Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination syst...Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination system for Connected and Automated Vehicles(CAVs)at single-lane intersections,particularly in the context of left-hand side driving on roads.The goal is to facilitate smooth right turns for certain vehicles without creating bottlenecks.We consider that all approaching vehicles share relevant information through vehicular communications.The Intersection Coordination Unit(ICU)processes this information and communicates the optimal crossing or turning times to the vehicles.The primary objective of this coordination is to minimize overall traffic delays,which also helps improve the fuel consumption of vehicles.By considering information from upcoming vehicles at the intersection,the coordination system solves an optimization problem to determine the best timing for executing right turns,ultimately minimizing the total delay for all vehicles.The proposed coordination system is evaluated at a typical urban intersection,and its performance is compared to traditional traffic systems.Numerical simulation results indicate that the proposed coordination system significantly enhances the average traffic speed and fuel consumption compared to the traditional traffic system in various scenarios.展开更多
In this paper,we investigate the distributed Nash equilibrium(NE)seeking problem for aggregative games with multiple uncertain Euler–Lagrange(EL)systems over jointly connected and weight-balanced switching networks.T...In this paper,we investigate the distributed Nash equilibrium(NE)seeking problem for aggregative games with multiple uncertain Euler–Lagrange(EL)systems over jointly connected and weight-balanced switching networks.The designed distributed controller consists of two parts:a dynamic average consensus part that asymptotically reproduces the unknown NE,and an adaptive reference-tracking module responsible for steering EL systems’positions to track a desired trajectory.The generalized Barbalat’s Lemma is used to overcome the discontinuity of the closed-loop system caused by the switching networks.The proposed algorithm is illustrated by a sensor network deployment problem.展开更多
With the development of fast communication technology between ego vehicle and other traffic participants,and automated driving technology,there is a big potential in the improvement of energy efficiency of hybrid elec...With the development of fast communication technology between ego vehicle and other traffic participants,and automated driving technology,there is a big potential in the improvement of energy efficiency of hybrid electric vehicles(HEVs).Moreover,the terrain along the driving route is a non-ignorable factor for energy efficiency of HEV running on the hilly streets.This paper proposes a look-ahead horizon-based optimal energy management strategy to jointly improve the efficiencies of powertrain and vehicle for connected and automated HEVs on the road with slope.Firstly,a rule-based framework is developed to guarantee the success of automated driving in the traffic scenario.Then a constrained optimal control problem is formulated to minimize the fuel consumption and the electricity consumption under the satisfaction of inter-vehicular distance constraint between ego vehicle and preceding vehicle.Both speed planning and torque split of hybrid powertrain are provided by the proposed approach.Moreover,the preceding vehicle speed in the look-ahead horizon is predicted by extreme learning machine with real-time data obtained from communication of vehicle-to-everything.The optimal solution is derived through the Pontryagin’s maximum principle.Finally,to verify the effectiveness of the proposed algorithm,a traffic-in-the-loop powertrain platform with data from real world traffic environment is built.It is found that the fuel economy for the proposed energy management strategy improves in average 17.0%in scenarios of different traffic densities,compared to the energy management strategy without prediction of preceding vehicle speed.展开更多
Pavement condition monitoring and its timely maintenance is necessary to ensure the safety and quality of the roadway infrastructure. The International Roughness Index (IRI) is a commonly used measure to quantify road...Pavement condition monitoring and its timely maintenance is necessary to ensure the safety and quality of the roadway infrastructure. The International Roughness Index (IRI) is a commonly used measure to quantify road surface roughness and is a critical input to asset management. In Indiana, the IRI statistic contributes to roughly half of the pavement quality index computation used for asset management. Most agencies inventory IRI once a year, however, pavement conditions vary much more frequently. The objective of this paper is to develop a framework using crowdsourced connected vehicle data to identify and detect temporal changes in IRI. Over 3 billion connected vehicle records in Indiana were analyzed across 30 months between 2022 and 2024 to understand the spatiotemporal variations in