Laplacian energy maximizationfor multi-layer air transportation networks

To increase airspace capacity, alleviate flight delay,and improve network robustness, an optimization method of multi-layer air transportation networks is put forward based on Laplacian energy maximization. The effectiveness of taking Laplacian energy as a measure of network robustness is validated through numerical experiments. The flight routes addition optimization model is proposed with the principle of maximizing Laplacian energy. Three methods including the depth-first search( DFS) algorithm, greedy algorithm and Monte-Carlo tree search( MCTS) algorithm are applied to solve the proposed problem. The trade-off between system performance and computational efficiency is compared through simulation experiments. Finally, a case study on Chinese airport network( CAN) is conducted using the proposed model. Through encapsulating it into multi-layer infrastructure via k-core decomposition algorithm, Laplacian energy maximization for the sub-networks is discussed which can provide a useful tool for the decision-makers to optimize the robustness of the air transportation network on different scales.

Distributed Contact Plan Design for Multi-Layer Satellite-Terrestrial Network

In multi-layer satellite-terrestrial network, Contact Graph Routing(CGR) uses the contact information among satellites to compute routes. However, due to the resource constraints in satellites, it is extravagant to configure lots of the potential contacts into contact plans. What's more, a huge contact plan makes the computing more complex, which further increases computing time. As a result, how to design an efficient contact plan becomes crucial for multi-layer satellite network, which usually has a large scaled topology. In this paper, we propose a distributed contact plan design scheme for multi-layer satellite network by dividing a large contact plan into several partial parts. Meanwhile, a duration based inter-layer contact selection algorithm is proposed to handle contacts disruption problem. The performance of the proposed design was evaluated on our Identifier/Locator split based satellite-terrestrial network testbed with 79 simulation nodes. Experiments showed that the proposed design is able to reduce the data delivery delay.

A Kind of Second-Order Learning Algorithm Based on Generalized Cost Criteria in Multi-Layer Feed-Forward Neural Networks

A kind of second order algorithm--recursive approximate Newton algorithm was given by Karayiannis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very reluctant, which led to the loss of valuable information and affected performance of the algorithm to certain extent. For multi layer feed forward neural networks, the second order back propagation recursive algorithm based generalized cost criteria was proposed. It is proved that it is equivalent to Newton recursive algorithm and has a second order convergent rate. The performance and application prospect are analyzed. Lots of simulation experiments indicate that the calculation of the new algorithm is almost equivalent to the recursive least square multiple algorithm. The algorithm and selection of networks parameters are significant and the performance is more excellent than BP algorithm and the second order learning algorithm that was given by Karayiannis.

Global forward-predicting dynamic routing for traffic concurrency space stereo multi-layer scale-free network

Many real communication networks, such as oceanic monitoring network and land environment observation network,can be described as space stereo multi-layer structure, and the traffic in these networks is concurrent. Understanding how traffic dynamics depend on these real communication networks and finding an effective routing strategy that can fit the circumstance of traffic concurrency and enhance the network performance are necessary. In this light, we propose a traffic model for space stereo multi-layer complex network and introduce two kinds of global forward-predicting dynamic routing strategies, global forward-predicting hybrid minimum queue（HMQ） routing strategy and global forward-predicting hybrid minimum degree and queue（HMDQ） routing strategy, for traffic concurrency space stereo multi-layer scale-free networks. By applying forward-predicting strategy, the proposed routing strategies achieve better performances in traffic concurrency space stereo multi-layer scale-free networks. Compared with the efficient routing strategy and global dynamic routing strategy, HMDQ and HMQ routing strategies can optimize the traffic distribution, alleviate the number of congested packets effectively and reach much higher network capacity.

Hausdorff Dimension of Multi-Layer Neural Networks

This elucidation investigates the Hausdorff dimension of the output space of multi-layer neural networks. When the factor map from the covering space of the output space to the output space has a synchronizing word, the Hausdorff dimension of the output space relates to its topological entropy. This clarifies the geometrical structure of the output space in more details.

Multi-layer Tectonic Model for Intraplate Deformation and Plastic-Flow Network in the Asian Continental Lithosphere

In a large area of the east—central Asian continent there is a unified seismic network system composed of two families of large—seismic belts that intersect conjugately. Such a seismic network in the middle—upper crust is actually a response to the plastic flow network in the lower lithosphere including the lower crust and lithospheric mantle. The existence of the unified plastic flow system confirms that the driving force for intraplate tectonic deformation results mainly from the compression of the India plate, while the long-range transmission of the force is carried out chiefly by means of plastic flow. The plastic flow network has a control over the intraplate tectonic deformation.

