现有安全稳定控制系统(简称稳控系统)的可靠性评估方法本质上属于静态建模,由于未能体现系统内各装置老化和检修等动态过程,在一定程度上影响了评估结果的准确性。为此,文中提出一种基于马尔可夫链蒙特卡洛(Markov chain Monte Carlo,MC...现有安全稳定控制系统(简称稳控系统)的可靠性评估方法本质上属于静态建模,由于未能体现系统内各装置老化和检修等动态过程,在一定程度上影响了评估结果的准确性。为此,文中提出一种基于马尔可夫链蒙特卡洛(Markov chain Monte Carlo,MCMC)的稳控系统动态可靠性评估方法。首先针对失效过程,构建四状态非齐次马尔可夫模型来模拟装置老化过程,并给出各状态评判方法;其次针对修复过程,分析不同检修策略对装置状态转移的影响以体现状态检修的差异性;最后考虑稳控装置状态转移过程的时序或条件相关性,对稳控系统可靠性进行动态建模。以实际稳控系统为例,仿真对比不同检修策略下的可靠性,并对模型参数进行灵敏度分析。评估结果表明,该方法可以求解稳控系统的时变可用度,用于指导稳控装置现场合理检修。展开更多
Statistical biases may be introduced by imprecisely quantifying background radiation reference levels. It is, therefore, imperative to devise a simple, adaptable approach for precisely describing the reference backgro...Statistical biases may be introduced by imprecisely quantifying background radiation reference levels. It is, therefore, imperative to devise a simple, adaptable approach for precisely describing the reference background levels of naturally occurring radionuclides (NOR) in mining sites. As a substitute statistical method, we suggest using Bayesian modeling in this work to examine the spatial distribution of NOR. For naturally occurring gamma-induced radionuclides like 232Th, 40K, and 238U, statistical parameters are inferred using the Markov Chain Monte Carlo (MCMC) method. After obtaining an accurate subsample using bootstrapping, we exclude any possible outliers that fall outside of the Highest Density Interval (HDI). We use MCMC to build a Bayesian model with the resampled data and make predictions about the posterior distribution of radionuclides produced by gamma irradiation. This method offers a strong and dependable way to describe NOR reference background values, which is important for managing and evaluating radiation risks in mining contexts.展开更多
为提高水文模型参数识别的可靠性,融合自回归模型与马尔可夫链-蒙特卡洛方法(auto regressive model based modified Markov Chain-Monte Carlo,AR-MCMC),利用自回归模型刻画残差序列的自相关性,修正MCMC方法中的残差协方差矩阵。通过...为提高水文模型参数识别的可靠性,融合自回归模型与马尔可夫链-蒙特卡洛方法(auto regressive model based modified Markov Chain-Monte Carlo,AR-MCMC),利用自回归模型刻画残差序列的自相关性,修正MCMC方法中的残差协方差矩阵。通过新疆提孜那甫河流域融雪径流模型(SRM)的案例分析发现:融雪径流模拟的残差序列具有显著的自相关性;修正残差协方差矩阵后,边缘似然值更大;综合考虑多项评价指标,AR-MCMC方法在识别期与验证期推求的预测区间均优于MCMC方法;对比2种方法在识别期与验证期的纳什系数,采用AR-MCMC方法依次为0.86、0.89,而采用MCMC方法依次为0.84、0.87,即AR-MCMC方法获取的模型拟合效果更好。分析结果表明,相对于传统的MCMC方法,AR-MCMC方法能够更好地对研究区融雪径流过程进行模拟预测。展开更多
文摘现有安全稳定控制系统(简称稳控系统)的可靠性评估方法本质上属于静态建模,由于未能体现系统内各装置老化和检修等动态过程,在一定程度上影响了评估结果的准确性。为此,文中提出一种基于马尔可夫链蒙特卡洛(Markov chain Monte Carlo,MCMC)的稳控系统动态可靠性评估方法。首先针对失效过程,构建四状态非齐次马尔可夫模型来模拟装置老化过程,并给出各状态评判方法;其次针对修复过程,分析不同检修策略对装置状态转移的影响以体现状态检修的差异性;最后考虑稳控装置状态转移过程的时序或条件相关性,对稳控系统可靠性进行动态建模。以实际稳控系统为例,仿真对比不同检修策略下的可靠性,并对模型参数进行灵敏度分析。评估结果表明,该方法可以求解稳控系统的时变可用度,用于指导稳控装置现场合理检修。
文摘Statistical biases may be introduced by imprecisely quantifying background radiation reference levels. It is, therefore, imperative to devise a simple, adaptable approach for precisely describing the reference background levels of naturally occurring radionuclides (NOR) in mining sites. As a substitute statistical method, we suggest using Bayesian modeling in this work to examine the spatial distribution of NOR. For naturally occurring gamma-induced radionuclides like 232Th, 40K, and 238U, statistical parameters are inferred using the Markov Chain Monte Carlo (MCMC) method. After obtaining an accurate subsample using bootstrapping, we exclude any possible outliers that fall outside of the Highest Density Interval (HDI). We use MCMC to build a Bayesian model with the resampled data and make predictions about the posterior distribution of radionuclides produced by gamma irradiation. This method offers a strong and dependable way to describe NOR reference background values, which is important for managing and evaluating radiation risks in mining contexts.
文摘为提高水文模型参数识别的可靠性,融合自回归模型与马尔可夫链-蒙特卡洛方法(auto regressive model based modified Markov Chain-Monte Carlo,AR-MCMC),利用自回归模型刻画残差序列的自相关性,修正MCMC方法中的残差协方差矩阵。通过新疆提孜那甫河流域融雪径流模型(SRM)的案例分析发现:融雪径流模拟的残差序列具有显著的自相关性;修正残差协方差矩阵后,边缘似然值更大;综合考虑多项评价指标,AR-MCMC方法在识别期与验证期推求的预测区间均优于MCMC方法;对比2种方法在识别期与验证期的纳什系数,采用AR-MCMC方法依次为0.86、0.89,而采用MCMC方法依次为0.84、0.87,即AR-MCMC方法获取的模型拟合效果更好。分析结果表明,相对于传统的MCMC方法,AR-MCMC方法能够更好地对研究区融雪径流过程进行模拟预测。
