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 charg...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.展开更多
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 in...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.展开更多
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 exis...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.展开更多
Integrating autonomous vehicles (AVs) and autonomous parking spaces (APS) marks a transformative development in urban mobility and sustainability. This paper reflects on these technologies’ historical evolution, curr...Integrating autonomous vehicles (AVs) and autonomous parking spaces (APS) marks a transformative development in urban mobility and sustainability. This paper reflects on these technologies’ historical evolution, current interdependence, and future potential through the lens of environmental, social, and economic sustainability. Historically, parking systems evolved from manual designs to automated processes yet remained focused on convenience rather than sustainability. Presently, advancements in smart infrastructure and vehicle-to-infrastructure (V2I) communication have enabled AVs and APS to operate as a cohesive system, optimizing space, energy, and transportation efficiency. Looking ahead, the seamless integration of AVs and APS into broader smart city ecosystems promises to redefine urban landscapes by repurposing traditional parking infrastructure into multifunctional spaces and supporting renewable energy initiatives. These technologies align with global sustainability goals by mitigating emissions, reducing urban sprawl, and fostering adaptive land uses. This reflection highlights the need for collaborative efforts among stakeholders to address regulatory and technological challenges, ensuring the equitable and efficient deployment of AVs and APS for smarter, greener cities.展开更多
The usage of electric vehicles holds a crucial role in lowering the diminishing of the ozone layer because electric vehicles are not dependent on fossil fuels.With more research,evaluation,and its characteristics on e...The usage of electric vehicles holds a crucial role in lowering the diminishing of the ozone layer because electric vehicles are not dependent on fossil fuels.With more research,evaluation,and its characteristics on electric vehicles,the infrastructure of charging points,production of electric vehicles,and network modelling,this paper provides a comprehensive overview of electric vehicles,and hybrid vehicles,including an analysis of their market growth,as well as different types of optimization used in the current scenario.In developing countries like India,the biggest barrier is their unfulfilled facility over the charging.Without renewable energy sources,vehicle-to-grid technology facilitates the enhancement of additional power requirements.The mobility factor has been considered an important and special characteristic of electric vehicles.展开更多
Small-drone technology has opened a range of new applications for aerial transportation. These drones leverage the Internet of Things (IoT) to offer cross-location services for navigation. However, they are susceptibl...Small-drone technology has opened a range of new applications for aerial transportation. These drones leverage the Internet of Things (IoT) to offer cross-location services for navigation. However, they are susceptible to security and privacy threats due to hardware and architectural issues. Although small drones hold promise for expansion in both civil and defense sectors, they have safety, security, and privacy threats. Addressing these challenges is crucial to maintaining the security and uninterrupted operations of these drones. In this regard, this study investigates security, and preservation concerning both the drones and Internet of Drones (IoD), emphasizing the significance of creating drone networks that are secure and can robustly withstand interceptions and intrusions. The proposed framework incorporates a weighted voting ensemble model comprising three convolutional neural network (CNN) models to enhance intrusion detection within the network. The employed CNNs are customized 1D models optimized to obtain better performance. The output from these CNNs is voted using a weighted criterion using a 0.4, 0.3, and 0.3 ratio for three CNNs, respectively. Experiments involve using multiple benchmark datasets, achieving an impressive accuracy of up to 99.89% on drone data. The proposed model shows promising results concerning precision, recall, and F1 as indicated by their obtained values of 99.92%, 99.98%, and 99.97%, respectively. Furthermore, cross-validation and performance comparison with existing works is also carried out. Findings indicate that the proposed approach offers a prospective solution for detecting security threats for aerial systems and satellite systems with high accuracy.展开更多
