网络结构和信息披露对网络参与主体的收益影响研究

网络结构和网络上的信息披露机制对网络功能的发挥有着直接的影响,建立相应的数学模型并采用数学分析和仿真验证的手段揭示其对于网络参与主体收益的影响规律,具有理论和应用价值。在明确网络中两类参与主体的基础上,定义了他们的收益,并根据信息披露的不同情形,分为两种情况求解模型,应用数学证明和仿真分析的手段得到了网络结构对收益影响规律的三个结论。在对结论分析的基础上,指出了模型的管理学意义,讨论了进一步的研究工作。     

基于城乡物流一体化的连锁经营配送网络优化研究

基于城乡物流一体化的目标设定和相应基础设施平台的支持,提出连锁经营配送网络的建设模式,依据对当前连锁经营网络发展的重点工作的分析,建立了关于配送中心和农产品收购中心选择的网络优化模型.并引入算例说明模型的可行性和适用性.     

区域物流的技术网络效应研究

建立了一个基于信息技术网络效应的物流信息化需求模型,来说明信息化带来的技术网络效应是物流厂商加入信息网络的动力所在.并基于1995-2010年30个省、直辖市、自治区的面板数据,运用面板单位根和面板协整方法,证明了区域物流中技术网络效应的存在.     

论“打开黑箱评价”的网络DEA模型

讨论了两阶段网络DEA模型KH,复合网络DEA模型WYP,以及两个模型之间的关系.并指出了上述两个模型能够打开"黑箱".     

线性代数网上学习系统的开发与研究

本文简要介绍线性代数学习网站的系统研究与开发设计．涉及到网络数据库、ASP、界面设计等多种技术.该线性代数网上学习系统为老师和学生提供了交流平台，其界面美观、操作简便．具有良好的应用前景.     

基于TOPSIS的网络舆情态势等级模糊多指标综合评价模型

网络舆情是社会舆情在网络空间中的映射,体现了社会稳定和谐程度.网络舆情态势分析对于有效预测和把握舆情导向具有重大的意义.文章主要研究构建基于TOPSIS的网络舆情态势等级模糊多指标综合评价模型.首先,根据网络舆情态势演化形成原因和发展规律,建立了网络舆情态势等级评价三级指标体系,提出了基于有序比值方法的网络舆情态势等级评价指标权重的确定方法.然后,构建了基于TOPSIS的网络舆情态势等级模糊多指标综合评价模型.最后,通过魏则西事件验证了该模型的有效性和适用性,并与线性加权综合评价模型进行对比分析,说明了文章模型的优越性.研究结果对政府和相关舆情监管人员及时有效的管理和引导网络舆情具有重要的指导意义与参考价值.     

基于睡眠规律的管制员认知能力分析

为了更加准确评估管制员工作状态,针对管制员认知能力评估问题,采用贝叶斯网络模型,提出了一种基于睡眠规律的分析方法.首先归纳了评价睡眠规律的主要因素,并采用PVT反应时间量化管制员的认知能力水平;然后根据先验知识构建了贝叶斯网络模型,在实验统计与模糊集理论的基础上得到条件概率表CPT,参数学习更新,并计算相关系数确定了关键因素;最后通过实际案例分析,预测了管制员认知水平,验证了评估的可行性与合理性.实验结果表明该分析方法能够根据睡眠规律对管制员值班前的认知能力进行准确的评估,降低了人为因素的安全隐患,增强了人员岗前管理的主动性.     

加工中心本体故障模式的统计研究

本文对加工中心整机及机床本体的故障模式及其分布规律进行研究,分析了产生故障的原因,指出了提高加工中心可靠性的途径.研究表明加工中心机床本体故障率最高,刀库与机械手是其可靠性最薄弱的环节.     

大规模定制模式下敏捷产品可拓概念设计研究

在大规模定制 (MC)模式下 ,利用可拓方法给出了敏捷产品概念设计的一种物元可拓模型 ,讨论了创造性思维的一般规律及开拓的方向和途径 .最后 ,指出 MC敏捷产品的构思能够通过可拓策略生成器快速实现 .     

基于AHP和DEA法的高校信息化水平评价研究

根据高校信息化水平评价的特点,构建了适合我国高校信息化水平评价的指标体系.在此基础上,建立了基于AHP和DEA多目标排序的评价模型,并运用该模型对高校信息化水平进行了评价与分析,为高校信息化评价探索了新的途径.     

基于混合患病兄弟对IBD数据的连锁分析

患病兄弟对(affected sib-pair,ASP)设计在遗传统计中有着广泛的应用,这种设计针对的是完全兄弟对(full-sib),而在实际问题中,被抽样的患病兄弟对中常会混有一定数目的半兄弟对(half-sib).论文对这种基于IBD信息的混有半兄弟对NASP(称为mixed affected sib-pair,...     

Experimenting with parallelism for the instantiation of ASP programs

In the last few years, microprocessor technologies have been moving towards multi-core architectures, in order to improve performance as well as reduce power consumption. This makes real Symmetric MultiProcessing (SMP) available even on non-dedicated machines, and paves the way to the development of better performing software. Notably, the recent application of Answer Set Programming (ASP) in different emerging areas, such as knowledge management or information extraction/integration, shows that performance is a crucial issue also for ASP systems. Among the tasks performed by such systems, the instantiation process, which consists of generating a variable-free program equivalent to the input one, is one of the most expensive from a computational viewpoint, especially in the case of huge input data. In this paper a new strategy exploiting parallelism for the instantiation of ASP programs is proposed. An implementation of this strategy and its integration with the grounding module of the DLV system is discussed. The results of an experimental analysis are also presented, which confirm that the strategy is effective in making ASP instantiation more efficient.     

