抗毁性WDM网状光网络中的P圈快速构造算法

p圈法是抗毁性网状WDM光网络中一种十分优秀的保护算法,在p圈配置到网状光网络的过程中,圈构造算法是设计的第一步.文中提出了一种快速圈挖掘算法(FCMA),本算法基于k-最短路由算法构造最小p图,通过圈扩展算法获得更多性能优良圈,文中给了启发式算法的具体步骤.通过计算机仿真表明,该算法实现快速圈构造,性能优于其它同类算法,适合网状光网络中的p圈快速构造.     

一种基于P圈的ASON节点保护方案

在研究自动交换光网络（ASON）中发生节点故障时的保护方法的基础上,提出了一种针对节点故障使用P圈实现业务保护的方案,同时提出了一种在确保发生单链路故障时实现100％业务恢复率的基础上进行节点故障保护的启发式算法,并通过OPNET软件搭建的ASON仿真平台,在泛欧COST239网络拓扑和北美NSFnet网络拓扑上,采用静态业务模型对该算法进行了仿真评估。仿真结果证明,该算法在拥有故障业务快速恢复能力的同时,相比已有算法具有更低的资源冗余度。     

一种基于P圈的ASON混合故障保护模型

在研究自动交换光网络(ASON)中发生节点/链路混合故障时的保护方法的基础上,设计了一种同时考虑节点故障和链路故障情况的基于P圈的保护模型,同时给出了一种保证在混合故障时可以达到100%业务恢复率的预配置P圈保护的启发式算法,并且综合考虑了实际网络中保护恢复速度和资源效率等因素,给出了具有最大P圈跳数限制的P圈配置方案.利用OPNET软件搭建了ASON仿真平台,在泛欧Cost239网络拓扑和北美NSFnet网络拓扑上,采用静态业务模型对该算法进行了仿真分析.仿真结果证明,该算法在拥有故障业务快速恢复能力的同时,相比已有算法具有更低的资源冗余度.     

圈图上的两类组合优化问题

在圈图上研究了两类组合优化问题.第一类问题主要研究在要求图中各边的最大调整费用不能超过给定预算时,如何对各边权进行调整,使得其他各顶点到给定顶点的距离之和最大,得到了线性时间算法;第二类问题主要研究在要求圈图上的所有边的调整费用之和不超过给定预算时,如何对各边权进行调整,使得某一固定顶点到给定顶点的距离尽可能的大,得到了求解该问题的多项式时间算法.     

HCONoC: a novel high-performance hierarchical optical network on chip

针对传统的片上电互连在带宽、时延等方面遇到的问题,提出一种基于光分组交换技术的分层簇的光片上网络(HCONoC).设计了该网络的拓扑结构连接方式、布局方法、节点编址命名方法以及扩展方式等.为HCONoC的不同层次结构分别构建了新的无阻塞片上光路由器结构,并分析了损耗特性.根据网络拓扑的具体特点和编址方式,设计了有效的路由算法,可提高网络吞吐同时减少时延.最后,使用OPNET搭建仿真平台,对所提网络结构的时延和吞吐性能进行了仿真,结果表明该网络结构在本地流量模式下显示出更强的性能优势.     

格网中p-cycles重配置的快速实现算法

提出了一种用于双链路故障恢复和为动态业务提供保护的p-cycles重配置启发式算法.该算法可以提供网络生存性设计和网络管理的高效性和简洁性,并能提供快速的恢复操作.仿真结果表明,这种算法用于双链路故障恢复的重配置容量冗余度可达74%,平均计算时间为48ms;用于增量重配置的双故障冗余度为118%,计算时间为81ms.基于该算法的业务恢复可以满足大多数业务的最低可中断时间要求.分析表明该算法亦适用于动态业务的p-cycles重配置.     

光互连网络中排序算法研究

通过对光互连网络排序算法的研究,提出利用二分法构造二分图依次确定内外节点开关的连接状态,得到可重排无阻塞的 Omega 网络, Banyan 网络和 Crossover 光网络,每种光互连网络都可实现 8×8 信号全排列无阻塞的输出和排序。针对二分法互连函数不一致的问题,继而采用优化的 Looping Algorithm 算法,生成路由标签确定各级节点开关的状态,从而得到互连函数相同结构简单性能优越的光网络。     

