改进的重要抽样法在电力系统可靠性评估中的应用

将分散抽样方法与重要抽样方法相结合，提出了一种应用于电力系统可靠性评估的改进重要抽样算法。应用该方法对IEEE-RTS标准系统的发电部分进行了可靠性评估，并与采用常规抽样方法和常规重要抽样方法的评估结果作了比较。结果表明，文中提出的方法在保证计算精度的前提下显著减小了抽样方差，加快了迭代计算的收敛速度。     

Bounding rare event probabilities in computer experiments

Bounding probabilities of rare events in the context of computer experiments is an important concern in reliability studies. These rare events depend on the output of a physical model with random input variables. Since the model is only known through an expensive black box function, standard efficient Monte Carlo methods designed for rare events cannot be used. That is why a strategy based on importance sampling methods is proposed. This strategy relies on Kriging meta-modeling and manages to achieve sharp upper confidence bounds on the rare events probabilities. The variability due to the Kriging meta-modeling step is properly taken into account. The proposed methodology is applied to an artificial example and compared with more standard Bayesian bounds. Eventually, a challenging real case is analyzed. It consists in finding an upper bound for the probability that the trajectory of an airborne load will collide with the aircraft that released it.     

On the use of marginal posteriors in marginal likelihood estimation via importance sampling

The efficiency of a marginal likelihood estimator where the product of the marginal posterior distributions is used as an importance sampling function is investigated. The approach is generally applicable to multi-block parameter vector settings, does not require additional Markov Chain Monte Carlo (MCMC) sampling and is not dependent on the type of MCMC scheme used to sample from the posterior. The proposed approach is applied to normal regression models, finite normal mixtures and longitudinal Poisson models, and leads to accurate marginal likelihood estimates.     

基于因果图基本事件重要度的故障分析方法研究

因果图理论可以进行故障分析，但是缺乏对基本事件的重要性的分析。文章在引入基本事件重要度概念．并将这种新方法成功运用于煤矿瓦斯爆炸的故障分析中。通过基本事件的重要度，分析了各基本事件对顶事件的影响程度和重要程度，找出了故障原因和关键环节，并提出了切实可行的预防措施。研究结果表明，基于基本事件重要度的因果图．是有效地进行故障诊断和安全分析的定性和定量方法．能为事故预防提供可行的对策。     

基于增强BiLSTM-CRF模型的推文恶意软件名称识别

针对推文中恶意软件名称识别任务存在的文本简短、非正式、实体类别单一以及实体歧义等问题,提出了一种基于BERT-BiLSTM-Self-attention-CRF的实体识别方法,以实现推文中恶意软件名称的自动识别。在BiLSTM-CRF模型的基础上,利用BERT模型编码单词语境信息,提升词嵌入的上下文语义质量,增强原有模型的语义消歧能力;同时,借助Self-attention机制学习单词间关系和句子结构特征,利用加权表征帮助单一类别实体的解码,以提升恶意软件名称实体的识别效果。通过构建包含恶意软件名称实体的推文标记数据集进行实验测试,结果表明,提出的方法可以实现更好的性能,其精确率、召回率、F1值分别为86.38%,84.73%,85.55%,相较于基线模型BiLSTM-CRF,F1值提升了12.61%。     

A comprehensive approach to optimal software rejuvenation

Software aging is caused by resource exhaustion and can lead to progressive performance degradation or result in a crash. We develop experiments that simulate an on-line bookstore application, using the standard configuration of TPC-W benchmark. We study application failures due to memory leaks, using the accelerated life testing (ALT). ALT significantly reduces the time needed to estimate the time to failure at normal level. We then select the Weibull time to failure distribution at normal level, to be used in a semi-Markov model so as to optimize the software rejuvenation trigger interval. Then we derive the optimal rejuvenation schedule interval by fixed point iteration and by an alternative non-parametric estimation algorithm. Finally, we develop a simulation model using importance sampling (IS) to cross validate the ALT experimental results and the semi-Markov model, and also we apply the non-parametric method to cross validate the optimized trigger intervals by comparing the availabilities obtained from the semi-Markov model and those from IS simulation using the non-parametric method.     

On the choice of importance distributions for unconstrained and constrained state estimation using particle filter

Recursive state estimation of constrained nonlinear dynamical system has attracted the attention of many researchers in recent years. For nonlinear/non-Gaussian state estimation problems, particle filters have been widely used (Arulampalam et al. [1]). As pointed out by Daum [2], particle filters require a proposal distribution and the choice of proposal distribution is the key design issue. In this paper, a novel approach for generating the proposal distribution based on a constrained Extended Kalman filter (C-EKF), Constrained Unscented Kalman filter (C-UKF) and constrained Ensemble Kalman filter (C-EnkF) has been proposed. The efficacy of the proposed state estimation algorithms using a particle filter is illustrated via a successful implementation on a simulated gas-phase reactor, involving constraints on estimated state variables and another example problem, which involves constraints on the process noise (Rao et al. [10]). We also propose a state estimation scheme for estimating state variables in an autonomous hybrid system using particle filter with Unscented Kalman filter as a proposal and unconstrained Ensemble Kalman filter (EnKF) as a proposal. The efficacy of the proposed state estimation scheme for an autonomous hybrid system is demonstrated by conducting simulation studies on a three-tank hybrid system. The simulation studies underline the crucial role played by the choice of proposal distribution in formulation of particle filters.     

我国建筑节能的现状和发展途径

从国外建筑节能技术的新趋势说明了建筑节能的重要性,通过介绍国内建筑节能的现状,分析建筑节能的必要性与紧迫性,并论述了建筑节能的发展途径。     

高等院校与战略性新兴产业发展的关系

战略性新兴产业是引导未来经济社会发展的重要力量,是从根本上转变经济发展方式的必然选择。扶持战略性新兴产业发展已经成为我国未来产业发展的重要举措。高等院校具有丰富的科教人力资源,能够为国家战略性新兴产业发展作出积极贡献,应突出科研人才对发展战略性新兴产业的支撑作用,以战略性新兴产业发展为导向,实现战略性新兴产业与科研人才的紧密结合,充分发挥大学教育的优势机制,积极推动战略性新兴产业的发展。