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.

Holistic Discourse in the Network Cognitive Modeling

The purpose of this paper is to pose a new question to speed-up mutual understanding among team members or/and group of experts when communicating over the Internet in forms of virtual collaboration, electronic brainstorming, network strategic conversation, etc. We have previously proposed an approach that the convergent control mechanism based on the fundamental principles of thermodynamic and inverse problem solution method, as well as various artificial intelligence techniques, be incorporated into the communicative process. This paper shows a further development of the approach in terms of applying The Fuzzy Tychonoff Theorem along with quantum techniques provide to reach a high level of holistic discourse which is achieved not only through the application of fundamental principles of compactness of the topological space, but also utilizing quantum entanglement and complementarity principles for discourse structuring in a special way. The approach is implemented as the Responsibility Thinking System （RTS） tested in the course of finding the decisions of the real life issues.

Scheduling optimization for UAV communication coverage using virtual force-based PSO model

When the ground communication base stations in the target area are severely destroyed,the deployment of Unmanned Aerial Vehicle(UAV)ad hoc networks can provide people with temporary communication services.Therefore,it is necessary to design a multi-UAVs cooperative control strategy to achieve better communication coverage and lower energy consumption.In this paper,we propose a multi-UAVs coverage model based on Adaptive Virtual Force-directed Particle Swarm Optimization(AVF-PSO)strategy.In particular,we first introduce the gravity model into the traditional Particle Swarm Optimization(PSO)algorithm so as to increase the probability of full coverage.Then,the energy consumption is included in the calculation of the fitness function so that maximum coverage and energy consumption can be balanced.Finally,in order to reduce the communication interference between UAVs,we design an adaptive lift control strategy based on the repulsion model to reduce the repeated coverage of multi-UAVs.Experimental results show that the proposed coverage strategy based on gravity model outperforms the existing state-of-the-art approaches.For example,in the target area of any size,the coverage rate and the repeated coverage rate of the proposed multi-UAVs scheduling are improved by 6.9–29.1%,and 2.0–56.1%,respectively.Moreover,the proposed scheduling algorithm is high adaptable to diverse execution environments.©2022 Published by Elsevier Ltd.

MULTI-CLASS TRAFFIC QOS ROUTING FOR LEO SATELLITE NETWORKS

Due to the diversified demands of quality of service（QoS） in volume multimedia application, QoS routings for multiservice are becoming a research hotspot in low earth orbit（LEO） satellite networks. A novel QoS satellite routing algorithm for multi-class traffic is proposed. The goal of the routing algorithm is to provide the distinct QoS for different traffic classes and improve the utilization of network resources. Traffic is classified into three classes and allocated priorities based on their QoS requirements, respectively. A priority queuing mechanism guarantees the algorithm to work better for high-priority classes. In order to control the congestion, a blocking probability analysis model is built up based on the Markov process theory. Finally, according to the classification link-cost metrics, routings for different classes are calculated with the distinct QoS requirments and the status of network resource. Simulations verify the performance of the routing algorithm at different time and in different regions, and results demonstrate that the algorithm has great advantages in terms of the average delay and the blocking probability. Meanwhile, the robustness issue is also discussed.

IMPLICIT REPRESENTATION FOR THE MODELLING OF HYBRID DYNAMIC SYSTEMS

Hybrid systems can be represented by a discrete event model interacting with a continuous model, and the interface by ideal switching components which modify the topology of a system at the switching time. This paper deals with the modelling of such systems using the bond graph approach. The paper shows the interest of the implicit representation: to derive a unique state equation with jumping parameters, to derive the implicit state equation with index of nilpotency one corresponding to each configuration, to analyze the properties of those models and to compute the discontinuity.

Impact of Wavelength-Routed Network Physical Topology on Blocking Probability Using a Dynamic Traffic Growth Model

We investigate the impact of network topology on blocking probability in wavelength-routed networks using a dynamic traffic growth model. The dependence of blocking on different physical parameters is assessed.

