Multi-Stage Voltage Control Optimization Strategy for Distribution Networks Considering Active-Reactive Co-Regulation of Electric Vehicles

The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the existing research has not deeply explored the EV active-reactive synergistic regulating characteristics,and failed to realize themulti-timescale synergistic control with other regulatingmeans,For this reason,this paper proposes amultilevel linkage coordinated optimization strategy to reduce the voltage deviation of the distribution network.Firstly,a capacitor bank reactive power compensation voltage control model and a distributed photovoltaic(PV)activereactive power regulationmodel are established.Additionally,an external characteristicmodel of EVactive-reactive power regulation is developed considering the four-quadrant operational characteristics of the EVcharger.Amultiobjective optimization model of the distribution network is then constructed considering the time-series coupling constraints of multiple types of voltage regulators.A multi-timescale control strategy is proposed by considering the impact of voltage regulators on active-reactive EV energy consumption and PV energy consumption.Then,a four-stage voltage control optimization strategy is proposed for various types of voltage regulators with multiple time scales.Themulti-objective optimization is solved with the improvedDrosophila algorithmto realize the power fluctuation control of the distribution network and themulti-stage voltage control optimization.Simulation results validate that the proposed voltage control optimization strategy achieves the coordinated control of decentralized voltage control resources in the distribution network.It effectively reduces the voltage deviation of the distribution network while ensuring the energy demand of EV users and enhancing the stability and economic efficiency of the distribution network.

Anomaly Detection of Controllable Electric Vehicles through Node Equation against Aggregation Attack

The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system.This study presents an innovative anomaly detection framework for EV charging stations,addressing the unique challenges posed by third-party aggregation platforms.Our approach integrates node equations-based on the parameter identification with a novel deep learning model,xDeepCIN,to detect abnormal data reporting indicative of aggregation attacks.We employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter estimation.The xDeepCIN model,incorporating a Compressed Interaction Network,has the ability to capture complex feature interactions in sparse,high-dimensional charging data.Experimental results on both proprietary and public datasets demonstrate significant improvements in anomaly detection performance,with F1-scores increasing by up to 32.3%for specific anomaly types compared to traditional methods,such as wide&deep and DeepFM(Factorization-Machine).Our framework exhibits robust scalability,effectively handling networks ranging from 8 to 85 charging points.Furthermore,we achieve real-time monitoring capabilities,with parameter identification completing within seconds for networks up to 1000 nodes.This research contributes to enhancing the security and reliability of renewable energy systems against evolving cyber threats,offering a comprehensive solution for safeguarding the rapidly expanding EV charging infrastructure.

Optimization of an Artificial Intelligence Database and Camera Installation for Recognition of Risky Passenger Behavior in Railway Vehicles

Urban railways are vital means of public transportation in Korea.More than 30%of metropolitan residents use the railways,and this proportion is expected to increase.To enhance safety,the government has mandated the installation of closed-circuit televisions in all carriages by 2024.However,cameras still monitored humans.To address this limitation,we developed a dataset of risk factors and a smart detection system that enables an immediate response to any abnormal behavior and intensive monitoring thereof.We created an innovative learning dataset that takes into account seven unique risk factors specific to Korean railway passengers.Detailed data collection was conducted across the Shinbundang Line of the Incheon Transportation Corporation,and the Ui-Shinseol Line.We observed several behavioral characteristics and assigned unique annotations to them.We also considered carriage congestion.Recognition performance was evaluated by camera placement and number.Then the camera installation plan was optimized.The dataset will find immediate applications in domestic railway operations.The artificial intelligence algorithms will be verified shortly.