As Internet of Vehicles(IoV)technology continues to advance,edge computing has become an important tool for assisting vehicles in handling complex tasks.However,the process of offloading tasks to edge servers may expo...As Internet of Vehicles(IoV)technology continues to advance,edge computing has become an important tool for assisting vehicles in handling complex tasks.However,the process of offloading tasks to edge servers may expose vehicles to malicious external attacks,resulting in information loss or even tampering,thereby creating serious security vulnerabilities.Blockchain technology can maintain a shared ledger among servers.In the Raft consensus mechanism,as long as more than half of the nodes remain operational,the system will not collapse,effectively maintaining the system’s robustness and security.To protect vehicle information,we propose a security framework that integrates the Raft consensus mechanism from blockchain technology with edge computing.To address the additional latency introduced by blockchain,we derived a theoretical formula for system delay and proposed a convex optimization solution to minimize the system latency,ensuring that the system meets the requirements for low latency and high reliability.Simulation results demonstrate that the optimized data extraction rate significantly reduces systemdelay,with relatively stable variations in latency.Moreover,the proposed optimization solution based on this model can provide valuable insights for enhancing security and efficiency in future network environments,such as 5G and next-generation smart city systems.展开更多
Realistic urban scene generation has been extensively studied for the sake of the development of autonomous vehicles. However, the research has primarily focused on the synthesis of vehicles and pedestrians, while the...Realistic urban scene generation has been extensively studied for the sake of the development of autonomous vehicles. However, the research has primarily focused on the synthesis of vehicles and pedestrians, while the generation of cyclists is rarely presented due to its complexity. This paper proposes a perspective-aware and realistic cyclist generation method via object retrieval. Images, semantic maps, and depth labels of objects are first collected from existing datasets, categorized by class and perspective, and calculated by an algorithm newly designed according to imaging principles. During scene generation, objects with the desired class and perspective are retrieved from the collection and inserted into the background, which is then sent to the modified 2D synthesis model to generate images. This pipeline introduces a perspective computing method, utilizes object retrieval to control the perspective accurately, and modifies a diffusion model to achieve high fidelity. Experiments show that our proposal gets a 2.36 Fréchet Inception Distance, which is lower than the competitive methods, indicating a superior realistic expression ability. When these images are used for augmentation in the semantic segmentation task, the performance of ResNet-50 on the target class can be improved by 4.47%. These results demonstrate that the proposed method can be used to generate cyclists in corner cases to augment model training data, further enhancing the perception capability of autonomous vehicles and improving the safety performance of autonomous driving technology.展开更多
Aerodynamic and dynamic interference from trains is a key issue of concern for the safety of road vehicles travelling on single-level rail-cum road bridges.Based on the wind-road vehicle-train-bridge(WRTB)coupled vibr...Aerodynamic and dynamic interference from trains is a key issue of concern for the safety of road vehicles travelling on single-level rail-cum road bridges.Based on the wind-road vehicle-train-bridge(WRTB)coupled vibration system developed herein,this study examines the dynamic characteristics when road vehicles meet trains in this situation.The influence of load combination,vehicle type and vehicle location is analyzed.A method to obtain the aerodynamic load of road vehicles encountering the train at an arbitrary wind speed is proposed.The results show that due to the windproof facilities and the large line distance between the railway and highway,the aerodynamic and dynamic influence of trains on road vehicles is slight,and the vibration of road vehicles depends on the road roughness.Among the road vehicles discussed,the bus is the easiest to rollover,and the truck-trailer is the easiest to sideslip.Compared with the aerodynamic impact of trains,the crosswind has a more significant influence on road vehicles.The first peak/valley value of aerodynamic loads determines the maximum dynamic response,and the quick method is optimized based on this conclusion.Test cases show that the optimized method can produce conservative results and can be used for relevant research or engineering applications.展开更多
With the adjustment of the energy structure and the rapid development of commercial complex buildings,building energy systems(BES)are playing an increasingly important role.To fully utilize smart building management t...With the adjustment of the energy structure and the rapid development of commercial complex buildings,building energy systems(BES)are playing an increasingly important role.To fully utilize smart building management techniques for coordinating and optimizing energy systems while limiting carbon emissions,this study proposes a smart building energy scheduling method based on distributionally robust optimization(DRO).First,a framework for day-ahead market interaction between the distribution grid(DG),buildings,and electric vehicles(EVs)is established.Based on the the price elasticity matrix principle,demand side management(DSM)technology is used to model the price-based demand response(PBDR)of building electricity load.Meanwhile,the thermal inertia and thermal load flexibility of the building heating system are utilized to leverage the energy storage capabilities of the heating system.Second,a Wasserstein DRO Stackelberg game model is constructed with the objective of maximizing the benefits for both buildings and EVs.This Wasserstein distributionally robust model is then transformed into a mixed-integer model by combining the Karush–Kuhn–Tucker(KKT)conditions and duality theory.Finally,the optimization effect of temperature load storage characteristics on BES flexible scheduling and the coordination of DRO indicators on the optimization results were verified through simulations.The strategy proposed in this article can reduce the total operating cost of BES by 26.37%,significantly enhancing economic efficiency and achieving electricity and heat substitution,resulting in a smoother load curve.This study provides a theoretical foundation and assurance for optimal daily energy scheduling of BES.展开更多
Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power o...Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is proposed.Firstly,the k-medoids clustering algorithm is used to divide the reduced power scene into periods.Then,the discrete variables and continuous variables are optimized in the same period of time.Finally,the number of input groups of parallel capacitor banks(CB)in multiple periods is fixed,and then the secondary static reactive power optimization correction is carried out by using the continuous reactive power output device based on the static reactive power compensation device(SVC),the new energy grid-connected inverter,and the electric vehicle charging station.According to the characteristics of the model,a hybrid optimization algorithm with a cross-feedback mechanism is used to solve different types of variables,and an improved artificial hummingbird algorithm based on tent chaotic mapping and adaptive mutation is proposed to improve the solution efficiency.The simulation results show that the proposed decoupling strategy can obtain satisfactory optimization resultswhile strictly guaranteeing the dynamic constraints of discrete variables,and the hybrid algorithm can effectively solve the mixed integer nonlinear optimization problem.展开更多
The maneuverability and stealth of aerial-aquatic vehicles(AAVs)is of significant importance for future integrated air-sea combat missions.To improve the maneuverability and stealth of AAVs near the water surface,this...The maneuverability and stealth of aerial-aquatic vehicles(AAVs)is of significant importance for future integrated air-sea combat missions.To improve the maneuverability and stealth of AAVs near the water surface,this paper proposed a high-maneuverability skipping motion strategy for the tandem twin-rotor AAV,inspired by the motion behavior of the flying fish to avoid aquatic and aerial predators near the water surface.The novel tandem twin-rotor AAV was employed as the research subject and a strategybased ADRC control method for validation,comparing it with a strategy-based PID control method.The results indicate that both control methods enable the designed AAV to achieve high stealth and maneuverability near the water surface with robust control stability.The strategy-based ADRC control method exhibits a certain advantage in controlling height,pitch angle,and reducing impact force.This motion strategy will offer an inspiring approach for the practical application of AAVs to some extent.展开更多
The integration of technologies like artificial intelligence,6G,and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle ap...The integration of technologies like artificial intelligence,6G,and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle applications.However,these advancements also generate a surge in data processing requirements,necessitating the offloading of vehicular tasks to edge servers due to the limited computational capacity of vehicles.Despite recent advancements,the robustness and scalability of the existing approaches with respect to the number of vehicles and edge servers and their resources,as well as privacy,remain a concern.In this paper,a lightweight offloading strategy that leverages ubiquitous connectivity through the Space Air Ground Integrated Vehicular Network architecture while ensuring privacy preservation is proposed.The Internet of Vehicles(IoV)environment is first modeled as a graph,with vehicles and base stations as nodes,and their communication links as edges.Secondly,vehicular applications are offloaded to suitable servers based on latency using an attention-based heterogeneous graph neural network(HetGNN)algorithm.Subsequently,a differential privacy stochastic gradient descent trainingmechanism is employed for privacypreserving of vehicles and offloading inference.Finally,the simulation results demonstrated that the proposedHetGNN method shows good performance with 0.321 s of inference time,which is 42.68%,63.93%,30.22%,and 76.04% less than baseline methods such as Deep Deterministic Policy Gradient,Deep Q Learning,Deep Neural Network,and Genetic Algorithm,respectively.展开更多
The Internet of Everything(IoE)coupled with Proactive Artificial Intelligence(AI)-Based Learning Agents(PLAs)through a cloud processing system is an idea that connects all computing resources to the Internet,making it...The Internet of Everything(IoE)coupled with Proactive Artificial Intelligence(AI)-Based Learning Agents(PLAs)through a cloud processing system is an idea that connects all computing resources to the Internet,making it possible for these devices to communicate with one another.Technologies featured in the IoE include embedding,networking,and sensing devices.To achieve the intended results of the IoE and ease life for everyone involved,sensing devices and monitoring systems are linked together.The IoE is used in several contexts,including intelligent cars’protection,navigation,security,and fuel efficiency.The Smart Things Monitoring System(STMS)framework,which has been proposed for early occurrence identification and theft prevention,is discussed in this article.The STMS uses technologies based on the IoE and PLAs to continuously and remotely observe,control,and monitor vehicles.The STMS is