roughness. Annual comparisons across all major interstates in Indiana showed the miles of interstates classified as “Good” decreased from 1896 to 1661 miles between 2022 and 2024. The miles of interstate classified as “Needs Maintenance” increased from 82 to 120 miles. A detailed case study showing monthly and daily changes of estimated IRI on I-65 are presented along with supporting dashcam images. Although the crowdsourced IRI estimates are not as robust as traditional specialized pavement profilers, they can be obtained on a monthly, weekly, or even daily basis. The paper concludes by suggesting a combination of frequent crowdsourced IRI and commercially available dashcam imagery of roadway can provide an agile and responsive mechanism for agencies to implement pavement asset management programs that can complement existing annual programs.展开更多
In recent years,forest therapy has become a popular method for improving human health.However,guided forest therapy is not always easily accessible,and forest walking is a more convenient and feasible alterna-tive.The...In recent years,forest therapy has become a popular method for improving human health.However,guided forest therapy is not always easily accessible,and forest walking is a more convenient and feasible alterna-tive.Therefore,it is important to determine whether forest walking has the same effect as guided forest therapy.To investigate this,we conducted a campus forest-based study in which 247 university students were randomly assigned to participate in either forest walking or guided forest therapy activities.The study measured physical and psychological interventions in participants,while controlling for the inten-sity of physical activity.The findings indicated that both approaches were effective in promoting stress relief and physical and mental recovery among university students.No significant difference in effectiveness was observed between the two approaches.Furthermore,we constructed a mediation model that combines the biophilia hypothesis,stress reduction theory,and attention restoration theory to investigate the psychological mechanisms underlying the restorative effects of forest activities.Our findings indi-cate that an increase in nature connectedness significantly predicts a reduction in state anxiety.This effect is medi-ated by perceived restorativeness and a combination chain of mediators from perceived restorativeness to mood.This study presents a justification for selecting forest walking as a means of stress relief when guided forest therapy is unavail-able.Additionally,it enhances our comprehension of how forests contribute to the restorative effects experienced by individuals.展开更多
During oilfield development,a comprehensive model for assessing inter-well connectivity and connected volume within reservoirs is crucial.Traditional capacitance(TC)models,widely used in inter-well data analysis,face ...During oilfield development,a comprehensive model for assessing inter-well connectivity and connected volume within reservoirs is crucial.Traditional capacitance(TC)models,widely used in inter-well data analysis,face challenges when dealing with rapidly changing reservoir conditions over time.Additionally,TC models struggle with complex,random noise primarily caused by measurement errors in production and injection rates.To address these challenges,this study introduces a dynamic capacitance(SV-DC)model based on state variables.By integrating the extended Kalman filter(EKF)algorithm,the SV-DC model provides more flexible predictions of inter-well connectivity and time-lag efficiency compared to the TC model.The robustness of the SV-DC model is verified by comparing relative errors between preset and calculated values through Monte Carlo simulations.Sensitivity analysis was performed to compare the model performance with the benchmark,using the Qinhuangdao Oilfield as a case study.The results show that the SV-DC model accurately predicts water breakthrough times.Increases in the liquid production index and water cut in two typical wells indicate the development time of ineffective circulation channels,further confirming the accuracy and reliability of the model.The SV-DC model offers significant advantages in addressing complex,dynamic oilfield production scenarios and serves as a valuable tool for the efficient and precise planning and management of future oilfield developments.展开更多
