Model of Computer Network Topology Optimization Based on Pattern Recognition Technology

The topological structure of the computer network has an important influence on the efficiency of the whole network system,the exertion of the technical performance, the reliability and the cost. The concept of computer network topology classification, introduces thecharacteristics, based on the analysis of the complex network structure, explore the effective construction of computer network topology model,for its practical application in the design of redundancy is studied, to improve the network system reliability and system safety design，

Subdivision surface modeling system based on arbitrary topological curves network and combined subdivision

Arbitrary topological curve network has no restriction in topology structure,so it has more powerful representing ability in defining complex surfaces.A complex surface modeling system is presented based on arbitrary topological curve network and the improved combined subdivision method,its functions including creating and editing curve network,and generating and modifying curve network＇s interpolated surface.This modeling system can be used to the process of products＇concept design,and its applications is also significant to the development of subdivision method.

Brain networks modeling for studying the mechanism underlying the development of Alzheimer’s disease

Alzheimer’s disease is a primary age-related neurodegenerative disorder that can result in impaired cognitive and memory functions.Although connections between changes in brain networks of Alzheimer’s disease patients have been established,the mechanisms that drive these alterations remain incompletely understood.This study,which was conducted in 2018 at Northeastern University in China,included data from 97 participants of the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset covering genetics,imaging,and clinical data.All participants were divided into two groups:normal control(n=52;20 males and 32 females;mean age 73.90±4.72 years)and Alzheimer’s disease(n=45,23 males and 22 females;mean age 74.85±5.66).To uncover the wiring mechanisms that shaped changes in the topology of human brain networks of Alzheimer’s disease patients,we proposed a local naive Bayes brain network model based on graph theory.Our results showed that the proposed model provided an excellent fit to observe networks in all properties examined,including clustering coefficient,modularity,characteristic path length,network efficiency,betweenness,and degree distribution compared with empirical methods.This proposed model simulated the wiring changes in human brain networks between controls and Alzheimer’s disease patients.Our results demonstrate its utility in understanding relationships between brain tissue structure and cognitive or behavioral functions.The ADNI was performed in accordance with the Good Clinical Practice guidelines,US 21 CFR Part 50-Protection of Human Subjects,and Part 56-Institutional Review Boards(IRBs)/Research Good Clinical Practice guidelines Institutional Review Boards(IRBs)/Research Ethics Boards(REBs).

A Topological Evolution Model Based on the Attraction of the Motif Vertex

As a fundamental problem in the field of the network science,the study of topological evolution model is of great significance for revealing the inherent dynamics and mechanisms of complex network evolution.In order to study the influence of different scales of preferential attachment on topological evolution,a topological evolution model based on the attraction of the motif vertex is proposed.From the perspective of network motif,this model proposes the concept of attraction of the motif vertex based on the degree of the motif,quantifies the influence of local structure on the node preferential attachment,and performs the preferential selection of the new link based on the Local World model.The simulation experiments show that the model has the small world characteristic apparently,and the clustering coefficient varies with the scale of the local world.The degree distribution of the model changes from power-law distribution to exponential distribution with the change of parameters.In some cases,the piecewise power-law distribution is presented.In addition,the proposed model can present a network with different matching patterns as the parameters change.

Deductive way of reasoning about the internet AS level topology

Our current understanding about the AS level topology of the Internet is based on measurements and inductive-type models which set up rules describing the behavior （node and edge dynamics） of the individual ASes and generalize the consequences of these individual actions for the complete AS ecosystem using induction. In this paper we suggest a third, deductive approach in which we have premises for the whole AS system and the consequences of these premises are determined through deductive reasoning. We show that such a deductive approach can give complementary insights into the topological properties of the AS graph. While inductive models can mostly reflect high level statistics （e.g., degree distribution, clustering, diameter）, deductive reasoning can identify omnipresent subgraphs and peering likelihood. We also propose a model, called YEAS, incorporating our deductive analytical findings that produces topologies contain both traditional and novel metrics for the AS level Internet.

