多
为了优化装备零件再制造激光熔覆工艺参数,提高熔覆层的质量,选取激光功率(A),送粉量(B),扫描速度(C)为优化变量,将熔覆层高度(H)和宽度(W)作为响应指标,基于响应面分析法,利用Design-Expert软件设计中心复合实验,对实验结果进行方差分...为了优化装备零件再制造激光熔覆工艺参数,提高熔覆层的质量,选取激光功率(A),送粉量(B),扫描速度(C)为优化变量,将熔覆层高度(H)和宽度(W)作为响应指标,基于响应面分析法,利用Design-Expert软件设计中心复合实验,对实验结果进行方差分析,建立工艺参数相对于响应指标的回归预测模型。通过分析建立的摄动图与3D响应面,结果表明:激光功率与送粉量对于熔覆层高度和宽度的影响最为显著,同时送粉量与扫描速度、激光功率与送粉量的交互作用分别对于熔覆层高度和宽度有显著影响。装备零件再制造激光熔覆的最优工艺参数为:激光功率3.94 k W,送粉量60 g/min,扫描速度4mm/s。展开更多
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.
文摘轴承的早期故障信号属于微弱信号,其故障特征提取一直是旋转机械故障诊断的一大难点。笔者将掩膜法引入到局部均值分解(local mean decomposition,简称LMD)分解中,提出了一种基于LMD和掩膜法(mask signal,简称MS)的滚动轴承微弱故障提取方法。由于LMD在噪声背景下分解出的功能分量(product function,简称PF)存在模态混叠现象,很难辨别故障频率的真伪,所以引入了掩膜信号法对LMD分解出的与原信号相关性强的PF分量进行处理,抑制模态混叠现象,提取故障频率。文中以滚动轴承实际故障信号为对象进行分析,通过将掩膜信号法与LMD方法相结合的方式,对存在噪声的故障信号进行处理,将故障频率处的峭度值提高了8倍,同时将信噪比提高了19.1%,成功提取了故障信号,为故障特征提取提供一种新的诊断方法。
文摘为了优化装备零件再制造激光熔覆工艺参数,提高熔覆层的质量,选取激光功率(A),送粉量(B),扫描速度(C)为优化变量,将熔覆层高度(H)和宽度(W)作为响应指标,基于响应面分析法,利用Design-Expert软件设计中心复合实验,对实验结果进行方差分析,建立工艺参数相对于响应指标的回归预测模型。通过分析建立的摄动图与3D响应面,结果表明:激光功率与送粉量对于熔覆层高度和宽度的影响最为显著,同时送粉量与扫描速度、激光功率与送粉量的交互作用分别对于熔覆层高度和宽度有显著影响。装备零件再制造激光熔覆的最优工艺参数为:激光功率3.94 k W,送粉量60 g/min,扫描速度4mm/s。
