基于综合效益的城市地下物流网络设计与优化方法

针对城市地下物流系统(Underground Logistics System,ULS)的特征,对一系列ULS网络节点选址与优化问题进行了建模分析.1)从解决城市交通拥堵的角度出发,探讨了物流地上地下分配的三种方案,并建立了地下货运OD评价模型.2)综合权衡货运量与货源距离,基于改进的模糊C均值聚类确定ULS—级节点的选址和辐射范围.3)对每个一级区域构建了ULS二级节点选址优化模型,通过人工免疫算法搜索最少覆盖节点群及节点的最优归属.4)建立多目标ULS网络规划模型,结合Prim算法与Dijkstra算法实现货物地下运输路径的最优选择,并采用栅格覆盖的思路在节点服务范围内对ULS网络进行费用优化.5)提出ULS网络效能评估指标,设置中心节点以提高系统运输效率和抗风险能力.     

基于能量变化的无线传感器网络覆盖算法

设计了一种基于能量变化的无线传感器网络覆盖算法.通常网络节点连接的通信范围应该大于两倍的感应范围,但这将导致网络能耗的增加.提出的算法采用最少数量的节点来解决传感器网络的覆盖率和网络的连通性问题.测量结果表明,提出的覆盖算法可以满足传感器节点的通信范围小于两倍的感应范围.这将降低传感器网络的能量损耗.算法通过使用不同能量水平的活跃节点来降低能量损耗,并且可以延长系统的使用寿命.     

Ad Hoc网络区域划分和资源分配问题研究

主要利用图论、概率统计及优化理论对Ad-Hoc网络进行了数学建模.研究了等圆(不等圆)区域覆盖、带障碍区域的覆盖、确定性点覆盖、信道分配、抗毁度、节能性和通信质量等问题.定义了覆盖效率、抗毁性概率指标、覆盖系数、期望覆盖系数、网络寿命等一系列评价系数和衡量标准,提出了基于单位距离覆盖系数和期望覆盖系数的启发式寻优算法,并编程加以实现,得到较满意的近似解.     

区域分解算法——偏微数值解新技术(Ⅰ)

区域分解算法是近年开辟的计算偏微分方程数值解的一个新方向.由于该方法能分解大型问题为小型问题;复杂区域问题为简单区域问题;串行问题为并行问题,故已成为计算大型科学与工程问题的重要方法.本文综述美、苏、法、意及我国数学家在这一领域的某些工作.全文分覆盖型、非覆盖型、虚拟型三章.必要的参考文献附于文章末,供读者参考.     

Ad Hoc网络中的区域划分和资源分配问题

建立了Ad Hoc网络中的区域划分和资源分配的模型,重点对网络的最少小区数覆盖、抗毁性、节点分簇等问题进行了研究,并对节能性、通信质量评估进行了初步探讨.     

椭圆型方程的并行迭代区域分裂法——多子区域情形

本文提出了一个有内交叉点的多子域区域分裂法。无交叉点的多子域分裂可归为两子域情形。在每一交叉点处取一子域覆盖之。称其为覆盖子域。不同交叉点的覆盖子域互不相交。本文的算法是把求解子区域上的Dirichlet问题,混合问题和覆盖子域上的Dirichlet问题相结合而得到的。利用了两子域Schwarz交替法和两子域并行迭代分裂法的思想。本算法具有高度并行性和通信局部性,适应于任意多个子区域的分裂。     

移动式喷灌系统优化设计的数学模型

基于MCM2006A题,建立了均匀喷洒的喷灌系统优化管理模型.首先通过水力学计算,得到喷头射程、间距、数量和布局.然后将喷灌规则的确定转化为用单一小矩形条覆盖矩形区域的二维覆盖问题,针对一般情况设计了算法,得到管道移动方案.对所给喷灌区域,计算结果表明了模型的可行性.     

基于罚函数方法的Leach协议

为延长无线传感器网络生存时长、减少网络能量消耗,首先将自适应粒子群优化算法应用于Leach协议,获得每一轮的最优簇头集;再基于罚函数方法,对集合中处于边缘位置的感知节点以及基站附近能量较低的感知节点进行惩罚,降低其当选为簇头的概率.通过大量仿真实验表明,协议对网络中簇头节点的选取更加合理,死亡节点分布由外而内,使节点能量负载更加均衡.     