A NETWORK MODEL DECOMPOSITION METHOD FOR THE CALCULATION OF CUT-OFF FREQUENCIES OF AN ARBITRARILY SHAPED WAVEGUIDE WITH ARBITRARY FILLING

A new method, termed network model decomposition method (NMDM), is pre-sented in this paper for the evaluation of cut-off frequencies of an arbitrarily shaped waveguidewith arbitrary filling. Through discretizing the cross section of the waveguide, a topologicalmodel is first constructed, and then the corresponding network model is established based on thedifferential forms in electromagnetic field theory. A general algorithm for evaluating the cut-offfrequencies of an arbitrarily shaped waveguide with arbitrary filling has also been developed byusing the diakoptic node analysis in electrical network theory. Finally the algorithm is applied toseveral typical waveguides, as a demonstration of the validity of the method.

基于拓扑复杂程度的实际供热管网建模方法研究与分析

提出了按照管网拓扑复杂程度建立水力工况仿真模型的思路,以及结合流量方向和最小闭合差判断环网水力交汇位置的方法。以某市区集中供热管网为研究对象,根据3种复杂程度建立管网水力工况仿真模型,并取3种工况验证分析。结果表明,该方法能准确判断环网水力交汇位置,每一级的模型仿真压力与实际值的最大相对误差的绝对值均低于10%,且管网拓扑结构复杂程度越高,模型模拟误差越小,建模成本越高。

多因素下基于路网拓扑的电动汽车充电路径规划策略

随着电动汽车产业的发展,电动汽车的充电需求也日益增加.为了满足电动汽车用户充电多样性需求并提高充电设施利用率,本文在考虑出行距离、充电电价以及充电站排队情况等三种影响因素下构建混合整数线性规划模型,提出了一种多因素下基于充电站路网拓扑结构的电动汽车充电路径规划方法,为用户规划充电路径与充电站选择.首先,该方法在能耗约束的前提下基于Dijkstra最短路径算法进行充电引导,为求解多目标最优引入信息熵的概念来确定各参数影响权重.其次,针对用户充电需求的差异性问题,提出了三种不同目标下的规划方法以降低用户充电成本.此外,本文构建了站点随机充电服务排队模型并进行敏感性分析以研究充电站服务能力对充电成本的影响.以某地区路网为算例进行仿真,结果表明本文提出的方法能够有效降低用户充电出行成本并合理规划出行路径,验证了所提模型的可行性和有效性,对充电选择和站点配置具有一定的决策参考意义.

带襟翼导轨翼肋后缘尺寸-拓扑综合优化的摄动神经网络代理模型法

带襟翼导轨的翼肋后缘设计需要确定肋缘条、腹板的尺寸和肋腹板的拓扑形状,对此提出了一种针对尺寸-拓扑综合优化的摄动神经网络(perturbation neural network, PNN)代理模型法.其基本思想是基于拓扑优化对参数的敏感性,引入了对试验设计(design of experiments, DOE)样本点的摄动,通过过滤手段捕获拓扑突变点,并降低数值噪声,极大地提高了代理模型的预测精度,将拓扑优化过程作为黑盒,直接建立起尺寸变量与拓扑优化后结构响应的代理模型.最后在代理模型上进行优化,得到了结构尺寸与拓扑形状的最优组合.该文完成了一个翼肋后缘优化典型算例,证明了该方法的有效性和优越性.

基于路网复杂度分区的轨迹分段地图匹配方法

针对现有大多数地图匹配方法在城市复杂环境下难以有效平衡匹配速度和精度的问题,提出了一种基于路网复杂度分区的轨迹分段地图匹配方法。该方法包括路网分区和轨迹分段匹配两个部分。通过构建的路网复杂度分区模型将路网划分为复杂区域和非复杂区域;对复杂区域内的轨迹段采用改进的隐马尔可夫模型进行匹配,非复杂区域内的轨迹段采用基于几何拓扑的快速匹配模型进行匹配;将不同区域内匹配的轨迹段进行拼接,得到完整轨迹的匹配结果。为得到路网复杂度分区模型的最优参数,进行了11组不同参数设置的对比实验,并将最终结果与ST-matching和传统隐马尔可夫模型两种地图匹配方法匹配的结果进行对比。结果表明,在三个数据集的匹配准确率均在96%以上,比其他两种对比算法匹配时间减少了60%,在保证匹配准确率的前提下有效提升了匹配效率。

