Neural Network Signal Processing Approach for Damage Assessment in Fiberoptic Smart Material Systems and Structures

An approach by using neural network signal processing in associate with embedded fiberoptic sensing array for the newly developed “smart material systems and structures” is discussed in this paper.The principle,structure of this approach and suitable neural network algorithms are described.The results of simulation experiments are also given.

Distributed Information Flow Verification for Secure Service Composition in Smart Sensor Network

Accelerate processor, efficient software and pervasive connections provide sensor nodes with more powerful computation and storage ability, which can offer various services to user. Based on these atomic services, different sensor nodes can cooperate and compose with each other to complete more complicated tasks for user. However, because of the regional characteristic of sensor nodes, merging data with different sensitivities become a primary requirement to the composite services, and information flow security should be intensively considered during service composition. In order to mitigate the great cost caused by the complexity of modeling and the heavy load of single-node verification to the energy-limited sensor node, in this paper, we propose a new distributed verification framework to enforce information flow security on composite services of smart sensor network. We analyze the information flows in composite services and specify security constraints for each service participant. Then we propose an algorithm over the distributed verification framework involving each sensor node to participate in the composite service verification based on the security constraints. The experimental results indicate that our approach can reduce the cost of verification and provide a better load balance.

Comprehensive evaluation of 5G+smart distribution network based on combined weighting method-cloud model

With the large-scale application of 5G technology in smart distribution networks,the operation effects of distribution networks are not clear.Herein,we propose a comprehensive evaluation model of a 5G+smart distribution network based on the combination weighting and cloud model of the improved Fuzzy Analytic Hierarchy-Entropy Weight Method(FAHP-EWM).First,we establish comprehensive evaluation indexes of a 5G+smart distribution network from five dimensions:reliable operation,economic operation,efficient interaction,technological intelligence,and green emission reduction.Second,by introducing the principle of variance minimization,we propose a combined weighting method based on the improved FAHP-EWM to calculate the comprehensive weight,so as to reduce the defects of subjective arbitrariness and promote objectivity.Finally,a comprehensive evaluation model of 5G+smart distribution network based on cloud model is proposed by considering the uncertainty of distribution network node information and equipment status information.The example analysis indicates that the overall operation of the 5G+smart distribution network project is decent,and the weight value calculated by the combined weighting method is more reasonable and accurate than that calculated by the single weighting method,which verifies the effectiveness and rationality of the proposed evaluation method.Moreover,the proposed evaluation method has a certain guiding role for the large-scale application of 5G communication technology in smart distribution networks.

Smart Rewiring:Improving Network Robustness Faster

Previous work puts forward a random edge rewiring method which is capable of improving the network robustness noticeably, while it lacks further discussions about how to improve the robustness faster. In this study, the detailed analysis of the structures of improved networks show that regenerating the edges between high-degree nodes can enhance the robustness against a targeted attack. Therefore, we propose a novel rewiring strategy based on regenerating more edges between high-degree nodes, called smart rewiring, which could speed up the increase of the robustness index effectively. The smart rewiring method also explains why positive degree-degree correlation could enhance network robustness.

Wireless Body Area Networks for Pervasive Healthcare and Smart Environments

Wireless body area networks （WBANs） use RF communication for interconnection of tiny sensor nodes located in, on, or in close prox- imity to the human body. A WBAN enables physiological signals, physical activity, and body position to be continuously monitored.

Progressive Transfer Learning-based Deep Q Network for DDOS Defence in WSN

In The Wireless Multimedia Sensor Network(WNSMs)have achieved popularity among diverse communities as a result of technological breakthroughs in sensor and current gadgets.By utilising portable technologies,it achieves solid and significant results in wireless communication,media transfer,and digital transmission.Sensor nodes have been used in agriculture and industry to detect characteristics such as temperature,moisture content,and other environmental conditions in recent decades.WNSMs have also made apps easier to use by giving devices self-governing access to send and process data connected with appro-priate audio and video information.Many video sensor network studies focus on lowering power consumption and increasing transmission capacity,but the main demand is data reliability.Because of the obstacles in the sensor nodes,WMSN is subjected to a variety of attacks,including Denial of Service(DoS)attacks.Deep Convolutional Neural Network is designed with the stateaction relationship mapping which is used to identify the DDOS Attackers present in the Wireless Sensor Networks for Smart Agriculture.The Proposed work it performs the data collection about the traffic conditions and identifies the deviation between the network conditions such as packet loss due to network congestion and the presence of attackers in the network.It reduces the attacker detection delay and improves the detection accuracy.In order to protect the network against DoS assaults,an improved machine learning technique must be offered.An efficient Deep Neural Network approach is provided for detecting DoS in WMSN.The required parameters are selected using an adaptive particle swarm optimization technique.The ratio of packet transmission,energy consumption,latency,network length,and throughput will be used to evaluate the approach’s efficiency.

