Application of fuzzy optimization model in the evaluationof urban flood-waterloggedvulnerabilitybased on the synthetic weight ofgame theory

The evaluation of urban flood-waterlogged vulnerability is very important to the safety of urban flood control. In this paper, the evaluation of consolidated index is used. Respectively, AHP and entropy method calculate the subjective and objective weight of the evaluation indicators, and combine them by game theory. So we can obtain synthetic weight based on objective and subjective weights. The evaluation of urban flood-waterlogged vulnerability as target layer, a single variable multi-objective fuzzy optimization model is established. We use the model to evaluate flood-waterlogged vulnerability of 13 prefecture-level city in Hunan, and compare it with other evaluation method. The results show that the evaluation method has certain adaptability and reliability, and it＇ s helpfid to the construction planning of urban flood control.

Dynamics of Advantageous Mutant Spread in Spatial Death-Birth and Birth-Death Moran Models

The spread of an advantageous mutation through a population is of fundamental interest in population genetics. While the classical Moran model is formulated for a well-mixed population, it has long been recognized that in real-world applications, the population usually has an explicit spatial structure which can significantly influence the dynamics. In the context of cancer initiation in epithelial tissue, several recent works have analyzed the dynamics of advantageous mutant spread on integer lattices, using the biased voter model from particle systems theory. In this spatial version of the Moran model, individuals first reproduce according to their fitness and then replace a neighboring individual. From a biological standpoint, the opposite dynamics, where individuals first die and are then replaced by a neighboring individual according to its fitness, are equally relevant. Here, we investigate this death-birth analogue of the biased voter model. We construct the process mathematically, derive the associated dual process, establish bounds on the survival probability of a single mutant, and prove that the process has an asymptotic shape. We also briefly discuss alternative birth-death and death-birth dynamics, depending on how the mutant fitness advantage affects the dynamics. We show that birth-death and death-birth formulations of the biased voter model are equivalent when fitness affects the former event of each update of the model, whereas the birth-death model is fundamentally different from the death-birth model when fitness affects the latter event.

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).

Analysis of Students’ Misconception Based on Rough Set Theory

The study analyzed students’ misconception based on rough set theory and combined with interpretive structural model (ISM) to compare students’ degree of two classes. The study then has provided an effective diagnostic assessment tool for teachers. The participants were 30 fourth grade students in Central Taiwan, and the exam tools were produced by teachers for math exams. The study has proposed three methods to get common misconception of the students in class. These methods are “Deleting conditional attributes”, “Using Boolean logic to calculate discernable matrix”, and “Calculating significance of conditional attributes.” The results showed that students of Class A had common misconceptions but students of Class B had not common misconception. In addition, the remedial decision-making for these two classes of students is pointed out. While remedial decision-making of two classes corresponded to structural graph of concepts, it can be found the overall performance of the Class B was higher than Class A.

Cybersecurity: A Statistical Predictive Model for the Expected Path Length

The object of this study is to propose a statistical model for predicting the Expected Path Length (expected number of steps the attacker will take, starting from the initial state to compromise the security goal—EPL) in a cyber-attack. The model we developed is based on utilizing vulnerability information along with having host centric attack graph. Utilizing the developed model, one can identify the interaction among the vulnerabilities and individual variables (risk factors) that drive the Expected Path Length. Gaining a better understanding of the relationship between vulnerabilities and their interactions can provide security administrators a better view and an understanding of their security status. In addition, we have also ranked the attributable variables and their contribution in estimating the subject length. Thus, one can utilize the ranking process to take precautions and actions to minimize Expected Path Length.

