Fortifying Smart Grids: A Holistic Assessment Strategy against Cyber Attacks and Physical Threats for Intelligent Electronic Devices

Intelligent electronic devices(IEDs)are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control instructions.In the context of the heightened security challenges within smart grids,IEDs pose significant risks due to inherent hardware and software vulner-abilities,as well as the openness and vulnerability of communication protocols.Smart grid security,distinct from traditional internet security,mainly relies on monitoring network security events at the platform layer,lacking an effective assessment mechanism for IEDs.Hence,we incorporate considerations for both cyber-attacks and physical faults,presenting security assessment indicators and methods specifically tailored for IEDs.Initially,we outline the security monitoring technology for IEDs,considering the necessary data sources for their security assessment.Subsequently,we classify IEDs and establish a comprehensive security monitoring index system,incorporating factors such as running states,network traffic,and abnormal behaviors.This index system contains 18 indicators in 3 categories.Additionally,we elucidate quantitative methods for various indicators and propose a hybrid security assessment method known as GRCW-hybrid,combining grey relational analysis(GRA),analytic hierarchy process(AHP),and entropy weight method(EWM).According to the proposed assessment method,the security risk level of IEDs can be graded into 6 levels,namely 0,1,2,3,4,and 5.The higher the level,the greater the security risk.Finally,we assess and simulate 15 scenarios in 3 categories,which are based on monitoring indicators and real-world situations encountered by IEDs.The results show that calculated security risk level based on the proposed assessment method are consistent with actual simulation.Thus,the reasonableness and effectiveness of the proposed index system and assessment method are validated.

In-plane micro-sized energy storage devices:From device fabrication to integration and intelligent designs

The rapid development of micro-electronics raises the demand of their power sources to be simplified,miniaturized and highly integratable with other electronics on a chip.In-plane Micro-sized energy storage devices(MESDs),which are composed of interdigitated electrodes on a single chip,have aroused particular attentions since they could be easily integrated with other miniaturized electronics,reducing the complexity of overall chip design via removing complex interconnections with bulky power sources.This review highlights the achievements in the device fabrication of in-plane MESDs,as well as their integration and intelligent designs.We also discussed the current challenges and future perspectives for the development of in-plane MESDs.

Research on urban intelligent fashion advertising service model based on wearable devices

We are developing a novel wearable devices called the urban intelligent fashion advertising.Such system is mobile information devices capable of supporting remote communication and intelligent interaction between terminals.In this paper,we explore the possible functions of such a wearable devices and will present the service-based architecture combing the hardware and the software.This architecture involves two major parts.The first part is hardware design,which includes microcontroller,display part,communication module,and positioning system module.The second part is software design,which is a real-time interactive system that includes signal reception,position detection,and user workload assessment.Then,we use the interactive concept and interactive technology to construct the urban fashion advertising service model,and elaborate on its business model.Finally,we present sustainability development recommendations for the proposed service model.

The accuracy of arrhythmia detection and its clinical significance of intelligent wearable electrocardiogram monitoring devices in asymptomatic myocardial ischemia patients during daily activities.

This study focuses on the accuracy of arrhythmia detection by intelligent wearable electrocardiogram(ECG)monitoring devices in asymptomatic myocardial ischemia patients during daily activities.It elaborates on the technical principles,features,and working modes of such devices and makes a comparison with traditional ECG monitoring methods.Through a well-designed experimental approach involving data collection and analysis using specific evaluation metrics and standards,the accuracy of arrhythmia detection is evaluated.The relationship between arrhythmia and myocardial ischemia is explored,along with its impact on diagnosis,prognosis,and treatment strategy development.The application of these devices in daily activities,including feasibility,compliance,and analysis during different activity states and long-term trends,is also examined.Despite the potential benefits,technical limitations and barriers to clinical acceptance are identified,and future research directions are proposed.The findings contribute to a better understanding of the role and value of intelligent wearable ECG monitoring devices in the management of asymptomatic myocardial ischemia patients.

