基于三维科研态势演化图谱的国防科技发展评估

当前随着信息技术的深入发展,大数据时代已经悄然而至。大数据为国防科技发展带来了重大机遇的同时,其所带来"数据眩晕"和"数据冗余"效应不容忽视。面对大数据对国防科技发展所带来的巨大挑战,准确、快速、实时地把握和预测科研发展态势,已经成为科研工作者和科研管理高层决策者的迫切需要。在对传统科学知识图谱深入研究的基础上,本文提出一种基于网络和单词分布式嵌入的三维态势演化图谱模型,模型充分利用了文献中的结构信息和语义信息,并对结果进行可视化展示,实验表明,该方法对于掌握国防科技发展动向,挖掘技术前沿,把握技术机会,促进国防科技发展具有较大的参考价值。     

Statistical analysis for masked system life data from Marshall‐Olkin Weibull distribution under progressive hybrid censoring

This paper considers the statistical analysis of masked data in a series system, where the components are assumed to have Marshall‐Olkin Weibull distribution. Based on type‐I progressive hybrid censored and masked data, we derive the maximum likelihood estimates, approximate confidence intervals, and bootstrap confidence intervals of unknown parameters. As the maximum likelihood estimate does not exist for small sample size, Gibbs sampling is used to obtain the Bayesian estimates and Monte Carlo method is employed to construct the credible intervals based on Jefferys prior with partial information. Numerical simulations are performed to compare the performances of the proposed methods and one data set is analyzed.     

采用多敏感器相互校正的卫星入轨姿态计算方法

针对卫星与运载分离后,由于星上姿态测量敏感器未开机或不具备工作条件而无法获悉初始入轨姿态的问题,设计了采用多敏感器相互校正的入轨姿态计算方法。建立运动学方程,结合陀螺测量角速度信息和星敏感器首次测量有效姿态,采用信赖域方法进行非线性优化求解,由后向前反向推算姿态;提出一种由模拟太阳敏感器测量对日姿态到对地姿态转化的便捷方法,由模拟太阳敏感器测量对日信息,采用本体系太阳矢量与星敏感器测量信息建立联系,达到了利用太阳敏感器测量信息校正星敏感器推算姿态的目的,最终得到的星箭分离时卫星姿态信息能够与运载方信息吻合。将入轨后卫星真实姿态与地面模型仿真结果对比,验证了模型的正确性,为后续卫星设计和飞行程序设计与改进提供了重要依据。     

利用开源大数据的社会工作实时评估方法

鉴于目前社会工作评估评价方面的学术研究还较薄弱,存在无针对性指标体系,现有相关评比周期长、成本高等突出问题,探索构建了利用互联网开源大数据的实时定量评估模型,建立了针对性指标体系,提出评分快速计算方法,并设计实现原型软件。针对选定的社会工作进行案例实证,通过采集某省14个市州的互联网真实数据来评估其工作力度,结果与多位受访者的主观感受及相关官方评估基本相符,验证了所提模型的有效性和合理性。     

Shock initiation performance of NTO-based polymer bonded explosive

3-nitro-1,2,4-tri-azol-5-one (NTO) is a high energy insensitive explosive. To study the shock initiation process of NTO-based polymer bonded explosive JEOL-1 (32%octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX), 32% NTO, 28% Al and 8% binder system), the cylinder test, the gap experiments and numerical simulation were carried out. Firstly, we got the detonation velocity (7746 m/s) and the parameters of Jones-Wilkins-Lee (JWL) equation of state (EOS) for detonation product by cylinder test and numerical simulation. Secondly, the Hugoniot curve of unreacted explosive for JEOL-1 was obtained calculating the data of pressure and time at different Lagrangian positions. Then the JWL EOS of unreacted explosive was obtained by utilizing the Hugoniot curve as the reference curve. Finally, we got the pressure growth history of JEOL-1 under shock wave stimulation and the parameters of the ignition and growth reaction rate equation were obtained by the pressure-time curves measured by the shock-initiation gap experiment and numerical simulation. The determined trinomial ignition and growth model (IG model) parameters can be applied to subsequently simulation analysis and design of insensitive ammunition with NTO-based polymer bonded explosive.     

分布式储能技术的探索与实践

文章阐述了分布式储能技术的重要意义,介绍了分布式储能技术在经济社会可持续发展重要领域——能源互联网发展中的重要地位,并对分布式储能的技术形态与载体进行了分析,最后以信息网络重要组成部分——数据中心为应用场景,对分布式储能技术的实践进行了探讨。     

多民族地区青少年对待欺负问题态度的比较研究

一直以来,欺负行为普遍存在于校园之中,对青少年的身心健康造成了很大的危害。因此,引起了社会各界的广泛关注。本研究利用初中生欺负/受欺负问卷对新疆少数民族和汉族的近500名初中生对待欺负问题的态度进行了调查,分析发现汉族学生对待欺负问题的态度比少数民族学生更积极;对待欺负问题的态度存在性别差异,女生比男生更积极,在对待欺负行为的态度上少数民族学生有极其显著的性别差异;少数民族学生对待欺负问题的态度与年级显著相关,而不同年级汉族学生对待欺负问题的态度差异并不显著。     

不确定数据流多维建模方法*

由于不确定数据流应用的出现,给传统的精确、静态数据环境下的多维建模带来了巨大挑战。针对不确定数据流动态、无限和不确定等特征,提出了一种不确定数据流多维模型。该模型中引入了不确定对象来描述不确定事实元组,并且通过定义时间维度的层次时间窗口,很好地反映了数据流的动态性和无限性,最后还对此多维不确定数据流模型的基本代数操作和分析代数操作进行了形式化定义,为不确定数据流多维查询与分析提供了理论依据。     

On the Determinants of Terrorism Risk Concern in Europe

We investigate whether differences in terrorism risk are mirrored on terrorism risk perception across European countries for the period 2003–2007. We find that the average propensity for terrorism risk concern is affected by actual risk levels. Country and individual heterogeneity contribute substantially to the variation of observed risk perception. Singles and individuals with white collar jobs are less likely to mention terrorism as one of the most pressing issues their country faces, while political positioning towards the right makes it more likely to be concerned about terrorism. As far as competing risks are concerned, we find that the likelihood terrorism is mentioned, decreases with spending on pensions as a percentage of GDP being higher. Finally, based on the Bayesian framework, we also examined the formation of terrorism risk perceptions, and decompose the observed country-level time series of terrorism activity into a long- and a short-run component. We concluded that the observed risk perception variation is only explained by the long-term trend of terrorism activity countries face.     

Detecting Problems in Military Expenditure Data Using Digital Analysis

The UN asks governments to report key figures of their annual military budgets with the aim of creating trust among member states. This goal can only be achieved if the data reported is accurate. However, although there are many reasons for governments to falsify data, the UN does not check for manipulation. In this paper, we apply Benford’s law to the military expenditure data of 27 states taken from the UN register. Our analysis of the first digits shows that the states with the greatest deviations from the expected Benford distribution and therefore the lowest data quality are the USA and the UK.