Discussion on the Application of Complex Power Network to Software Engineering

Along with the further development of science and technology, computer hardware and the Intemet are in a rapid development, and information technology has been widely used in all fields so that complex problems are simply solved. Because of the needs for the development, software starts to mutually integrate with complex power network, making the scale of software increase greatly. Such a growing trend of software promotes soft-ware development to go beyond a general understanding and control and thus a complex system is formed. It is necessary to strengthen the research of complex network theory, and this is a new way to help people study the complexity of software systems. In this paper, the development course of complex dynamic network is introduced simply and the use of complex power network in the software engineering is summarized. Hopefully, this paper can help the crossover study of complex power network and software engineering in the future.

NEW METHOD TO ESTIMATE SCALING EXPONENTS OF POWER-LAW DEGREE DISTRIBUTION AND HIERARCHICAL CLUSTERING FUNCTION FOR COMPLEX NETWORKS

A new method and corresponding numerical procedure are introduced to estimate scaling exponents of power-law degree distribution and hierarchical clustering function for complex networks. This method can overcome the biased and inaccurate faults of graphical linear fitting methods commonly used in current network research. Furthermore, it is verified to have higher goodness-of-fit than graphical methods by comparing the KS （Kolmogorov-Smirnov） test statistics for 10 CNN （Connecting Nearest-Neighbor） networks.

An Analytical Model of the Power Law Distributions in the Complex Network

It is known that complex networks in nature exhibit some significant statistical features. We notice power law distributions which frequently emerge with respect to network structures of various quantities. One example is the scale-freeness which is described by the degree distribution in the power law shape. In this paper, within an analytical approach, we investigate the analytical conditions under which the distribution is reduced to the power law. We show that power law distributions are obtained without introducing conditions specific to each system or variable. Conversely, if we demand no special condition to a distribution, it is imposed to follow the power law. This result explains the universality and the ubiquitous presence of the power law distributions in complex networks.

A novel decomposition and coordination algorithm for complex networks and its application to power grids

To analyze and control complex networks effectively, this paper puts forward a new kind of scheme, which takes control separately in each area and can achieve the network’s coordinated optimality. The proposed algorithm is made up of two parts: the first part decomposes the network into several independent areas based on community structure and decouples the information flow and control power among areas; the second part selects the center nodes from each area with the help of the control centrality index. As long as the status of center nodes is kept on a satisfactory level in each area, the whole system is under effective control. Finally, the algorithm is applied to power grids, and the simulations prove its effectiveness.

Optimal power allocation for complex field network coding scheme with the K-th best relay selection

Wireless relay and network coding are two critical techniques to increase the reliability and throughput of wireless cooperative communication systems. In this paper, a complex field network coding （CFNC） scheme with the K-th best relay selection （KBS） is proposed and investigated, wherein the K-th best relay is selected to forward the multiplexed signal to the destination. First, the upper bound of the symbol error probability （SEP）, the diversity order, and the coding gain are derived for the CFNC scheme with KBS. Then, the coding gain is utilized as the optimized cri- terion to determine the optimal power allocation. It is validated through analysis and simulation that the CFNC scheme with KBS can achieve full diversity only when K=I, while the diversity order decreases with increasing parameter K, and the optimal power allocation can significantly improve the performance of the CFNC scheme with KBS.

S-curve networks and an approximate method for estimating degree distributions of complex networks

In the study of complex networks almost all theoretical models have the property of infinite growth, but the size of actual networks is finite. According to statistics from the China Internet IPv4 （Internet Protocol version 4） addresses, this paper proposes a forecasting model by using S curve （logistic curve）. The growing trend of IPv4 addresses in China is forecasted. There are some reference values for optimizing the distribution of IPv4 address resource and the development of IPv6. Based on the laws of IPv4 growth, that is, the bulk growth and the finitely growing limit, it proposes a finite network model with a bulk growth. The model is said to be an S-curve network. Analysis demonstrates that the analytic method based on uniform distributions （i.e., Barabasi-Albert method） is not suitable for the network. It develops an approximate method to predict the growth dynamics of the individual nodes, and uses this to calculate analytically the degree distribution and the scaling exponents. The analytical result agrees with the simulation well, obeying an approximately power-law form. This method can overcome a shortcoming of Barabasi-Albert method commonly used in current network research.

THE STUDY ON DEGREE DISTRIBUTION PROPERTY OF COMPLEX NETWORK BASED ON COOPERATIVE COMMUNICATION

Complex networks have been widely studied. Recently,many results show that the degree distributions of some large networks follow the form of power-law and these networks possess better robustness against random nodes failure. As an effective technology on combating the channel fading,wireless cooperative communication is becoming one of the most important methods to improve the wireless communication performances. In this paper,the complex network models based on cooperative communication and non-cooperative communication are established; and the degree distribution properties for them are studied. The simulation results show that the degree distributions of these networks also follow the form of power-law,which means that the addition of cooperative communi-cation links will not change the property of degree distribution and then these networks will possess better robustness against random nodes failure as well.

