Phase Transitions of Majority-Vote Model on Modular Networks

We investigate the phase transitions behavior of the majority-vote model with noise on a topology that consists of two coupled random networks. A parameter p is used to measure the degree of modularity, defined as the ratio of intermodular to intramodular connectivity. For the networks of strong modularity （small p）, as the level of noise f increases, the system undergoes successively two transitions at two distinct critical noises, fc1 and fc2. The first transition is a discontinuous jump from a coexistence state of parallel and antiparallel order to a state that only parallel order survives, and the second one is continuous that separates the ordered state from a disordered state. As the network modularity worsens, fc1 becomes smaller and fc1 does not change, such that the antiparallel ordered state will vanish if p is larger than a critical value of pc. We propose a mean-field theory to explain the simulation results.

Prediction of NO_(x)concentration using modular long short-term memory neural network for municipal solid waste incineration

Air pollution control poses a major problem in the implementation of municipal solid waste incineration(MSWI).Accurate prediction of nitrogen oxides(NO_(x))concentration plays an important role in efficient NO_(x)emission controlling.In this study,a modular long short-term memory(M-LSTM)network is developed to design an efficient prediction model for NO_(x)concentration.First,the fuzzy C means(FCM)algorithm is utilized to divide the task into several sub-tasks,aiming to realize the divide-and-conquer ability for complex task.Second,long short-term memory(LSTM)neural networks are applied to tackle corresponding sub-tasks,which can improve the prediction accuracy of the sub-networks.Third,a cooperative decision strategy is designed to guarantee the generalization performance during the testing or application stage.Finally,after being evaluated by a benchmark simulation,the proposed method is applied to a real MSWI process.And the experimental results demonstrate the considerable prediction ability of the M-LSTM network.

Inverse stochastic resonance in modular neural network with synaptic plasticity

This work explores the inverse stochastic resonance(ISR) induced by bounded noise and the multiple inverse stochastic resonance induced by time delay by constructing a modular neural network, where the modified Oja’s synaptic learning rule is employed to characterize synaptic plasticity in this network. Meanwhile, the effects of synaptic plasticity on the ISR dynamics are investigated. Through numerical simulations, it is found that the mean firing rate curve under the influence of bounded noise has an inverted bell-like shape, which implies the appearance of ISR. Moreover, synaptic plasticity with smaller learning rate strengthens this ISR phenomenon, while synaptic plasticity with larger learning rate weakens or even destroys it. On the other hand, the mean firing rate curve under the influence of time delay is found to exhibit a decaying oscillatory process, which represents the emergence of multiple ISR. However, the multiple ISR phenomenon gradually weakens until it disappears with increasing noise amplitude. On the same time, synaptic plasticity with smaller learning rate also weakens this multiple ISR phenomenon, while synaptic plasticity with larger learning rate strengthens it. Furthermore, we find that changes of synaptic learning rate can induce the emergence of ISR phenomenon. We hope these obtained results would provide new insights into the study of ISR in neuroscience.

A Short-Term Climate Prediction Model Based on a Modular Fuzzy Neural Network

In terms of the modular fuzzy neural network (MFNN) combining fuzzy c-mean (FCM) cluster and single-layer neural network, a short-term climate prediction model is developed. It is found from modeling results that the MFNN model for short-term climate prediction has advantages of simple structure, no hidden layer and stable network parameters because of the assembling of sound functions of the self-adaptive learning, association and fuzzy information processing of fuzzy mathematics and neural network methods. The case computational results of Guangxi flood season (JJA) rainfall show that the mean absolute error (MAE) and mean relative error (MRE) of the prediction during 1998-2002 are 68.8 mm and 9.78%, and in comparison with the regression method, under the conditions of the same predictors and period they are 97.8 mm and 12.28% respectively. Furthermore, it is also found from the stability analysis of the modular model that the change of the prediction results of independent samples with training times in the stably convergent interval of the model is less than 1.3 mm. The obvious oscillation phenomenon of prediction results with training times, such as in the common back-propagation neural network (BPNN) model, does not occur, indicating a better practical application potential of the MFNN model.

Interpreting Nestedness and Modularity Structures in Affiliation Networks: An Application in Knowledge Networks Formed by Software Project Teams

An understanding of the knowledge creation and diffusion process in the organizational context is extremely relevant. Because from this understanding, organizations can restructure processes, reorient teams and implement methodologies to assist in the construction of an evolutionary process of knowledge creation and diffusion aimed at sustainable growth and innovation. The theory of complex social networks has been applied in several fields to help understand organizational cognitive processes. However, these approaches still insipiently consider the analysis of the nestedness and modularity of the studied networks. In this article, we presented an approach that sought to identify patterns of nestedness and modularity in networks of affiliation of people in projects in the organizational context. The study sought to identify these patterns in affiliation networks in a public organization providing information technology services in the period from 2006 to 2013. The detection of these patterns was performed using the NODF (Nestedness metric based on Overlap and Decreasing Fill) algorithm described by [1]. The nestedness and modularity metrics can influence patterns of knowledge creation and diffusion in formal and informal networks constituted for the execution of projects in organizations. This study showed that the network structures of the organization during the study period presented a high degree of nestedness, and it was possible to identify combined structures of nestedness and modularity.