多波长光网络的动态路由等效算法

针对多波长光网络的特点，提出了一种动态路由和波长分配的等效算法。采用波长图、增加虚拟源节点和目的节点等技术，把多波长网络转化为等效的单波长网络，避免了求解路由和波长分配两个复杂子问题，简化了算法的程序设计。利用最短径算法进行路由和波长分配可以求得问题的最优解，从而有效地降低了网络阻塞率。仿真结果表明：与FAR-2D算法相比，在4和8波长的全波长转换网络中，采用等效算法阻塞率最大降幅分别达到0．02、0．025。     

分组OTDM局域网中接收受限问题的扩展

在分组间插的OTDM局域网中，光分组的发送和接收不仅受限于电子处理速度的限制，而且受限于光分组压缩解压缩器的结构。本文提出了使用分组重排和光时分解复用器构建并行解压缩器来缓解该受限问题的方案。同时，通过对传统的周期预留多址接入CRMA(Cycle Reservation Multiple Access)协议进行进一步扩展，得到了在受限条件下既能避免将数据分组简单丢弃、又能获取很高网络性能可行的高速网络接入控制协议。     

锅壳式锅炉补强圈制造加工质量问题引发的思考

在锅炉产品安全性能进行监督检验的过程中，发现某锅壳式锅炉安全阀管座补强圈的制造加工质量存在问题，本文通过论述补强圈的作用及分析存在的原因，提出解决方案和改进意见。     

Capacity-efficient strategy for 100% dual-failure restorability in optical mesh networks utilising reconfigurable p-cycles and a forcer filling concept

The problem of achieving 100% dual-failure restorability utilising reconfigurable p-cycle mechanisms has been investigated via three different p-cycle mechanisms derived from the integer linear programming model; complete-repair (CRP), incremental-repair (IRP) and dynamic-repair (DRP). An enhanced 100% dual-failure restoration mechanism is proposed, designated as modified DRP with forcer filling concept (MDRP/wFF), in which network protection is achieved using only reconfigurable span-protecting p-cycles without the addition of path-segment-protecting p-cycles as in DRP. As a result, the overhead incurred in differentiating between the two p-cycles becomes non-existent upon network restoration. Furthermore, a FF concept is employed to reduce the amount of spare capacity required to restore the network. The performance of the MDRP/wFF scheme was benchmarked against that of the CRP, IRP and DRP mechanisms. The simulation results show that MDRP/wFF achieves a performance tradeoff among CRP, IRP and DRP in terms of its spare capacity requirements and average number of reconfigured spans during the reconfiguration process [designated as average reconfiguration overhead (ARO)]. The MDRP/wFF scheme was found to have a lower spare capacity requirement but incurs some additional ARO compared with DRP.     

基于神经网络的图像边缘检测方法

提出了一种基于神经网络的图像边缘检测新方法.该方法首先基于邻域灰度极值提取边界候选图像,然后以边界候选象素及其邻域象素的二值模式作为样本集,输入边缘检测神经网络进行训练.边缘检测神经网络采用BP网络,为加快网络的训练速度,采用了滚动训练和权值随机扰动的方法.实验表明,该方法提高了神经网络的学习效率,获得的边缘图像封闭性好,边缘描述真实.     

Superhydrophobic–superoleophilic stainless steel meshes by spray-coating of a POSS hybrid acrylic polymer for oil–water separation

Superhydrophobic–superoleophilic meshes with hierarchical structures were fabricated by spraying a POSS hybrid acrylic polymer on stainless steel mesh for oil–water separation in this paper. Fourier transform infrared spectroscopy, nuclear magnetic resonance (¹H NMR), and thermogravimetric analyses were used to verify the chemical composition and thermostability of the POSS hybrid acrylic polymer, which was synthesized via a free radical solution polymerization. The obtained mesh was characterized by scanning electron microscopy, X-ray photoelectron spectroscopy, and optical contact angle meter to confirm the morphology, composition, and wettability of the film surface. The coated mesh, with a static water contact angle of 153° and a sliding angle of 4.5°, was applied to separate a series of oil–water mixtures, such as n-hexane, isooctane, petroleum ether, kerosene, and vegetable oil, with separation efficiency nearly 99%. In addition, the coated mesh still kept separation efficiency approximately 99% even after 25 separation cycles for n-hexane/water mixture. After 20 abrasion cycles, the water contact angle of the mesh remained 145°, and separation efficiency for n-hexane/water mixture is approximately 99%, indicating the coated mesh possessed good mechanical stability. The as-prepared mesh will be a promising material in oil–water separation, due to the simple, low-cost, and easily scalable fabrication method and the excellent separation performance in radical oil–water separation.     