Impact of Artificial Intelligence on Corporate Leadership

Artificial Intelligence (AI) is transforming organizational dynamics, and revolutionizing corporate leadership practices. This research paper delves into the question of how AI influences corporate leadership, examining both its advantages and disadvantages. Positive impacts of AI are evident in communication, feedback systems, tracking mechanisms, and decision-making processes within organizations. AI-powered communication tools, as exemplified by Slack, facilitate seamless collaboration, transcending geographical barriers. Feedback systems, like Adobe’s Performance Management System, employ AI algorithms to provide personalized development opportunities, enhancing employee growth. AI-based tracking systems optimize resource allocation, as exemplified by studies like “AI-Based Tracking Systems: Enhancing Efficiency and Accountability.” Additionally, AI-powered decision support, demonstrated during the COVID-19 pandemic, showcases the capability to navigate complex challenges and maintain resilience. However, AI adoption poses challenges in human resources, potentially leading to job displacement and necessitating upskilling efforts. Managing AI errors becomes crucial, as illustrated by instances like Amazon’s biased recruiting tool. Data privacy concerns also arise, emphasizing the need for robust security measures. The proposed solution suggests leveraging Local Machine Learning Models (LLMs) to address data privacy issues. Approaches such as federated learning, on-device learning, differential privacy, and homomorphic encryption offer promising strategies. By exploring the evolving dynamics of AI and leadership, this research advocates for responsible AI adoption and proposes LLMs as a potential solution, fostering a balanced integration of AI benefits while mitigating associated risks in corporate settings.

Edge Device Fault Probability Based Intelligent Calculations for Fault Probability of Smart Systems

In a smart system, the faults of edge devices directly impact the system’s overall fault. Further, complexity arises when different edge devices provide varying fault data. To study the Smart System Fault Evolution Process (SSFEP) under different fault data conditions, an intelligent method for determining the Smart System Fault Probability (SSFP) is proposed. The data types provided by edge devices include the following: (1) only known edge device fault probability;(2) known Edge Device Fault Probability Distribution (EDFPD);(3) known edge device fault number and EDFPD;(4) known factor state of the edge device fault and EDFPD. Moreover, decision methods are proposed for each data case. Transfer Probability (TP) is divided into Continuity Transfer Probability (CTP) and Filterability Transfer Probability (FTP). CTP asserts that a Cause Event (CE) must lead to a Result Event (RE), while FTP requires CF probability to exceed a threshold before RF occurs. These probabilities are used to calculate SSFP. This paper introduces a decision method using the information diffusion principle for low-data SSFP determination, along with an improved method. The method is based on space fault network theory, abstracting SSFEP into a System Fault Evolution Process (SFEP) for research purposes.

Research on intelligent ultrasonic thickness measurement system applied to large area of hull

For a ship in service,seawater corrosion is unavoidable. In order to ensure navigation safety and master the steel plate thickness in service ship,thickness of the ship steel plate must be tested periodically by a scientific method. After consideration of an actual situation of thickness measurement,the bearing mechanism of ultrasonic thickness meter probe has been designed on the basis of wall-climbing robot,and preliminary experiments have been carried out. The device is mainly used for thickness measurement of a large area of ship hull plate when the docking ship has been sandblasted. Efficiency and safety can be improved to finish thickness measurement by using the device.

Flexible and breathable 3D porous SSE/MXene foam towards impact/electromagnetic interference/bacteria multiple protection performance for intelligent wearable devices