Link prediction in complex networks via modularity-based belief propagation

Link prediction aims at detecting missing, spurious or evolving links in a network, based on the topological information and/or nodes＇ attributes of the network. Under the assumption that the likelihood of the existence of a link between two nodes can be captured by nodes＇ similarity, several methods have been proposed to compute similarity directly or indirectly, with information on node degree. However, correctly predicting links is also crucial in revealing the link formation mechanisms and thus in providing more accurate modeling for networks. We here propose a novel method to predict links by incorporating stochastic-block-model link generating mechanisms with node degree. The proposed method first recov- ers the underlying block structure of a network by modularity-based belief propagation, and based on the recovered block structural information it models the link likelihood between two nodes to match the degree sequence of the network. Experiments on a set of real-world networks and synthetic networks generated by stochastic block model show that our proposed method is effective in detecting missing, spurious or evolving links of networks that can be well modeled by a stochastic block model. This approach efficiently complements the toolbox for complex network analysis, offering a novel tool to model links in stochastic block model networks that are fundamental in the modeling of real world complex networks.

An evolving network model with modular growth

In this paper, we propose an evolving network model growing fast in units of module, according to the analysis of the evolution characteristics in real complex networks. Each module is a small-world network containing several interconnected nodes and the nodes between the modules are linked by preferential attachment on degree of nodes. We study the modularity measure of the proposed model, which can be adjusted by changing the ratio of the number of inner- module edges and the number of inter-module edges. In view of the mean-field theory, we develop an analytical function of the degree distribution, which is verified by a numerical example and indicates that the degree distribution shows characteristics of the small-world network and the scale-free network distinctly at different segments. The clustering coefficient and the average path length of the network are simulated numerically, indicating that the network shows the small-world property and is affected little by the randomness of the new module.

MNN-XSS:Modular Neural Network Based Approach for XSS Attack Detection

The rapid growth and uptake of network-based communication technologies have made cybersecurity a significant challenge as the number of cyber-attacks is also increasing.A number of detection systems are used in an attempt to detect known attacks using signatures in network traffic.In recent years,researchers have used different machine learning methods to detect network attacks without relying on those signatures.The methods generally have a high false-positive rate which is not adequate for an industry-ready intrusion detection product.In this study,we propose and implement a new method that relies on a modular deep neural network for reducing the false positive rate in the XSS attack detection system.Experiments were performed using a dataset consists of 1000 malicious and 10000 benign sample.The model uses 50 features selected by using Pearson correlation method and will be used in the detection and preventions of XSS attacks.The results obtained from the experiments depict improvement in the detection accuracy as high as 99.96%compared to other approaches.

Detecting and describing the modular structures of weighted networks

In the functional properties of complex networks, modules play a central role. In this paper, we propose a new method to detect and describe the modular structures of weighted networks. In order to test the proposed method, as an example, we use our method to analyse the structural properties of the Chinese railway network. Here, the stations are regarded as the nodes and the track sections are regarded as the links. Rigorous analysis of the existing data shows that using the proposed algorithm, the nodes of network can be classified naturally. Moreover, there are several core nodes in each module. Remarkably, we introduce the correlation function Grs, and use it to distinguish the different modules in weighted networks.

模块化网络化企业的初次分配范式与最优剩余分割占比:以专精特新企业为例

专精特新企业是中国经济实现创新驱动的重要微观基础,而培育和发展专精特新企业的关键又在于科学界定企业剩余分割占比,从根本上均衡物质资本和人力资本所有者利益,激发两者积极性。当前,产业分工协作已由产业间分工协作、产业内分工协作向产品内分工协作转变,模块化网络组织(含模块制造商、系统集成商及规则设计商)已然成为专精特新企业的主流形态。运用博弈论及仿真方法研究一般企业及三类专精特新企业的最优剩余分割占比问题,结果显示:(1)企业剩余最优分割占比是物质资本和人力资本所有者讨价还价的动态博弈结果,重置成本、资产专用性和风险承受(意愿和能力)是影响博弈进程与结果的关键因素,这三种关键因素的动态强弱变化导致三类专精特新企业最优剩余分割占比存在很大差异。(2)专精特新模块制造商内部分配是物质资本主导下的“资本雇佣劳动”传统初次分配范式,其人力资本所有者分配的企业剩余最优分割占比处在0~0.5区间,具体呈正偏态分布,最优占比众数远小于0.5。(3)专精特新系统集成商内部分配是人力资本和物质资本共同主导下的“资本雇佣劳动与劳动雇佣资本并存”新型初次分配范式,其人力资本所有者分配的企业剩余最优分割占比呈正态分布,最优占比众数接近0.5。(4)专精特新规则设计商内部分配则为人力资本主导下的“劳动雇佣资本”新型初次分配范式,其人力资本所有者分配的企业剩余最优分割占比处在0.5~1区间,具体呈负偏态分布,最优占比众数远大于0.5。因此,专精特新企业需要根据自身在模块化网络组织中的位置和形态,采用合适的初次分配范式和企业剩余分割占比。