Development of an integrated dynamic model for supply security and resilience analysis of natural gas pipeline network systems

An integrated dynamic model of natural gas pipeline networks is developed in this paper.Components for gas supply,e.g.,pipelines,junctions,compressor stations,LNG terminals,regulation stations and gas storage facilities are included in the model.These components are firstly modeled with respect to their properties and functions and,then,integrated at the system level by Graph Theory.The model can be used for simulating the system response in different scenarios of operation,and evaluate the consequences from the perspectives of supply security and resilience.A case study is considered to evaluate the accuracy of the model by benchmarking its results against those from literature and the software Pipeline Studio.Finally,the model is applied on a relatively complex natural gas pipeline network and the results are analyzed in detail from the supply security and resilience points of view.The main contributions of the paper are:firstly,a novel model of a complex gas pipeline network is proposed as a dynamic state-space model at system level;a method,based on the dynamic model,is proposed to analyze the security and resilience of supply from a system perspective.

Stability analysis of multi-group deterministic and stochastic epidemic models with vaccination rate

We discuss in this paper a deterministic multi-group MSIR epidemic model with a vaccination rate, the basic reproduction number Ro, a key parameter in epidemiology, is a threshold which determines the persistence or extinction of the disease. By using Lyapunov function techniques, we show if Ro is greater than 1 and the deterministic model obeys some conditions, then the disease will prevail, the infective persists and the endemic state is asymptotically stable in a feasible region. If Ro is less than or equal to 1, then the infective disappear so the disease dies out. In addition, stochastic noises around the endemic equilibrium will be added to the deterministic MSIR model in order that the deterministic model is extended to a system of stochastic ordinary differential equations. In the stochastic version, we carry out a detailed analysis on the asymptotic behavior of the stochastic model. In addition, regarding the value of Ro, when the stochastic system obeys some conditions and Ro is greater than 1, we deduce the stochastic system is stochastically asymptotically stable. Finally, the deterministic and stochastic model dynamics are illustrated through computer simulations.

基于ISM的混合轻烃装运危险性分析

“混合轻烃”是油气田伴生气和进口液化天然气中烷烃的提取物,近年市场供应逐渐增加。由于其和传统的以液化石油气为代表的烃类混合物的组分上的差别,国内缺乏对应的生产规范与储运设备,为生产与贸易带来不便。为此针对混合轻烃在装卸与运输环节中的危险性,利用可解释结构模型(ISM)对衍生的安全管理措施之间的逻辑关系进行拓扑结构呈现。结果表明:若沿用市场现有的LPG槽车运输混合轻烃则必须充分计算其工艺可靠性,提前评估这类产品的事故风险以及选择合理的包装方式,并做好设备日常维护等保障性工作;其他因素的重要度依此变化,可为安全防范提供参考;在日常的安全管理工作中,安全专项资金的投入是最基本的保障,其次是生产规章制度与安全管理规程的科学性,以及如何平衡生产效率与安全之间的关系。

应用图论建模输电网的电力现货市场出清模型

电力现货市场优化出清模型的难点是考虑安全约束的机组组合(security constrained unit commitment,SCUC)模型。构建一个好的SCUC模型以提升求解速度是长期以来的研究热点。随着省电力系统规模的不断发展,以及国内省间电力现货市场的运行,现货市场对于出清模型的求解效率需求更为突出。该文对机组组合模型的研究历程进行详细综述,总结提出机组运行出力约束、出力上限约束、爬坡约束强化建模的机组运行模型;基于图论在线性最优潮流计算中的应用,构建输电网的环流潮流计算模型并引入SCUC模型,最终构建了一套可进一步提升求解效率的电力现货市场出清模型。最后通过多个大规模算例的计算分析表明,所提模型在保证不损失求解精度的同时,可有效提升求解效率,且对于阻塞频发的电力系统,优越性更为明显。

欺骗防御技术发展及其大语言模型应用探索

欺骗防御作为主动防御中最具发展前景的技术,帮助防御者面对高隐蔽未知威胁化被动为主动,打破攻守间天然存在的不平衡局面.面对潜在的威胁场景,如何利用欺骗防御技术有效地帮助防御者做到预知威胁、感知威胁、诱捕威胁,均为目前需要解决的关键问题.博弈理论与攻击图模型在主动防御策略制定、潜在风险分析等方面提供了有力支撑,总结回顾了近年来二者在欺骗防御中的相关工作.随着大模型技术的快速发展,大模型与网络安全领域的结合也愈加紧密,通过对传统欺骗防御技术的回顾,提出了一种基于大模型的智能化外网蜜点生成技术,实验分析验证了外网蜜点捕获网络威胁的有效性,与传统Web蜜罐相比较,在仿真性、稳定性与灵活性等方面均有所提升.为增强蜜点间协同合作、提升对攻击威胁的探查与感知能力,提出蜜阵的概念.针对如何利用蜜点和蜜阵技术,对构建集威胁预测、威胁感知和威胁诱捕为一体的主动防御机制进行了展望.