An Influence Maximization Algorithm Based on Improved K-Shell in Temporal Social Networks

Influence maximization of temporal social networks (IMT) is a problem that aims to find the most influential set of nodes in the temporal network so that their information can be the most widely spread. To solve the IMT problem, we propose an influence maximization algorithm based on an improved K-shell method, namely improved K-shell in temporal social networks (KT). The algorithm takes into account the global and local structures of temporal social networks. First, to obtain the kernel value Ks of each node, in the global scope, it layers the network according to the temporal characteristic of nodes by improving the K-shell method. Then, in the local scope, the calculation method of comprehensive degree is proposed to weigh the influence of nodes. Finally, the node with the highest comprehensive degree in each core layer is selected as the seed. However, the seed selection strategy of KT can easily lose some influential nodes. Thus, by optimizing the seed selection strategy, this paper proposes an efficient heuristic algorithm called improved K-shell in temporal social networks for influence maximization (KTIM). According to the hierarchical distribution of cores, the algorithm adds nodes near the central core to the candidate seed set. It then searches for seeds in the candidate seed set according to the comprehensive degree. Experiments show that KTIM is close to the best performing improved method for influence maximization of temporal graph (IMIT) algorithm in terms of effectiveness, but runs at least an order of magnitude faster than it. Therefore, considering the effectiveness and efficiency simultaneously in temporal social networks, the KTIM algorithm works better than other baseline algorithms.     

Simulated-annealing-based genetic algorithm for modeling the optical constants of solids

We propose a simulated-annealing-based genetic algorithm for solving model parameter estimation problems. The algorithm incorporates advantages of both genetic algorithms and simulated annealing. Tests on computer-generated synthetic data that closely resemble optical constants of a metal were performed to compare the efficiency of plain genetic algorithms against the simulated-annealing-based genetic algorithms. These tests assess the ability of the algorithms to find the global minimum and the accuracy of values obtained for model parameters. Finally, the algorithm with the best performance is used to fit the model dielectric function to data for platinum and aluminum.     

A novel evolutionary meta-heuristic for the multi-objective optimization of real-world water distribution networks

Genetic algorithms are currently one of the state-of-the-art meta-heuristic techniques for the optimization of large engineering systems such as the design and rehabilitation of water distribution networks. They are capable of finding near-optimal cost solutions to these problems given certain cost and hydraulic parameters. Recently, multi-objective genetic algorithms have become prevalent in the water industry due to the conflicting nature of these hydraulic and cost objectives. The Pareto-front of solutions can aid decision makers in the water industry as it provides a set of design solutions which can be examined by experienced engineers. However, multi-objective genetic algorithms tend to require a large number of objective function evaluations to arrive at an acceptable Pareto-front. This article investigates a novel hybrid cellular automaton and genetic approach to multi-objective optimization (known as CAMOGA). The proposed method is applied to two large, real-world networks taken from the UK water industry. The results show that the proposed cellular automaton approach can provide a good approximation of the Pareto-front with very few network simulations, and that CAMOGA outperforms the standard multi-objective genetic algorithm in terms of efficiency in discovering similar Pareto-fronts.     

NUMERICAL ALGORITHMS FOR SOLUTIONS OF LARGE EIGENVALUE PROBLEMS IN PIEZOELECTRIC RESONATORS

Two algorithms for eigenvalue problems in piezoelectric finite element analyses are introduced. The first algorithm involves the use of Lanczos method with a new matrix storage scheme, while the second algorithm uses a Rayleigh quotient iteration scheme. In both solution methods, schemes are implemented to reduce storage requirements and solution time. Both solution methods also seek to preserve the sparsity structure of the stiffness matrix to realize major savings in memory. In the Lanczos method with the new storage scheme, the bandwidth of the stiffness matrix is optimized by mixing the electrical degree of freedom with the mechanical degrees of freedom. The unique structural pattern of the consistent mass matrix is exploited to reduce storage requirements. These major reductions in memory requirements for both the stiffness and mass matrices also provided large savings in computational time. In the Rayleigh quotient iteration method, an algorithm for generating good initial eigenpairs is employed to improve its overall convergence rate, and its convergence stability in the regions of closely spaced eigenvalues and repeated eigenvalues. The initial eigenvectors are obtained by interpolation from a coarse mesh. In order for this multi-mesh iterative method to be effective, an eigenvector of interest in the fine mesh must resemble an eigenvector in the coarse mesh. Hence, the method is effective for finding the set of eigenpairs in the low-frequency range, while the Lanczos method with a mixed electromechanical matrix can be used for any frequency range. Results of example problems are presented to show the savings in solution time and storage requirements of the proposed algorithms when compared with the existing algorithms in the literature.