公共场所人员安全疏散评价方法探讨

公共场所安全疏散评价对于确保火灾中人员的生命安全具有重要意义。基于“性能化”防火设计的思想，对影响公共场所安全疏散的因素进行了分析，并对安全疏散的必要性能和评价程序进行了较为全面的探讨，其中包括烟气的评价、人员疏散能力的评价、疏散路线合理性的评价、疏散对策的评价等，目的在于通过合理的评价方法得出合理的疏散性能，为减少公共场所火灾造成的人员伤亡和财产损失提供帮助。     

高校现代教育技术应用现状及发展趋势

通过对高校现代教育技术应用现状的分析,阐述了高校现代教育技术应用的发展趋势。     

新形势下关于高校学生管理模式改革的思考

在新形势下,我国高校发生了一系列重大变化,学生管理模式必须进行相应的改革,其主要表现在以下三个方面:树立“以学生为本”的理念,强化服务意识;搜索管理盲点,改革管理体制;注重管理人员的素质,加强学生管理队伍的建设。     

信息化条件下基于大数据的后装一体化保障构想

由于契约的不完全性、专用性资产的存在,导致民企参军的谈判过程中,一方可能利用另一方因专用性资产投资的锁定效应,而采取机会主义行为将另一方套牢,攫取可占用专用性准租金。这种套牢风险不是单向的,而是双向的,双向套牢风险的存在不仅会降低军品科研生产项目建设质量,而且还会严重挫伤民营企业承担军品科研生产任务的积极性。为有效防范双向套牢风险,本文将从博弈论的角度,建立民营企业与军队采办部门的期望收益与专用性资产投资的函数关系和博弈矩阵,分阶段讨论民营企业与军队采办部门所面临的套牢风险大小及各自的最佳行为选择。以降低民企参军面临的套牢风险,引导更多优势民营企业进入军品科研生产与维修领域,推动军民融合的深度发展。     

发动机冷却燃料做功潜力?分析

在高马赫数飞行下,用燃料冷却超燃冲压发动机壁面的冷却需求量大于发动机燃烧量。为了降低燃料的冷却量以及实现燃料冷却量和燃烧量的匹配,采用?分析法对超燃冲压发动机壁面燃料冷却工质进行做功潜力分析。发动机壁面冷却燃料的特性决定其热量?大小。根据发动机壁面温度分布、热流密度分布计算热量?,建立稳定流动燃料工质的?平衡方程。结果表明：在壁面最高温度为1200K时,传入壁面的热量为562.4kW,其中理论热量?为541.3kW;冷却燃料工质流量的增加,最大输出功减小;燃料工质出口温度增加,输出功减小,燃料工质出口压力增加,输出功基本不变。     

舰载武器作战使用条件仿真量化分析

运用层次分析法、本征向量法和多属性决策问题的数据处理等方法,对舰载武器作战使用的约束条件进行了量化分析和数据处理,系统地描述了单一约束条件和多个约束条件对武器有效使用的概率问题,建立了约束条件下,舰载武器作战使用仿真流程。研究结果对舰载武器作战使用具有一定的指导作用。     

基于三方动态博弈模型的网络生存防御策略优化配置

针对网络攻防环境中防御方以提高系统生存能力为目的所进行的最优生存防御策略的选取问题,提出了一种基于完全信息动态博弈理论的生存防御策略优化配置算法。将恶意攻击方、故障意外事件及防御方作为博弈的参与人,提出了一种混合战略模式下的三方动态博弈模型,对博弈的主要信息要素进行了说明,以混合战略纳什均衡理论为基础,将原纳什均衡条件式的表达式转化为可计算数值结果的表达式,并据此增加了近似的概念,最后,将提出的模型和近似纳什均衡求解算法应用到一个网络实例中,结果证明了模型和算法的可行性和有效性。     

A model to predict the residual life of aircraft engines based upon oil analysis data

This paper reports on a study using the available oil monitoring information, such as the data obtained using the Spectrometric Oil Analysis Programme (SOAP), to predict the residual life of a set of aircraft engines. The relationship between oil monitoring information and the residual life is established using the concept of the proportional residual, which states that the predicted residual life may be proportional to the wear increment measured by the oil analysis programmes. Assuming such a relationship between wear and the residual life exists, we formulated a recursive prediction model for the item's residual life given measured oil monitoring information to date. A set of censored life data of 30 aircraft engines (right censored due to preventive overhaul) along with the history of their monitored metal concentration information are available to us. The metal concentration information includes many variables, such as Fe, Cu, Al, etc.; not all of them are useful, and some of them may be correlated. The principal component analysis (PCA) has been adopted to reduce the dimension of the original data set and to produce a new set of uncorrelated variables, which we shall use in the prediction model. The procedure associated with estimating model parameters is discussed. The model is fitted to the actual SOAP data from the aircraft engines, and the goodness‐of‐fit test has been carried out. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

基于油液分析的装备状态多阶段组合预测模型

状态预测是状态维修的关键步骤之一。针对装备状态变化规律复杂、特点难易准确掌握的关键问题,以延时时间概念和两阶段预知模型为基础,提出了根据不同阶段特点采用不同预测方法的多阶段组合预测模型。通过油液分析及其相关规定,对装备运行阶段进行了具体划分,给出了适应各阶段特点的预测方法。最后,通过实例验证了该模型的有效性。     

Model parameter estimation and residual life prediction for a partially observable failing system

We consider a partially observable degrading system subject to condition monitoring and random failure. The system's condition is categorized into one of three states: a healthy state, a warning state, and a failure state. Only the failure state is observable. While the system is operational, vector data that is stochastically related to the system state is obtained through condition monitoring at regular sampling epochs. The state process evolution follows a hidden semi‐Markov model (HSMM) and Erlang distribution is used for modeling the system's sojourn time in each of its operational states. The Expectation‐maximization (EM) algorithm is applied to estimate the state and observation parameters of the HSMM. Explicit formulas for several important quantities for the system residual life estimation such as the conditional reliability function and the mean residual life are derived in terms of the posterior probability that the system is in the warning state. Numerical examples are presented to demonstrate the applicability of the estimation procedure and failure prediction method. A comparison results with hidden Markov modeling are provided to illustrate the effectiveness of the proposed model. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 190–205, 2015