Design and Develop Function for Research Based Application of Intelligent Internet-of-Vehicles Model Based on Fog Computing

The fast growth in Internet-of-Vehicles(IoV)applications is rendering energy efficiency management of vehicular networks a highly important challenge.Most of the existing models are failing to handle the demand for energy conservation in large-scale heterogeneous environments.Based on Large Energy-Aware Fog(LEAF)computing,this paper proposes a new model to overcome energy-inefficient vehicular networks by simulating large-scale network scenarios.The main inspiration for this work is the ever-growing demand for energy efficiency in IoV-most particularly with the volume of generated data and connected devices.The proposed LEAF model enables researchers to perform simulations of thousands of streaming applications over distributed and heterogeneous infrastructures.Among the possible reasons is that it provides a realistic simulation environment in which compute nodes can dynamically join and leave,while different kinds of networking protocols-wired and wireless-can also be employed.The novelty of this work is threefold:for the first time,the LEAF model integrates online decision-making algorithms for energy-aware task placement and routing strategies that leverage power usage traces with efficiency optimization in mind.Unlike existing fog computing simulators,data flows and power consumption are modeled as parameterizable mathematical equations in LEAF to ensure scalability and ease of analysis across a wide range of devices and applications.The results of evaluation show that LEAF can cover up to 98.75%of the distance,with devices ranging between 1 and 1000,showing significant energy-saving potential through A wide-area network(WAN)usage reduction.These findings indicate great promise for fog computing in the future-in particular,models like LEAF for planning energy-efficient IoV infrastructures.

Deep Transfer Learning Techniques in Intrusion Detection System-Internet of Vehicles: A State-of-the-Art Review

The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)technology.The functional advantages of IoV include online communication services,accident prevention,cost reduction,and enhanced traffic regularity.Despite these benefits,IoV technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle immobilization.This paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for Intrusion Detection Systems in the Internet of Vehicles(IDS-IoV)based on anomaly detection.IDS-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by cyber-attacks.These systems can autonomously create specific models based on network data to differentiate between regular traffic and cyber-attacks.Among these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational complexity.We evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and stability.This review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-IoV systems.By examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments,ensuring safer and more efficient transportation networks.

Review on dynamic analysis of road pavements under moving vehicles and plane strain conditions

This paper reviews works on the dynamic analysis of flexible and rigid pavements under moving vehicles on the basis of continuum-based plane strain models and linear theories.The purpose of this review is to provide in-formation about the existing works on the subject,critically discuss them and make suggestions for further research.The reviewed papers are presented on the basis of the various models for pavement-vehicle systems and the various methods for dynamically analyzing these systems.Flexible pavements are modeled by a homogeneous or layered half-plane with isotropic or anisotropic and linear elastic,viscoelastic or poroelastic material behavior.Rigid pavements are modeled by a beam or plate on a homogeneous or layered half-plane with material properties like the ones for flexible pavements.The vehicles are modeled as concentrated or distributed over a finite area loads moving with constant or time dependent speed.The above pavement-vehicle models are dynamically analyzed by analytical,analytical/numerical or purely numerical methods working in the time or frequency domain.Representative examples are presented to illustrate the models and methods of analysis,demonstrate their merits and assess the effects of the various parameters on pavement response.The paper closes with con-clusions and suggestions for further research in the area.The significance of this research effort has to do with the presentation of the existing literature on the subject in a critical and easy to understand way with the aid of representative examples and the identification of new research areas.

Using machine learning to identify primary features in choosing electric vehicles based on income levels

An electric vehicle is becoming one of the popular choices when choosing a vehicle.People are generally impressed with electric vehicles’zero-emission and smooth drives,while unstable battery duration keeps people away.This study tries to identify the primary factors that affect the likelihood of owning an electric vehicle based on different income levels.We divide the dataset into three subgroups by household income from$50,000 to$150,000 or low-medium income level,$150,000 to$250,000 or medium-high income level,and$250,000 or above,the high-income level.We considered several machine learning classifiers,and naive Bayes gave us a relatively higher accuracy than other algorithms in terms of overall accuracy and F1 scores.Based on the probability analysis,we found that for each of these groups,one-way commuting distance is the most important for all three income levels.

