微波接力组网的节点再优化研究

为进一步完善常规方法构建的微波接力网组网拓扑,提出了一种基于最优链路集的网络节点再优化方法。该方法综合考虑节点吸引系数、链路衰落概率、节点通信冗余等因素,借助网络拓扑优化的思想,采用遗传算法构造了微波接力网的最优链路集;以节点在该集合中的度为依据,通过对节点的合理排序,以部分用户节点代替非必要的干线节点,完善了组网拓扑。在修改链路和节点价值集后,该方法还可解决其他网络干线、中继节点的选择问题。     

Combining multivariate volatility forecasts using weighted losses

The ability to improve out-of-sample forecasting performance by combining forecasts is well established in the literature. This paper advances this literature in the area of multivariate volatility forecasts by developing two combination weighting schemes that exploit volatility persistence to emphasise certain losses within the combination estimation period. A comprehensive empirical analysis of the out-of-sample forecast performance across varying dimensions, loss functions, sub-samples and forecast horizons show that new approaches significantly outperform their counterparts in terms of statistical accuracy. Within the financial applications considered, significant benefits from combination forecasts relative to the individual candidate models are observed. Although the more sophisticated combination approaches consistently rank higher relative to the equally weighted approach, their performance is statistically indistinguishable given the relatively low power of these loss functions. Finally, within the applications, further analysis highlights how combination forecasts dramatically reduce the variability in the parameter of interest, namely the portfolio weight or beta.     

一种由三角模所诱导的粗糙集间的相似性度量方法

给出两粗糙集间一种新的相似度计算方法,并将其应用到模糊模式识别问题中,通过算例说明这种方法的可行性和有效性.     

浮筒式液面调节器设计

该文设计了一种新型的液面调节器：对浮筒式液面调节器的原理和结构进行了设计，并对调节器的技术指标和调节方法做了阐述，经实际使用证明，该调节器对液面调节与控制效果良好。     

模的极小逼近的一个等价条件

设Λ为Artin代数,C,X,Y是有限生成右Λ-模,χ,γ为有限生成右Λ-模范畴的子范畴.证明了C有右极小χ-逼近0→Y→X→C→0当且仅当Ext_Λ~1(C,)|_γ以Hom_Λ(Y,)|_γ为投射盖.类似地给出了当C有左极小γ-逼近时的等价条件.     

自由代数F_m的E(n)-模代数的证明

对任一个m×n矩阵Γ∈Mm×n(k),给出E(n)在m个变量的自由代数Fm上有一个作用,证明了该作用使得自由代数Fm成为一个E(n)-模代数.     

模糊集范畴的性质

定义一种模糊集范畴F,讨论了F具有的范畴性质:F与子集范畴之间的关系;F上的态射用a-截集表示的条件;F上态射和映射的关系:证明了F的乘积、等子、余等子、拉回和极限等存在,并给出它们的具体形式.     

强G-预不变凸函数的性质研究

给出了强G-预不变凸函数的一些新的性质,这些性质在数学规划中有着重要的应用.     

帐篷映射归宿的完整性探析

应用三进制方法完整地研究了帐篷映射的动力学特性．当初值x0=0，1时，经帐篷映射迭代后的最终归宿为x∞→0；当x0 [0，1]时，x∞→-∞．当x0∈（O，1），x0为有限位小数时，x0→0或-∞；当x0为循环小数时，x∞将处在周期轨道上或趋于-∞；当x0为不循环小数时，x∞将处在混沌轨道上或趋于-∞．     

An Efficient Multidimensional Fusion Algorithm for IoT Data Based on Partitioning

The Internet of Things (IoT) implies a worldwide network of interconnected objects uniquely addressable, via standard communication protocols. The prevalence of IoT is bound to generate large amounts of multisource, heterogeneous, dynamic, and sparse data. However, IoT offers inconsequential practical benefits without the ability to integrate, fuse, and glean useful information from such massive amounts of data. Accordingly, preparing us for the imminent invasion of things, a tool called data fusion can be used to manipulate and manage such data in order to improve process efficiency and provide advanced intelligence. In order to determine an acceptable quality of intelligence, diverse and voluminous data have to be combined and fused. Therefore, it is imperative to improve the computational efficiency for fusing and mining multidimensional data. In this paper, we propose an efficient multidimensional fusion algorithm for IoT data based on partitioning. The basic concept involves the partitioning of dimensions (attributes), i.e., a big data set with higher dimensions can be transformed into certain number of relatively smaller data subsets that can be easily processed. Then, based on the partitioning of dimensions, the discernible matrixes of all data subsets in rough set theory are computed to obtain their core attribute sets. Furthermore, a global core attribute set can be determined. Finally, the attribute reduction and rule extraction methods are used to obtain the fusion results. By means of proving a few theorems and simulation, the correctness and effectiveness of this algorithm is illustrated.