familiar with the platform used by the global positioning system;as a result,the STMS can maintain a real-time record of current vehicle positions.This information is utilized to locate the vehicle in an accident or theft.The findings of the STMS system are promising for precisely identifying crashes,evaluating incident severity,and locating vehicles after collisions have occurred.Moreover,we formulate an ad hoc STMS network communication scenario to evaluate the efficacy of data communication by utilizing various network parameters,such as round-trip time(RTT),data packet transmission,data packet reception,and loss.From our experimentation,we obtained an improved communication efficiency for STMS across multiple PLAs compared to the standard greedy routing and traditional AODV approaches.Our framework facilitates adaptable solutions with communication competence by deploying Proactive PLAs in a cloud-connected smart vehicular environment.展开更多
The rapid development of the anti-missile weapon technology brings new challenges to the cooperative penetration strategy solution and the guidance law design for Hypersonic Vehicles(HVs).This paper studies the coordi...The rapid development of the anti-missile weapon technology brings new challenges to the cooperative penetration strategy solution and the guidance law design for Hypersonic Vehicles(HVs).This paper studies the coordinated game penetration guidance problem for multiple hypersonic vehicles faced with space threat areas.A scheme for seeking cooperative game penetration guidance strategy under safety critical control framework is presented.In this scheme,a multiHV cooperative game model is proposed in a minimum optimization form which can simplify the solving process and accelerate the computing speed.Then,a second-order control barrier function is developed to transform the implicit nonlinear constraints of the proposed model into linear ones.In order to obtain better performance of guidance strategy,a composite guidance law under the safety critical control framework is presented to allocate guidance strategies appropriately in the whole process.It is shown that the proposed scheme can guarantee successful penetration while avoiding threat areas.Finally,a comparative simulation with a two-on-three game is conducted to verify the effectiveness of the proposed method.展开更多
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 be...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.展开更多
This paper presents an integrated control scheme for enhancing the ride comfort and handling performance of a four-wheel-independent-drive electric vehicle through the coordination of active suspension system(ASS)and ...This paper presents an integrated control scheme for enhancing the ride comfort and handling performance of a four-wheel-independent-drive electric vehicle through the coordination of active suspension system(ASS)and anti-lock braking system(ABS).First,a longitudinal-vertical coupled vehicle dynamics model is established by integrating a road input model.Then the coupling mechanisms between longitudinal and vertical vehicle dynamics are analyzed.An ASS-ABS integrated control system is proposed,utilizing an H∞controller for ASS to optimize load transfer effect and a neural network sliding mode control for ABS implementation.Finally,the effectiveness of the proposed control scheme is evaluated through comprehensive tests conducted on a hardware-in-loop(HIL)test platform.The HIL test results demonstrate that the proposed control scheme can significantly improve the braking performance and ride comfort compared to conventional ABS control methods.展开更多
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(S...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.展开更多
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 issu...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.展开更多
Electric vehicles(EVs)are becoming more popular worldwide due to environmental concerns,fuel security,and price volatility.The performance of EVs relies on the energy stored in their batteries,which can be charged usi...Electric vehicles(EVs)are becoming more popular worldwide due to environmental concerns,fuel security,and price volatility.The performance of EVs relies on the energy stored in their batteries,which can be charged using either AC(slow)or DC(fast)chargers.Additionally,EVs can also be used as mobile power storage devices using vehicle-to-grid(V2G)technology.Power electronic converters(PECs)have a constructive role in EV applications,both in charging EVs and in V2G.Hence,this paper comprehensively investigates the state of the art of EV charging topologies and PEC solutions for EV applications.It examines PECs from the point of view of their classifications,configurations,control approaches,and future research prospects and their impacts on power quality.These can be classified into various topologies:DC-DC converters,AC-DC converters,DC-AC converters,and AC-AC converters.To address the limitations of traditional DC-DC converters such as switching losses,size,and high-electromagnetic interference(EMI),resonant converters and multiport converters are being used in high-voltage EV applications.Additionally,power-train converters have been modified for high-efficiency and reliability in EV applications.This paper offers an overview of charging topologies,PECs,challenges with solutions,and future trends in the field of the EV charging station applications.展开更多
基金supported by Jiangsu Provincial Science and Technology Project,grant number J2023124.Jing Guo received this grant,the URLs of sponsors’website is https://kxjst.jiangsu.gov.cn/(accessed on 06 June 2024).