Design and Implementation of a Novel HomePlug-Based Solution for Low Cost and High Performance Smart Home Networking

As the smart home is the end-point power consumer, it is the major part to be controlled in a smart micro grid. There are so many challenges for implementing a smart home system in which the most important ones are the cost and simplicity of the implementation method. It is clear that the major share of the total cost is referred to the internal controlling system network; although there are too many methods proposed but still there is not any satisfying method at the consumers＇ point of view. In this paper, a novel solution for this demand is proposed, which not only minimizes the implementation cost, but also provides a high level of reliability and simplicity of operation; feasibility, extendibility, and flexibility are other leading properties of the design.

New “Intellectual Networks” (Smart Grid) for Detecting Electrical Equipment Faults, Defects and Weaknesses

The most important elements of “intellectual networks” (Smart Grid) are the systems of monitoring the parameters of electrical equipment. Information-measuring systems (IMS), which described in this paper, were proposed to use together with rapid digital protection against short-circuit regimes in transformer windings. This paper presents an application’s experience of LVI-testing, some results of the use of Frequency Response Analysis (FRA) to check the condition of transformer windings and infra-red control results of electrical equipment. The LVI method and short-circuit inductive reactance measurements are sensitive for detecting such faults as radial, axial winding deformations, a twisting of low-voltage or regulating winding, a losing of winding’s pressing and others.

Context-Aware System Modeling Based on Boolean Control Network

Boolean control network consists of a set of Boolean variables whose state is determined by other variables in the network. Boolean network is used for modeling complex system. In this paper, we have presented a model of a context-aware system used in smart home based on Boolean control networks. This modeling describes the relationship between the context elements (person, time, location, and activity) and services (Morning Call, Sleeping, Guarding, Entertainment, and normal), which is effective to logical inference. We apply semi tensor matrix product to describe the dynamic of the system. This matrix form of expression is a convenient and reasonable way to design logic control system.

基于区块链和IPFS 远程医疗的安全方法研究

COVID-19的大规模感染给人民生活造成了不便,比如就医难、医疗学术交流共享不便、医疗资源不平衡,从而衍生远程医疗。目前远程医疗数据仍然以集中式存储、互联网协议(Internet Protocol,IP)传输和签证机构(Certificate Authority,CA)颁发的证书为主,导致数据安全性低、网络通讯延迟和CA维护成本高。本文提出一种SMSSS-CA签名机制、智能合约和私有区块链解决CA维护成本高、医疗数据隐私泄露和安全性低的问题。此外,还提出命名数据网络(Named Data Network,NDN)集成星际文件系统(InterPlanetary File System,IPFS)、联盟区块链和智能合约,用来提高远程会诊时SMSSS-CA签名或验签时获取患者病例和共享会诊信息的响应速度和降低区块链传播开销。实验分别从NS3和NDNSIM两种实验环境对比,结果表明,该方法比现有方案更安全和共享更及时。

基于无线传感网络的智慧农业监测系统设计

在信息化、智能化的时代背景下,智慧农业已成为推动农业现代化的重要手段。在这种情况下,基于无线传感网络的智慧农业监测系统应运而生,它融合了物联网、云计算、大数据等多项先进技术,为农业生产提供了全新的解决方案。文中介绍了智慧农业监测系统的研究背景,详细阐述了系统的总体设计、硬软件设计及数据分析方法,设计了一种基于无线传感网络的智慧农业监测系统,为农业生产提供了支持。

基于自智网络的网络运营支撑系统智能化演进

网络运营支撑系统(OSS)是当前运营商通信网络数智转型的重要抓手,参考TM Forum的自智网络方法,结合IT领域的中台理念,通过业务和技术2个层面双轮驱动,以数字化转型和智能化提升为主要方向,确定集约的“平台+应用”OSS目标架构,分阶段整合原有分散的烟囱系统,推动网络智慧运营从信息化走向数字化、从自动化走向智能化,在实践中践行“数智强企”发展理念。

基于深度学习和大数据分析的智慧交通流量预测模型研究

本文探讨了智慧交通系统中交通流量预测现状及深度学习在交通数据处理中的应用,在此基础上提出一种新的深度学习模型,专门用于智慧交通系统中的交通流量预测。该模型结合深度卷积神经网络和门控循环单元,可高效处理交通数据中的空间和时间特征。通过验证,该模型在不同城市、路段类型和天气条件下均展现出卓越的预测能力和强大的数据适应性。在主干道上,模型的准确率达到89.4%,均方误差(MSE)为0.045;在支路上,准确率达到82.7%~85.2%,MSE介于0.039~0.055之间。与传统预测方法相比,特别是在复杂交通场景下,本模型在准确率和计算效率方面均有显著提升。

Reinforcement Learning-Empowered Graph Convolutional Network Framework for Data Integrity Attack Detection in Cyber-Physical Systems

The massive integration of communication and information technology with the large-scale power grid has enhanced the efficiency, safety, and economical operation of cyber-physical systems. However, the open and diversified communication environment of the smart grid is exposed to cyber-attacks. Data integrity attacks that can bypass conventional security techniques have been considered critical threats to the operation of the grid. Current detection techniques cannot learn the dynamic and heterogeneous characteristics of the smart grid and are unable to deal with non-euclidean data types. To address the issue, we propose a novel Deep-Q-Network scheme empowered with a graph convolutional network (GCN) framework to detect data integrity attacks in cyber-physical systems. The simulation results show that the proposed framework is scalable and achieves higher detection accuracy, unlike other benchmark techniques.