In the rapidly evolving landscape of intelligent transportation systems,the security and authenticity of vehicular communication have emerged as critical challenges.As vehicles become increasingly interconnected,the n...In the rapidly evolving landscape of intelligent transportation systems,the security and authenticity of vehicular communication have emerged as critical challenges.As vehicles become increasingly interconnected,the need for robust authentication mechanisms to safeguard against cyber threats and ensure trust in an autonomous ecosystem becomes essential.On the other hand,using intelligence in the authentication system is a significant attraction.While existing surveys broadly address vehicular security,a critical gap remains in the systematic exploration of Deep Learning(DL)-based authentication methods tailored to these communication paradigms.This survey fills that gap by offering a comprehensive analysis of DL techniques—including supervised,unsupervised,reinforcement,and hybrid learning—for vehicular authentication.This survey highlights novel contributions,such as a taxonomy of DL-driven authentication protocols,real-world case studies,and a critical evaluation of scalability and privacy-preserving techniques.Additionally,this paper identifies unresolved challenges,such as adversarial resilience and real-time processing constraints,and proposes actionable future directions,including lightweight model optimization and blockchain integration.By grounding the discussion in concrete applications,such as biometric authentication for driver safety and adaptive key management for infrastructure security,this survey bridges theoretical advancements with practical deployment needs,offering a roadmap for next-generation secure intelligent vehicular ecosystems for the modern world.展开更多
Generative artificial intelligence(AI),as an emerging paradigm in content generation,has demonstrated its great potentials in creating high-fidelity data including images,texts,and videos.Nowadays wireless networks an...Generative artificial intelligence(AI),as an emerging paradigm in content generation,has demonstrated its great potentials in creating high-fidelity data including images,texts,and videos.Nowadays wireless networks and applications have been rapidly evolving from achieving“connected things”to embracing“connected intelligence”.展开更多
Vehicle overtaking poses significant risks and leads to injuries and losses on Malaysia’s roads.In most scenarios,insufficient and untimely information available to drivers for accessing road conditions and their sur...Vehicle overtaking poses significant risks and leads to injuries and losses on Malaysia’s roads.In most scenarios,insufficient and untimely information available to drivers for accessing road conditions and their surrounding environment is the primary factor that causes these incidents.To address these issues,a comprehensive system is required to provide real-time assistance to drivers.Building upon our previous research on a LoRa-based lane change decision-aid system,this study proposes an enhanced Vehicle Overtaking System(VOS).This system utilizes long-range(LoRa)communication for reliable real-time data exchange between vehicles(V2V)and the cloud(V2C).By providing drivers with critical information,including surrounding vehicle movements,through visual and audible warnings,the VOS aims to support vehicle overtaking decisions by calculating the safe distance between vehicles as per the Association of State Highway and Transportation Officials(AASHTO)guidelines.This study also examines the performance of LoRa communication strength and data transmission at various distances using a cloud monitoring tool or dashboard.展开更多
The connectivity of shale pores and the occurrence of movable oil in shales have long been the focus of research.In this study,samples from wells BX7 and BYY2 in the Eq3^4-10 cyclothem of Qianjiang Formation in the Qi...The connectivity of shale pores and the occurrence of movable oil in shales have long been the focus of research.In this study,samples from wells BX7 and BYY2 in the Eq3^4-10 cyclothem of Qianjiang Formation in the Qianjiang depression,were analyzed.A double mercury injection method was used to distinguish between invalid and effective connected pores.The pore characteristics for occurrence of retained hydrocarbons and movable shale oil were identified by comparing pore changes in low temperature nitrogen adsorption and high pressure mercury injection experiments before and after extraction and the change in the mercury injection amounts in the pores between two separate mercury injections.The results show that less than 50%of the total connected pores in the Eq34-10 cyclothem samples are effective.The development of effective connected pores affects the mobility of shale oil but varies with different lithofacies.The main factor limiting shale oil mobility in Well BX7 is the presence of pores with throat sizes less than 15 nm.In Well BYY2,residual mercury in injection testing of lamellar dolomitic mudstone facies was mainly concentrated in pores with throats of 10-200 nm,and in bulk argillaceous dolomite facies,it was mainly concentrated at 60-300 nm.The throats of hydrocarbon-retaining pores can be 5 nm or even smaller,but pores with movable shale oil in the well were found to have throat sizes greater than 40 nm.Excluding the influence of differences in wettability,the movability of shale oil is mainly affected by differences in lithofacies,the degree of pore deformation caused by diagenesis,the complexity of pore structures,and the connectivity of pore throats.Dissolution and reprecipitation of halite also inhibit the mobility of shale oil.展开更多
