Exponential synchronization of complex dynamical networks with Markovian jumping parameters using sampled-data and mode-dependent probabilistic time-varying delays

In this paper, the problem of exponential synchronization of complex dynamical networks with Markovian jumping parameters using sampled-data and Mode-dependent probabilistic time-varying coupling delays is investigated. The sam- pling period is assumed to be time-varying and bounded. The information of probability distribution of the time-varying delay is considered and transformed into parameter matrices of the transferred complex dynamical network model. Based on the condition, the design method of the desired sampled data controller is proposed. By constructing a new Lyapunov functional with triple integral terms, delay-distribution-dependent exponential synchronization criteria are derived in the form of linear matrix inequalities. Finally, two numerical examples are given to illustrate the effectiveness of the proposed methods.

Stochastic asymptotical synchronization of chaotic Markovian jumping fuzzy cellular neural networks with mixed delays and the Wiener process based on sampled-data control

We investigate the stochastic asymptotical synchronization of chaotic Markovian jumping fuzzy cellular neural networks （MJFCNNs） with discrete, unbounded distributed delays, and the Wiener process based on sampled-data control using the linear matrix inequality （LMI） approach. The Lyapunov–Krasovskii functional combined with the input delay approach as well as the free-weighting matrix approach is employed to derive several sufficient criteria in terms of LMIs to ensure that the delayed MJFCNNs with the Wiener process is stochastic asymptotical synchronous. Restrictions （e.g., time derivative is smaller than one） are removed to obtain a proposed sampled-data controller. Finally, a numerical example is provided to demonstrate the reliability of the derived results.

Linear Network Coding Based Fast Data Synchronization for Wireless Ad Hoc Networks with Controlled Topology

Fast data synchronization in wireless ad hoc networks is a challenging and critical problem.It is fundamental for efficient information fusion,control and decision in distributed systems.Previously,distributed data synchronization was mainly studied in the latency-tolerant distributed databases,or assuming the general model of wireless ad hoc networks.In this paper,we propose a pair of linear network coding(NC)and all-to-all broadcast based fast data synchronization algorithms for wireless ad hoc networks whose topology is under operator’s control.We consider both data block selection and transmitting node selection for exploiting the benefits of NC.Instead of using the store-and-forward protocol as in the conventional uncoded approach,a compute-and-forward protocol is used in our scheme,which improves the transmission efficiency.The performance of the proposed algorithms is studied under different values of network size,network connection degree,and per-hop packet error rate.Simulation results demonstrate that our algorithms significantly reduce the times slots used for data synchronization compared with the baseline that does not use NC.

Free-matrix-based time-dependent discontinuous Lyapunov functional for synchronization of delayed neural networks with sampled-data control

This paper is concerned with the synchronization of delayed neural networks via sampled-data control. A new technique, namely, the free-matrix-based time-dependent discontinuous Lyapunov functional approach, is adopted in constructing the Lyapunov functional, which takes advantage of the sampling characteristic of sawtooth input delay. Based on this discontinuous Lyapunov functional, some less conservative synchronization criteria are established to ensure that the slave system is synchronous with the master system. The desired sampled-data controller can be obtained through the use of the linear matrix inequality（LMI） technique. Finally, two numerical examples are provided to demonstrate the effectiveness and the improvements of the proposed methods.

Pinning sampled-data synchronization for complex networks with probabilistic coupling delay

We deal with the problem of pinning sampled-data synchronization for a complex network with probabilistic time-varying coupling delay. The sampling period considered here is assumed to be less than a given bound. Without using the Kronecker product, a new synchronization error system is constructed by using the property of the random variable and input delay approach. Based on the Lyapunov theory, a delay-dependent pinning sampled-data synchronization criterion is derived in terms of linear matrix inequalities （LMIs） that can be solved effectively by using MATLAB LMI toolbox. Numerical examples are provided to demonstrate the effectiveness of the proposed scheme.