As intelligent wearable devices,they will inevitably be subjected to various damages and disturbances from the external environment during daily use.Therefore,it is urgent to develop safeguarding materials with multiple protective properties.Herein,this work developed a flexible and breathable three-dimensional(3D)porous shear stiffening elastomer(SSE)/MXene(M-SSE)foam with impact/electromagnetic interference(EMI)/bacteria multiple protection performance for intelligent wearable devices.The continuous conductive MXene network in the 3D SSE porous structure made M-SSE foam exhibit excellent electromagnetic interference shielding property with a high shielding effectiveness of 34 dB.Attributed to the shear stiffening effect of porous SSE matrix,M-SSE foam possessed unique anti-impact and protection properties.The energy dissipation rate reached up to more than 85%,illustrating M-SSE foam could effectively attenuate the external impact force and absorb the impact energy.Inherited from the excellent photothermal performance of MXene,M-SSE foam achieved a considerable saturated temperature of 98℃ under 0.57 W/cm^(2) laser power.Therefore,M-SSE foam showed extraordinary antimicrobial property for Staphylococcus aureus according to the principle of photothermal sterilization.Finally,for the development of intelligent wearable devices,conductive MSSE foam could be used as an intelligent sensor to monitor various human movements owing to the highly sensitive property.This work greatly expanded the application prospect of multifunctional protective materials in various complex environments and promoted the development of multifunctional smart wearable devices in protection field.

Application of Mobile Intelligent Inspection System in Substation Equipment Management

Intelligent inspection is more and more widely applied in all walks of life. This paper introduces the application of the intelligent inspection system in substation equipment management based on mobile phone terminal. It can make the power supply enterprise have more effective means to ensure the safe operation of equipment, improve equipment management level, and improve the economic benefits of power supply.

The Research of Intelligent Substation Time Synchronization System and the Influence of Its Fault to Relay Protection

The intelligent substation realizes the digitization of information in the whole substation and thus time synchronization system becomes more and more important. This paper introduces time synchronization technology of intelligent substation and puts forward the principles for designing intelligent substation time synchronization system. According to some relay protection malfunction examples caused by time synchronization system fault, analyze the influence of time synchronization system fault to relay protection and correspondingly put forward some improving measures.

2D materials for intelligent devices

Neuromorphic intelligent hardware technologies have undergone rapid advancement during the past decade,with the goal of building intelligent devices and systems capable of overcoming challenges associated with conventional hardware.Realization of neuromorphic intelligent hardware depends on major advances in materials science,condensed matter physics,device physics and engineering.As a revolutionary discovery,two-dimensional(2D)materials with atomically-thin thickness and exceptionally high tunability introduce a new physical paradigm and show great promise in the development of intelligent devices.Here,we give prominence to three categories of tunable properties(i.e.,charge carrier,band structure,lattice structure)that are inherent for 2D materials and review their superiorities in constructing intelligent devices particularly in electronics and optoelectronics.Furthermore,we provide insight into how the unique physical mechanisms emerging in 2D materials offer a fertile ground for the design of diverse intelligence devices.

Artificial Intelligence Providing a More Optimized Assessment Tool for Comprehensive Geriatric Assessment

With the continuous development of science and technology,artificial intelligence(AI)is coming into our lives and changing our lives.Since China entered the aging society in 2000,the degree of population aging has deepened.Comprehensive geriatric assessment(CGA)is now the accepted gold standard for the care of older people in hospitals.However,some problems limit the clinical application,such as complexity and time consuming.Therefore,by analyzing previous studies,we summarize some existing AI tools in order to find a more optimized assessment tool to complete the entire CGA process.

鲜食玉米收获机高净度清选智能测产装置研究

为改善国内小区鲜食玉米育种测产机械装备缺乏、小区鲜食玉米种子清选和测产过程完全依靠人工的现状,设计一种小区鲜食玉米种子高净度清选智能测产装置。该装置采用双层网筛、风力清选、智能测产流程完成高净度清选及测产作业,实现种子高净度清选和智能测产的作业要求。以上筛夹角、下筛夹角和螺旋分离装置入口风速为试验因素,以作业时千克籽粒含杂率为试验指标进行正交试验,建立因素与指标的响应面数学模型,并进行试验验证。结果表明:上筛夹角为14.0°,下筛夹角为4.0°,螺旋清选装置入口风速为27.0 m/s时为最佳作业组合,此参数组合下,鲜食玉米籽粒千克籽粒含杂率为1.2%,籽粒残留清理合格率为99.69%,测产平均误差为0.412%,籽粒含水率测量平均误差为3.57%,玉米种子千粒质量测量平均误差为0.276%。为小区鲜食玉米种子的高净度清选以及智能测产提供研究基础。

