Most earth-dam failures are mainly due to seepage,and an accurate assessment of the permeability coefficient provides an indication to avoid a disaster.Parametric uncertainties are encountered in the seepage analysis,...Most earth-dam failures are mainly due to seepage,and an accurate assessment of the permeability coefficient provides an indication to avoid a disaster.Parametric uncertainties are encountered in the seepage analysis,and may be reduced by an inverse procedure that calibrates the simulation results to observations on the real system being simulated.This work proposes an adaptive Bayesian inversion method solved using artificial neural network(ANN)based Markov Chain Monte Carlo simulation.The optimized surrogate model achieves a coefficient of determination at 0.98 by ANN with 247 samples,whereby the computational workload can be greatly reduced.It is also significant to balance the accuracy and efficiency of the ANN model by adaptively updating the sample database.The enrichment samples are obtained from the posterior distribution after iteration,which allows a more accurate and rapid manner to the target posterior.The method was then applied to the hydraulic analysis of an earth dam.After calibrating the global permeability coefficient of the earth dam with the pore water pressure at the downstream unsaturated location,it was validated by the pore water pressure monitoring values at the upstream saturated location.In addition,the uncertainty in the permeability coefficient was reduced,from 0.5 to 0.05.It is shown that the provision of adequate prior information is valuable for improving the efficiency of the Bayesian inversion.展开更多
为有效预防矿山重大事故的发生,降低矿山事故风险,以故障树分析法(Fault Tree Analysis,FTA)为理论基础,揭示矿山重大风险节点的耦合机制,然后建立通用的矿山重大风险评估动态贝叶斯网络(Dynamic Bayesian Network,DBN)。从人、机、环...为有效预防矿山重大事故的发生,降低矿山事故风险,以故障树分析法(Fault Tree Analysis,FTA)为理论基础,揭示矿山重大风险节点的耦合机制,然后建立通用的矿山重大风险评估动态贝叶斯网络(Dynamic Bayesian Network,DBN)。从人、机、环、管四方面筛选影响因素,构建故障树模型,涵盖45个基本事件。通过专家评价语言模糊转化及改进的相似聚合法确定DBN模型参数,以7级语言量表收集6位不同权重专家意见,基于经处理得到的各基本事件先验概率,构建DBN模型进行正向推理。将时间划分为九个时间片,在无证据输入下,发现部分节点风险概率随时间上升,矿山风险总体呈上升趋势。反向诊断假设矿山典型重大风险预测风险状态概率100%,计算节点后验概率及变异风险(Risk of Variability,ROV)值并排序,确定人员技术水平差、文化水平低等为主要因素,产品储存量过多、车辆违规操作等为关键因素。最后以某矿山为例开展分析验证工作。研究表明:所构建模型能够基于输入证据准确预测出矿山重大风险概率的变化;通过分析新疆某矿山,成功对关键风险因素进行识别,并对这些风险因素进行排序,从而识别出系统的薄弱环节,并实现风险监控,决策者因此可以迅速做出反应,减少事故风险。展开更多
Multi-agent systems often require good interoperability in the process of completing their assigned tasks.This paper first models the static structure and dynamic behavior of multiagent systems based on layered weight...Multi-agent systems often require good interoperability in the process of completing their assigned tasks.This paper first models the static structure and dynamic behavior of multiagent systems based on layered weighted scale-free community network and susceptible-infected-recovered(SIR)model.To solve the problem of difficulty in describing the changes in the structure and collaboration mode of the system under external factors,a two-dimensional Monte Carlo method and an improved dynamic Bayesian network are used to simulate the impact of external environmental factors on multi-agent systems.A collaborative information flow path optimization algorithm for agents under environmental factors is designed based on the Dijkstra algorithm.A method for evaluating system interoperability is designed based on simulation experiments,providing reference for the construction planning and optimization of organizational application of the system.Finally,the feasibility of the method is verified through case studies.展开更多
Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific ...Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts.In this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of data.Graph generative network(GGN)is used to process time-series meteorological data with complex structures.The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the model.In addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval prediction.With the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale data.The PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.展开更多
