基于抽象故障树的化工事故预警

情景是分析事件的发生、发展及可能的后果的有效机制,然而,基于情景的预警机制或缺乏有效的模型支撑或受制于模型的局限性,实践中难以推广.抽象故障树是同类事故故障树的高层抽象,综合历史案例与专家经验,能够刻画事故的成因的机理、情景演化过程及可能的后果,能够有效支撑基于情景的预警分析.提出一种基于抽象故障树的化工事故预警方法,基于抽象映射计算事件危害度及节点重要度,将情景演化的割集模型转换为贝叶斯网络模型,采用Board法对事故危害进行风险度量和防御事件排序,实现基于情景的不同演化路径的事故风险预测及最佳应对策略推荐,实验结果显示了该方法用于事故分析预警的有效性.     

Quantitative risk assessment of railway intrusions with text mining and fuzzy Rule-Based Bow-Tie model

With the increasing traveling speed of railway transportation, rail right-of-way intrusions can cause high-consequence accidents and pose severe challenges to railway safety. Although intrusion detection technologies have been widely studied and applied, they can only support in-event inspection and post-event control. In the current complex environment, there is a critical need to analyze the causal chain of railway intrusions and mitigate safety risks before or during the risk evolution process. This paper developed a novel methodological framework on the cause-consequence model based on the text mining techniques and fuzzy bow-tie modeling to systematically investigate the railway intrusion risks. In order to mine both critical factors and their interrelationships, a lexical co-occurrence analysis was carried out on a customized corpus of intrusion accident recordings. Then structured bow-tie diagrams were developed based on the networks generated by unstructured data. To overcome the data uncertainty issue, this paper utilized cause-consequence-based probabilistic analysis and fuzzy theory to quantify the risks involving the occurrence probability of top events and outcomes in terms of expert judgements. The application of the proposed bow-tie model was demonstrated based on the case of the Hualien Derailment accident. The findings based on the bow-tie model and historical accidents in this research have systematically summarized basic events and causal chains. Ultimately, they can be utilized by researchers and practitioners both to identify the critical risk factors and enhance railway safety via proactive and reactive measures.     

基于故障树的事故分类方法

故障树的割集分析技术是判定事故原因的常用技术,然而,基于割集的技术仅能通过基本事件的组合判定事故的发生,无法分析事故演化过程的中间事件.本文针对事故分析报告描述的事故成因机理,结合文本分类和故障树分析技术,提出一种面向故障树的事故报告分类方法,实现面向事故演化路径的事故报告的因果定位,能够自动关联事故报告与故障树结构演化信息,实现借鉴专家经验的事故因果演化的精确分析.     

Performance of a Hedged Stochastic Portfolio Model in the Presence of Extreme Events

Classical methods for computing the value-at-risk(VaR) do not account for the large price variationsobserved in financial markets. The historical methodis subject to event risk and may miss some fundamentalmarket evolution relevant to VaR; thevariance/covariance method tends to underestimate thedistribution tails and Monte Carlo simulation issubject to model risk. These methods are therebyusually completed with analyses derived fromcatastrophe scenarios.We propose a special case of the extreme-valueapproach for computing the value-at-risk of a stochasticmulticurrency portfolio when alternative hedgingstrategies are considered. This approach is able tocover market conditions ranging from the usual VaRenvironment to financial crises.We implement a multistage portfolio model with anexchange rate dynamic with stochastic volatility. Theparameters are estimated by GARCH-t models. Thesimulations are used to select multicurrencyportfolios whose exchange rate risk is hedged andrebalanced each ten days, accounting for VaR. Wecompare the performances of the two most classicalinstitutional options strategies – protective puts andcovered calls – to that of holding an unhedgedportfolio in presence of extreme events.     