文摘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.
基金supported by a Korean Agency for Infrastructure Technology Advancement(KAIA)grant funded by the Ministry of Land,Infrastructure and Transport(grant no.RS-2023-00239464).
文摘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.
基金funded by the State Grid Corporation Science and Technology Project(5108-202218280A-2-391-XG).
文摘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.
文摘Integrating autonomous vehicles (AVs) and autonomous parking spaces (APS) marks a transformative development in urban mobility and sustainability. This paper reflects on these technologies’ historical evolution, current interdependence, and future potential through the lens of environmental, social, and economic sustainability. Historically, parking systems evolved from manual designs to automated processes yet remained focused on convenience rather than sustainability. Presently, advancements in smart infrastructure and vehicle-to-infrastructure (V2I) communication have enabled AVs and APS to operate as a cohesive system, optimizing space, energy, and transportation efficiency. Looking ahead, the seamless integration of AVs and APS into broader smart city ecosystems promises to redefine urban landscapes by repurposing traditional parking infrastructure into multifunctional spaces and supporting renewable energy initiatives. These technologies align with global sustainability goals by mitigating emissions, reducing urban sprawl, and fostering adaptive land uses. This reflection highlights the need for collaborative efforts among stakeholders to address regulatory and technological challenges, ensuring the equitable and efficient deployment of AVs and APS for smarter, greener cities.
文摘The usage of electric vehicles holds a crucial role in lowering the diminishing of the ozone layer because electric vehicles are not dependent on fossil fuels.With more research,evaluation,and its characteristics on electric vehicles,the infrastructure of charging points,production of electric vehicles,and network modelling,this paper provides a comprehensive overview of electric vehicles,and hybrid vehicles,including an analysis of their market growth,as well as different types of optimization used in the current scenario.In developing countries like India,the biggest barrier is their unfulfilled facility over the charging.Without renewable energy sources,vehicle-to-grid technology facilitates the enhancement of additional power requirements.The mobility factor has been considered an important and special characteristic of electric vehicles.
文摘Small-drone technology has opened a range of new applications for aerial transportation. These drones leverage the Internet of Things (IoT) to offer cross-location services for navigation. However, they are susceptible to security and privacy threats due to hardware and architectural issues. Although small drones hold promise for expansion in both civil and defense sectors, they have safety, security, and privacy threats. Addressing these challenges is crucial to maintaining the security and uninterrupted operations of these drones. In this regard, this study investigates security, and preservation concerning both the drones and Internet of Drones (IoD), emphasizing the significance of creating drone networks that are secure and can robustly withstand interceptions and intrusions. The proposed framework incorporates a weighted voting ensemble model comprising three convolutional neural network (CNN) models to enhance intrusion detection within the network. The employed CNNs are customized 1D models optimized to obtain better performance. The output from these CNNs is voted using a weighted criterion using a 0.4, 0.3, and 0.3 ratio for three CNNs, respectively. Experiments involve using multiple benchmark datasets, achieving an impressive accuracy of up to 99.89% on drone data. The proposed model shows promising results concerning precision, recall, and F1 as indicated by their obtained values of 99.92%, 99.98%, and 99.97%, respectively. Furthermore, cross-validation and performance comparison with existing works is also carried out. Findings indicate that the proposed approach offers a prospective solution for detecting security threats for aerial systems and satellite systems with high accuracy.
基金supported in part by the National Natural Science Foundation of China under Grant No.61701197in part by the National Key Research and Development Program of China under Grant No.2021YFA1000500(4)in part by the 111 project under Grant No.B23008.
文摘As Internet of Vehicles(IoV)technology continues to advance,edge computing has become an important tool for assisting vehicles in handling complex tasks.However,the process of offloading tasks to edge servers may expose vehicles to malicious external attacks,resulting in information loss or even tampering,thereby creating serious security vulnerabilities.Blockchain technology can maintain a shared ledger among servers.In the Raft consensus mechanism,as long as more than half of the nodes remain operational,the system will not collapse,effectively maintaining the system’s robustness and security.To protect vehicle information,we propose a security framework that integrates the Raft consensus mechanism from blockchain technology with edge computing.To address the additional latency introduced by blockchain,we derived a theoretical formula for system delay and proposed a convex optimization solution to minimize the system latency,ensuring that the system meets the requirements for low latency and high reliability.Simulation results demonstrate that the optimized data extraction rate significantly reduces systemdelay,with relatively stable variations in latency.Moreover,the proposed optimization solution based on this model can provide valuable insights for enhancing security and efficiency in future network environments,such as 5G and next-generation smart city systems.