A Non-symmetric Digital Image Secure Communication Scheme Based on Generalized Chaos Synchronization System

Based on a generalized chaos synchronization system and a discrete Sinai map, a non-symmetric true color （RGB） digital image secure communication scheme is proposed. The scheme first changes an ordinary RGB digital image with 8 bits into unrecognizable disorder codes and then transforms the disorder codes into an RGB digital image with 16 bits for transmitting. A receiver uses a non-symmetric key to verify the authentication of the received data origin, and decrypts the ciphertext. The scheme can encrypt and decz：Fpt most formatted digital RGB images recognized by computers, and recover the plaintext almost without any errors. The scheme is suitable to be applied in network image communications. The analysis of the key space, sensitivity of key parameters, and correlation of encrypted images imply that this scheme has sound security.

Data classification method based on network dynamics analysis and cloud model

In order to reduce amount of data storage and improve processing capacity of the system, this paper proposes a new classification method of data source by combining phase synchronization model in network clustering with cloud model. Firstly, taking data source as a complex network, after the topography of network is obtained, the cloud model of each node data is determined by fuzzy analytic hierarchy process （AHP）. Secondly, by calculating expectation, entropy and hyper entropy of the cloud model, comprehensive coupling strength is got and then it is regarded as the edge weight of topography. Finally, distribution curve is obtained by iterating the phase of each node by means of phase synchronization model. Thus classification of data source is completed. This method can not only provide convenience for storage, cleaning and compression of data, but also improve the efficiency of data analysis.

道路环境与驾驶行为数据同步采集系统研发

高精度、可移植、多维度的在途车辆及其周边驾驶环境数据在L3级及以上自动驾驶汽车场景库测试、不良驾驶行为谱构建以及在途实时交通管控等领域有至关重要的作用,由单一传感器构成的数据采集系统在精度、维度上都受到了较大限制,导致其可靠度较低;为实现多类数据传感器协同一体化,研发了一款包含激光雷达、车载运动测量与组合定位、车载踏板与方向盘位移传感器和雷视道路多维感知一体机的多模块道路环境数据与驾驶行为同步采集系统,支持多项交通数据采集任务的并行处理,融合了用户的真实驾驶行为,并基于该系统设计了依维柯某车型的数据实车采集平台;该系统由上位机与下位机两个子系统构成,集成了多模块数据采集、储存、传输与标定等功能,且对各个模块完成了时间同步处理,适用于两客一危场景,并设计了易操作的上位机用户交互界面,对多模块数据实现实时可视化,直观查看各时段数据的变化规律,具有较高的应用价值。

我国数据空间建设:核心要件、发展路径与推进策略

数字经济时代,算力和数据成为新质生产力和新型生产要素。构建国家数据空间,高效统筹利用算力和数据等战略资源,是加快推进网络强国、数字中国建设的战略举措。本文立足我国数字化发展实际,借鉴国外数据空间建设经验,提出了网络传输体系、算力调度体系、数据应用体系、安全保障体系“四位一体”的数据空间总体架构,明确了起步验证、打造样板、创新驱动的“三步走”发展路径,并从统筹推进机制、示范工程建设、关键技术创新、标准规范建立、算力与数据基础设施化、国际交流合作等方面提出了具体举措,以期为我国加快数据空间建设提供参考借鉴。

基于物理信息神经网络的同步发电机建模

采用物理机理建立的同步发电机模型在表达发电机真实的非线性特性方面存在不足,电力系统正在不断研究和采纳新的同步发电机模型以增强模型的准确性。数据驱动模型具有更强的非线性表达能力,但在同步发电机建模实际应用中面临着模型泛化性不强和所需数据量大等问题。为克服上述问题,该文结合神经元建模原理,以循环神经网络为基本框架,在同步发电机物理机理的引导下,提出基于物理信息神经网络的同步发电机模型。经过算例验证,所提模型可准确表达同步发电机磁饱和特性,并且具有较强的泛化性。该模型可在小规模数据下对同步发电机各阶模型达到更高的拟合准确度,并可应用于现有机电暂态仿真算法。

