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.     

融合空间偏好和语义的个体活动识别方法

个体活动识别对用户画像、个性化推荐、异常行为检测、群体行为分析和基于活动的资源配置优化具有重要价值。提出了一种基于稀疏的社交媒体签到数据的个体活动语义识别方法,从签到数据中提取活动行为的时间周期性和趋势性特征,并采用空间偏好量化算法,从个体与群体活动的空间关联中提取群体和个体的空间访问偏好,使用自然语言嵌入工具BERT模型提取访问兴趣点的语义。时间特征、空间偏好特征和访问兴趣点名称语义特征共同构成表征群体、个体偏好的时空联合特征,通过极限梯度提升分类器对其进行分类,得到活动语义识别结果。在Foursquare数据集上的对比实验和消融实验中验证了所提活动语义识别模型可以有效提升活动语义识别的准确性。     

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。     

基于本体技术的分布式模拟训练系统操作监控平台

针对分布式模拟训练系统操作训练特点,在基于HLA（High Level Architecture）的模拟训练系统中,提出并建立了基于本体技术的操作训练监控平台,解决了训练系统和监控平台中操作消息的语义异构问题,实现了操作训练情况的动态实时显示。在平台实现中,采用RTI（Run-Time Infrastructure）提供的服务实现了联邦数据收发;建立了操作信息领域本体,采用计算语义相似度的方法实现操作信息由分布交互层向语义层映射;通过专家知识库和操作监控引擎判断操作逻辑合理性;利用View Graph以图形化的方式展示出操作训练情况。最后,通过一个炮车操作训练监控实例,验证了该平台的可行性和有效性。     

一种基于语义关系的情感词典自动构建方法

构建英文情感词典研究相对成熟,形成了丰富可靠的词典资源。而针对中文的研究时间短,中文情感分析词典资源较少。借鉴现有可靠的英文词典资源,提出了基于语义关系的情感词典自动构建算法,算法先从HowNet的概念中进行中文义原和词语抽取及语义分析,再利用HowNet概念中DEF中英文属性值,在英文情感词典SentWordNet中进行义原和词语情感值查询,最后根据词语和义原之间的语义关系进行词语的情感值计算。算法直接利用现有的英文情感词典,无需人工标注,生成的情感词典记录了词语的语义关系、情感极性值等多种信息,弥补了现有词典的不足。评测实验结果表明,根据算法实现的情感词典相比其他词典在准确率接近的情况下,召回率和F值最高,取得了较好的评测性能。     

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.