基于无限重复博弈的管理决策建模分析

分析了无限重复博弈成功应用的条件,并以此结论建立博弈模型,确定收益矩阵参数,该模型在实际应用中能得到合理的解释,其博弈结果是双赢的博弈平衡.     

Context-aware end-to-end QoS diagnosis and guarantee based on Bayesian network

A systematic approach for end-to-end QoS qualitative diagnosis and quantitative guarantee is proposed to support quality of service (QoS) management on current Internet.An automatic unwatched discretiz...     

基于DCT域特征和Bayes语义分类的教学图像资源库检索技术

提出了一种根据DCT域特征提取和Bayes语义分类结合的图像检索技术。针对图像库数据结构的语义需求,采用朴素Bayes算法对图像库进行分类;其中视觉特征提取部分,采用改进的DCT域的边缘空间分布概率特征提取方法,获取目标对象边缘信息,建立20个特征值;通过Bayes分类器的分类,获取每类的索引特征向量,用于初次语义类别过滤,提高了检索效率。通过实验,建立教学需要的图像资源库,与其它检索算法进行查准率对比试验,证明了本算法的优越性。     

Multi-sources information fusion algorithm in airborne detection systems

To aim at the multimode character of the data from the airplane detecting system, the paper combines Dempster- Shafer evidence theory and subjective Bayesian algorithm and makes to propose a mixed structure multimode data fusion algorithm. The algorithm adopts a prorated algorithm relate to the incertitude evaluation to convert the probability evaluation into the precognition probability in an identity frame, and ensures the adaptability of different data from different source to the mixed system. To guarantee real time fusion, a combination of time domain fusion and space domain fusion is established, this not only assure the fusion of data chain in different time of the same sensor, but also the data fusion from different sensors distributed in different platforms and the data fusion among different modes. The feasibility and practicability are approved through computer simulation.     

基于动态贝叶斯网络的无人机路径规划研究

针对威胁可变及威胁体不尽相同的无人机路径规划问题,提出了一种局部路径重规划的算法。该算法首先构造出战场具有n类威胁体的初始路径图———“改进型Voronoi图”,后应用Dijkstra算法搜索威胁分布图,求解粗略最短路径。在无人战斗机飞行过程中,威胁体威胁等级不断变化,无人战斗机通过多传感器数据融合知识构建动态贝叶斯网络图,感知环境,获取信息,应用Viterbi解码算法获得实时威胁等级,进行局部改进型Voronoi图的重构,以完成局部路径重规划,提高了无人战斗机在实战环境下生存概率。给出了应用该方法的具体步骤,通过仿真实验证明该算法的有效性。     

供应链计划建模中的博弈方法

基于供应链管理思想,针对供应链上下游的主要合作伙伴关系,建立一种基于协商的上下游合作计划模型·根据外界情况(如价格、库存等因素),确定企业与战略伙伴的合作关系·在合作计划模型的基础上,分别讨论了动态博弈的纳什均衡理论和协商对策理论在模型中的应用·指出供应链的上下游合作企业达到"双赢"目标的条件及可行性·结果表明,非合作博弈体现了一种个人理性,而协商对策满足个人和群体合理性·仿真验证了模型和算法的有效性·     

收费公路和公共交通之间的定价博弈分析

研究了收费公路和公共交通的定价博弈问题.运用博弈论的模型和方法,在自由竞争和充分合作的情况下分别建立了两者间的定价模型;探讨了在自由竞争的情况下公路收费和公交票价达到的均衡状态,以及在多种政府政策下均衡状态的变化,同时分析了运营者相互合作下的公路收费和公交票价.结论表明,在自由竞争的情况下,政府税收对最优均衡定价没有影响,在固定出行需求的情况下,政府对公交进行补贴会导致两者的定价上升.     

基于双因子Lee-Carter模型的死亡率预测及年金产品风险评估

本文针对传统单因子Lee-Carter模型中死亡率的改善呈常数速率的明显弊端,利用贝叶斯MCMC方法和中国实际人口死亡率数据,考察对比了双因子Lee-Carter模型的预测效果.检验结果表明双因子模型的拟合优度和离差信息准则DIC明显优于单因子模型,较好地抓住了死亡率随时间演进的波动性.文章还进一步比较了基于两种模型预测结果的年金的定价、统计特征、风险度量和资本要求.结果表明双因子模型下,年金价格核密度图的尖峰厚尾现象较为突出,宜考虑TVaR风险度量来弥补SolvencyⅡ中基于VaR的资本额度计算方法的不足,以因应年金产品中死亡率超预期改善的长寿风险.     

Description of the aberrant Leptopilina lasallei n. sp., with an updated phylogeny of Leptopilina Förster (Hymenoptera: Figitidae: Eucoilinae)

ABSTRACT

In the search for native Asian parasitoids of Drosophila suzukii, the notorious spotted-wing Drosophila (SWD), an odd new species of Eucoilinae was discovered. Leptopilina lasallei sp. nov. is herein described and diagnosed relative to other eucoilines associated with drosophilid hosts. Morphologically, L. lasallei is somewhat aberrant within Leptopilina; phylogenetically, L. lasallei is sister group to the core Leptopilina. In the process of investigating L. lasallei, a de novo molecular phylogeny of Leptopilina was generated and is included here. The integrated approach used for the characterisation of L. lasallei, and the resulting phylogeny of Leptopilina, produced data useful to select parasitoid species for SWD biological control.http://www.zoobank.org/urn:lsid:zoobank.org:act:402D504A-4616-4524-85D7-1C13A6276F06 http://www.zoobank.org/urn:lsid:zoobank.org:act:402D504A-4616-4524-85D7-1C13A6276F06     

Spatial dependence model with feature difference

This paper presents a new spatial dependence model with an adjustment of feature difference. The model accounts for the spatial autocorrelation in both the outcome variables and residuals. The feature difference adjustment in the model helps to emphasize feature changes across neighboring units, while suppressing unobserved covariates that are present in the same neighborhood. The prediction at a given unit incorporates components that depend on the differences between the values of its main features and those of its neighboring units. In contrast to conventional spatial regression models, our model does not require a comprehensive list of global covariates necessary to estimate the outcome variable at the unit, as common macro-level covariates are differenced away in the regression analysis. Using the real estate market data in Hong Kong, we applied Gibbs sampling to determine the posterior distribution of each model parameter. The result of our empirical analysis confirms that the adjustment of feature difference with an inclusion of the spatial error autocorrelation produces better out-of-sample prediction performance than other conventional spatial dependence models. In addition, our empirical analysis can identify components with more significant contributions.