We investigated the application of Causal Bayesian Networks (CBNs) to large data sets in order to predict user intent via internet search prediction. Here, sample data are taken from search engine logs (Excite, Altavi...We investigated the application of Causal Bayesian Networks (CBNs) to large data sets in order to predict user intent via internet search prediction. Here, sample data are taken from search engine logs (Excite, Altavista, and Alltheweb). These logs are parsed and sorted in order to create a data structure that was used to build a CBN. This network is used to predict the next term or terms that the user may be about to search (type). We looked at the application of CBNs, compared with Naive Bays and Bays Net classifiers on very large datasets. To simulate our proposed results, we took a small sample of search data logs to predict intentional query typing. Additionally, problems that arise with the use of such a data structure are addressed individually along with the solutions used and their prediction accuracy and sensitivity.展开更多
为揭示养老机构安全事故的致因机制,基于2015—2024年89起司法案例、事故报告和理赔案例,构建融合故障树分析(Fault Tree Analysis,FTA)与贝叶斯网络(Bayesian Network,BN)的致因链模型,系统识别并量化关键风险因素及其复杂交互关系。...为揭示养老机构安全事故的致因机制,基于2015—2024年89起司法案例、事故报告和理赔案例,构建融合故障树分析(Fault Tree Analysis,FTA)与贝叶斯网络(Bayesian Network,BN)的致因链模型,系统识别并量化关键风险因素及其复杂交互关系。结果表明,养老机构安全事故致因链呈现出多路径耦合与节点聚集的结构特征;少数高敏感性因素如安全意识淡薄、护理人员培训不足在不同事故类型中反复出现,成为事故链条中的关键驱动要素;不同事故类型在致因链上既呈现出独特的演化路径,又在关键致因节点上存在显著共性特征。展开更多
通过系统理论过程分析(system-theoretic process analysis,STPA)方法识别航空事故危险因素,属于定性分析过程,无法定量地评估各因素对事故的影响程度。针对上述问题,提出STPA与贝叶斯网络(Bayesian network,BN)结合的定性与定量分析方...通过系统理论过程分析(system-theoretic process analysis,STPA)方法识别航空事故危险因素,属于定性分析过程,无法定量地评估各因素对事故的影响程度。针对上述问题,提出STPA与贝叶斯网络(Bayesian network,BN)结合的定性与定量分析方法。以捷蓝航空A320飞机襟翼事故为例,通过STPA方法构建了襟翼控制系统的控制结构模型并全面地分析了潜在的不安全控制行为及相关致因场景。随后将STPA定性分析结果转化为可定量分析的贝叶斯网络模型,从而识别出事故中的内部交互逻辑以及影响度较高的致因因素,提出全面的安全性建议。分析结果表明:导致事故的主要因素为液压源故障,而动力传输组件(power transmission unit,PTU)故障和液压管路泄漏是导致液压源失效的主要原因,关键重要度分别为0.688和0.299。展开更多
文摘We investigated the application of Causal Bayesian Networks (CBNs) to large data sets in order to predict user intent via internet search prediction. Here, sample data are taken from search engine logs (Excite, Altavista, and Alltheweb). These logs are parsed and sorted in order to create a data structure that was used to build a CBN. This network is used to predict the next term or terms that the user may be about to search (type). We looked at the application of CBNs, compared with Naive Bays and Bays Net classifiers on very large datasets. To simulate our proposed results, we took a small sample of search data logs to predict intentional query typing. Additionally, problems that arise with the use of such a data structure are addressed individually along with the solutions used and their prediction accuracy and sensitivity.
文摘为揭示养老机构安全事故的致因机制,基于2015—2024年89起司法案例、事故报告和理赔案例,构建融合故障树分析(Fault Tree Analysis,FTA)与贝叶斯网络(Bayesian Network,BN)的致因链模型,系统识别并量化关键风险因素及其复杂交互关系。结果表明,养老机构安全事故致因链呈现出多路径耦合与节点聚集的结构特征;少数高敏感性因素如安全意识淡薄、护理人员培训不足在不同事故类型中反复出现,成为事故链条中的关键驱动要素;不同事故类型在致因链上既呈现出独特的演化路径,又在关键致因节点上存在显著共性特征。