分布式无线组网超宽带穿墙雷达系统设计与联合定位

单发单收穿墙雷达具备便携、系统简单、可独立工作等优势,但难以实现目标二维定位与跟踪。该文基于便携式单发单收雷达设计了一套分布式无线组网穿墙雷达系统,同时提出了一种目标联合定位方法,能够兼顾系统便携、低成本和目标二维信息估计。首先,设计了基于互补格雷码发射波形的超宽带雷达系统,解决了同频段多台雷达同时工作互相干扰的问题;分布式无线组网超宽带穿墙雷达系统包括3个雷达节点,并通过无线模块与数据处理中心通信。其次,提出了一种基于行为认知理论和模板匹配相结合的数据同步方法,通过识别各雷达数据中的相同运动状态来解决无线组网雷达慢时同步问题,摆脱了传统同步方法对硬件的苛刻要求。最后,提出基于Levenberg-Marquardt(L-M)最优化算法的雷达位置自估计和目标位置求解方法,实现了无先验雷达节点位置信息下的目标快速定位与跟踪。通过仿真分析与实验验证,该文设计的无线组网雷达系统可以实现目标二维定位与实时跟踪,雷达自身位置的估计精度优于0.06 m,对运动人体目标定位精度优于0.62 m。

面向时隙光交换网络的纳秒级时间同步技术

随着互联网和通信服务的飞速发展,数据中心组网架构的研究重心由电交换转向光交换。由于光交换网络进行时隙交换之前需要同步时钟信号,时间同步的精度和稳定性是提升光网络性能的重要参数。提出基于FPGA实现的时间同步方案,与现有方案相比,该方案在降低了同步复杂性的同时具有更高的精度和稳定性。测试结果表明时间同步后的偏移量不超过2.56 ns,在不同光纤长度、运行时长和环境温度下的时钟抖动为皮秒级。

深度神经网络动态分层梯度稀疏化及梯度合并优化方法

针对数据并行方法加速大规模深度神经网络时易出现的通信开销大、训练耗时长、资源利用率不高的问题,提出了一种深度神经网络动态分层梯度稀疏化及梯度合并优化方法。首先,将梯度稀疏化压缩与流水线并行技术相结合,提出动态分层梯度稀疏优化方法,为每层神经网络匹配一个合适的阈值,通过在后续迭代时动态调整该阈值,实现对每层网络传输梯度的自适应压缩。然后,提出了层梯度合并方法,利用动态规划算法对层梯度合并时的通信开销、稀疏化及层梯度计算时间进行权衡优化,求解出最佳的层梯度合并组合,并将多层小尺度梯度张量合并为一层通信,以降低分层梯度决策时引入的过高通信延迟开销。最后,将求解出的最佳层梯度合并组合应用于具体的训练迭代过程。实验结果表明:与已有方法相比,所提方法可在保证模型训练精度的同时大大降低通信开销,提升模型的训练速度;与未压缩方法相比,训练速度最大可提升1.99倍。

基于QUIC的无线自组网数据同步算法

针对轮询和gossip 2种数据同步存在的应用局限性问题,结合快速UDP网络连接(quick UDP internet connections,QUIC)传输协议和gossip算法的特性,提出一种基于QUIC的无线自组网数据同步算法。介绍QUIC-gossip算法的核心原理、数据传输策略和动态拓扑适应性,并通过实验和性能评估进行验证。结果表明:该算法能够降低同步数据的延迟,具有鲁棒性较强、数据传输较快和适应性较强的特点,为解决无线自组网中的数据同步问题提供了一种新方法。

重载铁路云数据中心架构方案研究

为解决重载铁路多专业信息系统建设资源投资重复、数据共享不畅的问题,通过分析云平台、数据中台、数据中心机房的构成,研究重载铁路云数据中心的架构及其信息安全防护、网络可靠性保障、内外网隔离与互通等,提出包括服务、网络、安全防护、数据中台等多个维度构建的专有云数据中心总体架构,研究可为重载铁路云数据中心的建设提供技术支撑。通过需求分析、云平台架构设计和关键技术设计研究重载铁路云数据中心架构,包括设计总体架构、网络架构、数据中台架构和时钟同步架构,并从信息安全防护、网络可靠性、网络隔离与互通角度进行关键设计。通过方案研究,可有效支撑重载铁路云数据中心的建设。

