Observation of Topological Links Associated with Hopf Insulators in a Solid-State Quantum Simulator

Hopf insulators are intriguing three-dimensional topological insulators characterized by an integer topological invariant. They originate from the mathematical theory of Hopf fibration and epitomize the deep connection between knot theory and topological phases of matter, which distinguishes them from other classes of topological insulators. Here, we implement a model Hamiltonian for Hopf insulators in a solid-state quantum simulator and report the first experimental observation of their topological properties, including nontrivial topological links associated with the Hopf fibration and the integer-valued topological invariant obtained from a direct tomographic measurement. Our observation of topological links and Hopf fibration in a quantum simulator opens the door to probe rich topological properties of Hopf insulators in experiments. The quantum simulation and probing methods are also applicable to the study of other intricate three-dimensional topological model Hamiltonians.

Link-Vertex Topology Aggregation for Multi-domain Path Computation with Hierarchical PCE Architecture

Inter-domain path computing is one big issue in multi-domain networks. The Hierarchical Path Computing Element (H-PCE) is a semi-central architecture for computing inter-domain path. To facilitate H-PCE in inter-domain path computing, this paper proposed a topology aggregation scheme to abstract the edge nodes and their connected inter-domain link as one vertex to achieve more optimal paths and confidentiality guarantee. The effectiveness of the scheme has been demonstrated on solving wavelength routing in multi-domain Wavelength Division Multiplexing (WDM) network via simulation. Simulation results show that this scheme reduces at least 10% inter-domain blocking probability, compared with the traditional Domain-to-the-Node (DtN) scheme.

TOPOLOGICAL STRUCTURES OF VORTEX AND HELICITY ANALYSIS

The topological structures of the vortex filaments and vortex tubes with an exact solution of a straight spiral vortex tube are discussed. It is found that there are some confusions about the calculation of the helicity of a knotted vortex filament and some linked vortex filaments by using different methods. How to unify these methods is explained and the right results are given. (Edited author abstract) 5 Refs.

基于离散时间量子漫步的链路预测算法

量子漫步算法能模拟游走粒子在图上的量子相干演化,粒子的运动状态由量子态的相干叠加而成.与经典随机游走算法相比,量子漫步算法具有寻找目标节点时间少和源节点扩散至其他节点时间少的优点.提出一种基于离散时间量子漫步的链路预测(link predictionbased on discrete time quantum walk,简称LP-DTQW)算法.研究结果表明:相对于其他7种算法,LP-DTQW算法有更高的预测精度;LP-DTQW算法的时间复杂度远低于经典RWR(random walk with restart)链路预测算法的时间复杂度.因此,LP-DTQW算法具有更强的预测性能.

基于SCL-CMM模型的超网络链路预测方法

图为建模现实系统的内在交互提供了一种有效的方式,但却无法显示捕获的多个实体之间广泛存在的高阶异质性,超图则可以很好地突破低阶关系的限制。超网络的链路预测就是根据观测到的超图结构来预测未知的超链路,因其可以充分地刻画复杂系统的关联模式而成为网络科学中的热点问题。现有的方法通常针对整个拓扑结构设计推理模型,忽略了网络中隐含的聚集特性,导致预测的超链路类别不全面。针对上述问题,提出了基于超图谱聚类解析器的协调矩阵最小化(coordination matrix minimization based on hyper graph spectral clustering parser,SCL-CMM)模型的超网络链路预测方法。该方法将高阶超网络映射到具有一定语义的异质超图上,然后利用谱聚类解析器来提取超链路的结构特征,将原始超图重构为多个同质子图,进而在子图的观测空间而不是整个网络的邻接空间推断潜在超链路的分布情况,还原完整的超网络结构。该方法联合学习超网络的结构特征与集聚属性来建模各个子图的高阶非线性行为,解决了异构超图链路预测类别单一、精度低的问题。在9个真实数据集上进行了大量的对比实验表明,该方法在AUC(area under curve)评分和召回率方面都显著优于现有方法。