基金supported by the Cultivation Program for Major Scientific Research Projects of Harbin Institute of Technology(ZDXMPY20180109).
文摘Realistic urban scene generation has been extensively studied for the sake of the development of autonomous vehicles. However, the research has primarily focused on the synthesis of vehicles and pedestrians, while the generation of cyclists is rarely presented due to its complexity. This paper proposes a perspective-aware and realistic cyclist generation method via object retrieval. Images, semantic maps, and depth labels of objects are first collected from existing datasets, categorized by class and perspective, and calculated by an algorithm newly designed according to imaging principles. During scene generation, objects with the desired class and perspective are retrieved from the collection and inserted into the background, which is then sent to the modified 2D synthesis model to generate images. This pipeline introduces a perspective computing method, utilizes object retrieval to control the perspective accurately, and modifies a diffusion model to achieve high fidelity. Experiments show that our proposal gets a 2.36 Fréchet Inception Distance, which is lower than the competitive methods, indicating a superior realistic expression ability. When these images are used for augmentation in the semantic segmentation task, the performance of ResNet-50 on the target class can be improved by 4.47%. These results demonstrate that the proposed method can be used to generate cyclists in corner cases to augment model training data, further enhancing the perception capability of autonomous vehicles and improving the safety performance of autonomous driving technology.
基金The Research Project of Southwest Municipal Design&Research Institute of China under Grant No.2023KY-KT-02-I。
文摘Aerodynamic and dynamic interference from trains is a key issue of concern for the safety of road vehicles travelling on single-level rail-cum road bridges.Based on the wind-road vehicle-train-bridge(WRTB)coupled vibration system developed herein,this study examines the dynamic characteristics when road vehicles meet trains in this situation.The influence of load combination,vehicle type and vehicle location is analyzed.A method to obtain the aerodynamic load of road vehicles encountering the train at an arbitrary wind speed is proposed.The results show that due to the windproof facilities and the large line distance between the railway and highway,the aerodynamic and dynamic influence of trains on road vehicles is slight,and the vibration of road vehicles depends on the road roughness.Among the road vehicles discussed,the bus is the easiest to rollover,and the truck-trailer is the easiest to sideslip.Compared with the aerodynamic impact of trains,the crosswind has a more significant influence on road vehicles.The first peak/valley value of aerodynamic loads determines the maximum dynamic response,and the quick method is optimized based on this conclusion.Test cases show that the optimized method can produce conservative results and can be used for relevant research or engineering applications.
基金funded by the National Natural Science Foundation of China(52008014)the Fundamental Research Funds for the Central Universities(JKF-20240037)supported by the“111 Center”and Beihang World TOP University Cooperation Program.
文摘With the adjustment of the energy structure and the rapid development of commercial complex buildings,building energy systems(BES)are playing an increasingly important role.To fully utilize smart building management techniques for coordinating and optimizing energy systems while limiting carbon emissions,this study proposes a smart building energy scheduling method based on distributionally robust optimization(DRO).First,a framework for day-ahead market interaction between the distribution grid(DG),buildings,and electric vehicles(EVs)is established.Based on the the price elasticity matrix principle,demand side management(DSM)technology is used to model the price-based demand response(PBDR)of building electricity load.Meanwhile,the thermal inertia and thermal load flexibility of the building heating system are utilized to leverage the energy storage capabilities of the heating system.Second,a Wasserstein DRO Stackelberg game model is constructed with the objective of maximizing the benefits for both buildings and EVs.This Wasserstein distributionally robust model is then transformed into a mixed-integer model by combining the Karush–Kuhn–Tucker(KKT)conditions and duality theory.Finally,the optimization effect of temperature load storage characteristics on BES flexible scheduling and the coordination of DRO indicators on the optimization results were verified through simulations.The strategy proposed in this article can reduce the total operating cost of BES by 26.37%,significantly enhancing economic efficiency and achieving electricity and heat substitution,resulting in a smoother load curve.This study provides a theoretical foundation and assurance for optimal daily energy scheduling of BES.
