目的:观察麝香保心丸对老年急性冠脉综合征(ACS)心率变异(HRV)的影响,评价其心肌保护作用。方法:90名老年ACS患者,随机分为麝香保心丸及常规治疗组,分别于用药前、后记录24 h Holter,观察心绞痛症状及心电图变化,进行HRV分析。结果:麝...目的:观察麝香保心丸对老年急性冠脉综合征(ACS)心率变异(HRV)的影响,评价其心肌保护作用。方法:90名老年ACS患者,随机分为麝香保心丸及常规治疗组,分别于用药前、后记录24 h Holter,观察心绞痛症状及心电图变化,进行HRV分析。结果:麝香保心丸组心绞痛明显改善(P<0.05)。SDNN、SDANN、rMSSD、PNN 均升高,极低频(VLF)、低频(LF)、LF/HF均降低(P均<0.05)。结论:麝香保心丸能有效改善ACS患者心肌缺血症状及心率变异。展开更多
为在低复杂度约束条件下提升电磁信号调制识别的性能,提出了一种基于稀疏深度神经网络(Sparse Deep Neural Network,SDNN)的电磁信号调制识别方法。首先,通过提取电磁信号同相和正交两路数据绘制出信号的星座图,作为信号的浅层特征表达...为在低复杂度约束条件下提升电磁信号调制识别的性能,提出了一种基于稀疏深度神经网络(Sparse Deep Neural Network,SDNN)的电磁信号调制识别方法。首先,通过提取电磁信号同相和正交两路数据绘制出信号的星座图,作为信号的浅层特征表达;然后,基于星座图中各信号点密度大小对星座图进行上色,增强星座图中信号特征;最后,通过SDNN对增强后的星座图进行识别分类。实验结果表明,SDNN模型选取合适的剪枝率后,能够有效降低模型存储规模和计算量,其中模型参数压缩了72%,浮点运算量压缩了45%,与原模型97%的综合识别率相比,稀疏化处理后模型的综合识别率为96.8%,在小幅度识别精度损失范围内大幅降低了模型复杂度。展开更多
Unmanned Aerial Vehicle(UAV)has emerged as a promising technology for the support of human activities,such as target tracking,disaster rescue,and surveillance.However,these tasks require a large computation load of im...Unmanned Aerial Vehicle(UAV)has emerged as a promising technology for the support of human activities,such as target tracking,disaster rescue,and surveillance.However,these tasks require a large computation load of image or video processing,which imposes enormous pressure on the UAV computation platform.To solve this issue,in this work,we propose an intelligent Task Offloading Algorithm(iTOA)for UAV edge computing network.Compared with existing methods,iTOA is able to perceive the network’s environment intelligently to decide the offloading action based on deep Monte Calor Tree Search(MCTS),the core algorithm of Alpha Go.MCTS will simulate the offloading decision trajectories to acquire the best decision by maximizing the reward,such as lowest latency or power consumption.To accelerate the search convergence of MCTS,we also proposed a splitting Deep Neural Network(sDNN)to supply the prior probability for MCTS.The sDNN is trained by a self-supervised learning manager.Here,the training data set is obtained from iTOA itself as its own teacher.Compared with game theory and greedy search-based methods,the proposed iTOA improves service latency performance by 33%and 60%,respectively.展开更多
文摘目的:观察麝香保心丸对老年急性冠脉综合征(ACS)心率变异(HRV)的影响,评价其心肌保护作用。方法:90名老年ACS患者,随机分为麝香保心丸及常规治疗组,分别于用药前、后记录24 h Holter,观察心绞痛症状及心电图变化,进行HRV分析。结果:麝香保心丸组心绞痛明显改善(P<0.05)。SDNN、SDANN、rMSSD、PNN 均升高,极低频(VLF)、低频(LF)、LF/HF均降低(P均<0.05)。结论:麝香保心丸能有效改善ACS患者心肌缺血症状及心率变异。
文摘为在低复杂度约束条件下提升电磁信号调制识别的性能,提出了一种基于稀疏深度神经网络(Sparse Deep Neural Network,SDNN)的电磁信号调制识别方法。首先,通过提取电磁信号同相和正交两路数据绘制出信号的星座图,作为信号的浅层特征表达;然后,基于星座图中各信号点密度大小对星座图进行上色,增强星座图中信号特征;最后,通过SDNN对增强后的星座图进行识别分类。实验结果表明,SDNN模型选取合适的剪枝率后,能够有效降低模型存储规模和计算量,其中模型参数压缩了72%,浮点运算量压缩了45%,与原模型97%的综合识别率相比,稀疏化处理后模型的综合识别率为96.8%,在小幅度识别精度损失范围内大幅降低了模型复杂度。
基金the Artificial Intelligence Key Laboratory of Sichuan Province(Nos.2019RYJ05)National Natural Science Foundation of China(Nos.61971107).
文摘Unmanned Aerial Vehicle(UAV)has emerged as a promising technology for the support of human activities,such as target tracking,disaster rescue,and surveillance.However,these tasks require a large computation load of image or video processing,which imposes enormous pressure on the UAV computation platform.To solve this issue,in this work,we propose an intelligent Task Offloading Algorithm(iTOA)for UAV edge computing network.Compared with existing methods,iTOA is able to perceive the network’s environment intelligently to decide the offloading action based on deep Monte Calor Tree Search(MCTS),the core algorithm of Alpha Go.MCTS will simulate the offloading decision trajectories to acquire the best decision by maximizing the reward,such as lowest latency or power consumption.To accelerate the search convergence of MCTS,we also proposed a splitting Deep Neural Network(sDNN)to supply the prior probability for MCTS.The sDNN is trained by a self-supervised learning manager.Here,the training data set is obtained from iTOA itself as its own teacher.Compared with game theory and greedy search-based methods,the proposed iTOA improves service latency performance by 33%and 60%,respectively.
