Analysis on Connectivity of Inter-Orbit-Links in a MEO/LEO Double-Layer Satellite Network

As an important scheme of future global mobile satellite communication systems to provide multimedia service, a Double-Layer Satellite Network （DLSN） with MEO satellites and LEO satellites is proposed. The Inter-Orbit-Links （IOLs） between layers is an essential factor, which affects the performances of the DLSN systems. Considering certain constellation parameters, the geometric characteristics of IOLs are described and the connectivity of MEO satellites and LEO satellites in the DLSN is analyzed. By computer simulation, the results show that IOLs should be selectively established according to certain parameters rather than the simple in-sight principle.

Exploring reservoir computing:Implementation via double stochastic nanowire networks

Neuromorphic computing,inspired by the human brain,uses memristor devices for complex tasks.Recent studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data analysis.This paper presents a model based on these nanowire networks,with an improved conductance variation profile.We suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage pulses.The nanowire network layer generates dynamic behaviors for pulse voltages,allowing time series prediction analysis.Our experiment uses a double stochastic nanowire network architecture for processing multiple input signals,outperforming traditional reservoir computing in terms of fewer nodes,enriched dynamics and improved prediction accuracy.Experimental results confirm the high accuracy of this architecture on multiple real-time series datasets,making neuromorphic nanowire networks promising for physical implementation of reservoir computing.

Eigenvalue Spectrum and Synchronizability of Double-Layer Directed Ring Networks

The synchronizability of multiplex undirected regular networks has been intensively studied based on the study of the synchronizability of single-layer networks. However, most real networks are characterized by some degree of directionality. So multiplex directed networks can better explain the synchronizability phenomenon. Here, based on the theory of master stability function (MSF), we study the eigenvalue spectrum and synchronizability of double-layer inter-layer directed ring networks (Networks-A) and double-layer intra-layer directed ring networks (Networks-B). The eigenvalue spectrum of the supra-Laplacian matrix of the networks is rigorously derived, and the influence of the networks structure parameters on the network’s synchronizability is analyzed. The correctness of the theory is further verified by numerical simulation analysis. Finally, the synchronizability of four kinds of double-layer ring networks with different coupling modes, namely, Networks-A, Networks-B, Networks-C (double-layer undirected ring networks), and Networks-D (double-layer undirected inter-layer random-added-edge ring networks), is compared and the results can provide guidance for constructing the optimal synchronization network.

A decision hyper plane heuristic based artificial immune network classification algorithm

Most of the developed immune based classifiers generate antibodies randomly, which has negative effect on the classification performance. In order to guide the antibody generation effectively, a decision hyper plane heuristic based artificial immune network classification algorithm （DHPA1NC） is proposed. DHPAINC taboos the inner regions of the class domain, thus, the antibody generation is limited near the class domain boundary. Then, the antibodies are evaluated by their recognition abilities, and the antibodies of low recognition abilities are removed to avoid over-fitting. Finally, the high quality antibodies tend to be stable in the immune network. The algorithm was applied to two simulated datasets classification, and the results show that the decision hyper planes determined by the antibodies fit the class domain boundaries well. Moreover, the algorithm was applied to UCI datasets classification and emotional speech recognition, and the results show that the algorithm has good performance, which means that DHPAINC is a promising classifier.

One-way hash function based on hyper-chaotic cellular neural network

The design of an efficient one-way hash function with good performance is a hot spot in modern cryptography researches. In this paper, a hash function construction method based on cell neural network with hyper-chaos characteristics is proposed. First, the chaos sequence is gotten by iterating cellular neural network with Runge Kutta algorithm, and then the chaos sequence is iterated with the message. The hash code is obtained through the corre- sponding transform of the latter chaos sequence. Simulation and analysis demonstrate that the new method has the merit of convenience, high sensitivity to initial values, good hash performance, especially the strong stability.