Mobile user forecast and power-law acceleration invariance of scale-free networks

This paper studies and predicts the number growth of China＇s mobile users by using the power-law regression. We find that the number growth of the mobile users follows a power law. Motivated by the data on the evolution of the mobile users, we consider scenarios of self-organization of accelerating growth networks into scale-free structures and propose a directed network model, in which the nodes grow following a power-law acceleration. The expressions for the transient and the stationary average degree distributions are obtained by using the Poisson process. This result shows that the model generates appropriate power-law connectivity distributions. Therefore, we find a power-law acceleration invariance of the scale-free networks. The numerical simulations of the models agree with the analytical results well.

Logistic Mapping-Based Complex Network Modeling

In this paper, a new topological approach for studying an integer sequence constructed from Logistic mapping is proposed. By evenly segmenting [0,1]?into N non-overlapping subintervals which is marked as , representing the nodes identities, a network is constructed for analysis. Wherein the undirected edges symbolize their relation of adjacency in an integer sequence obtained from the Logistic mapping and the top integral function. By observation, we find that nodes’ degree changes with different values of??instead of the initial value—X0, and the degree distribution presents the characteristics of scale free network, presenting power law distribution. The results presented in this paper provide some insight into degree distribution of the network constructed from integer sequence that may help better understanding of the nature of Logistic mapping.

基于深度强化学习的航天器功率信号复合网络优化算法

为了实现航天器电源系统的灵活高效并网,最大化有限能量的利用,提出一种基于深度强化学习(deep reinforcement learning,DRL)的功率传输与信号传输复合网络拓扑优化模型,并使用知识蒸馏原理的多种可解释组件模型对优化过程进行剖析。首先,分析在轨运行阶段航天器母线电压调节控制域变换规律,并结合节点传播性参数,建立功率传输与信号通信的复合网络拓扑模型。然后,利用A3C(asynchronous advantage actor-critic)算法,对信号传输网络路由分布、拓扑结构等方面潜在的运行可靠性风险进行自适应性优化。最后,结合多种可解释组件对已训练的DRL模型进行知识蒸馏,形成一种可解释的量化分析方法。所提方法可以指导空间电源在随机阴影影响下选择最佳并网方案,并为更高任务要求和复杂环境下空间电源控制器设计提供理论支持。

基于复杂网络理论的船舶电网脆性源辨识方法

[目的]针对船舶电网关键元件辨识方法的考虑角度单一,未计及节点的局部和全局特征、对节点承载负荷的重要度考虑不足等问题,提出一种基于复杂网络理论的博弈组合赋权脆性源辨识方法。[方法]船舶电力系统发生中断和连锁崩溃的内在机理是其本身具有脆性。首先,从脆性角度出发,基于复杂网络理论建立节点度、特征向量中心性、节点凝聚度、接近中心性、脆性电气介数等5项指标;然后,提出博弈组合赋权法,以纳什均衡为协调目标,确定直觉模糊层次分析法(IFAHP)和熵权法(EWM)的最优组合权重,并综合赋值5项指标以得到综合脆性指标;最后,利用典型四电站环形船舶电网,验证该脆性源辨识方法的有效性和可行性。[结果]根据不同脆性指标的网络效能,得到了4个等级的脆性源。对比结果表明,所提方法可以更全面合理地辨识脆性源。[结论]研究成果可为船舶电网连锁故障的预防控制提供有效指导。

基于潮流和复杂网络理论的综合能源系统关键环节识别

综合能源系统(integrated energy system, IES)的关键环节识别对于系统的脆弱性研究和改善有重要意义。针对传统还原论方法无法解释系统层面且复杂网络分析中未考虑能流带来影响的问题,提出一种基于潮流和复杂网络结构的IES关键环节识别方法。首先,建立基于潮流的综合能源复杂网络模型,将IES的数学模型参数和潮流结果引入复杂网络的建模以使模型更加贴合现实的运行工况;其次,基于潮流计算结果和复杂网络参数,提出节点能量度和能量边介数2个评价指标,提高了关键环节识别精度;最后,根据所建立的模型对关键环节进行破坏并观察网络效率的变化,与其他攻击模式进行对比分析,验证了所提方法的可行性。

复杂任务情境中技术如何促进部门协同——基于S市市域社会治理现代化指挥中心的案例研究

已有研究多将技术促进部门协同理解为技术与组织互动视角下,基层政府为应对复杂任务情境采取的一种策略性行为,但对其背后的权力维度关注甚少。论文围绕权力视角下技术如何实现政府部门协同这一核心问题,选取S市市域社会治理现代化指挥中心为案例,探讨技术是否影响,以及如何影响政府部门间的权力关系。研究认为,从案例中析出的分布式治理遵从了“任务情境-技术应用-治理行为”相互适配的行动逻辑,对标复杂性任务情境,通过平台技术的协调功能,能够推动政府部门围绕“高效处置一件事”组建任务共同体。这种任务共同体暗含了以“科层为体、技术为用”的嵌套性组织结构和松散耦合性部门关系互为表里的新型权力结构——分布式权力网络。分布式治理作为根植于中国城市基层治理场景的微观理论,一方面有助于丰富基层治理机制的理论图谱,对传统基层治理机制选择的理论预设以及技术治理与科层治理共生理论进行了部分修正和改进;另一方面,通过反思技术与科层组织关系的传统研究路径,将权力维度重新拉入技术与组织关系研究,在一定程度上扩展了既有研究视域。同时,分布式治理的提出也为回答“优化基层治理数字化转型和技术化创新”之问提供了一个探索性答案。

