The differential equations having delays take paramount interest in the research community due to their fundamental role to interpret and analyze the mathematical models arising in biological studies.This study deals ...The differential equations having delays take paramount interest in the research community due to their fundamental role to interpret and analyze the mathematical models arising in biological studies.This study deals with the exploitation of knack of artificial intelligence-based computing paradigm for numerical treatment of the functional delay differential systems that portray the dynamics of the nonlinear influenza-A epidemic model(IA-EM)by implementation of neural network backpropagation with Levenberg-Marquardt scheme(NNBLMS).The nonlinear IA-EM represented four classes of the population dynamics including susceptible,exposed,infectious and recovered individuals.The referenced datasets for NNBLMS are assembled by employing the Adams method for sufficient large number of scenarios of nonlinear IA-EM through the variation in the infection,turnover,disease associated death and recovery rates.The arbitrary selection of training,testing as well as validation samples of dataset are utilizing by designed NNBLMS to calculate the approximate numerical solutions of the nonlinear IA-EM develop a good agreement with the reference results.The proficiency,reliability and accuracy of the designed NNBLMS are further substantiated via exhaustive simulations-based outcomes in terms of mean square error,regression index and error histogram studies.展开更多
为解决当前城市轨道交通(简称:城轨)列车客流分析存在的检测精度不高和适用场景单一等问题,设计了一种基于异质集成学习方法的城轨列车智能客流分析系统。该系统基于云边协同架构,采用分组Voting方法,将YOLOv5s(You Only Look Once v5s)...为解决当前城市轨道交通(简称:城轨)列车客流分析存在的检测精度不高和适用场景单一等问题,设计了一种基于异质集成学习方法的城轨列车智能客流分析系统。该系统基于云边协同架构,采用分组Voting方法,将YOLOv5s(You Only Look Once v5s)、FCHD(Fully Convolutional Head Detector)、CSRNet(Network for Congested Scene Recognition)模型作为基模型进行集成,最终实现客流统计、拥挤度分析和辅助清客等功能。利用北京城轨某线路列车的监控图像数据进行实验,结果表明,与其他各基模型相比,该系统采用的模型检测效果更佳,有效提升了检测精度,丰富了可适用的检测场景。展开更多
Based on principal component analysis, the rules of clayey soil's behaviors affected by varied indices were studied. It was discovered that the common method of the single liquidity index IL used to determine the ...Based on principal component analysis, the rules of clayey soil's behaviors affected by varied indices were studied. It was discovered that the common method of the single liquidity index IL used to determine the consistency of silt-clay or silt-loam was not rational. It was more rational that the liquidity index IL combined with the void ratio e characterized the behavior of silt-clay. Similarly the index of e depicted the nature of sandy loam more rationally than IL. The method of predicting the pile shafted resistance by the two indices of e and IL, which was more accurate, was obtained by the methodology of back propagation (BP) artificial neural networks combined with principal component analysis. It was also observed that the pile shaft resistance increased with the increase of depth within a critical affect-depth ranging from 20 to 30 m, and the harder the clayey soil consistency was, the shallower the critical depth was.展开更多
文摘The differential equations having delays take paramount interest in the research community due to their fundamental role to interpret and analyze the mathematical models arising in biological studies.This study deals with the exploitation of knack of artificial intelligence-based computing paradigm for numerical treatment of the functional delay differential systems that portray the dynamics of the nonlinear influenza-A epidemic model(IA-EM)by implementation of neural network backpropagation with Levenberg-Marquardt scheme(NNBLMS).The nonlinear IA-EM represented four classes of the population dynamics including susceptible,exposed,infectious and recovered individuals.The referenced datasets for NNBLMS are assembled by employing the Adams method for sufficient large number of scenarios of nonlinear IA-EM through the variation in the infection,turnover,disease associated death and recovery rates.The arbitrary selection of training,testing as well as validation samples of dataset are utilizing by designed NNBLMS to calculate the approximate numerical solutions of the nonlinear IA-EM develop a good agreement with the reference results.The proficiency,reliability and accuracy of the designed NNBLMS are further substantiated via exhaustive simulations-based outcomes in terms of mean square error,regression index and error histogram studies.
文摘为解决当前城市轨道交通(简称:城轨)列车客流分析存在的检测精度不高和适用场景单一等问题,设计了一种基于异质集成学习方法的城轨列车智能客流分析系统。该系统基于云边协同架构,采用分组Voting方法,将YOLOv5s(You Only Look Once v5s)、FCHD(Fully Convolutional Head Detector)、CSRNet(Network for Congested Scene Recognition)模型作为基模型进行集成,最终实现客流统计、拥挤度分析和辅助清客等功能。利用北京城轨某线路列车的监控图像数据进行实验,结果表明,与其他各基模型相比,该系统采用的模型检测效果更佳,有效提升了检测精度,丰富了可适用的检测场景。
文摘Based on principal component analysis, the rules of clayey soil's behaviors affected by varied indices were studied. It was discovered that the common method of the single liquidity index IL used to determine the consistency of silt-clay or silt-loam was not rational. It was more rational that the liquidity index IL combined with the void ratio e characterized the behavior of silt-clay. Similarly the index of e depicted the nature of sandy loam more rationally than IL. The method of predicting the pile shafted resistance by the two indices of e and IL, which was more accurate, was obtained by the methodology of back propagation (BP) artificial neural networks combined with principal component analysis. It was also observed that the pile shaft resistance increased with the increase of depth within a critical affect-depth ranging from 20 to 30 m, and the harder the clayey soil consistency was, the shallower the critical depth was.