面向边缘智能的神经网络模型生成与部署研究

随着移动计算、第五代移动通信技术(5G)以及物联网(IoT)技术的不断演进,各类终端设备的数量呈现指数级增长。这种激增的终端设备连接到网络产生了巨大的数据流,对于需要实时处理和快速响应用户任务的需求提出了新的挑战。尤其是在这些海量数据中,半结构化和非结构化数据所占比例较大,这使得神经网络因其独特的优势而得到了广泛应用。为了提高数据处理能力和推理精度,神经网络模型会被设计得非常复杂,其存储和运行均需要消耗大量的计算资源。然而,边缘设备通常只配置有限的计算资源,无法满足存储和运行复杂神经网络模型的需求,需要借助云计算中心来完成这些任务。这种云协同会引发响应延时和增加网络带宽消耗,并带来用户隐私数据泄露等潜在风险。为了解决这些问题,提出一种面向边缘智能的神经网络模型快速生成与自动化部署(NGD)方法,根据边缘设备的硬件配置和承载的具体计算任务需求,生成与其匹配的神经网络模型,并将其快速部署在目标设备上,实现设备本地推理。在3种典型的硬件平台上的神经网络模型生成与部署实验结果表明,NGD方法能够高效地为资源受限的边缘设备生成匹配的神经网络模型,并快速地将其部署在设备上进行推理任务。

新能源材料与器件课程O2O智慧教学模式探索

新能源材料对“碳达峰、碳中和”国家战略目标的实现具有重要的支撑作用。新能源材料与器件课程是新能源材料研究与应用的基础,也是新能源人才培养过程中的主干课程,现有教学模式很难满足对高质量新能源人才的培养,因此该文提出将“智慧教学”理念及“O2O模式”应用到课程教学的各个环节。通过信息化技术搭建智慧教育平台,延伸教与学的边界,建构出一种虚实相生、情境结合、意象交融的教学空间;通过智能化的方式提高教学效率,增强教学互动,提升学生学习效率,加强信息反馈和教学管理。从而开发教师和学生的聪明才智和主观能动性,使投入得到最大化产出、获得最优学习效果,支撑我国新能源人才的培养。

高压断路器操动机构驱动电机及其控制技术研究

高压断路器操动机构用电机驱动提高了断路器运行的可靠性与可控性,因此设计了一套适用于126 kV真空断路器的电机操动机构。基于操动机构动力学分析结果确定驱动电机转矩、转速要求,提出一种有限转角永磁无刷电机设计方案,研制样机进行联机试验完成动作要求检验。在此基础上,设计分段转矩控制策略,结合驱动电机输出转矩需求将操动机构的运动过程分为4个阶段,从降低触头碰撞、避免预击穿现象发生、提高断路器工作可靠性角度对各阶段电机输出转矩进行动态调节。结果表明:所研制的驱动电机配合分段转矩控制策略,在保证灭弧室对操动机构动作时间、动作速度要求的前提下,实现了操动机构的运动过程优化和工作可靠性提高,促进了断路器智能化操作进程。

骨科医疗器械智能验收设备结合医疗器械唯一标识编码在骨科器械管理中的应用效果分析

目的:分析骨科医疗器械智能验收设备结合医疗器械唯一标识(UDI)编码在骨科器械管理中的应用效果。方法:基于UDI编码制定骨科医疗器械UDI应用流程,通过骨科医疗器械智能验收设备识别比对,完成骨科器械的验货和精准计费等全部操作。抽取2023年9-12月份中国中医科学院望京医院骨科手术使用的8317件骨科器械,按照骨科医疗器械智能验收设备使用时间的不同,将2023年9-10月份使用的4072件骨科器械标记为实施前,采用传统模式管理,将2023年11-12月份使用的4245件骨科器械标记为实施后,实施智能验收模式管理。对比骨科医疗器械智能验收设备使用前后的管理效果和不良事件发生率。结果:骨科医疗器械智能验收设备实施后器械验收准确性的管理总有效率为98.78%,明显高于实施前,差异有统计学意义(x^(2)=272.03,P<0.01)。骨科医疗器械智能验收设备实施后不良事件总发生率为8.83%,明显低于实施前,差异有统计学意义(x^(2)=281.38,P<0.01)。结论:骨科医疗器械智能验收设备结合UDI编码能够实现骨科器械的高效配货、精准验收和计费,减少不良事件发生,满足一物一码全流程追溯管理要求,提升骨科医疗器械管理水平。