基于Fuzzy-Rough-TOPSIS的工程项目投标风险评价模型研究

在工程项目招投标阶段,项目风险评价是建筑施工企业进行投标决策和作出项目选择的重要依据.基于理想化与主客观相结合的思想,提出了一种确定风险评价指标综合权重的方法;采用梯形模糊数处理模糊性信息,并将其与粗集理论和TOPSIS(a technique for order preference by similarity to ideal solution)方法相融合,建立了工程项目风险评价的Fuzzy-Rough-TOPSIS模型.实例运行表明,模型可操作性强,适用于多个项目的风险分析和比较,并能够在一定程度上克服以往模型存在的主观性强、应用条件限制严格等不足.     

Answer set programming in intuitionistic logic

As a demonstration of the flexibility of constructive mathematics, we propose an interpretation of propositional answer set programming (ASP) in terms of intuitionistic proof theory, in particular in terms of simply typed lambda calculus. While connections between ASP and intuitionistic logic are well-known, they usually take the form of characterizations of stable models with the help of some intuitionistic theories represented by specific classes of Kripke models. As such the known results are model-theoretic rather than proof-theoretic. In contrast, we offer an explanation of ASP using constructive proofs.     

FIDIC合同条件下工程索赔的博弈模型*

索赔是工程项目各参与主体共同关注的焦点,索赔决策直接影响业主方和承包方的利益。研究通过分析工程索赔的特性,结合FIDIC合同条件下的索赔条款,提出索赔的博弈基础;分析博弈论和贝叶斯法则与索赔决策之间的契合程度,构建工程索赔的基本博弈模型;基于工程索赔的四种情境,构建完全信息有限多轮博弈模型、资金时间价值对博弈模型的影响、不完全信息有限多轮博弈模型和非完全对立的不完全信息有限多轮博弈模型并求解“纳什均衡”,模拟工程参与方做出索赔决策的路径,寻求相应情形下承包方和业主方的最优策略,提出工程参与方优化索赔策略的建议。     

Parametric mortality indexes: From index construction to hedging strategies

In this paper, we investigate the construction of mortality indexes using the time-varying parameters in common stochastic mortality models. We first study how existing models can be adapted to satisfy the new-data-invariant property, a property that is required to ensure the resulting mortality indexes are tractable by market participants. Among the collection of adapted models, we find that the adapted Model M7 (the Cairns–Blake–Dowd model with cohort and quadratic age effects) is the most suitable model for constructing mortality indexes. One basis of this conclusion is that the adapted model M7 gives the best fitting and forecasting performance when applied to data over the age range of 40–90 for various populations. Another basis is that the three time-varying parameters in it are highly interpretable and rich in information content. Based on the three indexes created from this model, one can write a standardized mortality derivative called K-forward, which can be used to hedge longevity risk exposures. Another contribution of this paper is a method called key K-duration that permits one to calibrate a longevity hedge formed by K-forward contracts. Our numerical illustrations indicate that a K-forward hedge has a potential to outperform a q-forward hedge in terms of the number of hedging instruments required.     

Twisted Extensions of Spin Models

A spin model is one of the statistical mechanical models which were introduced by V.F.R. Jones to construct invariants of links. In this paper, we give a new construction of spin models of size 4n from a given spin model of size n. The process is similar to taking tensor product with a spin model of size four, but we add some sign exchange. This construction also gives symmetric four-weight spin models of the type introduced by E. Bannai and E. Bannai.     

Granular fuzzy models: Analysis,design, and evaluation

The study is concerned with a design of granular fuzzy models. We exploit a concept of information granularity by developing a model coming as a network of intuitively structured collection of interval information granules described in the output space and a family of induced information granules (in the form of fuzzy sets) formed in the input space. In contrast to most fuzzy models encountered in the literature, the results produced by granular models are information granules rather than plain numeric entities. The design of the model concentrates on a construction of information granules that form a backbone of the overall construct. Interval information granules positioned in the output space are built by considering intervals of equal length, equal probability, and developing an optimized version of the intervals. The induced fuzzy information granules localized in the input space are realized by running a conditional Fuzzy C-Means (FCM). The performance of the model is assessed by considering criteria of coverage and information specificity (information granularity). Further optimization of the model is proposed along the line of an optimal re-distribution of input information granules induced by the individual interval information granules located in the output space. Experimental results involve some synthetic low-dimensional data and publicly available benchmark data sets.     

An approximate algorithm for prognostic modelling using condition monitoring information

Established condition based maintenance modelling techniques can be computationally expensive. In this paper we propose an approximate methodology using extended Kalman-filtering and condition monitoring information to recursively establish a conditional probability density function for the residual life of a component. The conditional density is then used in the construction of a maintenance/replacement decision model. The advantages of the methodology, when compared with alternative approaches, are the direct use of the often multi-dimensional condition monitoring data and the on-line automation opportunity provided by the computational efficiency of the model that potentially enables the simultaneous condition monitoring and associated inference for a large number of components and monitored variables. The methodology is applied to a vibration monitoring scenario and compared with alternative models using the case data.