基于度中心性的AFDX网络拓扑优化

随着航空技术的飞速发展,航空电子全双工交换以太网(AFDX)取代了传统航电传输总线标准,成为新一代机载设备网络。针对目前航电网络的带宽资源分配不均的问题,提出了一种基于度中心性理论的AFDX网络拓扑优化算法,并依据某型号真实网络拓扑中终端之间的虚拟链路关系对其进行了仿真和实验。通过将带宽资源占用率较高的终端调整至一个交换机,使目标交换机级联带宽降低了11.7%和9.72%;并通过基于OPNET的全数字仿真平台对本算法进行了仿真验证,全网交换机带宽占用率均有不同程度的降低。结果表明,上述方法能够对网络拓扑架构进行优化,不仅能够解决飞机网络局部带宽分配不足的问题,同时能够优化网络性能,具有重要的工程实践意义。

基于特征学习的链路预测模型TNTlink

在合作作者网络中,链路预测可以预测当前网络中缺失的链接,以及新的或已解散的链接,根据网络中观测到的信息来推断两位作者在不久的将来是否会产生合作,对于挖掘和分析网络的演化、重塑网络模型具有重要意义。链路预测是计算机科学和物理学的重要研究方向,对此已有较深入的研究,其主要研究思路是基于马尔可夫链、机器学习和无监督的学习。然而,这些工作大多只使用单一的特征,即基于网络拓扑特征或者属性特征进行预测,很少将这些跨学科的特征组合考虑,结合多学科特征进行链路预测的研究非常少。文中设计开发了TNTlink模型,该模型结合网络拓扑特征、基本特征和附加特征,并结合物理学和计算机科学的领域知识,利用深度神经网络将这些特征集成到一个深度学习框架中,其在解决链路预测问题时取得了不错的效果。文中使用了5个数据集(ca-AstroPh,ca-CondMat,ca-GrQc,ca-HepPh和ca-HepTh),包含69032个节点和450617条边,从捕获的信息中利用二进制相似度和模糊余弦相似度计算和识别特征。如果节点在这些特征中表现出更多的相似性(如相似的节点、相同的关键字或彼此之间密切的关系),则两个节点间更有可能生成链接。除了考虑节点的特征外,还考虑了节点重要性对链路形成的影响,进而提出了一种新的链路预测指标MI,以区分强影响和弱影响,对节点的重要影响进行建模。将所提模型与主流分类器在5个数据集上进行比较,结果表明MI和TNTlink有效地提高了链路预测的AUC值。

基于拓扑优化和链路感知的无线体域网路由协议

为解决无线体域网(Wireless Body Area Networks,WBAN)在人体运动过程中网络拓扑结构频繁变化导致链路质量和WBAN性能下降等问题,首先根据人体结构对WBAN网络拓扑进行优化,通过添加中继节点建立WBAN主干网,提供节点和hub之间相对稳定的链接,然后提出了适用于WBAN拓扑优化后的路由策略(Routing Protocol Based on Topology Optimization and Link Awareness,R-TOLA)。R-TOLA综合了链路质量感知和代价函数,通过调整主干网中继和节点中继获得最优化路径。仿真实验表明,基于拓扑结构优化和链路感知的R-TOLA协议和其他路由协议相比,在人体拓扑网络结构频繁变化的环境下具有网络生存时间更长、吞吐量更大等优势。