A Hyper-Heuristic Framework for Lifetime Maximization in Wireless Sensor Networks With A Mobile Sink

Maximizing the lifetime of wireless sensor networks(WSNs) is an important and challenging research problem. Properly scheduling the movements of mobile sinks to balance the energy consumption of wireless sensor network is one of the most effective approaches to prolong the lifetime of wireless sensor networks. However, the existing mobile sink scheduling methods either require a great amount of computational time or lack effectiveness in finding high-quality scheduling solutions. To address the above issues, this paper proposes a novel hyperheuristic framework, which can automatically construct high-level heuristics to schedule the sink movements and prolong the network lifetime. In the proposed framework, a set of low-level heuristics are defined as building blocks to construct high-level heuristics and a set of random networks with different features are designed for training. Further, a genetic programming algorithm is adopted to automatically evolve promising high-level heuristics based on the building blocks and the training networks. By using the genetic programming to evolve more effective heuristics and applying these heuristics in a greedy scheme, our proposed hyper-heuristic framework can prolong the network lifetime competitively with other methods, with small time consumption. A series of comprehensive experiments, including both static and dynamic networks,are designed. The simulation results have demonstrated that the proposed method can offer a very promising performance in terms of network lifetime and response time.

策略梯度的超启发算法求解带容量约束车辆路径问题

有容量车辆路径问题是组合优化问题中比较热门的问题,它属于经典的NP-hard问题并且时间复杂度高.本文提出了一种基于策略梯度的超启发算法,将强化学习中的确定性策略梯度算法引入到超启发算法的高层策略中的底层算法选择策略,确定性策略梯度算法采用Actor-Critic框架,另外为了能够在后续计算和神经网络参数更新中引用历史经验数据,在确定性策略梯度算法中设计了经验池用于存储状态转移数据.在超启发算法解的接受准则方面,文中通过实验对比了3种接受准则的效果,最终选择了自适应接受准则作为高层策略中解的接受准则.通过对有容量车辆路径问题标准算例的计算,并将求解结果与其他算法对比,验证了所提算法在该问题求解上的有效性和稳定性.

基于m×2正则化交叉验证的神经网络超参数调优方法

超参数调优是神经网络建模的关键问题。针对传统的超参数调优方法存在的问题,该文提出了一种基于m×2正则化交叉验证的超参数调优方法。目的是给出一种适用于复杂模型、大数据集背景下的计算开销较小且稳健的超参数调优方法。该方法的思想是从完整的数据集上选取少部分数据进行调优,避免模型在数据集较大时非常耗时的超参数调优难题;在m×2交叉验证的基础上设置正则化条件均衡训练集与验证集之间的分布差异,从而减少分布不一致带来的性能波动;使用信噪比作为调优的优化目标,从而可以综合考虑模型性能评价指标的均值和方差;并采用正交设计选择相关性较低的超参数组合以提高调优效率。以命名实体任务为例进行实验,在CoNLL 2003数据集上的实验结果显示,提出的调优方法能够选到和网格搜索性能上没有显著差异的超参数组合,且调优时间可显著降低约66%。

基于实验方案设计的卷积神经网络超参数优化方法

卷积神经网络是人工智能的重要组成部分,在自然语言处理、图像识别等领域表现优异。卷积神经网络模型超参数配置涉及训练策略,在卷积神经网络大模型优化方面起着至关重要的作用。现有超参数优化方法耗时耗力,遍历整个超参数空间,容易陷入局部最优解。首先,构建3个不同深度的自建卷积神经网络作为优化对象,以提高模型在验证集上的准确率为优化目标找到最佳的超参数配置。其次,考虑优化神经网络大模型的训练过程并提高模型性能的需求,提出一种基于实验方案设计的卷积神经网络超参数优化方法。最后,为了验证方法的有效性,依据均匀设计理念构建训练方案,生成超参数优化组合,进行主观经验生成训练方案的对比实验。结果表明:所提出的优化方法在收敛速度、准确率和计算效率上更具优势。该方法为实现卷积神经网络大模型的高效训练提供支持,具有良好的通用性,可以应用于不同规模的卷积神经网络训练任务。

