光伏发电系统的输出功率具有明显的非线性特性,易受外界环境因素的影响,现有的最大功率点追踪(maximum power point tracking,MPPT)技术的追踪精度和追踪时间都有待进一步提高。对此,提出了一种结合了整体分布(overall distribution,OD...光伏发电系统的输出功率具有明显的非线性特性,易受外界环境因素的影响,现有的最大功率点追踪(maximum power point tracking,MPPT)技术的追踪精度和追踪时间都有待进一步提高。对此,提出了一种结合了整体分布(overall distribution,OD)算法和粒子群优化(particle swarm optimization,PSO)算法的MPPT算法(改进ODPSO-MPPT算法),以解决全局最大功率点追踪问题。对传统PSO-MPPT算法的速度分量加以约束,使其避免陷入局部最优并能准确地追踪最大功率,同时优化传统PSO算法的惯性权重并融合OD算法,使其能在更短时间内找到全局最大功率点。最后,搭建光伏发电系统仿真模型对所提算法进行验证。仿真结果表明,改进ODPSO-MPPT算法在标准测试条件和局部遮阴条件下均能更快速准确地追踪到全局最大功率。展开更多
Community-acquired pneumonia(CAP)is considered a sort of pneumonia developed outside hospitals and clinics.To diagnose community-acquired pneumonia(CAP)more efficiently,we proposed a novel neural network model.We intr...Community-acquired pneumonia(CAP)is considered a sort of pneumonia developed outside hospitals and clinics.To diagnose community-acquired pneumonia(CAP)more efficiently,we proposed a novel neural network model.We introduce the 2-dimensional wavelet entropy(2d-WE)layer and an adaptive chaotic particle swarm optimization(ACP)algorithm to train the feed-forward neural network.The ACP uses adaptive inertia weight factor(AIWF)and Rossler attractor(RA)to improve the performance of standard particle swarm optimization.The final combined model is named WE-layer ACP-based network(WACPN),which attains a sensitivity of 91.87±1.37%,a specificity of 90.70±1.19%,a precision of 91.01±1.12%,an accuracy of 91.29±1.09%,F1 score of 91.43±1.09%,an MCC of 82.59±2.19%,and an FMI of 91.44±1.09%.The AUC of this WACPN model is 0.9577.We find that the maximum deposition level chosen as four can obtain the best result.Experiments demonstrate the effectiveness of both AIWF and RA.Finally,this proposed WACPN is efficient in diagnosing CAP and superior to six state-of-the-art models.Our model will be distributed to the cloud computing environment.展开更多
文摘光伏发电系统的输出功率具有明显的非线性特性,易受外界环境因素的影响,现有的最大功率点追踪(maximum power point tracking,MPPT)技术的追踪精度和追踪时间都有待进一步提高。对此,提出了一种结合了整体分布(overall distribution,OD)算法和粒子群优化(particle swarm optimization,PSO)算法的MPPT算法(改进ODPSO-MPPT算法),以解决全局最大功率点追踪问题。对传统PSO-MPPT算法的速度分量加以约束,使其避免陷入局部最优并能准确地追踪最大功率,同时优化传统PSO算法的惯性权重并融合OD算法,使其能在更短时间内找到全局最大功率点。最后,搭建光伏发电系统仿真模型对所提算法进行验证。仿真结果表明,改进ODPSO-MPPT算法在标准测试条件和局部遮阴条件下均能更快速准确地追踪到全局最大功率。
基金This paper is partially supported by Medical Research Council Confidence in Concept Award,UK(MC_PC_17171)Royal Society International Exchanges Cost Share Award,UK(RP202G0230)+5 种基金British Heart Foundation Accelerator Award,UK(AA/18/3/34220)Hope Foundation for Cancer Research,UK(RM60G0680)Global Challenges Research Fund(GCRF),UK(P202PF11)Sino-UK Industrial Fund,UK(RP202G0289)LIAS Pioneering Partnerships award,UK(P202ED10)Data Science Enhancement Fund,UK(P202RE237).
文摘Community-acquired pneumonia(CAP)is considered a sort of pneumonia developed outside hospitals and clinics.To diagnose community-acquired pneumonia(CAP)more efficiently,we proposed a novel neural network model.We introduce the 2-dimensional wavelet entropy(2d-WE)layer and an adaptive chaotic particle swarm optimization(ACP)algorithm to train the feed-forward neural network.The ACP uses adaptive inertia weight factor(AIWF)and Rossler attractor(RA)to improve the performance of standard particle swarm optimization.The final combined model is named WE-layer ACP-based network(WACPN),which attains a sensitivity of 91.87±1.37%,a specificity of 90.70±1.19%,a precision of 91.01±1.12%,an accuracy of 91.29±1.09%,F1 score of 91.43±1.09%,an MCC of 82.59±2.19%,and an FMI of 91.44±1.09%.The AUC of this WACPN model is 0.9577.We find that the maximum deposition level chosen as four can obtain the best result.Experiments demonstrate the effectiveness of both AIWF and RA.Finally,this proposed WACPN is efficient in diagnosing CAP and superior to six state-of-the-art models.Our model will be distributed to the cloud computing environment.