基于新型编码解码网络的复杂输电线识别

构建新型编码解码网络实现图像中多根交叉复杂输电线的高精度快速识别.为了减少网络参数,编码器取常规MobileNetV3的前16层,并且采用坐标注意力机制代替常规MobileNetV3的挤压和激励注意力机制,获取特征图的通道信息和位置信息;解码器通过金字塔池化模块获取输电线多尺度特征信息,提高识别精度;采用跳跃链接将编码器第2、4、7、11和13层特征图经锐化核卷积处理后分别与解码器的特征图堆叠,加强复杂输电线边缘特征提取;引入混合损失函数解决图像中输电线像素少、背景像素多的类别不平衡问题;利用迁移学习加快网络训练速度.实验结果表明,新型编码解码网络的平均像素精度(MPA)、平均交并比(MIOU)和识别速度分别为92.18%、84.27%和32帧/s,优于PSPNet、U2Net和其他输电线识别网络的.

基于复杂网络理论的电力通信网脆弱性分析研究

基于电力通信网在现代电力系统中的核心作用,文章深入分析了其网络拓扑结构特性及在不同攻击和故障模式下的脆弱性。研究先通过构建数学模型,分析电力通信网的小世界和无标度特性,进而探讨了网络的度分布、节点脆弱性及故障模式。在此基础上,文章提出了针对性的保护策略,如对高度数节点的保护、低度数节点的加边策略以及网络的重构策略,旨在增强网络的抗攻击能力和提升其稳定性。这些策略的有效性通过定量方法进行了评估,结果显示所提出的措施能显著提高电力通信网的健壮性和效率。本研究不仅为电力通信网络的脆弱性分析提供了理论基础,也为网络的优化和保护提供了实践指导,对于保障电力系统的稳定运行具有重要意义。

基于深度强化学习的电网关键节点识别

电网中某个关键节点发生故障,可能会引起级联失效,导致整个区域大面积停电。针对电网关键节点识别问题,提出了基于深度强化学习的电网关键节点识别模型。该模型通过图神经网络GCN对电网网络结构进行感知,对动作和状态进行向量表示,通过DQN的决策,选择最优动作,进行梯度回传,不断迭代优化参数,得到最优模型。基于该模型设计了电网关键节点检测算法KNDDRL,并在不同网络上进行了大量的实验,实验结果表明该算法能够有效识别电力网络中的关键节点,其效果优于所比较的代表性算法。

柔性双工网络功率分配:边剪枝加速的GNN计算

由于用户间干扰的存在,无线通信网络中的功率分配问题往往是非凸的、计算量巨大。当前图神经网络(graph neural network,GNN)成为一种有效的计算方法被用来解决该问题。为了最大限度地提高网络传输速率的同时降低计算复杂度,提出一种将设备属性和通信连接属性纳入GNN的柔性双工网络图表示方法,并构建了相应的柔性双工图神经网络(flexible duplex GNN,FD-GNN)模型,首次将节点对之间的距离、信道增益和邻居作为动态阈值引入到FD-GNN中,以适应动态环境。排除GNN中邻居的信道状态信息,通过修剪FD-GNN中的边来减少计算时间降低网络时间复杂度。仿真表明,所提出的基于信道增益邻居的阈值设定方法,性能最优且达到加权最小均方误差(weighted minimum mean square error,WMMSE)的97%,相较于Full-GNN所需的训练时间下降24%。提出的基于阈值的边剪枝有效降低了GNN运算的时间复杂度,提高了算法有效性。

基于复杂网络的中国南方航空网络研究

随着经济的快速发展和全球化进程的加快,国内航空运输网络的不断扩大,航空业的迅速发展使得对航空网络的研究具有较大的意义。文中以中国南方航空公司2021-2022年冬春航班计划为基础,运用复杂网络的基础理论分析中国南方航空航线网络的特性,将南方航空网络的特性与中国航空网络的特性进行对比。再运用度中心性、介数中心性、接近度中心性三种中心性分析指标对南方航空网络进行节点重要性排序,分析在不同中心性指标因素下节点的重要性。最后对研究成果进行分析和展望。

A new local-world evolving network model

In some real complex networks, only a few nodes can obtain the global information about the entire networks, but most of the nodes own only local connections therefore own only local information of the networks. A new local-world evolving network model is proposed in this paper. In the model, not all the nodes obtain local network information, which is different from the local world network model proposed by Li and Chen （LC model）. In the LC model, each node has only the local connections therefore owns only local information about the entire networks. Theoretical analysis and numerical simulation show that adjusting the ratio of the number of nodes obtaining the global information of the network to the total number of nodes can effectively control the valuing range for the power-law exponent of the new network. Therefore, if the topological structure of a complex network, especially its exponent of power-law degree distribution, needs controlling, we just add or take away a few nodes which own the global information of the network.