基于超网络的军事交通运输网络节点价值评估方法

针对军事交通运输网络中的高价值目标选择问题,提出一种基于超网络的节点价值评估方法。考虑多层网络内部和网络之间的交互机制,构建由通行枢纽子网、军事力量子网和后勤保障子网组成的军事交通运输超网络模型;提出综合拓扑维度和功能维度的超网络综合效能评价算法,在此基础上运用节点删除法,获得超网络中节点的体系价值和优先打击顺序。实验结果表明:节点的功能属性对其体系价值起决定性作用,且所处位置越优、邻近节点越重要,其体系价值越高;经过多轮打击后,通过该方法获得的节点打击排序相近,表明了该方法的鲁棒性;与现有方法相比,该方法能够全面客观地判断目标的重要度,有效提高决策的准确率,具有较高的战场普适性。

基于超图理论的中医方剂网络药对挖掘方法

随着复杂网络研究的深入发展,利用复杂网络理论对中药药方数据进行建模分析已成为研究中药配伍规律的重要方法之一。然而,传统的“药-药”网络结构只基于“点-边”关系,无法体现方剂和中药之间的高阶交互关系,从而影响药对挖掘的准确率和效果。针对该问题,提出了一种基于超图的方药超网络建模方法来刻画方剂与中药之间的高阶关联关系,以方剂为超边、方剂中的中药为节点构建方药超网络,对比传统“药-药”网络和方药超网络的统计特性发现,方药超网络节点超度分布更加符合幂律分布特征,具有小世界和无标度特性,证明了方药超网络建模方法具有合理性和可行性;基于方药超网络提出一种融合多因素的双权重度中心性指标(double weight degree centrality,DWDC)挖掘方剂中的常见中药,引入超边属性对节点特性的影响,以网络的脆弱性和鲁棒性为依据,通过各算法之间的对比实验,验证所提出方法能够更有效筛选出方剂超网络中的常见中药;再将DWDC指标推广到二阶常见药对挖掘,提出高阶指标H-DWDC,基于真实数据集的实验结果表明,该方法能够有效地从海量数据中挖掘出常见药对。

基于超参数优化的电力负荷预测模型研究

电力负荷数据的多样性与复杂性,会导致负荷预测过程中出现超参数难以确定、拟合效果较差和预测精度不高等问题。针对以上问题,提出一种基于樽海鞘群算法的融入注意力机制的双向长短期记忆神经网络模型——SSA-AM-BiLSTM模型。该模型使用BiLSTM学习特征的内部变化规律,引入注意力机制为特征进行权重分配,并且利用樽海鞘群算法优化网络超参数。基于具体数据集进行的负荷预测仿真实验表明,相较于GRU、LSTM、AM-BiLSTM和PSO-AM-BiLSTM模型,所提出的SSA-AM-BiLSTM模型的MAPE分别减少了2.15%、1.93%、1.42%和0.45%,并且优化了拟合效果,显著提高了预测精度。

Hyper-V虚拟环境下高职院校计算机实验室网络设计研究

针对传统模式下计算机实验环境存在的实验空间与时间的局限性、机房维护成本过高,通过使用Hyper-V服务器虚拟化技术高效运行虚拟机、不仅使计算机实验室信息服务平台在硬件层面具有高可用性,而且在网络层面也具有高可用性和灵活性,为计算机实验室提供更好的服务打下了坚实的基础[1]。

铁路通信网管云平台建设方案

为准确把握铁路通信网管云技术的发展方向,研究提出铁路网管云平台的建设方案。针对铁路专网领域独立建设各通信网管系统的模式,分析其现状及存在问题。在充分研究云平台技术基础上,结合《新时代交通强国铁路先行规划纲要》,提出铁路通信骨干网管及铁路局集团公司局干网管云平台的建设方案。分析既有拟整合网管系统资源的使用情况,计算搭建云平台所需计算与存储资源,配置适宜需求能力的云平台软硬件数量,并提出平台网络架构。构建资源可全局调度、能力可全面开放、容量可弹性伸缩、态势感知的智能化运维、管控平台,为铁路通信网管云的建设提供参考。