整合EVE-NG、GNS3及HCL搭建多厂商网络仿真实验平台

文章针对EVE-NG、GNS3仿真平台主要仿真思科设备,HCL仿真平台只能仿真H3C设备,而实际工程项目往往涵盖多厂商设备的问题,探讨将EVE-NG、GNS3、HCL等网络模拟器通过与本地物理网卡桥接的方法进行整合,搭建集成多厂商设备,可用于实际网络工程项目的网络仿真实验平台。文章不仅给出了上述三款网络模拟器桥接整合的基本原理,还给出了与本地物理网卡的具体桥接过程。测试后证实,整合后的网络仿真实验平台可兼容模拟多厂商网络设备运行。

铜绿山矿床蚀变矿物光谱EVS三维可视化建模及指示意义

随着浅表金属矿产资源的逐渐枯竭,深部隐伏矿体找矿勘查成为未来趋势,相应找矿勘查技术和方法的创新势在必行。近年来,短波红外(SWIR)光谱技术在全球典型斑岩–浅成低温热液矿床和矽卡岩型矿床的深部找矿勘查中发挥了重要作用,但目前主要是基于二维剖面的工作。EVS(earth volumetric studio)建模系统是应用于地球科学领域的3D建模软件,此软件基于数据驱动,具有数据可视化及模型动态更新的特点,可以满足地质建模的需求。本文利用EVS软件,对鄂东南矿集区铜绿山矽卡岩型Cu-Fe-Au矿床ⅩⅢ和ⅩⅣ号隐伏矿体不同蚀变矿物的空间分布、绿泥石和高岭石族矿物SWIR光谱特征参数的空间变化进行了三维可视化建模,探讨不同蚀变矿物和SWIR光谱参数与隐伏矿体之间的三维空间耦合关系;通过MATLAB原创程序计算含绿泥石SWIR光谱参数的样品点与深部矿化中心的三维最短距离,评价了SWIR光谱参数对深部热液矿化中心的精准指示潜力。结果表明,EVS软件三维建模在未来隐伏矿体勘查和预测中具有较大的应用潜力。

基于EVS建模分析物探-钻探在Cr(Ⅵ)污染场地勘察中的应用

为了探索物探和钻探技术在污染场地勘察中的综合应用,该研究选取河北省某铬污染场地作为案例。通过EVS软件对钻探数据进行三维建模,并结合高密度电法勘测结果综合分析了污染物的分布特征。建模结果揭示了试验地块的总体积为306.822 m^(3),其中Cr(Ⅵ)含量超标的体积达299.049 m^(3)。具体来看,超标1倍的体积为287.71 m^(3),2倍和3倍的分别仅为10.522 m^(3)和0.817 m^(3)。Cr(Ⅵ)含量在1倍超标时在地层中分布较为均匀,但在2~3倍超标时,主要集中于粉质黏土层,且底层Cr(Ⅵ)浓度较高。高密度电法勘测显示,粉砂、粉土和粉质黏土的视电阻率分别为2.5~5Ω·m、1~2.5Ω·m和小于1Ω·m,表明该技术对地层的响应良好。研究结果表明,EVS建模在清晰度和完整性方面优于传统分析方法,有助于更准确地评估污染场地的状况。此外,该方法通过区分不同土层,增强了物探分析的效能。然而,由于依赖钻探数据,模型可能受到钻孔数量的限制。高密度电法勘测作为一种有效的辅助手段,在钻探前能够识别不同地层,减少钻孔数量,同时确保模型建立的可信度。尽管如此,由于影响因素众多,视电阻率变化的原因难以简单归结。总体而言,结合EVS建模和高密度电法勘测不仅可以有效降低钻探工作量,还能准确识别污染严重区域,为污染场地的修复提供重要信息。

A blockchain based privacy-preserving federated learning scheme for Internet of Vehicles

The application of artificial intelligence technology in Internet of Vehicles(lov)has attracted great research interests with the goal of enabling smart transportation and traffic management.Meanwhile,concerns have been raised over the security and privacy of the tons of traffic and vehicle data.In this regard,Federated Learning(FL)with privacy protection features is considered a highly promising solution.However,in the FL process,the server side may take advantage of its dominant role in model aggregation to steal sensitive information of users,while the client side may also upload malicious data to compromise the training of the global model.Most existing privacy-preserving FL schemes in IoV fail to deal with threats from both of these two sides at the same time.In this paper,we propose a Blockchain based Privacy-preserving Federated Learning scheme named BPFL,which uses blockchain as the underlying distributed framework of FL.We improve the Multi-Krum technology and combine it with the homomorphic encryption to achieve ciphertext-level model aggregation and model filtering,which can enable the verifiability of the local models while achieving privacy-preservation.Additionally,we develop a reputation-based incentive mechanism to encourage users in IoV to actively participate in the federated learning and to practice honesty.The security analysis and performance evaluations are conducted to show that the proposed scheme can meet the security requirements and improve the performance of the FL model.