基于FPGA的两阶段配电网拓扑实时辨识算法

对配电网拓扑进行准确的实时辨识是电力系统安全稳定运行的基础,但随着新能源的接入以及配电网规模不断增大,配电网拓扑结构的动态变化愈加频繁且难以辨识。然而,现有配电网拓扑辨识算法所使用的历史数据需要人工对其进行拓扑标注,且拓扑辨识时间长,难以实现配电网拓扑实时辨识。因此,文中提出了一种基于现场可编程逻辑门阵列(FPAG)的两阶段配电网拓扑结构实时辨识算法。该算法不需要预先给出配电网拓扑类别的数量,即可对已有历史数据进行相应的拓扑标注及分类,并且基于FPGA实现了对配电网拓扑的实时辨别。该算法分为2个阶段:第1阶段采用变分贝叶斯高斯混合模型,对已有历史数据进行相应的拓扑标注及分类;第2阶段采用麻雀搜索算法,使得支持向量机快速收敛得到最优参数,以实现对配电网拓扑结构的精准辨识。基于该算法,利用FPGA并行架构以及高速高密度特性建立了实时拓扑结构辨识平台。最后,通过算例分析验证了所提辨识方法的有效性和优越性。

损管指挥信息系统复杂网络拓扑结构研究

现代舰船灾害发生的突然性、灾害传播的快速性及其影响的广泛性显著增强,迫切需要提升损管指挥体系的信息化水平。基于复杂网络理论,建立了舰艇损管指挥信息系统的网络拓扑模型。通过网络拓扑模型的特征指标计算,对不同的损管指挥信息网络模式进行评估分析,给出提升损管指挥信息化水平的优化方法。完成损管指挥信息系统组织结构模型的构建、分析和优化工作,开发允许拓扑结构方案自定义的性能计算软件,改善传统树状损管指挥结构的不足,为损管指挥信息化研究提供一个新的方向。

基于城市雨洪模型的长治市某城区应急救援路径规划研究

在洪水应急管理中,合理设计救援路线,有助于提高救援效率,减少灾害损失。在建立的内涝灾害救援路径规划框架中,采用高效高分辨率雨洪模型,模拟区域的内涝过程,基于内涝结果,采用原始法重构路网拓扑结构。考虑道路风险及水深对车速的影响,构建以时间为指标的路段权重矩阵,采用Floyd算法推求最短路径。另构建了以路径长度为指标的权重矩阵,作为对照。以长治市某城区为例,设置5种情景条件(无降雨、50 a、100 a、200 a、500 a),比较不同情景下的救援路线。结果表明:分别以通行时间、路径距离为指标构建路网权重进行计算,其结果有明显变化。500 a降雨条件下,以通行时间为指标构建的路网权重得到的规划路线,在通行距离上增加了12.43%,而在行驶时间上少了14.07%。

A novel matrix topology-based analysis method of thermal circuits for reliability optimization of HSER

Hermetically Sealed Electromagnetic Relay(HSER), used in aviation and aerospace,demands high reliability due to its critical applications. Given its complex operating conditions, efficient thermal analysis is essential for optimizing reliability. The commonly used Finite Element Method(FEM) is often time-consuming and may not be efficient or adaptable for complex multi-dimensional system calculations and design processes. This paper introduces an analysis method for thermal networks based on matrix perspective technology, encompassing matrix transformation, backpropagation of the heat path model, temperature rise calculation, solution comparison, and product implementation. Using the similarity theory of heat circuits, a basic thermal unit is established. Based on the fundamental connection between key components, a thermal network for a typical HSER is designed. An experimental system is set up, and the thermal network model's accuracy is confirmed using test data. Employing the topology analysis method, the topology of the thermal network is analyzed under both coil-energized and de-energized states. Potential thermal paths are identified, leading to optimized solutions for the HSER. Utilizing these solutions, the thermal path matrix topology model is backpropagated to the thermal path for temperature rise calculations. When compared to prototype HSER test data, the efficiency and accuracy of this matrix topology-based analysis method are confirmed.

