Generalized linear complementarity in a problem ofn-person games

In this paper, we introduce a generalization of the polymatrix game (a nonzero sum noncooperativen-person game) considered by Howson and relate the problem of computing an equilibrium set of strategies for such a game to the generalized linear complementarity problem of Cottle and Dantzig. For an even more general version of the game we prove the existence of an-equilibrium set of strategies. We also present a result on the stability of the equilibria based on degree theory.     

双自由度后坐保险机构敏感因子对比

目前国内对双自由度后坐保险机构的研究大多集中于对其动态特性的分析和仿真,对机构运动影响敏感因子的分析很少。借助计算机仿真,运用正交试验设计方法对双自由度后坐保险机构敏感因子进行分析,确定各敏感因子对机构影响程度的主次顺序以及主要敏感因子。结果表明上质量块质量对其运动特性影响最大并为主要敏感因子,然后依次是惯性簧刚度,惯性簧长度和下质量块质量的影响。     

基于集对分析的保障能力薄弱环节改进模型研究

确定保障能力薄弱环节对提高部队保障能力至关重要,目前保障能力薄弱环节确定有指标评估得分法和权重法两种方法,两种方法均存在一定的片面性。针对存在的问题,提出一种基于指标裕度空间和权重的求指标薄弱度的方法查找、确定保障能力薄弱环节,并且将集对分析的思想应用到指标优化和指标裕度空间的分析求取上,使指标薄弱度更加精确。最后结合某基层一级保障单位进行实例,运用Matlab软件仿真模型,进行保障能力指标薄弱度分析,并与目前存在的两种方法对比分析,实践证明所提出的方法更适用于保障能力薄弱环节的确定。     

灰色理论在古陶瓷断代中的应用

利用模式识别方法可以推测古陶瓷出产年代。将古陶瓷中各化学成分的含量组成一个向量,并与该向量的灰色模型GM(1, 1)参数构成新向量,用于描述古陶瓷样品。以灰色关联度为距离定义,使用近邻法对古陶瓷样品年代进行预测,其留一法预测精度达79.55%。实验结果验证了灰色理论可以用于古陶瓷断代。     

基于最大隶属度的区间概率灰色随机多准则决策方法

定义了一种灰色隶属函数.针对概率为区间数、准则值为区间灰数的灰色随机多准则决策问题,提出一种基于最大隶属度的决策方法.首先,运用区间数可能度排序向量将区间概率转化为点概率,并将其转化为无风险决策问题;然后,计算各方案在负理想方案到正理想方案上的灰色隶属度,并计算各方案准则值的相对灰度,进而根据灰色隶属度和相对灰度大小对方案进行排序;最后,通过算例验证了该方法的可行性和有效性.     

Adaptive beam search lookahead algorithms for the circular packing problem

This paper addresses the circular packing problem (CPP), which consists in packing n circles C_i, each of known radius r_i, i∈N={1, …, n}, into the smallest containing circle C. The objective is to determine the radius r of C as well as the coordinates (x_i, y_i) of the center of C_i, i∈N. CPP is solved using two adaptive algorithms that adopt a binary search to determine r, and a beam search to check the feasibility of packing n circles into C when the radius is fixed at r. A node of level ?, ?=1, …, n, of the beam search tree corresponds to a partial packing of ? circles of N into C. The potential of each node of the tree is assessed using a lookahead strategy that, starting with the partial packing of the current node, assigns each unpacked circle to its maximum hole degree position. The beam search stops either when the lookahead strategy identifies a feasible packing or when it has fathomed all nodes. The computational tests on a set of benchmark instances show the effectiveness of the proposed adaptive algorithms.     

基于样本块的破损唐卡图像修复算法的改进

基于样本块的修复算法由于能同时有效地修复图像的受损纹理和结构的优点被引入到唐卡图像的数字化保护中,而且它对特定破损唐卡图像修复效果良好,但是由于优先权计算和最佳样本块不唯一等问题的影响,算法对其他类唐卡图像修复效果不佳。针对此算法的不足,对信任度计算方法和等照线计算方法进行了改进,解决了最佳样本块不唯一的问题。实验结果证明,改进后的算法不仅能够得到令人满意的修复结果而且能够提高修复效率。     

社会网络中紧密度模型分析

针对社会网络中节点的复杂性,以及节点间关系的不确定性,本文采用中心性和凝聚子群对社会网络进行建模分析,并通过MATLAB进行仿真实验验证,从而在复杂的网络中选择出有效的中心节点。通过对节点的管理和利用,实现对整个社会网络管理的科学性,严谨性。通过模型的理论分析,我们可以设想该方法将对社会网络知识的传播,有效信息的发布,社会网络的稳定等起到有的放矢的作用。因而此研究具有良好的实践价值。     

改进Dijkstra算法在PGIS中的应用

传统Dijkstra算法用于路径诱导会使路网节点的数量增多、搜索范围扩大,从而耗费大量时间和空间,降低停车诱导信息系统(PGIS)的运行效率和实时性。针对城市路网的特定环境和路径诱导需求,根据2点之间直线最短的原理,在Dijkstra算法的基础上,提出一种应用于PGIS、基于矩形搜索范围的改进Dijkstra算法,设计并实现城市路网模型中单行、禁行、交叉点时间延误等问题的解决方案。实验结果表明,改进Dijkstra算法可以减少路网节点搜索范围和计算复杂度,提高用户搜索路径的实时性。     

Self-stabilizing minimum degree spanning tree within one from the optimal degree

We propose a self-stabilizing algorithm for constructing a Minimum Degree Spanning Tree (MDST) in undirected networks. Starting from an arbitrary state, our algorithm is guaranteed to converge to a legitimate state describing a spanning tree whose maximum node degree is at most Δ^∗+1, where Δ^∗ is the minimum possible maximum degree of a spanning tree of the network.To the best of our knowledge, our algorithm is the first self-stabilizing solution for the construction of a minimum degree spanning tree in undirected graphs. The algorithm uses only local communications (nodes interact only with the neighbors at one hop distance). Moreover, the algorithm is designed to work in any asynchronous message passing network with reliable FIFO channels. Additionally, we use a fine grained atomicity model (i.e., the send/receive atomicity). The time complexity of our solution is O(mn²logn) where m is the number of edges and n is the number of nodes. The memory complexity is O(δlogn) in the send-receive atomicity model (δ is the maximal degree of the network).