基金Project(202006430012)supported by the China Scholarship Council。
文摘Most earth-dam failures are mainly due to seepage,and an accurate assessment of the permeability coefficient provides an indication to avoid a disaster.Parametric uncertainties are encountered in the seepage analysis,and may be reduced by an inverse procedure that calibrates the simulation results to observations on the real system being simulated.This work proposes an adaptive Bayesian inversion method solved using artificial neural network(ANN)based Markov Chain Monte Carlo simulation.The optimized surrogate model achieves a coefficient of determination at 0.98 by ANN with 247 samples,whereby the computational workload can be greatly reduced.It is also significant to balance the accuracy and efficiency of the ANN model by adaptively updating the sample database.The enrichment samples are obtained from the posterior distribution after iteration,which allows a more accurate and rapid manner to the target posterior.The method was then applied to the hydraulic analysis of an earth dam.After calibrating the global permeability coefficient of the earth dam with the pore water pressure at the downstream unsaturated location,it was validated by the pore water pressure monitoring values at the upstream saturated location.In addition,the uncertainty in the permeability coefficient was reduced,from 0.5 to 0.05.It is shown that the provision of adequate prior information is valuable for improving the efficiency of the Bayesian inversion.
文摘为有效预防矿山重大事故的发生,降低矿山事故风险,以故障树分析法(Fault Tree Analysis,FTA)为理论基础,揭示矿山重大风险节点的耦合机制,然后建立通用的矿山重大风险评估动态贝叶斯网络(Dynamic Bayesian Network,DBN)。从人、机、环、管四方面筛选影响因素,构建故障树模型,涵盖45个基本事件。通过专家评价语言模糊转化及改进的相似聚合法确定DBN模型参数,以7级语言量表收集6位不同权重专家意见,基于经处理得到的各基本事件先验概率,构建DBN模型进行正向推理。将时间划分为九个时间片,在无证据输入下,发现部分节点风险概率随时间上升,矿山风险总体呈上升趋势。反向诊断假设矿山典型重大风险预测风险状态概率100%,计算节点后验概率及变异风险(Risk of Variability,ROV)值并排序,确定人员技术水平差、文化水平低等为主要因素,产品储存量过多、车辆违规操作等为关键因素。最后以某矿山为例开展分析验证工作。研究表明:所构建模型能够基于输入证据准确预测出矿山重大风险概率的变化;通过分析新疆某矿山,成功对关键风险因素进行识别,并对这些风险因素进行排序,从而识别出系统的薄弱环节,并实现风险监控,决策者因此可以迅速做出反应,减少事故风险。
基金supported by the Key R&D Projects in Jiangsu Province(BE2021729)the Key Primary Research Project of Primary Strengthening Program(KYZYJKKCJC23001).
文摘Multi-agent systems often require good interoperability in the process of completing their assigned tasks.This paper first models the static structure and dynamic behavior of multiagent systems based on layered weighted scale-free community network and susceptible-infected-recovered(SIR)model.To solve the problem of difficulty in describing the changes in the structure and collaboration mode of the system under external factors,a two-dimensional Monte Carlo method and an improved dynamic Bayesian network are used to simulate the impact of external environmental factors on multi-agent systems.A collaborative information flow path optimization algorithm for agents under environmental factors is designed based on the Dijkstra algorithm.A method for evaluating system interoperability is designed based on simulation experiments,providing reference for the construction planning and optimization of organizational application of the system.Finally,the feasibility of the method is verified through case studies.
基金Project(2020YFC2008605)supported by the National Key Research and Development Project of ChinaProject(52072412)supported by the National Natural Science Foundation of ChinaProject(2021JJ30359)supported by the Natural Science Foundation of Hunan Province,China。
文摘Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts.In this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of data.Graph generative network(GGN)is used to process time-series meteorological data with complex structures.The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the model.In addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval prediction.With the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale data.The PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.
