Data analysis on tokamak plasmas is mainly based on various diagnostic systems,which are usually modularized and independent of each other.This leads to a large amount of data not being fully and effectively exploited...Data analysis on tokamak plasmas is mainly based on various diagnostic systems,which are usually modularized and independent of each other.This leads to a large amount of data not being fully and effectively exploited so that it is not conducive to revealing the deep physical mechanism.In this work,Bayesian probability inference with machine learning methods have been applied to the electron cyclotron emission and Thomson scattering diagnostic systems on HL-2A/2M,and the effects of integrated data analysis(IDA)on the electron temperature of HL-2A with Bayesian probability inference are demonstrated.A program is developed to infer the whole electron temperature profile with a confidence interval,and the program can be applied in online analysis.The IDA results show that the full profile of the electron temperature can be obtained and the diagnostic information is more comprehensive and abundant with IDA.The inference models for electron temperature analysis are established and the developed programs will serve as an experimental data analysis tool for HL-2A/2M in the near future.展开更多
Against the backdrop of China's High-Speed Rail(HSR)network achieving global leadership in scale(48,000 km,70%worldwide)yet facing structural fragility,disaster-chain coupling,and regional imbalance,this study con...Against the backdrop of China's High-Speed Rail(HSR)network achieving global leadership in scale(48,000 km,70%worldwide)yet facing structural fragility,disaster-chain coupling,and regional imbalance,this study constructs a"structural resilience-propagation dynamics-intelligent governance"trinity framework.By integrating multi-layer coupled network theory,this paper develop:(1)A spatiotemporal-weighted betweenness centrality algorithm dynamically identifying critical nodes using Beidou data;(2)A multi-layer SEIR model quantifying epidemic-economic dual diffusion with linear threshold mechanisms;(3)A reinforcement learning framework optimizing anti-siphoning policies.Key findings reveal:Wuhan hub fault recovery time reduced by 50%through backup topology,Beijing West Station achieved 89%epidemic blocking via dual-channel quarantine,and the Chengdu-Chongqing regional Gini coefficient decreased 29%via frequency regulation.The paradigm shift from static planning to real-time adaptive governance offers a"China solution"for global railway resilience,evidenced by magnetic levitation standards(e.g.,18%energy savings in Saudi NEOM line)and multi-disaster defense systems(35%faster ASEAN flood recovery).展开更多
基金supported by the National Magnetic Confinement Fusion Energy Research and Development Program of China(Nos.2019YFE03090100,2019YFE03040004)the National Science Foundation for Young Scientists of China(No.12005052)。
文摘Data analysis on tokamak plasmas is mainly based on various diagnostic systems,which are usually modularized and independent of each other.This leads to a large amount of data not being fully and effectively exploited so that it is not conducive to revealing the deep physical mechanism.In this work,Bayesian probability inference with machine learning methods have been applied to the electron cyclotron emission and Thomson scattering diagnostic systems on HL-2A/2M,and the effects of integrated data analysis(IDA)on the electron temperature of HL-2A with Bayesian probability inference are demonstrated.A program is developed to infer the whole electron temperature profile with a confidence interval,and the program can be applied in online analysis.The IDA results show that the full profile of the electron temperature can be obtained and the diagnostic information is more comprehensive and abundant with IDA.The inference models for electron temperature analysis are established and the developed programs will serve as an experimental data analysis tool for HL-2A/2M in the near future.
文摘Against the backdrop of China's High-Speed Rail(HSR)network achieving global leadership in scale(48,000 km,70%worldwide)yet facing structural fragility,disaster-chain coupling,and regional imbalance,this study constructs a"structural resilience-propagation dynamics-intelligent governance"trinity framework.By integrating multi-layer coupled network theory,this paper develop:(1)A spatiotemporal-weighted betweenness centrality algorithm dynamically identifying critical nodes using Beidou data;(2)A multi-layer SEIR model quantifying epidemic-economic dual diffusion with linear threshold mechanisms;(3)A reinforcement learning framework optimizing anti-siphoning policies.Key findings reveal:Wuhan hub fault recovery time reduced by 50%through backup topology,Beijing West Station achieved 89%epidemic blocking via dual-channel quarantine,and the Chengdu-Chongqing regional Gini coefficient decreased 29%via frequency regulation.The paradigm shift from static planning to real-time adaptive governance offers a"China solution"for global railway resilience,evidenced by magnetic levitation standards(e.g.,18%energy savings in Saudi NEOM line)and multi-disaster defense systems(35%faster ASEAN flood recovery).