H-CRAN中IRS辅助的D2D系统资源分配与RCG波束成形优化

以异构云无线电接入网(heterogeneous cloud radio access network,H-CRAN)中智能反射面(intelli‐gent reflecting surface,IRS)辅助的端到端(device-to-device,D2D)通信系统为背景,研究了该系统中以和速率最大化为目标的资源分配与黎曼共轭梯度(Riemannian conjugate gradient,RCG)波束成形优化方法。以最大化系统和速率为优化目标,构造子信道复用系数、发射功率门限以及IRS反射系数模约束等多约束优化问题。对于该非线性混合整数规划问题,提出了一种基于相对信道强度的延迟接受算法,以获得信道复用系数。随后将目标优化问题分解为两个子问题进行交替优化。对于发射功率优化子问题,使用逐次凸逼近(successive convex approximation,SCA)方法进行求解。对于IRS波束成形子问题,将IRS相移约束转化为复圆流形后,采用RCG算法进行求解。仿真结果表明,当IRS反射阵源数为50、基站最大发射功率为46 dBm时,与现有信道分配方案和随机信道分配方案相比,所提信道分配方案的和速率性能分别提高了5.2 bit/(s·Hz)和14.6 bit/(s·Hz)。与无IRS通信场景相比,部署IRS的和速率性能显著提高约31.2 bit/(s·Hz)。

有限理性决策理论下驾驶信息过载研究及应用

随着汽车网联化和智能化的发展,汽车驾驶过程面临着车内外多源信息过载的影响,这可能会产生交通安全问题.驾驶信息过载包含感知、认知和决策等复杂神经机制.在知识、时间和精力有限的情况下,当驾驶员处于庞大、繁杂、多变环境中排查信息对象并进行决策时,决策都是在有限度的理性条件下进行,且常带有主观因素,这是有限理性决策理论的基本原理.汽车工业界基于有限理性决策理论,根据人的特性研究了AR-HUD控制驾驶信息过载的汽车驾驶辅助技术以及OLED车载显示的应用,驾驶员在面临信息过载时可快速筛选关键信息,做出正确驾驶决策,以此来提升驾驶的安全性.本文通过综述国内外学者在驾驶信息过载原因、生理心理机制以及控制驾驶信息过载的技术和装备等方面的研究成果,为安全驾驶、汽车智能化发展的产学研结合提供参考.

汽车轮毂表面缺陷检测技术分析与装置设计

机器视觉作为代替人工检测轮毂表面缺陷的重要手段,是目前该领域的主要研究方向,因此针对汽车轮毂表面缺陷检测技术的研究现状进行了综述与分析。首先,从轮毂表面缺陷的类别和人工检测流程入手,阐述了基于机器视觉的轮毂表面缺陷检测技术的要求和难点。其次,分析了基于机器视觉的智能检测算法的发展历程,包括传统的机器视觉方法在缺陷图像预处理、缺陷定位和特征提取、缺陷分类识别中的应用;卷积神经网络(CNN)等深度学习方法在缺陷检测、分割以及其他方面的应用。最后,介绍了现有轮毂型号识别装置、轮毂缺陷X射线图像采集装置、轮毂表面缺陷图像采集装置,并在分析当前基于机器视觉的智能检测装置在实际应用中的局限性及需要解决的若干关键技术问题的基础上,提出了3种智能检测实验装置设计方案,为全自动快速检测装置的研制与性能提升提供理论与技术支撑。