关联驱动下配电网同期线损异常数据辨识

配电网同期线损数据的可靠性对于有效实现电网降损与节能是非常关键的,辨识异常数据能够提升配电网同期线损数据的可靠性。为此,设计了关联驱动下配电网同期线损异常数据辨识方法。采用基于多值属性的关联规则挖掘算法,挖掘配电网同期线损数据。利用改进小波阈值去噪算法,对挖掘的配电网同期线损数据实施去噪处理。基于K-means聚类算法、改进型萤火虫算法与聚类可靠性评估指标,设计线损异常数据辨识模型,实现配电网同期线损异常数据辨识。测试结果表明,设计方法的平均误辨识点数和漏辨识点数分别低于10个和5个,平均相对辨识误差保持在1.0以下,具有较好的同期线损异常数据辨识性能。

基于数据挖掘和网络药理学探讨活血祛瘀法治疗代谢相关脂肪性肝病用药规律及作用机制

目的基于数据挖掘和网络药理学探讨活血祛瘀法治疗代谢相关脂肪性肝病(MAFLD)的用药规律及代表药物的作用机制。方法检索中国知识资源总库(CNKI)、中国学术期刊数据库(万方数据)、中文科技期刊数据库(VIP)、中国生物医学文献数据库(CBM)建库至2023年6月1日活血祛瘀法治疗MAFLD的随机对照试验,利用古今医案云平台V2.3.7进行中药频次、关联规则及聚类分析。选取核心中药丹参进行网络药理学分析,基于TCMSP数据库筛选活性成分与靶点,检索GeneCards、OMIM等数据库获取疾病靶点,预测丹参治疗疾病的潜在作用基因,采用R语言4.0.3、Cytoscape3.7.1与STRING数据库进行通路分析、构建网络图,采用AutoDuck Vina 1.1.2和PyMOL2.3.0软件进行分子对接验证。结果共纳入处方126首,涉及中药182味,丹参为使用频次最高的中药;用药药性以温、微寒、寒、平、微温为主,药味以辛、甘、苦为主,归经以肝经、脾经、肺经、胃经为主;活血祛瘀法核心配伍药物为丹参、山楂、泽泻、柴胡等,聚类分析得到4个聚类方。选取丹参作为代表药进行网络药理学分析。丹参存在65个活性成分,并可能通过RELA、EGFR、VEGFA等关键靶点及脂质与动脉粥样硬化、PI3K-Akt等通路发挥治疗MAFLD的作用。预测成分与对应靶点蛋白结合性好。结论本研究总结了活血祛瘀方药治疗MAFLD的配伍规律,其代表药丹参可通过多靶点、多通路治疗MAFLD。

基于433 MHz的多点位高精度数据采集同步与辅助决策支持系统

文中提出了一种基于433 MHz无线通信技术的多点位高精度数据采集同步与辅助决策支持系统.该系统通过分布式节点实现对环境参数的实时监测,并将采集到的数据通过433 MHz无线模块传输至中央处理单元.系统采用时分多址技术,保证了数据传输的可靠性和同步性;系统集成了数据分析和辅助决策算法,能为相关应用提供智能化支持.实际应用验证,该系统各项性能指标良好,能满足多场景下的数据采集与辅助决策需求.

基于优先级的电力调度数据多信道同步传输方法

针对电力通信网络中调度数据传输延迟较高,且多个信道之间延迟差异较大的问题,提出基于优先级的电力调度数据多信道同步传输方法。通过评估节点间的信噪比和贡献度,在高信噪比条件下实现电力通信网络的多信道同步传输。实验结果表明,所提方法可以有效降低传输延迟,同时缩小多个信道之间的延迟差距。该方法具备有效性和实用性,可为电力通信网络的优化与升级提供有力的技术支持。

基于EPON通信的馈线差动保护技术研究

该文研究了适用于智能配电网的基于EPON通信的馈线差动保护技术。首先介绍了智能配电网和EPON通信技术的特点和应用,然后阐述了馈线差动保护的基本原理和挑战。在此基础上,提出了基于EPON通信的馈线差动保护系统架构和关键技术,并通过实验验证了该技术的性能和优势,希望能为相关工作提供一些借鉴。