基金funded by the“Research and Application Project of Collaborative Optimization Control Technology for Distribution Station Area for High Proportion Distributed PV Consumption(4000-202318079A-1-1-ZN)”of the Headquarters of the State Grid Corporation.
文摘Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is proposed.Firstly,the k-medoids clustering algorithm is used to divide the reduced power scene into periods.Then,the discrete variables and continuous variables are optimized in the same period of time.Finally,the number of input groups of parallel capacitor banks(CB)in multiple periods is fixed,and then the secondary static reactive power optimization correction is carried out by using the continuous reactive power output device based on the static reactive power compensation device(SVC),the new energy grid-connected inverter,and the electric vehicle charging station.According to the characteristics of the model,a hybrid optimization algorithm with a cross-feedback mechanism is used to solve different types of variables,and an improved artificial hummingbird algorithm based on tent chaotic mapping and adaptive mutation is proposed to improve the solution efficiency.The simulation results show that the proposed decoupling strategy can obtain satisfactory optimization resultswhile strictly guaranteeing the dynamic constraints of discrete variables,and the hybrid algorithm can effectively solve the mixed integer nonlinear optimization problem.
基金supported by Southern Marine Science and Guangdong Laboratory(Zhuhai)(Grant No.SML2023SP229)。
文摘The maneuverability and stealth of aerial-aquatic vehicles(AAVs)is of significant importance for future integrated air-sea combat missions.To improve the maneuverability and stealth of AAVs near the water surface,this paper proposed a high-maneuverability skipping motion strategy for the tandem twin-rotor AAV,inspired by the motion behavior of the flying fish to avoid aquatic and aerial predators near the water surface.The novel tandem twin-rotor AAV was employed as the research subject and a strategybased ADRC control method for validation,comparing it with a strategy-based PID control method.The results indicate that both control methods enable the designed AAV to achieve high stealth and maneuverability near the water surface with robust control stability.The strategy-based ADRC control method exhibits a certain advantage in controlling height,pitch angle,and reducing impact force.This motion strategy will offer an inspiring approach for the practical application of AAVs to some extent.
文摘The integration of technologies like artificial intelligence,6G,and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle applications.However,these advancements also generate a surge in data processing requirements,necessitating the offloading of vehicular tasks to edge servers due to the limited computational capacity of vehicles.Despite recent advancements,the robustness and scalability of the existing approaches with respect to the number of vehicles and edge servers and their resources,as well as privacy,remain a concern.In this paper,a lightweight offloading strategy that leverages ubiquitous connectivity through the Space Air Ground Integrated Vehicular Network architecture while ensuring privacy preservation is proposed.The Internet of Vehicles(IoV)environment is first modeled as a graph,with vehicles and base stations as nodes,and their communication links as edges.Secondly,vehicular applications are offloaded to suitable servers based on latency using an attention-based heterogeneous graph neural network(HetGNN)algorithm.Subsequently,a differential privacy stochastic gradient descent trainingmechanism is employed for privacypreserving of vehicles and offloading inference.Finally,the simulation results demonstrated that the proposedHetGNN method shows good performance with 0.321 s of inference time,which is 42.68%,63.93%,30.22%,and 76.04% less than baseline methods such as Deep Deterministic Policy Gradient,Deep Q Learning,Deep Neural Network,and Genetic Algorithm,respectively.
基金funded by the Ministry of Science and Technology,Taiwan,grant number(MOST 111-2221-E167-025-MY2).
文摘The Internet of Everything(IoE)coupled with Proactive Artificial Intelligence(AI)-Based Learning Agents(PLAs)through a cloud processing system is an idea that connects all computing resources to the Internet,making it possible for these devices to communicate with one another.Technologies featured in the IoE include embedding,networking,and sensing devices.To achieve the intended results of the IoE and ease life for everyone involved,sensing devices and monitoring systems are linked together.The IoE is used in several contexts,including intelligent cars’protection,navigation,security,and fuel efficiency.The Smart Things Monitoring System(STMS)framework,which has been proposed for early occurrence identification and theft prevention,is discussed in this article.The STMS uses technologies based on the IoE and PLAs to continuously and remotely observe,control,and monitor vehicles.The STMS is familiar with the platform used by the global positioning system;as a result,the STMS can maintain a real-time record of current vehicle positions.This information is utilized to locate the vehicle in an accident or theft.The findings of the STMS system are promising for precisely identifying crashes,evaluating incident severity,and locating vehicles after collisions have occurred.Moreover,we formulate an ad hoc STMS network communication scenario to evaluate the efficacy of data communication by utilizing various network parameters,such as round-trip time(RTT),data packet transmission,data packet reception,and loss.From our experimentation,we obtained an improved communication efficiency for STMS across multiple PLAs compared to the standard greedy routing and traditional AODV approaches.Our framework facilitates adaptable solutions with communication competence by deploying Proactive PLAs in a cloud-connected smart vehicular environment.