Intra-layer synchronization in duplex networks

This paper explores the intra-layer synchronization in duplex networks with different topologies within layers and different inner coupling patterns between, within, and across layers. Based on the Lyapunov stability method, we prove theoretically that the duplex network can achieve intra-layer synchronization under some appropriate conditions, and give the thresholds of coupling strength within layers for different types of inner coupling matrices across layers. Interestingly,for a certain class of coupling matrices across layers, it needs larger coupling strength within layers to ensure the intra-layer synchronization when the coupling strength across layers become larger, intuitively opposing the fact that the intra-layer synchronization is seemly independent of the coupling strength across layers. Finally, numerical simulations further verify the theoretical results.

基于多层耦合网络的装备保障体系级联失效及抗毁性分析

为准确描述装备保障体系结构特征与毁伤失效流程,进而提升装备保障体系功能与抗毁能力,基于杀伤层、保障层和储供层构建装备保障体系的多层耦合网络模型。基于杀伤链给出装备维修保障链与储供保障链的特征和典型样式,在此基础上,提出考虑装备保障体系多层耦合网络特性的级联失效模型与节点重要度评估方法。给出对应的网络抗毁性评估指标。仿真结果表明,考虑级联失效的多层耦合网络模型能较好地反映遭受攻击时整个装备保障体系的性能变化情况,所提出的使命任务节点重要度能准确识别关键节点。与传统网络抗毁性指标相比,杀伤链数量对节点失效产生的装备保障体系抗毁性变化更加敏感。

考虑OD需求聚类的区域多层公路交通网络混合路径诱导模型

随着交通出行需求的快速增长,我国高速公路网络面临的运输压力日渐增加,经常出现严重的交通拥堵现象。为了缓解高速公路交通拥堵,并实施更有针对性的交通管控措施,提出了考虑OD需求聚类的区域多层公路交通网络混合路径诱导模型。首先,利用湖南省高速公路以及国道、省道的地理信息数据构建区域多层公路交通网络。然后,根据OD对间距离和OD交通量的差异,利用K-均值聚类算法对OD对进行聚类分析,将OD对划分为3个不同的类别。最后,应用遗传算法筛选出各类OD对中对拥堵贡献较大的出行群体,并建立有针对性的混合路径诱导模型,对拥堵贡献较大和拥堵贡献较小的出行群体分别应用不同的路径诱导方案。当OD需求扩样系数设置为6时,对OD对聚类可以将总出行成本进一步降低35186.03 min。在不进行OD对聚类时,使用规划路径的出行总数为79140,而实施OD对聚类后,使用规划路径的出行总数为70374。使用诱导路径的出行的平均出行时间由121.47 min下降为85.61 min,极少数出行(3.75%)的时间增加,且增加最大值低于3 min。对多个不同扩样系数进行敏感性分析进一步说明了考虑OD需求聚类的混合路径诱导模型具有良好的拥堵缓解效果。考虑OD需求聚类的区域多层公路交通网络混合路径诱导模型可以用于识别对拥堵贡献较大的关键出行群体,进而制定有针对性的路径诱导策略,在缓解高速公路交通拥堵的同时能够减少对大多数出行者的影响,降低路径诱导策略的实施难度。另外,研究结果还表明:对出行距离较长的出行群体实施路径诱导能够更加有效地缓解区域多层公路交通网络中的交通拥堵。

基于可达性的多层动态航空网络鲁棒性分析

为更准确地描述航空网络以分析其鲁棒性,提出1种以同一始发机场及其离场航班为子网络层,随出发时刻动态变化的多层动态航空网络模型。针对我国国内(不含港澳台)多层动态航空网络,以可达性、度中心性和加权介数中心性为节点特征指标,网络效率为网络性能测度指标,从随机攻击和蓄意攻击2种策略下对网络进行鲁棒性分析。研究结果表明:我国国内(不含港澳台)航空网络的鲁棒性主要由20%的机场提供,且在不同时间段内鲁棒性不同,在2023年夏秋航季,7∶00~20∶00间鲁棒性较好,4∶00~7∶00和20∶00~24∶00鲁棒性较差;用可达性指标衡量航空网络中机场节点的重要程度比度中心性和加权介数中心性指标更准确。研究结果可为保障航空运输网络安全提供一定参考。