MIP耦合EVS在挥发性有机污染地块精细化调查中的应用

为解决挥发性有机污染地块传统调查手段存在的周期长、精度低、无效点位多等问题,采用膜界面探测(MIP)技术、实验室检测分析和EVS软件对挥发性有机物(VOCs)污染地块进行精细化调查。结果表明,MIP-PID信号值与实验室检测数据多项式拟合曲线相关系数(R)> 0.90;MIP技术与传统调查技术结合可减少采样点位,缩短调查周期;EVS可精准计算污染土壤方量和刻画污染空间分布。基于MIP辅助判断、靶向采样验证、EVS污染范围刻画的精准调查方法,可为VOCs污染地块精准调查及绿色低碳风险管控和修复提供一定参考。

A Survey on an Emerging Safety Challenge for Autonomous Vehicles:Safety of the Intended Functionality

As the complexity of autonomous vehicles(AVs)continues to increase and artificial intelligence algorithms are becoming increasingly ubiquitous,a novel safety concern known as the safety of the intended functionality(SOTIF)has emerged,presenting significant challenges to the widespread deployment of AVs.SOTIF focuses on issues arising from the functional insufficiencies of the AVs’intended functionality or its implementation,apart from conventional safety considerations.From the systems engineering standpoint,this study offers a comprehensive exploration of the SOTIF landscape by reviewing academic research,practical activities,challenges,and perspectives across the development,verification,validation,and operation phases.Academic research encompasses system-level SOTIF studies and algorithm-related SOTIF issues and solutions.Moreover,it encapsulates practical SOTIF activities undertaken by corporations,government entities,and academic institutions spanning international and Chinese contexts,focusing on the overarching methodologies and practices in different phases.Finally,the paper presents future challenges and outlook pertaining to the development,verification,validation,and operation phases,motivating stakeholders to address the remaining obstacles and challenges.

Reliability evaluation of IGBT power module on electric vehicle using big data

There are challenges to the reliability evaluation for insulated gate bipolar transistors(IGBT)on electric vehicles,such as junction temperature measurement,computational and storage resources.In this paper,a junction temperature estimation approach based on neural network without additional cost is proposed and the lifetime calculation for IGBT using electric vehicle big data is performed.The direct current(DC)voltage,operation current,switching frequency,negative thermal coefficient thermistor(NTC)temperature and IGBT lifetime are inputs.And the junction temperature(T_(j))is output.With the rain flow counting method,the classified irregular temperatures are brought into the life model for the failure cycles.The fatigue accumulation method is then used to calculate the IGBT lifetime.To solve the limited computational and storage resources of electric vehicle controllers,the operation of IGBT lifetime calculation is running on a big data platform.The lifetime is then transmitted wirelessly to electric vehicles as input for neural network.Thus the junction temperature of IGBT under long-term operating conditions can be accurately estimated.A test platform of the motor controller combined with the vehicle big data server is built for the IGBT accelerated aging test.Subsequently,the IGBT lifetime predictions are derived from the junction temperature estimation by the neural network method and the thermal network method.The experiment shows that the lifetime prediction based on a neural network with big data demonstrates a higher accuracy than that of the thermal network,which improves the reliability evaluation of system.

Distributed Platooning Control of Automated Vehicles Subject to Replay Attacks Based on Proportional Integral Observers

Secure platooning control plays an important role in enhancing the cooperative driving safety of automated vehicles subject to various security vulnerabilities.This paper focuses on the distributed secure control issue of automated vehicles affected by replay attacks.A proportional-integral-observer(PIO)with predetermined forgetting parameters is first constructed to acquire the dynamical information of vehicles.Then,a time-varying parameter and two positive scalars are employed to describe the temporal behavior of replay attacks.In light of such a scheme and the common properties of Laplace matrices,the closed-loop system with PIO-based controllers is transformed into a switched and time-delayed one.Furthermore,some sufficient conditions are derived to achieve the desired platooning performance by the view of the Lyapunov stability theory.The controller gains are analytically determined by resorting to the solution of certain matrix inequalities only dependent on maximum and minimum eigenvalues of communication topologies.Finally,a simulation example is provided to illustrate the effectiveness of the proposed control strategy.