基金supported by the Natural Science Foundation of Jiangsu Province,China(No.BK20220945)。
文摘The rapid development of the anti-missile weapon technology brings new challenges to the cooperative penetration strategy solution and the guidance law design for Hypersonic Vehicles(HVs).This paper studies the coordinated game penetration guidance problem for multiple hypersonic vehicles faced with space threat areas.A scheme for seeking cooperative game penetration guidance strategy under safety critical control framework is presented.In this scheme,a multiHV cooperative game model is proposed in a minimum optimization form which can simplify the solving process and accelerate the computing speed.Then,a second-order control barrier function is developed to transform the implicit nonlinear constraints of the proposed model into linear ones.In order to obtain better performance of guidance strategy,a composite guidance law under the safety critical control framework is presented to allocate guidance strategies appropriately in the whole process.It is shown that the proposed scheme can guarantee successful penetration while avoiding threat areas.Finally,a comparative simulation with a two-on-three game is conducted to verify the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China under Grant 61972148.
文摘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.
基金Supported by National Natural Science Foundation of China(Grant No.52272387)State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures,Shijiazhuang Tiedao University of China(Grant No.KF2020-29)Beijing Municipal Science and Technology Commission through Beijing Nova Program of China(Grant No.20230484475).
文摘This paper presents an integrated control scheme for enhancing the ride comfort and handling performance of a four-wheel-independent-drive electric vehicle through the coordination of active suspension system(ASS)and anti-lock braking system(ABS).First,a longitudinal-vertical coupled vehicle dynamics model is established by integrating a road input model.Then the coupling mechanisms between longitudinal and vertical vehicle dynamics are analyzed.An ASS-ABS integrated control system is proposed,utilizing an H∞controller for ASS to optimize load transfer effect and a neural network sliding mode control for ABS implementation.Finally,the effectiveness of the proposed control scheme is evaluated through comprehensive tests conducted on a hardware-in-loop(HIL)test platform.The HIL test results demonstrate that the proposed control scheme can significantly improve the braking performance and ride comfort compared to conventional ABS control methods.
基金supported by the National Science Foundation of China Project(52072215,U1964203,52242213,and 52221005)National Key Research and Development(R&D)Program of China(2022YFB2503003)State Key Laboratory of Intelligent Green Vehicle and Mobility。
文摘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.
基金supported in part by the National Natural Science Foundation of China (61973219,U21A2019,61873058)the Hainan Province Science and Technology Special Fund (ZDYF2022SHFZ105)。
文摘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.
基金supported by the American University of Sharjah(No.FRG20-L-E112)。
文摘Electric vehicles(EVs)are becoming more popular worldwide due to environmental concerns,fuel security,and price volatility.The performance of EVs relies on the energy stored in their batteries,which can be charged using either AC(slow)or DC(fast)chargers.Additionally,EVs can also be used as mobile power storage devices using vehicle-to-grid(V2G)technology.Power electronic converters(PECs)have a constructive role in EV applications,both in charging EVs and in V2G.Hence,this paper comprehensively investigates the state of the art of EV charging topologies and PEC solutions for EV applications.It examines PECs from the point of view of their classifications,configurations,control approaches,and future research prospects and their impacts on power quality.These can be classified into various topologies:DC-DC converters,AC-DC converters,DC-AC converters,and AC-AC converters.To address the limitations of traditional DC-DC converters such as switching losses,size,and high-electromagnetic interference(EMI),resonant converters and multiport converters are being used in high-voltage EV applications.Additionally,power-train converters have been modified for high-efficiency and reliability in EV applications.This paper offers an overview of charging topologies,PECs,challenges with solutions,and future trends in the field of the EV charging station applications.
