大数据条件下结构区间可靠性与概率可靠性比较

对于结构服役期间收集到的结构相关大数据，首先对数据进行整理与分析，根据数据的特征进行分类；然后利用应力-强度干涉理论建立结构大数据条件下区间可靠性计算模型与随机可靠性计算模型，按大数据所分类型，分别计算每一类型大数据中结构区间非概率可靠度指标与随机概率可靠度，在区间非概率可靠度指标与随机概率可靠度计算结果中，最小区间可靠性指标是结构最终的区间可靠性指标，最小随机可靠度是结构最终的随机可靠度．最后，利用两个算例说明了大数据条件下结构区间可靠性计算方法与概率可靠性计算方法的优点与缺点．     

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??Recently, big data, could computing and internet of things provide some new information technologies for organization and management of complex systems, and they have caused multifaceted changes on organization framework and operations mechanism of enterprises. Based on this, we first construct a new stochastic model for a big data driven large-scale bike-sharing system, which expresses the important role played by big data, and describes the operations mechanism of the large-scale bike-sharing system, and specifically, the rebalancing of bikes in various stations in terms of trucks. Then, we present a mean-field limit theory, which is applied to analyzing the big data driven large-scale bike-sharing system, including establishing a time-inhomogeneous queueing system by means of the mean field theory, and setting up the mean-field equations through the time-inhomogeneous queueing system; providing an empirical measure process by means of a nonlinear birth-death process, giving algorithms for computing the fixed point in terms of a segmented structural birth-death processes, and computing the average number of bikes in each station; and providing numerical examples to analyze how the steady average number of bikes in each station depends on some key parameters of the bike-sharing system. Using these results, this paper analyzes physical effect of big data on performance of the large-scale bike-sharing. Therefore, this paper gives a promising research direction of stochastic model in the study of large-scale bike-sharing systems.     

On some classes of modules and characterization of rings

An abstract class of R-Mod is termed Additive Class if it is closed under taking submodules, homomorphic images and finite direct sums. In this paper we study the lattice properties of R-ad, the big lattice of all additive classes in R-mod for R any ring. Also, we study the lattice properties of R-ad when R is a Noetherian or semisimple ring. We describe a characterization of some rings through the additive classes and their properties. We study some properties of additive classes related with the associated linear filters.     

基于大数据的制造业作业成本控制体系构建

制造业成本控制和管理对企业发展是非常重要的,将作业成本法与成本控制相结合,为制造企业成本管理提供了一种新的思路.大数据时代为企业的生存与发展带来新的契机,大数据思维将极大的影响企业战略.将大数据与企业具体目标相结合,基于大数据环境,利用大数据技术和工具,探求新的成本控制模式.考虑到整个价值链,并利用作业成本思想,完善作业成本核算,期望为制造业成本控制提供一定的指引.     

An analytic framework for the two-dimensional infinite Toda hierarchy associated with a commutative algebra

Starting with the group of operators on a separable Hilbert space that differ from the identity by a trace-class operator, we construct a solution of the two-dimensional infinite Toda hierarchy associated with a maximal commutative subalgebra in complex k×k matrices. __________ Translated from Teoreticheskaya i Matematicheskaya Fizika, Vol. 155, No. 1, pp. 177–192, April, 2008.     

基于手机大数据的中国人口迁徙模式及疫情影响研究

新型冠状病毒感染的肺炎(COVID-19)可通过人员接触与流动迅速传播,因此研究人类迁徙和出行模式的变化对疫情防控至关重要.本文基于手机运营商2020年春运及疫情暴发前后连续两个月的全国地级市之间的人口流动数据,运用时序网络分析方法构建人口流动网络拓扑结构指标,并通过引入地理衰减因子提出Spatial-Louvain社团检测算法,研究平时、春运、疫情防控隔离和生产复工四阶段的人口迁徙模式的时空演化规律.研究发现:受各地疫情防控措施影响,武汉封城后全国城市间人口流量急剧下降,并持续至2月中旬.疫情期间人口流动网络结构呈现四阶段的时空演化模式;本文提出的空间网络社团检测算法比传统Louvain算法平均模块度值提高了14%;中国城市分布以经济交互和地理位置为基础,形成了以核心城市为中心,向周边辐射的城市群格局;疫情因素仅能在短暂时间内改变部分城市的城市群归属,当该因素消失或减弱后,城市群能迅速恢复原有格局.     

Unifying order structures for Colombeau algebras

We define a general notion of set of indices which, using concepts from pre‐ordered sets theory, permits to unify the presentation of several Colombeau‐type algebras of nonlinear generalized functions. In every set of indices it is possible to generalize Landau's notion of big‐O such that its usual properties continue to hold. Using this generalized notion of big‐O, these algebras can be formally defined the same way as the special Colombeau algebra. Finally, we examine the scope of this formalism and show its effectiveness by applying it to the proof of the pointwise characterization in Colombeau algebras.     

Hadoop云平台下的基于用户协同过滤算法研究

随着互联网的高速发展,海量新闻的个性化推荐成为一个重要课题,针对海量新闻的个性化推荐算法进行研究,以MapReduce的并行方法设计了基于Hadoop云平台下的协同过滤算法,利用MapReduce的并行方法,将传统的协同过滤算法并行化,并详细说明了并行化步骤和实现细节。最后用实验结果验证了改进的并行化的协同过滤算法在运行速度和执行效率方面有明显的提高,更适合处理大数据。     

大数据预处理中属性约简的特性保持分析

伴随着工业及社会信息化程度的增强,各个领域的自动化程度越来越高,大规模及超大规模海量数据应运而生,呈现出大数据特征。这些海量数据在提升行业发展动力的同时,也带来了巨大的挑战性问题—数据可用性。为了从海量数据中甄别出无用信息、挖掘有利于相关领域发展的有价值信息,就需要对其进行数据分析。数据预处理技术可以极大地减少数据分析时的处理量,提高数据分析处理的效率,而属性约简在数据预处理中是一个比较重要的环节。在分析大数据属性特征的基础上,较系统地分析了目前几种主流的属性约简算法,对各类算法的性能进行了剖析,并展望了今后大数据预处理的研究工作思路。