基于多层残差网络的地震提频处理在薄储集层识别中的应用

基于多层残差网络的地震提频处理方法,通过智能化网络将测井高频信息与地震数据相结合,能有效提升纵向分辨率,保持横向连续可追踪,利于薄储集层识别。针对AMH地区常规处理的地震数据仅能识别厚度大于30 m的碳酸盐岩层,无法有效识别厚度较小的薄储集层的问题,提出基于多层残差网络的地震提频处理方法,以井旁地震振幅作为训练数据,测井相对波阻抗作为训练标签,利用深度学习网络多层残差网络开展训练,获取相对波阻抗曲线的预测模型;通过将地震数据作为输入,利用深度网络训练模型计算得到相对波阻抗数据体,进而得到提频后的地震数据体相对应的反射系数体。通过对靶区地质情况的分析认识,对宽频子波进行标定后提取合适的宽频子波,与反射系数体进行褶积,得到提频后的地震数据体;利用提频后的地震数据体开展储集层反演,反演结果纵向具有较高分辨率,与主要目的层能够较好匹配,横向可以进行识别和追踪,利用高分辨地震数据反演结果实现AMH地区的薄储集层识别。结果表明,通过基于多层残差网络的地震提频处理及相应的高分辨模型反演,在AMH地区能够识别厚度大于10 m的薄储集层,较好地解决由于地震分辨率低无法识别薄储集层的问题,有效提高了薄储集层预测的精度,对同类型薄储集层识别具有借鉴意义。

Koopman原理内嵌MLP神经网络模型驱动的电力系统非线性振荡特征分析方法

针对电力系统非线性动态特性表征与物理机理融合不清晰、精度低的问题,提出了一种Koopman原理内嵌多层感知机(MLP)神经网络模型驱动的电力系统非线性特性表征与分析方法。阐明了Koopman算子的基本原理,分析了Koopman算子在非线性系统时序演化中的作用。采用MLP神经网络构建编码、解码映射,进而形成Koopman原理内嵌的神经网络深度学习模型,通过深度学习实现非线性系统“编码映射-线性演化-解码映射”3种结构的演化逼近。分析了将所提方法应用于电力系统动态特性分析的物理机理,建立了所提方法的求解与应用流程。通过单机与4机系统算例对所提方法进行对比验证,结果表明所提方法可以精确表征平衡点稳定域内的系统动态过程,可用于电力系统非线性振荡动态特性解析。

基于多层全连接神经网络的6C地震波极化向量识别研究

利用机器学习原理,提出一种基于多层全连接(multi-layer fully connected, MFC)神经网络的六分量(six-component, 6C)地震波极化向量识别方法。首先利用6C地震波各波型极化向量数学模型和一系列仿真参数得到5种波型和噪声波型各5 000个极化向量数据集,然后随机选取其中5 000个作为测试集,其余划分为训练集,进行MFC神经网络与支持向量机(support vector machine, SVM)的综合辨识性能对比实验。结果表明,MFC神经网络模型识别5种极化向量类型(SH波和Love波视为一类)和6种极化向量类型的效果均显著优于SVM模型,平均识别率分别达到99.786%和87.940%。

基于图神经网络的多层银企网络融合研究

针对金融行业内潜在系统性风险难以精准识别问题,基于直接系统性风险传染渠道的借贷数据以及间接渠道的互联网文本信息,构建多层银企网络,并利用图卷积神经网络(GCN)设计多层银企网络融合模型,根据融合网络量化评估29家银行和75家房地产机构的不同渠道系统性风险传染过程。实验结果表明,在多层金融网络融合任务上,本文融合模型的准确率达到0.8559,优于对比模型。融合网络分析表明,多层网络共同冲击下的银企系统性风险传染能力明显大于单一或者2层网络的系统性风险,且基于间接渠道的企业间网络系统性风险更明显。金融审慎监管应该更多关注文本数据、深度学习等技术对于整合庞大金融资源的能力和有效提高风险监测预警的能力。

基于多层卷积的红外与可见光图像融合算法

针对复杂背景下纹理细节信息丢失、融合图像视觉感受较差等问题,提出了一种基于多层卷积的红外与可见光图像融合算法。该算法的网络框架分为编码器、解码器和融合网络3个部分。在编码器中引入高效通道注意力机制对源图像进行编码处理,融合多层卷积块、梯度卷积块、下采样卷积块以及卷积空间通道注意力机制等形成多层卷积融合网络(MCFN),通过该融合网络进行特征融合,利用解码器重建输出融合图像。选取了5种现有算法与所提算法用8种客观评价指标在两种数据集上进行比较,结果表明,所提算法融合后的图像目标突出、细节清晰、轮廓明显、指标提升显著,符合人体视觉感受。