基于网络节点极大团的社团检测算法

社团结构检测有助于揭示复杂网络的结构-功能特性,目前已有的社团检测算法在其研究过程中存在着分辨率限制、节点不确定性以及需要先验参数等问题.为了解决此类问题,提出了一种基于网络节点极大团的社团检测算法(BMC).BMC算法将网络中的节点极大团设为初始节点群组,依据提出的极大团局部相似度和局部团组关系对节点群组进行分级聚类合并,以此挖掘出网络中的社团结构.针对在社团结构挖掘过程中出现的节点不确定性问题,通过模块度矩阵提出了模块隶属度对网络中的单邻居节点和重叠节点进行优化.为了验证BMC算法对网络社团结构挖掘的准确性,在5个真实网络数据集上与5种算法进行实验对比.通过3种衡量指标得到的实验结果表明,BMC算法能够准确地检测出网络中的社团结构.

一种可实现双向故障闭锁的双极Y型模块化多电平直流变换器

直流电网互联通常采用隔离型高压大功率直流变换器,但其存在体积大、成本高及传输效率较低等问题。该文提出一种双极Y型模块化多电平DC/DC变换器,其避免使用中间变压器,实现直流功率双向传输,并且可以有效闭锁双向直流故障。首先分析了变换器拓扑结构及故障闭锁工作原理,并根据桥臂内电势等效原理建立了数学模型。基于变换器臂间、相间能量平衡约束,在闭环控制的基础上引入桥臂电流补偿控制,提升变换器暂态性能。最后,在Matlab/Simulink搭建了采用模块化多电平DC/DC变换器(modularmultilevelDC/DCconverter,DC-MMC)的直流输电系统仿真模型,通过对端口电流、桥臂电流及桥臂电容电压等动态指标的分析,验证了所提DC-MMC双向功率传输控制及双向故障闭锁能力的可行性和有效性。

面向中压直流配电网的模块化多电平电池储能系统电荷吞吐量抑制策略

模块化多电平电池储能系统(modular multilevel converter-battery energy storage system,MMC-BESS)中各子模块功率周期性波动,与其相连的电池组充放电状态频繁切换,产生额外的电荷吞吐量,危害电池储能系统寿命。当MMC-BESS用于中压直流配电网时,其交直流侧功率传输情况复杂多变,增大电荷吞吐量的抑制难度。因此,文中针对中压直流配电网中的MMC-BESS,提出一种基于二倍频环流注入的电荷吞吐量抑制策略。首先,构建中压直流配电网中MMC-BESS的数学模型;其次,剖析MMC-BESS电荷吞吐量的产生机理,分析不同运行方式下电荷吞吐量的影响因素;再次,提出基于二倍频环流注入的电荷吞吐量抑制策略,以系统总电荷吞吐量最小为目标,选取不同工况下需注入的最优二倍频环流分量;最后,多种工况下的仿真及实验结果表明,所提方法可有效抑制中压直流配电网中MMC-BESS的电荷吞吐量。

基于模糊Modular神经网络的官厅水库及邻区的地震危险性估计

首先介绍了模糊Modular神经网络的原理、建模方法与仿真实验,然后利用该方法把一些常用的地震学指标作为神经网络的输入,未来50年最大震级则作为网络的期望输出,对官厅水库及邻区的地震活动进行学习与最大震级序列建模,进行危险性预测。通过分析,认为该方法在一定程度上具有学习、建模与外推预测泛化能力,具有很好的中长期地震危险性预测效果,可以作为中长期地震危险性分析的工具。