运用人工神经网络预测一代粘虫发生程度的研究

根据历年的积累资料,采用人工神经网络方法,对黑龙江东南农田一代粘虫发生程度进行了预测预报研究。结果表明：该方法性能良好,预测准确,可望成为植物病虫害预测预报一种新的有效辅助手段     

融合事故表征和CBR的特种设备事故预测研究

为了更好地预测特种设备事故,提出了融合事故表征和CBR（case-based reasoning）的特种设备事故预测研究。详细说明了面向全类特种设备的通用型事故表征技术,包括事故表征信息的结构模型、规范化方法和编码规则。介绍了融合事故表征和CBR的特种设备事故预测方法,其中为了更精准地检索相似案例,提出了针对不同属性类型的属性相似度计算方法、基于专家置信度的改进型AHP法、考虑时间衰减效应的案例相似度计算方法以及案例相似度阈值确定函数。通过上海某公司的汽车起重机案例验证了该方法。结果表明该方法不但能根据事故前兆信息,给出特种设备的事故原因故障树,以了解事故发展趋势,还能提供如事故特征、事故发生部位等事故表征信息的发生概率,以及应采取的预防措施。     

Integrating expert knowledge with data in Bayesian networks: Preserving data-driven expectations when the expert variables remain unobserved

When developing a causal probabilistic model, i.e. a Bayesian network (BN), it is common to incorporate expert knowledge of factors that are important for decision analysis but where historical data are unavailable or difficult to obtain. This paper focuses on the problem whereby the distribution of some continuous variable in a BN is known from data, but where we wish to explicitly model the impact of some additional expert variable (for which there is expert judgment but no data). Because the statistical outcomes are already influenced by the causes an expert might identify as variables missing from the dataset, the incentive here is to add the expert factor to the model in such a way that the distribution of the data variable is preserved when the expert factor remains unobserved. We provide a method for eliciting expert judgment that ensures the expected values of a data variable are preserved under all the known conditions. We show that it is generally neither possible, nor realistic, to preserve the variance of the data variable, but we provide a method towards determining the accuracy of expertise in terms of the extent to which the variability of the revised empirical distribution is minimised. We also describe how to incorporate the assessment of extremely rare or previously unobserved events.     

飞行事故调查辅助分析系统的开发及应用

针对飞行事故调查的实际需要,提出基于点对象构建飞行事故分析模型的思路.在该模型中,点对象可被用来描述事故的关键特征,如失事位置、飞行航迹、事故现场残骸分布等.基于该模型,将同一事故中的飞行数据、驾驶舱话音数据、雷达数据、目击数据以及现场残骸分布等信息整合起来,并在AutoCAD Map平台基础上开发了事故调查辅助分析系统.该系统通过绘制事故过程图和事故现场残骸分布图等手段,为事故调查提供直观、科学的辅助分析,从而有效地提高事故调查的水平.     

一种基于贝叶斯网络模型的交通事故预测方法

大部分的交通事故都可以预测.有效的交通事故预测能从很大程度上减少人员伤亡和交通阻塞.贝叶斯网络是目前不确定知识和推理领域最有效的理论模型之一.该文提出了一种基于贝叶斯网络模型理论的交通事故预测方法.在综合考虑交通事故成因的基础上利用领域专家知识构建网络模型,在已有的事故数据的基础上提出基于贝叶斯法则的学习算法,并通过计算变量间的条件概率来计算事故发生的可能性,达到事故预测的目的.文章的最后,通过历史数据进行仿真实验,对仿真结果和该模型的适用范围进行了分析.     

两级回归的流式大数据事件自适应预警方法

流式数据事件具有时间持续性,受采集器频率及外部环境干扰等因素影响,流式数据具有规模大、数据漂移等特征,且事件发生具有随机性特点,导致现有流式数据事件预警方法准确性很低,且在事件完全结束前无法得出判识结果,预警具有滞后性。针对这些问题提出一种两级回归的流式大数据事件自适应预警方法。该方法首先基于海量历史灾害事件,引入一级移动回归法建立权重支持域,提取事件的数据特征点,通过二级线性回归法建立事件回归模型,并对模型进行最小二乘误差分析建立事件置信域,构成预警模型;基于事件预警模型提出判识因子概念,提出流式数据事件分阶预警方法,通过判识因子自适应变换策略对事件未来发展趋势进行预估计,实现事件的实时预警。实验结果表明,该方法对比现有方法在事件预警实时性、预警效率及预警准确性等方面均具有很大优势。     