限制步长法及其推广

本文给出一个新的限制步长算法并讨论了算法的收敛性质.考虑问题这里f(x)∈C~2,C是R~n中的闭凸集.对于给定集合K,实数h及点y,定义hK={hx|x∈K},y+K={y+x|x∈K},而(?)表示K之边界点集.1.限制步长算法算法Ⅰ任意取定     

于改进UGF算法的线性拓扑WSN系统可靠性评估

提出了面向感知数据融合的通用发生函数(UGF)改进算法,并使用该算法对线性拓扑结构的无线传感网络(WSN)可靠性进行了评估。首先对PEGASIS协议下WSN的线性拓扑结构及数据传输过程进行抽象,建立了双向连续k/n:F系统模型。然后根据WSN感知数据传输及融合方式,在改进算法中重新定义了传感节点的UGF表达式和组合算子。最后对双向连续k/n:F模型进行单向化分解,根据得到的单向模型可靠性推导出双向模型的可靠性表达式。通过具体实例对提出的改进算法进行了验证,计算结果显示改进的算法可有效解决传感网络线性拓扑结构可靠性评估问题。     

Infeasibility spheres for finding robust solutions of blending problems with quadratic constraints

The blending problem is studied as a problem of finding cheap robust feasible solutions on the unit simplex fulfilling linear and quadratic inequalities. Properties of a regular grid over the unit simplex are discussed. Several tests based on spherical regions are described and evaluated to check the feasibility of subsets and robustness of products. These tests have been implemented into a Branch-and-Bound algorithm that reduces the set of points evaluated on the regular grid. The whole is illustrated numerically.     

MEA-BP模型在遥感影像分类中的应用研究

遥感影像分类作为遥感技术的一个重要应用,对遥感技术的发展具有重要作用.针对遥感影像数据特点,在目前的非线性研究方法中主要用到的是BP神经网络模型.但是BP神经网络模型存在对初始权阈值敏感、易陷入局部极小值和收敛速度慢的问题.因此,为了提高模型遥感影像分类精度,提出采用MEA-BP模型进行遥感影像数据分类.首先采用思维进化算法代替BP神经网络算法进行初始寻优,再用改进BP算法对优化的网络模型权阈值进一步精确优化,随后建立基于思维进化算法的BP神经网络分类模型,并将其应用到遥感影像数据分类研究中.仿真结果表明,新模型有效提高了遥感影像分类准确性,为遥感影像分类提出了一种新的方法,具有广泛研究价值.     

基于移动Agent技术的网格负载均衡调度方法

为了实现网格计算资源的动态自适应性管理和负载均衡调度,将移动Agent技术引入网格资源管理,并提出了一种基于Agent的网格资源调度模型.在该模型基础上,采用遗传克隆算法解决网格计算中的任务调度问题.仿真实验表明该算法不仅充分发挥了Agent的智能性、自主性,还具有良好的扩展性,提高了网格资源调度的效率.     

Using Discrete and Continuous Models to Solve Nanoporous Flow Optimization Problems

We consider using a discrete network model in combination with continuous nonlinear optimization models to solve the problem of optimizing channels in nanoporous materials. The problem and the hierarchical optimization algorithm are described in [2]. A key feature of the model is the fact that we use the edges of the finite element grid as the locations of the channels. The focus here is on the use of the discrete model within that algorithm. We develop several approximations to the relevant flow and a greedy algorithm for quickly generating a "good" tree connecting all of the nodes in the finite-element mesh to a designated root node. We also consider Metropolis-Hastings (MH) improvements to the greedy result. We consider both a regular triangulation and a Delaunay triangulation of the region, and present some numerical results.     

Local and parallel finite element algorithm for stationary incompressible magnetohydrodynamics

This article presents a local and parallel finite element method for the stationary incompressible magnetohydrodynamics problem. The key idea of this algorithm comes from the two‐grid discretization technique. Specifically, we solve the nonlinear system on a global coarse mesh, and then solve a series of linear problems on several subdomains in parallel. Furthermore, local a priori estimates are obtained on a general shape regular grid. The efficiency of the algorithm is also illustrated by some numerical experiments.© 2017 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 33: 1513–1539, 2017     

A modification of scarf's algorithm allowing restarting

A modification is proposed for Scarf's algorithm for computing fixed points, which allows restarting, and hence overcoming a serious difficulty of this well known algorithm. A particular feature of the new algorithm is that it uses for restarting an idea quite different from the sandwiching procedure of Merrill-Mackinnon, and operates with a non regular grid, in contrast with most other algorithms which use regular grids. From the computational point of view, it is very simple and seems to be quite competitive with other existing methods.     