基于卷积神经网络的HTTP隧道检测

超文本传输协议(Hyper Text Transfer Protocol,HTTP)隧道具有穿越防火墙和规避入侵检测系统识别的能力,给信息安全带来严重威胁。然而现阶段的HTTP隧道检测方法识别能力不足、难以应对特征复杂的HTTP隧道。文中分析了HTTP隧道数据包与正常HTTP数据包之间的差别,针对目前HTTP隧道检测方法存在的不足,提出了一种仅需提取小部分流量数据的基于卷积神经网络的HTTP隧道检测方法。实验结果表明,基于卷积神经网络的HTTP隧道检测方法能有效识别网络中的HTTP隧道流量,检测精确率、召回率、F1分数均达到99%以上,且不需要人工选择大量的专家特征,对网络流量监管有重要意义。

Hyper-V虚拟网络应用模型分析

Hyper-V3.0是微软推出的虚拟化产品,效率高,功能强大,在业界得到了普遍应用。本文从Hyper-V的体系结构、Hyper-V虚拟交换机架构、虚拟交换机类型以及Hyper-V VLAN的应用四个方面对Hyper-V进行了原理性分析,并对三种虚拟网络的应用给出了应用模型,为Hyper-V的正确使用,提供理论基础。

超网络视角下区域协同创新特征与模式——以济南都市圈数字产业为例

城市群是国家创新体系的空间组织形式,是区域发展战略实施的关键载体,研究城市群协同创新对国家创新驱动发展战略实施、区域协调发展战略制定均具有重要现实意义。从超网络视角进行城市群协同创新原理分析,以知识协同、主体协同、城市协同及其之间协同关系为研究对象,探究多元主体城市群协同创新结构特征、模式以及发展路径等。首先围绕城市群协同创新多主体、多维度、多要素、多层次的结构与特征,以数据挖掘为基础构建知识层协同网络,并延伸构建创新主体层面和城市层面协同网络,进而分析知识、主体、城市交互作用关系,将多层协同网络耦合建立城市群协同超网络模型,涵盖了知识、多元创新主体、城市子系统要素及其结构。经过上述理论分析,利用专利数据建立济南都市圈数字技术产业协同创新超网络模型,进行网络结构测度、创新关系刻画与单层网络、二模网络指标测度等研究,探讨了济南都市圈数字技术产业协同创新特征,并总结出济南都市圈数字技术产业协同创新的有效模式。最后,从空间、创新主体、知识供给等一体协同角度,提出加强顶层研发设计、推动产业链融通创新、激发产学研协同创新活力等对策建议。

高校中的服务器超融合技术应用分析

随着信息技术的飞速发展,服务器已成为高校教学、科研、办公和管理的重要基础设施之一。本文探讨了高校网络环境下服务器超融合技术的应用场景和优势,通过分析高校教学、办公和管理的特点和需求,以及服务器超融合技术的优势,探究了其在高校服务器管理和运行中的实际应用场景。本文通过案例分析和未来发展趋势展望,指明了服务器超融合技术对高校办公、管理和教学的重要意义。

数据中心网络架构设计与高可用性保障探索

确保数据中心网络架构的高稳定性是信息技术领域中的一个关键议题,涉及布局与设计规划。该文首先深入探讨了传统数据中心网络架构、软件定义网络(SDN)和超融合基础设施(HCI)在数据中心的应用,并审视了它们各自的优势。其次,为增强系统的稳定性,研究了包括网络冗余、容错能力、故障转移、均衡负载、防护框架及应急备份在内的多种方法。最后,对数据中心网络的未来发展趋势进行了预测,深入分析了新兴技术的影响,讨论了未来网络的设计理念,并探讨了在追求可持续发展过程中所面临的挑战和可能的机会。

一种基于Hyper-V和WSFC集群的主机多路径网络存储架构方案

本文通过Hyper-V虚拟化、WSFC故障转移集群和主机多路径技术的整合,利用网络存储技术设计了一种网络存储架构解决方案。此方案能在一定程度上节约硬件资源部署成本、减少系统非计划停机时间、提高硬件资源利用率、避免主机单路径造成的硬件故障,同时增强IO数据传输效率,从而提高整个信息系统运行的可靠性、安全性、稳定性及可持续性。