Integer Programming Formulations for Maximum Lifetime Broadcasting Problems in Wireless Sensor Networks

Approaches based on integer linear programming have been recently proposed for topology optimization in wireless sensor networks. They are, however, based on over-theoretical, unrealistic models. Our aim is to show that it is possible to accommodate realistic models for energy consumption and communication protocols into integer linear programming. We analyze the maximum lifetime broadcasting topology problem and we present realistic models that are also shown to provide efficient and practical solving tools. We present a strategy to substantially speed up the convergence of the solving process of our algorithm. This strategy introduces a practical drawback, however, in the characteristics of the optimal solutions retrieved. A method to overcome this drawback is discussed. Computational experiments are reported.

Semantic model and optimization of creative processes at mathematical knowledge formation

The aim of this work is mathematical education through the knowledge system and mathematical modeling. A net model of formation of mathematical knowledge as a deductive theory is suggested here. Within this model the formation of deductive theory is represented as the development of a certain informational space, the elements of which are structured in the form of the orientated semantic net. This net is properly metrized and characterized by a certain system of coverings. It allows injecting net optimization parameters, regulating qualitative aspects of knowledge system under consideration. To regulate the creative processes of the formation and realization of mathematical know- edge, stochastic model of formation deductive theory is suggested here in the form of branching Markovian process, which is realized in the corresponding informational space as a semantic net. According to this stochastic model we can get correct foundation of criterion of optimization creative processes that leads to “great main points” strategy (GMP-strategy) in the process of realization of the effective control in the research work in the sphere of mathematics and its applications.

基于自动分层分区的通信网络拓扑建模方法

通信系统抗震韧性评估对提升系统整体抗震能力和减轻灾害损失具有重要意义,基于通信系统组网规则快速建立合理的通信网络拓扑能够为抗震韧性评估提供网络拓扑数据,同时为建设经济且稳定的城市通信网络提供指导。针对这一问题提出了一种建立合理通信网络拓扑的方法:在获得通信节点基础信息的前提下,基于通信网络组网规则采用优化算法自动建立给出通信网络拓扑。该方法首先采用聚类算法实现通信节点的自动分区;然后,基于通信线路的拓扑规则,以线路长度最短为优化目标,采用蚁群算法和Dijkstra算法优化建立同一分区内节点间线路拓扑。为了验证该方法的可用性,以两座不同规模城市为例,建立了两座城市通信网络拓扑模型。建模结果显示:针对不同规模城市该方法能够结合具体情况快速建立合理的城市通信网络拓扑模型。

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.

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。

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。

HSEGRL:一种分层可自解释的图表示学习模型

近年来,随着图神经网络(graph neural network,GNN)技术在社交、信息、化学、生物等领域的广泛应用,GNN可解释性也受到广泛的关注.然而,现有的解释方法无法捕获层次化的解释信息,同时,这些层次信息未能被充分利用以提升图分类任务的准确率.基于这一问题,提出了一种层次化自解释的图表示学习(hierarchical self-explanation graph representation learning,HSEGRL)模型,该模型通过发现图结构中的层次信息进行图分类预测的同时,输出层次化的模型自解释结果.具体而言,针对图层次信息的发现设计了提取信息的基本单元——解释子,该解释子由提取节点特征的编码器获取层次化解释感知子图的池化层和抽取高阶解释信息的解码器组成.其中,为了准确提取层次化的解释子图,针对该模型的池化操作进行了解释感知优化设计,该设计通过评估模型的拓扑及特征重要性,层次化地筛选解释子图,实现分层自解释的同时完成图分类任务.HSEGRL是一个功能完备且便于迁移的图表示学习自解释模型,可以层次化综合考虑模型的拓扑信息与节点特征信息.在模型有效性验证层面,分别在分子、蛋白质和社交数据集上进行大量实验,实验结果表明所提模型在图分类任务中的分类准确率高于已有的先进的GNN自解释模型和GNN模型,并通过可视化分层解释结果的信息证明了该解释方法可信.