用于风险管理的贝叶斯网络学习

结合专家知识和数据进行贝叶斯网络学习.首先利用专家知识建立初始贝叶斯网络结构和参数;然后基于变量之间基本依赖关系、基本结构和依赖分析方法,对初始贝叶斯网络结构进行修正和调整,得到新的贝叶斯网络结构;最后将由专家和数据确定的参数合成为新的参数,得到融合专家知识和数据的贝叶斯网络.该方法可避免现有的贝叶斯网络学习过于依赖数据、对数据的数量和质量要求过高等问题.     

一种尾矿库风险评估的可能性处理方法

尾矿库作为一种危险源,对其进行风险评估是很必要的。首先在统计国内尾矿事故的基础上分析不同事故类型发生的概率;然后根据概率可能性转换方法,将概率分布转换为可能性分布,引入可能性理论中的可能性测度;最后考虑各个事故发生时的风险,结合专家赋值法,将其综合起来,计算出尾矿库的风险值。以此风险值为基础,建立风险评估模型。结合工程实例,验证该方法的可行性和有效性。     

主成分线性回归模型分析应用程序性能

应用程序的性能分析能够给体系架构设计者和性能优化者提供有效的参考和指导.采用主成分线性回归模型分析了SPEC CPU2006的整型程序性能.模型选取性能监测单元采样到的事件为自变量,每条指令的时钟周期数(CPI)作为因变量.模型中采用主成分分析法消除了性能事件之间的相关性.实验结果表明,模型的拟合优度在90%以上,对性能进行预测的平均相对误差为15%.模型从量化上分析了L1,L2高速缓存缺失作为影响性能的关键因素是怎样影响程序性能的.     

贝叶斯神经网络在股票时间序列预测中的应用

针对BP神经网络初始权值随机获取所导致的易陷入局部最优的问题,采用贝叶斯正则化（Bayesian Regularization,BR）算法改进传统BP神经网络模型。该算法在保证网络误差最小的前提下,通过历史数据的先验概率调整BP模型的适应度函数,使网络的泛化能力得到提升。在股票时间序列预测的实证性研究中表明,比传统BP模型在预测精度上提高42.81%。     

Robust aerodynamic shape design based on an adaptive stochastic optimization framework

Optimization techniques combined with uncertainty quantification are computationally expensive for robust aerodynamic optimization due to expensive CFD costs. Surrogate model technology can be used to improve the efficiency of robust optimization. In this paper, non-intrusive polynomial chaos method and Kriging model are used to construct a surrogate model that associate stochastic aerodynamic statistics with airfoil shapes. Then, global search algorithm is used to optimize the model to obtain optimal airfoil fast. However, optimization results always depend on the approximation accuracy of the surrogate model. Actually, it is difficult to achieve a high accuracy of the model in the whole design space. Therefore, we introduce the idea of adaptive strategy to robust aerodynamic optimization and propose an adaptive stochastic optimization framework. The surrogate model is updated adaptively by increasing training airfoils according to historical optimization results to guarantee the accuracy near the optimal design point, which can greatly reduce the number of training airfoils. The proposed method is applied to a robust aerodynamic shape optimization for drag minimization considering uncertainty of Mach number in transonic region. It can be concluded that the proposed method can obtain better optimal results more efficiently than the traditional robust optimization method and global surrogate model method.     