Quick Hosting Capacity Evaluation Based on Distributed Dispatching for Smart Distribution Network Planning with Distributed Generation

The smart distribution network(SDN)is integrat ing increasing distributed generation(DG)and energy storage(ES).Hosting capacity evaluation is important for SDN plan ning with DG.DG and ES are usually invested by users or a third party,and they may form friendly microgrids(MGs)and operate independently.Traditional centralized dispatching meth od no longer suits for hosting capacity evaluation of SDN.A quick hosting capacity evaluation method based on distributed optimal dispatching is proposed.Firstly,a multi-objective DG hosting capacity evaluation model is established,and the host ing capacity for DG is determined by the optimal DG planning schemes.The steady-state security region method is applied to speed up the solving process of the DG hosting capacity evalua tion model.Then,the optimal dispatching models are estab lished for MG and SDN respectively to realize the operating simulation.Under the distributed dispatching strategy,the dual-side optimal operation of SDN-MGs can be realized by several iterations of power exchange requirement.Finally,an SDN with four MGs is conducted considering multiple flexible resources.It shows that the DG hosting capacity of SDN oversteps the sum of the maximum active power demand and the rated branch capacity.Besides,the annual DG electricity oversteps the maximum active power demand value.

新型电力负荷管理系统的多模态通信组网研究

针对新型电力系统背景下电力负荷管理通信终端海量接入带来的通信可靠性问题,提出了一种基于多模态智慧网络的通信组网模型及其应用部署方案。首先在分析新型电力负荷管理系统业务通信可靠性需求基础上,构建了基于多模态智慧网络的层次化通信组网模型与部署架构;然后在所提组网模型框架下,针对现阶段新型电力负荷管理系统通信接入网层部署可靠性,设计了适应负控业务场景包含有线专网、无线专网和无线虚拟专网的通信接入网层工程应用部署方案;最后分析了新型电力负荷管理系统通信组网应用过程中尚待解决的关键技术问题,为新型电力负荷管理系统通信通道建设提供技术理论参考。

基于无线传感网及边缘网关的智慧农业大棚监控系统设计

在不断发展的物联网、大数据及云计算等技术的交叉融合下,基于无线传感自组网及边缘网关,应用边缘计算,设计实现了一种智慧农业大棚监控系统。系统设计基于物联网架构,传感层以CC2530芯片为核心分别构建了传感型采集节点和控制型节点;采用正三角形网格的节点部署方案,高质量实现了无线传感网构建。边缘网关以STM32F103ZET6 MCU为核心,一方面负责与无线传感器网络中的各节点进行数据通信,完成边缘计算并对网络中的节点进行管理和控制;另一方面通过WiFi、4G/5G等通信模块进行数据的无线传输,把采集到的数据封装处理打包传输到服务器端,实现边缘网关作为连接传感节点与服务器监控中心的相应功能。应用层通过EMQTT服务器和Node-RED搭建云平台构建,用户可以通过PC端、手机端登录数据处理中心的WEB服务器,实现对物联网设备的控制及环境的监测。

基于电力企业的智慧网络安全体系架构设计研究

随着电力企业数字化全面发展和新技术的广泛应用,新兴技术在提高生产、管理和运营效率的同时,也带来了前所未有的网络安全风险和威胁。电力系统作为国家经济增长和繁荣的关键基础设施,其安全稳定运行至关重要。为了应对新形势、新环境下的网络安全挑战,有效化解网络安全风险,本文提出了一种基于电力企业的智慧网络安全体系架构。该架构综合应用管理、技术以及运营等手段,构建一个综合性的防护体系,实现主动性的防御能力,切实保障电力系统及其生产经营的安全稳定。该智慧网络安全体系架构的实施,能够实现电力企业精细化、标准化的安全保障目标,推动各项网络安全举措落地,提升对网络安全事件的快速响应和处理能力,形成安全防护和运营的闭环管理,有效降低网络安全风险。

OS2架构下智能配网资源优化配置系统设计

为了降低智能配网资源配置过程中的网损,在第二代操作系统(OS2)架构下设计智能配网资源优化配置系统。将OS2架构作为资源优化配置系统的基础调度依附结构,搭建主网设备协同调度系统的硬件执行环境。在硬件设计的基础上,采用系统总调端运行管理模块,驱动主网协同电路使用烟花算法求解,以获取配网网损最小、经济效益最大的多目标优化式配置方案。通过运行控制模块驱动电子调度主机,实现配网资源的优化配置。测试结果表明,该系统可结合配网资源中变电站、线路、台区的具体信息,为管理人员提供全景数据统一采集共享服务;能够以配网网损最小、经济效益最大为前提,完成智能配网资源的优化配置。该系统能够提供有效的决策支持,帮助能源管理者作出科学的决策和进行合理的资源配置。