基于节点交互度的社会网络链路预测

链路预测是通过已知的网络拓扑和节点属性挖掘未来时刻节点潜在关系的重要手段,是预测缺失链路和识别虚假链路的有效方法,在研究社会网络结构演化中具有现实意义.传统的链路预测方法基于节点信息或路径信息相似性进行预测,然而,前者考虑指标单一导致预测精度受限,后者由于计算复杂度过高不适合在规模较大网络中应用.通过对网络拓扑结构的分析,本文提出一种基于节点交互度(interacting degree of nodes,IDN)的社会网络链路预测方法.该方法首先根据网络中节点间的路径特征,引入了节点效率的概念,从而提高对于没有公共邻居节点之间链路预测的准确性;为了进一步挖掘节点间共同邻居的相关属性,借助分析节点间共同邻居的拓扑结构,该方法还创新性地整合了路径特征和局部信息,提出了社会网络节点交互度的定义,准确刻画出节点间的相似度,从而增强网络链路的预测能力;最后,本文借助6个真实网络数据集对IDN方法进行验证,实验结果表明,相比于目前的主流算法,本文提出的方法在AUC和Precision两个评价指标上均表现出更优的预测性能,预测结果平均分别提升22%和54%.因此节点交互度的提出在链路预测方面具有很高的可行性和有效性.

基于SOP拓扑结构的配电网系统故障定位方法

针对传统配电网系统中故障定位方法存在故障定位信息回馈速度较慢的问题,研究了基于SOP拓扑结构的改进配电网系统,减少了系统因自身原因导致的故障。基于数据型乘法器加强了系统全局的故障检出定位的能力,同时增强硬件的抗干扰能力。研究基于MTG定位算法,可以通过对于故障产生原因权重的判断,提高对于定位的精确度。通过仿真试验表明,所设计的硬件抗干扰电路在干扰数据量为10G时抗干扰率能达到97.25%,具有很好的抗干扰能力。

无人卫通链路的动态建立机制及协议设计

针对无人卫通链路动态建立面临的挑战,文中提出了一种基于动态协议设计的解决方案。首先,分析了当前无人卫通链路存在的问题,包括传统静态建立机制的不足。其次,探讨了动态建立机制的必要性,并提出了一种基于深度学习的协议设计。该设计充分考虑了网络拓扑变化和资源分配的动态性,有效实现了无人卫通链路的动态建立,提高了链路稳定性和通信效率。最后,通过模拟和实验验证,检验了该设计的有效性和可行性。

浅谈工业以太网CC-LINK IE

基于以太网的整合网络CC-LINK IE系列推出的控制网络与现场网络,实现了从上位信息系统到下位现场系统的无缝协作。CC-LINK IE在数据容量与传输速度方面都与商用以太网相差无几,又很大程度上突破了传统工业以太网的延时性。除此之外,CC-LINK IE采用双环路的网络结构,任何一个站出现故障都不会干扰整个网络的数据交换,很大程度上提高了可靠性。本文比较了CCLINK IE与其他工业以太网的优缺点,最后提出有关应用案例。

Evaluating the Topology Coverage of BGP Monitors

BGP monitors are currently the main data resource of AS-level topology measurement,and the integrity of measurement result is limited to the location of such BGP monitors.However,there is currently no work to conduct a comprehensive study of the range of measurement results for a single BGP monitor.In this paper,we take the first step to describe the observed topology of each BGP monitor.To that end,we first investigate the construction and theoretical up-limit of the measured topology of a BGP monitor based on the valley-free model,then we evaluate the individual parts of the measured topology by comparing such theoretical results with the actually observed data.We find that:1)for more than 90%of the monitors,the actually observed peer-peer links merely takes a small part of all theoretical visible links;2)increasing the BGP monitors in the same AS may improve the measurement result,but with limited improvement;and 3)deploying multiple BGP monitors in different ASs can significantly improve the measurement results,but non-local BGP monitors can hardly replace the local AS BGP monitors.We also propose a metric for monitor selection optimization,and prove its effectiveness with experiment evaluation.