面向故障间格兰杰因果发现的霍克斯过程研究

现有因果关系建模方法应用于故障事件序列时,难以有效引入因果先验,使得算法结果过于稠密,同时在稀疏、时间精度低的数据上因果关系可靠性较差。将不同故障类型事件的因果关系建模为基于霍克斯过程的格兰杰因果关系,提出一种面向故障序列的格兰杰因果发现的霍克斯过程模型。将霍克斯过程拓展到离散时间域,解决低时间精度数据的建模问题,并通过构造基于贝叶斯信息准则的目标函数,保证因果结构稀疏性,进而利用基于EM算法与爬山法的迭代优化算法引入因果先验,提高模型的可靠性。实验结果表明,该方法在由不同参数生成的模拟数据上均表现突出,且在两个通信网络的真实数据集中,F1评分相比ADM4、MLE-SGL、TSSO和PCMCI算法提升15.18%以上。而通过引入根因标注和因果依赖性先验,算法的F1评分进一步提升22.43%以上,验证了引入先验的有效性。     

基于贝叶斯网络的隧道围岩失稳风险预警方法

为了对隧道围岩失稳风险做出准确判断,预防围岩失稳灾害的发生,将贝叶斯网络方法应用于隧道围岩失稳的风险预警。在系统分析隧道支护参数影响围岩稳定性的基础上,通过引入基于贝叶斯网的知识表达和相应的不确定性推理原理,构造了隧道围岩失稳风险预警专家系统;并通过工程案例验证了隧道围岩失稳风险预警专家系统好的适用性。贝叶斯网专家系统可以充分利用专家的先验知识和已有案例提供的或概率分布,可以使推理在输入数据不完备的基础上进行,能够有效地实现围岩失稳风险预警。     

A multivariate Bayesian scan statistic for early event detection and characterization

We present the multivariate Bayesian scan statistic (MBSS), a general framework for event detection and characterization in multivariate spatial time series data. MBSS integrates prior information and observations from multiple data streams in a principled Bayesian framework, computing the posterior probability of each type of event in each space-time region. MBSS learns a multivariate Gamma-Poisson model from historical data, and models the effects of each event type on each stream using expert knowledge or labeled training examples. We evaluate MBSS on various disease surveillance tasks, detecting and characterizing outbreaks injected into three streams of Pennsylvania medication sales data. We demonstrate that MBSS can be used both as a “general” event detector, with high detection power across a variety of event types, and a “specific” detector that incorporates prior knowledge of an event’s effects to achieve much higher detection power. MBSS has many other advantages over previous event detection approaches, including faster computation and easy interpretation and visualization of results, and allows faster and more accurate event detection by integrating information from the multiple streams. Most importantly, MBSS can model and differentiate between multiple event types, thus distinguishing between events requiring urgent responses and other, less relevant patterns in the data.     

CycleSense: Detecting near miss incidents in bicycle traffic from mobile motion sensors

In cities worldwide, cars cause health and traffic problems which could be partly mitigated through an increased modal share of bicycles. Many people, however, avoid cycling due to a lack of perceived safety. For city planners, addressing this is hard as they lack insights into where cyclists feel safe and where they do not. To gain such insights, we have in previous work proposed the crowdsourcing platform SimRa, which allows cyclists to record their rides and report near miss incidents via a smartphone app.In this paper, we present CycleSense, a combination of signal processing and Machine Learning techniques, which partially automates the detection of near miss incidents, thus making the reporting of near miss incidents easier. Using the SimRa data set, we evaluate CycleSense by comparing it to a baseline method used by SimRa and show that it significantly improves incident detection.     

A systemic framework for work accident analysis in judicial procedures

This article intends to demonstrate the need for changing analytical methods used in work accidents for judiciary purposes. It proposes to replace the commonly used simplistic model of unsafe act performed by workers, which has been used to invoke admission of guilt on the part of the victim of an accident, without attacking its root cause or preventing future events. The systemic framework for work accident analysis aims to shift the focus of the investigation, analyzing in a broad sense and carefully investigating circumstances by using a sociotechnical approach, especially when it comes to evaluating human performance in real work circumstances, as opposed to the evaluation based on prescribed tasks. Ergonomic modeling techniques are used for a better understanding of real work situations. We use a real accident to demonstrate how an accident analysis report performed by a legal expert witness, using a systemic framework, was able to prevail in the labor court. © 2011 Wiley Periodicals, Inc.