面向产业人才需求识别的多层职技网络构建研究

[目的/意义]传统职业分类体系作为观察劳动力市场的重要工具,难以及时反映产业人才需求的变化。为化解这一难题,文章聚焦探究如何利用数字化手段描绘职业与技能要求间的关系,建构多层职技网络的方法与实施路径。[方法/过程]首先,设计一套中文语境下描述不同岗位的通用语言——跨工作描述符,并据此建立起面向招聘数据资源开发利用的岗位描述框架;其次,建立基于岗位描述的职业智能分类技术,进而形成一套体现技能要求的多层职技网络构建方法与模式;最后,以上海集成电路产业为例,对该方法进行验证。[结果/结论]源自企业实际用工活动的多层职技网络能够辅助细粒度、灵活地观测多样化的产业人才需求。

SSA-MLP模型在岩质边坡稳定性预测中的应用

岩质边坡的力学参数量化及稳定性分析对岩质边坡灾害的防治具有重要意义。Hoek-Brown(H B)准则是一种用于确定岩体力学参数的经典方法,能反映出边坡岩体变形和位移的非线性破坏特征。在此基础上,首先,提出一种麻雀搜索算法(Sparrow Search Algorithm,SSA)改进多层感知器(Multi-Layer Perceptron,MLP)的神经网络模型,并用于边坡稳定性预测、指标敏感性分析及参数反演。其次,将收集的1085组岩质边坡的几何参数和H B准则参数等作为输入变量,极限平衡理论Bishop法求解的安全系数作为输出变量,对SSA MLP模型进行训练学习和性能评估。最后,将该模型运用于25个边坡实例,验证模型的有效性。结果显示,该模型收敛速度快、精度高,为边坡稳定性分析和参数量化提供了一种新思路。

基于改进天鹰的三维室内多层结构定位算法

在面向一些大型商超、医院、教学楼等大规模室内多层结构定位中,针对多层WSN结构的非测距定位问题,提出一种基于改进天鹰的三维室内多层结构定位算法IAODV-HOP算法。首先,为节点划分3类通信半径以细化跳数,同时利用最小均方差和权重因子修正节点的平均跳距。其次,用IAO算法对未知节点坐标进行寻优,通过佳点集策略对种群初始化,解决天鹰算法因初始种群随机分布而导致的种群的质量和多样性难以保证的问题,并且在局部搜索中加入黄金正弦的搜索策略完善种群的位置更新方式,增强了算法的局部搜索能力。通过仿真实验,本文所提算法IAODV-HOP相较于传统3D-DV-Hop、PSO-3DDV-Hop、N3-3DDV-Hop以及N3-ACO-3DDV-Hop算法,归一化平均定位误差分别下降70.33%、62.67%、64%、53.67%,表现出更优的性能,具有更好的稳定性和更高的定位精度。

基于锥形追踪和网络分解的NeRF三维重建方法

在计算机视觉领域,神经辐射场(NeRF)是以空间坐标或者时间、相机位姿等其他维度作为输入,通过多层感知机(MLP)网络模拟目标函数,生成颜色、深度等目标标量的过程。NeRF的应用包括对三维场景进行高质量的重建,而其在处理不同分辨率的场景时会产生过度模糊或者伪影的渲染效果,且存在训练耗时较长的问题。为了解决上述问题,提出基于锥形追踪和网络分解的NeRF三维重建方法。使用锥形追踪的方法,为每个像素投射一个圆锥体,并将投射的圆锥体切割成一系列的圆锥台,沿着该圆锥体进行特征化,通过高效渲染抗锯齿的圆锥台来降低模糊或者伪影效果。为了缩短训练时间,使用网络分解的方法,将原始NeRF接收5维数据的神经网络分解为两个网络,有效地缩短训练时间。实验结果表明,在NeRF_Synthetic、LLFF和Multiresolution数据集中,相比于NeRF、F 2-NeRF等方法,所提方法的峰值信噪比(PSNR)提升了14.4%~24.6%,能够重建出更丰富的细节特征,视觉效果更好,且训练时间大幅降低。