基于SEM-MIP状态估计的配电网拓扑辨识方法

配电网拓扑辨识是配电系统操作的关键问题和重要前提。由于缺乏足够的量测设备和配电系统拓扑的频繁变化,导致实际的配电网拓扑辨识问题十分具有挑战性。为此提出了基于同步量测数据状态估计的实时拓扑辨识方法。该方法可以利用少量相量测量单元的测量数据准确估计配电网拓扑结构。首先,在配电网中引入结构方程模型(structural equation model,SEM),用于捕捉拓扑与支路电流的关系。然后,针对拓扑辨识问题提出了混合整数规划(mixed integer programming,MIP)方法,通过复数方程的虚实部解耦,利用泰勒展开与等效松弛等手段对复数方程和约束条件进行线性化。此外,将配电网运行方式约束以节点为单位进行了重新规划。最后,所提基于结构方程模型的混合整数规划方法(SEM-MIP)可以使用现有的商业求解器求解,并通过一个实际12节点配电系统和IEEE 33节点测试系统验证了所提方法的有效性。

基于有限关键节点及Wasserstein距离的配网拓扑识别

明确配电网结构是配电网最优潮流、安全评估、网络重建、故障定位的基础。针对现有配电网拓扑识别方法缺乏结合现有网络结构参数和潮流信息,仅通过量测数据来进行拓扑识别效率低的问题,提出一种基于有限关键节点及Wasserstein距离的配电网拓扑识别方法。首先,利用子空间扰动模型证明配网拓扑变化时,可以利用有限的关键节点来进行拓扑识别,基于熵值法的混合K-Shell算法引入影响度概念,通过影响度与节点电气距离得出节点的重要度,确定配电网拓扑结构中的关键节点。其次,基于密度的噪声应用聚类算法通过电压、电流、有功、无功等4个特征来进行节点的聚类,将其他节点与关键节点进行类别归属,再结合Wasserstein距离得出节点间的连接关系从而得出配电网的拓扑结构。最后,通过IEEE 33节点算例和某小区实例,验证该方法的有效性。该方法极大地提高配电网拓扑识别效率与正确率,实现了配网拓扑结构的动态识别。

离散变量影响下激光通信网络拓扑结构优化研究

激光通信网络在进行信息传输时容易受到离散变量的影响,降低信息传输质量。为此,研究离散变量影响下激光通信网络拓扑结构优化方法。生成激光通信网络初始拓扑结构,分析离散变量带来的影响。根据激光通信网络拓扑结构优化目标,建立激光通信网络的拓扑结构优化目标模型。采用多目标进化方法,剔除拓扑结构中的冗余链路,实现对激光通信网络拓扑结构的优化。实验结果表明,所提方法优化后的网络拓扑结构链路平均长度在50 cm以下,通信节点能量在48 J以上,网络生存轮数高于371轮,优化性能好,有效剔除了网络拓结构中的冗余路径。

基于改进TOPSIS算法的电力通信网络路由优化

为了解决通信网络业务分布不均带来的电网运行风险问题,针对通信网络路由优化提出了基于相对熵的TOPSIS改进算法.应用该算法对建立的通信网络业务风险度和网络风险度评估模型进行业务重要度计算,并利用实际网络拓扑模型对算法有效性进行验证.结果表明,在针对单个业务或者整体网络时,TOPSIS改进算法较负载均衡算法和风险平衡算法能更有效地减少路径分配时的风险值,降低业务风险度.所提的TOPSIS改进算法有效改善了网络均衡度,进而解决了因业务集中带来的网络中断风险问题.

基于JSON自描述模型的配网拓扑重构技术

在中低压配电网中,配电自动化系统依靠线路拓扑和台区拓扑实施状态监控及故障处理,当前配电网网架结构愈加复杂且变更频繁,尤其是低压配电台区的现场拓扑实施困难,提出一种基于Json自描述模型的配网拓扑重构技术。基于Json格式实现配网终端标准化自描述模型,同时基于该模型提出终端设备注册机制和配网拓扑重构机制,实现中压线路拓扑及低压台区拓扑的自适应拓扑重构。基于动模仿真平台和现场测试,验证所提方法的有效性与实用性。