基于注意力模块化神经网络的城市固废焚烧过程氮氧化物排放预测

氮氧化物(nitrogen oxides,NO_(x))浓度的实时精准检测是实现脱硝过程闭环控制的前提,对提高城市固废焚烧(municipal solid waste incineration,MSWI)过程脱硝效率具有重要意义。为此,提出了一种基于注意力模块化神经网络(attention modular neural network,AMNN)的MSWI过程NO_(x)排放预测方法。首先,模拟脑网络“分而治之”处理复杂任务的特性,利用模糊C均值(fuzzy C-means,FCM)聚类算法将待预测任务划分为多个子任务,从而降低预测任务复杂度;其次,针对各子任务,设计一种自组织模糊神经网络(self-organizing fuzzy neural network,SOFNN)构建子模型,通过神经元增删机制和二阶学习算法提高子模型的学习效率和学习精度;然后,提出了一种基于注意力机制的子模型整合策略,进一步提高预测模型的泛化性能;最后,通过基准实验Mackey-Glass时间序列预测和北京某MSWI厂实际数据验证了AMNN的可行性和有效性。

中小容量VSC-HVDC在解决电力系统电磁环网问题的应用

电磁环网可以提高电网供电可靠性,但也会造成故障后短路电流增加、线路过载等问题。南方电网公司目前主要采取解环运行和方式预控的措施来降低电磁环网运行风险,但解环不适用于所有场景,方式预控又可能造成电源出力受限。因此提出将中小容量柔性直流输电系统(VSC-HVDC)应用于含电磁环网的低压电网,利用其灵活可控的特点来降低电磁环网运行风险。首先综合考虑《电力系统安全稳定导则》要求以及经济性确定VSC-HVDC在电磁环网中的接入位置和容量,随后给出了VSC-HVDC直流电压、拓扑结构以及参数的确定原则。其次,设计了VSC-HVDC在电磁环网中的控制策略,该控制策略可以降低网损,提高电磁环网稳定性。最后通过实例仿真验证了提出的中小容量柔性直流方案在降低电磁环网运行风险方面的有效性。

改进的模糊Modular神经网络在既有建筑可靠性鉴定中的应用

在Takagi-Sugeno模糊逻辑系统的基础上,提出了改进的模糊Modular神经网络模型(IF-MNN),并将该模型应用于既有建筑的可靠性鉴定。改进的模型是将传统的模糊Modular神经网络模型中的单输出改进为多输出。这种改进的多输入多输出的模糊Modular神经网络模型具有预测性能好、训练学习速度快的优点,它的系统门网络采用模糊C均值聚类算法代替K-means算法,专家网络的训练中引进了先进的Levenberg-Marquardt算法。在应用改进的模糊Modular神经网络模型对既有建筑进行可靠性鉴定的过程中,综合考虑了各主要因素对既有建筑可靠性鉴定等级的影响,并将经量化处理的影响因素作为网络的外部输入,将网络计算得到的4个输出值分别作为样本对应于不同可靠性等级的隶属度,建筑可靠性鉴定的最终评判等级为最大隶属度所对应的等级。训练和预测样本的计算结果证明了改进的模糊Modular神经网络模型在既有建筑可靠性鉴定中应用的可行性和有效性。

一种模糊Modular神经网络模型及其应用

将神经网络模糊系统与模糊C均值聚类法相结合 ,对模糊Modular神经网络进行研究 ,提出了该模糊神经网络模型的多输出结构及其学习算法 ,据此开发了模糊神经网络诊断系统 ,并将其用于某电源分系统的诊断分析 ,运行的结果表明 。

基于模块度的生态网络结构评价方法研究

当前,我国正在进行以生态文明为主题的国土空间规划,国土生态空间的生态网络结构优化方法仅从斑块及廊道的功能重要性进行评价,未考虑生态斑块在网络中的结构重要性。为解决网络中生态斑块的结构重要性评价的缺失问题,文中从复杂网络结构的角度分析,提出基于模块度的生态网络结构重要性评价方法,并以哈尔滨市为例进行应用验证。研究结果表明,根据模块度可以用于生态网络结构优化,模块度高的网络结构更稳定;对关键战略点的识别可以维持生态网络结构的稳定性,为国土空间规划中生态空间与城镇空间和农业空间的博弈提供依据。

近距无线通信组网拓扑技术

针对精确制导武器的新质作战需求,文中以无线电气设计为核心技术方向实现产品模块化设计,以解决灵活组网、抗毁重构和动态拓扑等问题。采用Ad Hoc组网架构、广义编码和无源多址接入技术,实现快速、灵活、稳定的信息交互。通过混合式路由设计,在金属舱网络中划分子网并实施先应式路由和反应式路由,并引入地界标思想以减少多制式通信影响。通过提前规定所有事件的时序和优先级,建立简易事件类型库,并在节点间备份存储,同时采用边界广播协议实现目的节点快速抵达网关节点。研究结果表明,所提方案满足了武器功能模块化设计的需求,通过无线通信网络的组网和路由协议设计,实现了灵活组网、抗毁重构、动态拓扑和多制式通信等能力特点。