基于改进图注意力网络的电力系统脆弱性关键环节辨识

随着电网的扩大与新能源比例的增加,电网的不确定性和随机性因素增加,危及系统安全运行,寻找出电网中的脆弱性关键环节来保障电网运行时的可靠性就显得尤为重要。针对当前传统电网脆弱性关键环节辨别方法识别速度慢、难以满足电网实际运行要求的问题,提出了基于改进图注意力网络算法(improved graph attention network,IGAT)的电网脆弱性关键环节辨识方法。首先,结合复杂网络理论和电网实际运行数据建立评价指标集。其次,利用IGAT挖掘出电网运行时的各项指标与脆弱性关键环节之间的映射关系,建立脆弱性关键环节辨识模型,并且考虑到训练准确性和效率等需求,对原始的图注意力网络进行优化。再次,通过仿真得到原始数据集,对辨识模型进行训练、验证和测试。最后,利用所述模型应用于改进的IEEE 30节点系统和实际电网中,结果表明所提方法具有可行性,且准确性和速度优于传统方法,有一定的工程利用价值。

基于传播图理论的真空管飞行列车的无线信道建模与容量分析

无线信道建模是分析和评估无线通信系统特性的重要环节。为了满足下一代轨道列车的通信需求,本文基于传播图信道建模理论,对真空管环境下列车在管道内运行过程中的无线电传播信道进行研究。结合朗伯散射模型和几何绕射理论模型进行仿真,过程中主要考虑了视距传输、单散射径、单散射加绕射三种情况。仿真得到了多输入多输出(multiple-input multiple-output,MIMO)条件下的信道传输矩阵,提取并分析了功率时延谱、K因子、多普勒谱和信道容量在列车处于不同位置时的变化情况,结果表明,所提出的模型有助于分析在真空管场景下MIMO系统的信道衰落特性和信道容量。

基于预训练与新型时序图神经网络的智能合约漏洞检测方法

针对现有深度学习漏洞检测方法对合约字节码特征挖掘不足、漏洞语义表征不精准,且传统图神经网络模型对合约语句的时序信息学习能力不足,提出一种基于预训练与时序图神经网络的智能合约漏洞检测方法。首先,通过预训练模型将智能合约字节码建模为漏洞语义感知的合约图结构。其次,结合自注意力机制,设计了一种新颖的基于事件驱动的时序图神经网络模型,实现对合约执行中时序信息的有效抽取。最后,聚焦于可重入漏洞、时间戳依赖漏洞以及Tx.origin身份认证漏洞,通过120932份真实合约数据集进行大量的评估实验,结果表明所提方法的检测效果显著优于现有方法。

城市复杂环境下蜂窝接入式无人机多层次路径规划方法

近年来蜂窝接入式无人机在解决传统无人机模型的局限性方面被广泛应用,但还未形成有效应对城市多建筑群及突发障碍物环境下的路径规划方法。本文提出一种新的蜂窝式无人机多层次路径规划方法,由连通性感知的全局规划层和无碰撞的局部规划层组成,全局规划以地面基站信号覆盖范围为规划单元,基于图论算法求解最优宏观路径,以提高求解效率,局部规划侧重飞行避障,以改进的DQN算法求解最优无碰撞飞行路径。仿真实验表明,相较于传统方法,该方法能将飞行距离有效缩短16.02%,并将求解效率提升76.6%,满足复杂城市环境下的无人机寻路需求,为复杂城市环境中的无人机路径规划提供了一种有效的解决方案。

