高层建筑电气火灾隐患因子定量分析方法研究

为了对高层建筑电气火灾隐患排查进行定量化分析,采用解释结构模型构建高层建筑电气火灾隐患因子的复杂逻辑模型;在利用德尔菲法确定隐患因子初始概率的基础上,经过交叉影响分析法求解交叉影响概率矩阵;引入马尔科夫链修正各隐患因子交叉影响后的概率,得到火灾隐患因子的稳态概率矩阵。研究结果表明:各隐患因子的马尔科夫链校正概率比德尔菲法测定的初始概率更加准确,该计算排序结果可明确高层电气火灾隐患排查和管控的重点。     

基于HMM和DT的无人机异常检测方法

为了实时检测无人机异常状态,提出基于隐马尔可夫模型(Hidden Markov Model,HMM)和决策树(Decision Tree,DT)的无人机异常检测方法(HMMDT).首先根据异常致因将无人机异常分为干扰异常和硬件异常;然后结合HMM和DT建立无人机异常检测模型,定义无人机异常度衡量异常状态的严重程度,确定...     

Optimization of gas detector placement considering scenario probability and detector reliability in oil refinery installation

Gas detection system is a critical layer of protection in process safety. Leak scenario probability and detector reliability are two key factors in the optimization of gas detector placement. However, they are easily neglected in previous studies, which may lead to an inaccurate evaluation of the optimization solutions. In this study, a stochastic programming (SP) optimization method is proposed considering these two factors. In order to quantitatively represent the probability of leak scenarios, a complete accident scenario set (CASS) is built combining leak sources and wind fields. Then, the computational fluid dynamics (CFD) method is adopted for consequence modeling of gas dispersion. The Markov model is developed to predict the detector reliability. With the objective of minimal cumulative detection time (MCDT), the SP formulation considering scenario probability and detector reliability (MCDT-SPR) is proposed. By introducing the particle swarm optimization (PSO) algorithm, the optimization formulations can be solved. A case study is investigated on a diesel hydrogenation refining unit. Results validate this approach is promising to improve the detection efficiency. This method is more practical and matching with the actual industrial environment, where the leak scenarios and the detector reliability can change dynamically in real process setting.     

用Markov模型预测长江水质

由于长江水质的污染程度日益严重,为了说明治理长江对长江水质进行了简单的评价,保护长江迫在眉睫,首先,根据长江流域的17个观测站近两年多的水质检测数据统计,说明了近两年多来长江的防污治理工作有一定的效果。然后,根据1995~2004年长江流域水质的数据报告,考虑各类水之间的相互转化,构造了马尔柯夫(Markov)转移矩阵,建立了马尔柯夫预测模型,通过已有的观测数据验证了该模型的正确性及有效性。运用该马尔柯夫模型预测未来10年水质的变化趋势,即Ⅰ、Ⅱ、Ⅲ、Ⅳ类水逐年减少,而Ⅴ、劣Ⅴ类水逐年增加。到2014年,长江的第Ⅰ类水只有0.4 059%,劣Ⅴ类水达到26.2 714%,不可饮用水（即第Ⅳ,Ⅴ,劣Ⅴ类水）将达到47.468%,为此应采取更加有效的治理措施,控制长江水质的恶化。最后,通过计算,得到了每年需要处理污水的最小百分比,才能杜绝劣Ⅴ类水,将第Ⅳ、Ⅴ类水控制在20%内,从而才能保证我们有足够的饮用水     

Optimal monitoring and control under state uncertainty: Application to lionfish management

State variables in many renewable resource management problems, such as the abundance of a fish stock, are imperfectly observed over time. In systems characterized by state uncertainty, decision makers often invest in monitoring to learn about the level of a stock. We develop a stochastic bioeconomic model of marine invasive species management under state uncertainty. The decision maker in our model simultaneously evaluates optimal investment in monitoring and population control. Using a recently-devised method for solving continuous-state Partially Observable Markov Decision Processes (POMDPs), we find that the ability to learn through monitoring can alter the role of population control in the optimal policy function, for example by reducing control intensity in favor of monitoring. Optimal monitoring depends on the management context, including in our application lionfish population structure. The rich transient dynamics of our model depend critically on the relationship between the initial conditions for information and invader abundance.     

Valuing environmental amenities through inverse optimization: Theory and case study

Amenity values of private forests are implicit but play a critical role in decision and policy making. This study presents an innovative valuation approach integrating techniques of operations research and economic theory of pricing environmental goods. A forest planning problem was inversed through altering its reward function of timber values so that the observed harvesting behavior became optimal. The discrepancy between the original and new rewards uncovered amenities values which were linked to forest attributes via hedonic models. This method was applied to a case study of the U.S. southern pine region. Depending on forest conditions, total economic values of amenities varied from zero to just under one thousand dollar per ha. At a discount rate of 3%, a typically managed forest generated on average $243 ha⁻¹ of amenities values per annum, more than twice of harvested timber values. Structural diversity and density of large pine trees were the key determinants to preserving forests for environmental amenities.     

基于灰色马尔可夫的道路交通事故预测

探讨灰色马尔可夫模型在道路交通事故中的具体应用。灰色模型适用于短期、数据量少和波动不大的预测问题,在长期预测时,数据序列拟合较差,预测精度偏低;而马尔可夫链适用于长期、数据序列随机波动大的预测问题。灰色马尔可夫模型结合了灰色GM(1,1)模型和马尔可夫理论的优点,利用灰色模型进行长期预测,再利用马尔可夫链理论进行波动状态预测,最后得到期望值。该模型克服了随机波动性数据对道路交通事故预测精度的影响,提高了灰色预测的准确度。实例结果,证明灰色马尔可夫GM(1,1)模型具有较好的应用价值,为道路交通安全管理提供了有用依据。     

远洋船员的机体功能状态与适航性研究

将远洋航行船员的内在和外在环境作为人因失效根原因和关键环节进行分析研讨,指出其对船员机体功能造成的负面影响,而产生失效行为,造成船舶事故.基于马尔可夫链原理,建立远洋船员的机体功能状态在航行链中的变化过程矩阵.根据对中远集团部分远洋船员统计数据,建立机体状态初始向量,结合过程矩阵进行仿真,结果显示船员从第5个月时,开始出现再适应期(休船期).考虑到航运业具体特点,远洋船员的航行时间以4个月为益,不要超过6个月.从船员机体状态适航性的角度,根据船员的状态向量的转换结果,分析和预测船员的适航性,对船员进行合理配置能有效提高船舶的航行安全.     

马尔科夫过程在河流综合水质预报中的应用

本文运用随机理论，建立了一个马尔科夫链综合水质预报模型。并通过实例的应用分析，证明了模型的可靠性，从而为水质预报的科学化提供了依据。     

灰色马尔柯夫模型在地表水体DO浓度预测中的应用

灰色马尔柯夫模型是一种随机预测模型，被广泛的应用于社会，经济的预测中，本文利用灰色马尔柯夫模型对某平原河段DO浓度进行了预测。经验证，灰色马尔柯夫模型适用于对地表水体DO浓度预测，并具有相当高的精度，在此基础上，作者提出了改进的建议，使模型更具有实用性和可操作性。