Research and development of electric vehicles for clean transportation

This article presents the research and development of an electric vehicle（EV） in Department of Human-Robotics Saitama Institute of Technology,Japan.Electric mobile systems developed in our laboratory include a converted electric automobile,electric wheelchair and personal mobile robot.These mobile systems contribute to realize clean transportation since energy sources and devices from all vehicles,i.e.,batteries and electric motors,does not deteriorate the environment.To drive motors for vehicle traveling,robotic technologies were applied.

A credibility-aware swarm-federated deep learning framework in internet of vehicles

Although Federated Deep Learning(FDL)enables distributed machine learning in the Internet of Vehicles(IoV),it requires multiple clients to upload model parameters,thus still existing unavoidable communication overhead and data privacy risks.The recently proposed Swarm Learning(SL)provides a decentralized machine learning approach for unit edge computing and blockchain-based coordination.A Swarm-Federated Deep Learning framework in the IoV system(IoV-SFDL)that integrates SL into the FDL framework is proposed in this paper.The IoV-SFDL organizes vehicles to generate local SL models with adjacent vehicles based on the blockchain empowered SL,then aggregates the global FDL model among different SL groups with a credibility weights prediction algorithm.Extensive experimental results show that compared with the baseline frameworks,the proposed IoV-SFDL framework reduces the overhead of client-to-server communication by 16.72%,while the model performance improves by about 5.02%for the same training iterations.

Ensuring Secure Platooning of Constrained Intelligent and Connected Vehicles Against Byzantine Attacks:A Distributed MPC Framework

This study investigates resilient platoon control for constrained intelligent and connected vehicles(ICVs)against F-local Byzantine attacks.We introduce a resilient distributed model-predictive platooning control framework for such ICVs.This framework seamlessly integrates the predesigned optimal control with distributed model predictive control(DMPC)optimization and introduces a unique distributed attack detector to ensure the reliability of the transmitted information among vehicles.Notably,our strategy uses previously broadcasted information and a specialized convex set,termed the“resilience set”,to identify unreliable data.This approach significantly eases graph robustness prerequisites,requiring only an(F+1)-robust graph,in contrast to the established mean sequence reduced algorithms,which require a minimum(2F+1)-robust graph.Additionally,we introduce a verification algorithm to restore trust in vehicles under minor attacks,further reducing communication network robustness.Our analysis demonstrates the recursive feasibility of the DMPC optimization.Furthermore,the proposed method achieves exceptional control performance by minimizing the discrepancies between the DMPC control inputs and predesigned platoon control inputs,while ensuring constraint compliance and cybersecurity.Simulation results verify the effectiveness of our theoretical findings.

Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

Path-Following Control With Obstacle Avoidance of Autonomous Surface Vehicles Subject to Actuator Faults

This paper investigates the path-following control problem with obstacle avoidance of autonomous surface vehicles in the presence of actuator faults,uncertainty and external disturbances.Autonomous surface vehicles inevitably suffer from actuator faults in complex sea environments,which may cause existing obstacle avoidance strategies to fail.To reduce the influence of actuator faults,an improved artificial potential function is constructed by introducing the lower bound of actuator efficiency factors.The nonlinear state observer,which only depends on measurable position information of the autonomous surface vehicle,is used to address uncertainties and external disturbances.By using a backstepping technique and adaptive mechanism,a path-following control strategy with obstacle avoidance and fault tolerance is designed which can ensure that the tracking errors converge to a small neighborhood of zero.Compared with existing results,the proposed control strategy has the capability of obstacle avoidance and fault tolerance simultaneously.Finally,the comparison results through simulations are given to verify the effectiveness of the proposed method.