基于图论的复杂系统潜通路分析方法

潜通路分析方法是可靠性分析中一种常用的方法,是减少设计问题,提升可靠性的一种重要手段。当前的潜通路分析方法主要是应用于电路系统分析,也有部分包含管路系统的应用,但是还没有适用于复杂系统的潜通路分析方法。针对上述问题,提出了一种基于图论的系统潜通路分析方法,首先构建基于流的系统模型,然后将模型中不影响分析的连接关系及模型节点简化,最后将系统模型转换为抽象的图模型,并利用节点识别和路径追踪的方法进行分析。最后,将所提方法应用于直升机起落架误收问题的分析,证明了所提方法的有效性。

面向动态信道的传播图论建模方法优化

动态信道建模方法是6G研发的重要内容。传播图论作为一种基于有向图拓扑结构的信道建模方法,具有计算高效和信道多径的功率指数衰减描述准确的特点,也能较好地满足6G中球面波波面模型研究和信道建模需求。针对6G通感一体化技术研发中动态信道建模的实际需求,梳理了传播图论基本理论,总结和提出了基于传播图论的面向动态信道建模框架和具体帧结构,基于反射波功率机制对传播图论的算法进行了优化,有效提升了传播图论对动态环境信道仿真的准确度。通过对该建模方法的仿真分析和实测验证,证明了其可行性和带来的性能提升。

基于改进FPPN的飞控系统故障传播路径分析方法

针对飞控系统多冗余、多闭环的结构特性,结合有向图模型和模糊Petri网(FPN)模型,构建飞控系统故障传播模糊概率Petri网(FPPN)模型,以解决飞控系统特定结构下的故障传播路径问题。改进的FPPN模型包含飞控系统有向图模型、故障传播特性量化计算模型和故障传播FPPN模型3个部分。采用面向对象技术分析飞控系统的功能行为和物理结构,综合复杂网络理论,构建系统故障传播有向图模型;引入Floyd算法,开展系统耦合关联分析,基于节点的度和边介数定义系统故障传播特性;在有向图模型的基础上,提出相应的结构映射规则,构建飞控系统故障传播FPPN模型,融合改进后的参数量化方法,设定2种推理算法对系统多冗余、闭环结构特性下的故障传播路径进行有效分析。通过数值分析与实例验证,获取飞控系统典型故障传播路径及路径上相关节点的状态值,验证所提方法的有效性。

一种基于GMM和Graph Cuts的图像分割方法

图像分割是图像处理中的基础问题。研究利用GMM构造T链的可行性,并探索一种可以兼顾精度与速度的确定GMM中K值的方法,结合GMM和Graph Cuts理论完成了对不同图像的分割实验。实验表明,文中的分割方法准确、有效。

变电工程项目图结构数据模型研究

[目的]为打通变电工程全生命周期各阶段之间的数据隔阂,实现几何模型和工程信息的流转,提出了基于图结构的变电工程数据模型构建方法。[方法]首先分析模型几何信息和工程数据信息在变电工程各阶段的流转,将数据模型分为核心模型与场景模型,其次按照变电工程的土建部分和电气设备来组织图的拓扑结构,然后将具有树形结构的IFC模型之中的部件之间的关系转换为“边”的形式,模型部件转为“节点”的形式,构建图结构并导入图数据库,最后设计电气设备部件级别的变电工程数据模型结构,并将该模型与核心模型的部件建立关联关系,形成变电工程图结构数据模型。[结果]测试结果表明变电工程图结构数据模型导入图数据库后可以实现几何模型与工程信息模型的灵活组合和分解,并可在面对大量几何结构和属性信息的复杂关联关系时实现高效的模型组件信息的修改和查询。[结论]基于图结构的数据模型可以同时承载几何信息和工程信息场景模型。它实现了各阶段对于模型颗粒度调整和工程信息增补和移除的需求,相比于基于表单管理的COBie(Construction Operations Building information exchange)标准可以为用户提供更容易理解的数据流转路径。

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。