工业以太网CC-LINK IE的性能分析

基于以太网的整合网络CC-LINKIE系列推出的控制网络与现场网络,实现了从上位信息系统到下位现场系统的无缝协作。CC-LINKIE在数据容量与传输速度方面都与商用以太网相差无几,又很大程度上突破了传统工业以太网的延时性。除此之外,CC-LINKIE采用双环路的网络结构,任何一个站出现故障都不会干扰整个网络的数据交换,很大程度上提高了可靠性。本文比较了CC-LINKIE与其他工业以太网的优缺点,最后提出有关应用案例。

Resources Allocation and Failures in Step Topology under Distributed Computing System

In the past years, distributed computing is gaining the popularity due to reduction in execution time and low cost involvement. On the basis of this, Mobile Adhoc Network (MANET) is also increasing worldwide with major advantage that it has no involvement of wire and transfer of data can be done by the virtual paths if the existing path is congested. In the present work, MANET is considered in the form of step topology which consists of heterogeneous collection of the devices. The work demonstrates the resources allocation for execution of tasks and it consists of selection of right path if the link failures and by pass link failures. It also consists of the resource management over the new proposed step topology. Entire work is modeled with the help of well known modeling language known as Unified Modeling Language (UML) and model demonstrates the resources allocation for execution of the tasks.

Detection of Link Fault Using Risk Modelling and Improving QOS Parameters in IP/WDM Networks

An operational backbone network is connected with many routers and other devices. Identifying faults in the network is very difficult, so a fault localization mechanism is necessary to identify fault and alleviate it and correct the faults in order to reduce the network performance degradation. A risk model needs to be devised based on the dynamic database by creating alternate path and the network is reconfigured by identifying dynamic paths. In this paper, an on-demand link state routing approach is used for handling failures in IP backbone networks and a localization algorithm is used to improve QOS parameters based on threshold value of gateway. It is proved that on-demand link state routing guarantees loop-free forwarding to reachable destinations regardless of the number of failures in the network, and in case of localization algorithm using modification process packet loss is avoided based on threshold value of gateway. Heuristic algorithm is also used for reconfiguration of dynamic path for effective fault localization. In this paper, in order to change the traffic condition, reconfiguration strategic is dynamically used. Dijikstra’s shortest path algorithm has been used to determine the shortest path between node pairs. Using risk modeling mechanism, a small set of candidate faults is identified. The concept of Fault Localization is used to minimize the fault occurring in the node and sends original path to node pairs. The localization algorithm based on MODIFICATION PROCESS, packet loss is avoided in the network by checking threshold value of gateway. If the threshold value is maximum, router directly forwards the packet to destination through gateway and if the threshold value is minimum, router compresses the packet and forwards the packet to destination with notification via gateway.

面向新能源并网的低压用户链路识别算法

针对新能源并网带来的电压特性变化和低压拓扑关系辨识颗粒度细化需求,提出了基于图信号处理的用户链路识别模型。该识别模型利用相邻节点间的电压相似性表征图信号的平滑性,结合节点电流定律对网络结构的约束,克服新能源并网带来的同相用户电压相似性变差的影响;利用图结构固有的链路属性,实现低压拓扑识别下沉至用户之间的上下游连接关系识别。最后,利用真实用户数据搭建仿真模型验证了所提算法的有效性,探讨了所提算法在不同场景下的性能表现,并与已有识别算法进行了比较分析。算例表明,所提算法与已有算法相比,可有效识别用户链路关系,且对新能源并网渗透率和数据误差率有较好的鲁棒性。

基于Louvain算法的复杂网络链路预测仿真

针对复杂网络拓扑结构相似的链路预测结果精度较差、适应度不高的问题,提出基于Louvain算法的复杂网络链路预测方法。计算所有编码复杂网络中社团内部连边总值,运用Louvain算法划分复杂网络社团结构,采用标准化互信息衡量划分结果差异,明确复杂网络结构与特征;计算复杂网络拓扑结构的链路权重,将相似性指标作为共同邻居信息,考虑复杂网络边的聚类与扩散特征,利用链接拓扑权重实现权重加权预测,完成高精度复杂网络链路预测任务。实验对比结果表明,所提方法可获得较高水准的曲线下面积值,有效增强链路预测准确率与运算效率,可广泛应用于复杂网络链路预测现实场景中。