Sub-exponential graph coloring algorithm for stencil-based Jacobian computations

Partial differential equations can be discretized using a regular Cartesian grid and a stencil-based method to approximate the partial derivatives. The computational effort for determining the associated Jacobian matrix can be reduced. This reduction can be modeled as a (grid) coloring problem. Currently, this problem is solved by using a heuristic approach for general graphs or by developing a formula for every single stencil. We introduce a sub-exponential algorithm using the Lipton–Tarjan separator in a divide-and-conquer approach to compute an optimal coloring. The practical relevance of the algorithm is evaluated when compared with an exponential algorithm and a greedy heuristic.     

An evidential approach to SLAM,path planning,and active exploration

Probability theory has become the standard framework in the field of mobile robotics because of the inherent uncertainty associated with sensing and acting. In this paper, we show that the theory of belief functions with its ability to distinguish between different types of uncertainty is able to provide significant advantages over probabilistic approaches in the context of robotics. We do so by presenting solutions to the essential problems of simultaneous localization and mapping (SLAM) and planning based on belief functions. For SLAM, we show how the joint belief function over the map and the robot's poses can be factored and efficiently approximated using a Rao-Blackwellized particle filter, resulting in a generalization of the popular probabilistic FastSLAM algorithm. Our SLAM algorithm produces occupancy grid maps where belief functions explicitly represent additional information about missing and conflicting measurements compared to probabilistic grid maps. The basis for this SLAM algorithm are forward and inverse sensor models, and we present general evidential models for range sensors like sonar and laser scanners. Using the generated evidential grid maps, we show how optimal decisions can be made for path planning and active exploration. To demonstrate the effectiveness of our evidential approach, we apply it to two real-world datasets where a mobile robot has to explore unknown environments and solve different planning problems. Finally, we provide a quantitative evaluation and show that the evidential approach outperforms a probabilistic one both in terms of map quality and navigation performance.     

停产后服务备件存储点的选择与库存问题研究

分析了停产后服务备件的三种存储模式,以主要基层仓库和常规基层仓库模式为基本模式,建立了选址库存模型,将两级库存体系不变模式和单级库存体系模式作为特例加以讨论,并设计了基于遗传算法的迭代算法求解选址库存问题,通过仿真算例验证了模型和方法的可行性和有效性.通过分析,主要基层仓库和常规基层仓库模式能够将成本优势与客户服务水平良好结合,是一种较好的模式.     

Indoor positioning system based on BLE location fingerprinting with classification approach

Position estimation is an important technique for location-based services. Many services and applications, such as navigation assistance, surveillance of patients and social networking, have been developed based on users’ position. Although the GPS plays an important role in positioning systems, its signal strength is extremely weak inside buildings. Thus, other sensing devices are necessary to improve the accuracy of indoor localisation. In the past decade, researchers have developed a series of indoor positioning technologies based on the received signal strength (RSS) of WiFi, ZigBee or Bluetooth devices under the infrastructure of wireless sensor network for location estimation. We can compute the distance of the devices by measuring their RSS, but the correctness of the result is unsatisfactory because the radio signal interference is a considerable issue and the indoor radio propagation is too complicated to model. Using the location fingerprint to estimate a target position is a feasible strategy because the location fingerprint records the characteristics of the signals and the signal strength is related to the space relation. This type of algorithm estimates the location of a target by matching online measurements with the closest a-priori location fingerprints. The matching or classification algorithm is a key issue in the correctness of location fingerprinting. In this paper, we propose an effective location fingerprinting algorithm based on the general and weighted k-nearest neighbour algorithms to estimate the position of the target node. The grid points are trained with an interval of 2 m, and the estimated position error is about 1.8 m. Thus, the proposed method is low computation consumption, and with an acceptable accuracy.