A hybrid method for simulation factor screening

Factor screening is performed to eliminate unimportant factors so that the remaining important factors can be more thoroughly studied in later experiments. Controlled sequential bifurcation (CSB) and controlled sequential factorial design (CSFD) are two new screening methods for discrete‐event simulations. Both methods use hypothesis testing procedures to control the Type I Error and power of the screening results. The scenarios for which each method is most efficient are complementary. This study proposes a two‐stage hybrid approach that combines CSFD and an improved CSB called CSB‐X. In Phase 1, a prescreening procedure will estimate each effect and determine whether CSB‐X or CSFD will be used for further screening. In Phase 2, CSB‐X and CSFD are performed separately based on the assignment of Phase 1. The new method usually has the same error control as CSB‐X and CSFD. The efficiency, on the other hand, is usually much better than either component method. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

船用蒸汽发生器传热管破损事故研究

综合应用事件树分析和确定论分析方法,建立船用蒸汽发生器传热管破损事故动态分析模型;基于运行安全分析研究选用典型事件序列,并利用仿真应用平台对该模型进行了仿真计算分析;明确了故事的发展过程和处置措施,以及多重故障和人工干预等因素对事故的影响结果.该研究结果对该事故操作规程的制定有一定的指导意义.     

批量独立可修件的备件需求预测仿真算法

利用Monte Carlo随机仿真技术,采用事件调度法的仿真策略对可修件的使用与维修过程进行仿真,给出了在给定备件和维修分队数量下批量独立可修件的备件保障概率仿真算法,并用一系列计算实例证明了算法的可行性和正确性,该算法能有效解决安装在不同装备上,具有不同已工作时间、不同故障间隔时间分布、不同修复时间分布的可修件的备件保障概率计算问题,为备件的科学储备提供了有力的决策依据。     

Modeling of unsupervised knowledge graph of events based on mutual information among neighbor domains and sparse representation

Text event mining, as an indispensable method of text mining processing, has attracted the extensive attention of researchers. A modeling method for knowledge graph of events based on mutual information among neighbor domains and sparse representation is proposed in this paper, i.e. UKGE-MS. Specifically, UKGE-MS can improve the existing text mining technology's ability of understanding and discovering high-dimensional unmarked information, and solves the problems of traditional unsupervised feature selection methods, which only focus on selecting features from a global perspective and ignoring the impact of local connection of samples. Firstly, considering the influence of local information of samples in feature correlation evaluation, a feature clustering algorithm based on average neighborhood mutual information is proposed, and the feature clusters with certain event correlation are obtained; Secondly, an unsupervised feature selection method based on the high-order correlation of multi-dimensional statistical data is designed by combining the dimension reduction advantage of local linear embedding algorithm and the feature selection ability of sparse representation, so as to enhance the generalization ability of the selected feature items. Finally, the events knowledge graph is constructed by means of sparse representation and l₁ norm. Extensive experiments are carried out on five real datasets and synthetic datasets, and the UKGE-MS are compared with five corresponding algorithms. The experimental results show that UKGE-MS is better than the traditional method in event clustering and feature selection, and has some advantages over other methods in text event recognition and discovery.     

临近空间中子环境及其探测

在临近空间辐射环境的诸多因素中,中子对飞行器的安全、可靠运行以及人员健康的威胁最为严重。文章介绍了临近空间中子的来源及其对飞行器和人体的危害,指出临近空间中子探测研究的重要意义,简要阐述了临近空间中子探测的原理与方法,最后探讨了临近空间中子探测领域所涉及的几个关键问题。     

脑功能光学成像研究进展

首先对脑功能成像技术的研究背景和发展历程进行了简单的回顾 ,然后对脑功能光学成像方法进行了分类综述 ,主要介绍了电压敏感性染色成像、内源光学成像、光学相干层析成像、神经元活动相关光信号成像等成像方法的原理和最新进展 ,并总结了选择光学成像系统所应考虑的因素 ,最后 ,通过对各种成像方法的分析和比较 ,提出了改进和完善脑功能光学成像技术的建议 ,并对脑功能光学成像技术的发展前景指出了一些可能的方向     

条件随机变量与条件数据融合

本文讨论基于条件事件代数系统上的条件随机变量的表示问题,并给出了在多个信息源提供的不同类型的条件数据时之统一表示方法,以及利用条件随机变量分布函数性质,对所得数据进行关联等。     

极大代数及其在离散事件动态系统中的应用

在人类社会活动中,有许多活动属于离散事件或离散事件动态系统。在极大代数意义下,这类系统可用线性系统的理论和方法建立数学模型、求解状态方程,讨论可控性、可观性,以及实现系统运行的优化设计。     

单片微机应用系统功能模拟的研究

本文研究了一个在 PC 机上运行的模拟系统,可以用来对一般的单片微机应用系统进行功能模拟和时序分析,具有以下两个特点:(1)系统模拟;(2)软、硬件同时模拟。本文介绍了模拟系统的基本组成,讨论了元件模型的建立,并给出了相应的模拟算法。     

Combined variance reduction techniques in fully sequential selection procedures

In the past several decades, many ranking‐and‐selection (R&S) procedures have been developed to select the best simulated system with the largest (or smallest) mean performance measure from a finite number of alternatives. A major issue to address in these R&S problems is to balance the trade‐off between the effectiveness (ie, making a correct selection with a high probability) and the efficiency (ie, using a small total number of observations). In this paper, we take a frequentist's point of view by setting a predetermined probability of correct selection while trying to reduce the total sample size, that is, to improve the efficiency but also maintain the effectiveness. In particular, in order to achieve this goal, we investigate combining various variance reduction techniques into the fully sequential framework, resulting in different R&S procedures with either finite‐time or asymptotic statistical validity. Extensive numerical experiments show great improvement in the efficiency of our proposed procedures as compared with several existing procedures.