Using satellites to complete spectrum monitoring tasks can effectively receive and process electromagnetic spectrum signals emitted by radiation sources.However,due to the shortage of satellite storage,computing and n...Using satellites to complete spectrum monitoring tasks can effectively receive and process electromagnetic spectrum signals emitted by radiation sources.However,due to the shortage of satellite storage,computing and network resources,the intersatellite coordination is weak,and with the massive growth of spectrum data,the traditional cloud computing mode cannot meet the requirements of electromagnetic spectrum monitoring in terms of real-time,bandwidth,and security.We apply edge computing technology and deep learning technology to the satellite.Aiming at the problems of distributed satellite management and control,we propose a space-based distributed electromagnetic spectrum monitoring intelligent connected cloud-edge collaborative architecture SpaceEdge.SpaceEdge applies edge computing and artificial intelligence technology to space-based spectrum monitoring.SpaceEdge deploys intelligent monitoring algorithms to edge nodes to form edge intelligent satellite,and uses the cloud to uniformly manage and control heterogeneous edge satellite and monitor satellite resources.In addition,SpaceEdge can also adjust edge intelligent spectrum monitoring applications as needed to achieve effective coordination of inter-satellite algorithms and data to achieve the purpose of collaborative monitoring.Finally,SpaceEdge was experimentally verified,and the results proved the feasibility of SpaceEdge and can improve the timeliness and autonomy of the distributed satellite’s coordinated signal monitoring.展开更多
The ITU Global Youth Celebration(GYC-25)is more than just a celebration.It reaffirms our commitment to youth as essential partners in building a connected,inclusive,and sustainable digital future.This is where bold id...The ITU Global Youth Celebration(GYC-25)is more than just a celebration.It reaffirms our commitment to youth as essential partners in building a connected,inclusive,and sustainable digital future.This is where bold ideas meet real action.Get ready for an electrifying mix of inspiration,learning,and co-creation all fueled by youth leadership and digital innovation.展开更多
New materials reshape daily life through safer,smarter technology From connected clothing to domestic objects and smart medical devices,new materials are permeating every aspect of our daily lives,transforming our rel...New materials reshape daily life through safer,smarter technology From connected clothing to domestic objects and smart medical devices,new materials are permeating every aspect of our daily lives,transforming our relationship with comfort,health and technology.展开更多
The coupling effect of dual-parallel rotor connected stator permanent magnet synchronous motor not only affects the magnetic field in the coupling area, but also generates an additional magnetic field in the uncoupled...The coupling effect of dual-parallel rotor connected stator permanent magnet synchronous motor not only affects the magnetic field in the coupling area, but also generates an additional magnetic field in the uncoupled area.The characteristics of the additional magnetic field and its influence on electromagnetic torque are studied in this paper.The topology and parameters of motor are described briefly.The existence of additional magnetic field is proved by the simulation models under two boundary conditions, and its characteristics and source are analyzed. The analytical model is established, and the influence of key parameters on the additional magnetic field is discussed. On this basis, the influence of the additional magnetic field on the electromagnetic torque of the motor is studied, and the analytical expression of the additional torque is constructed.The fluctuation rule is analyzed, and the additional magnetic field separation model is proposed. The theoretical analysis and simulation results reveal and improve the internal mechanism of reducing motor torque ripple by optimizing the duty angle and coupling distance. Finally, a prototype test platform is built to verify the correctness of the proposed theory and the accuracy of the simulation model.展开更多
Last year,China Standardization Press interviewed Mr.Jo Cops,IEC President,during the IEC Global Impact Fund Forum in Nanjing city,East China’s Jiangsu province.He talked about IEC’s contribution to an all-electric,...Last year,China Standardization Press interviewed Mr.Jo Cops,IEC President,during the IEC Global Impact Fund Forum in Nanjing city,East China’s Jiangsu province.He talked about IEC’s contribution to an all-electric,connected,and sustainable world,and further expounded on the vision and practice of the newly established IEC Global Impact Fund.展开更多
We present the design of two interacting harmonic non-elliptical compressible liquid inclusions embedded in an infinite isotropic elastic matrix subjected to uniform remote in-plane stresses.The original constant mean...We present the design of two interacting harmonic non-elliptical compressible liquid inclusions embedded in an infinite isotropic elastic matrix subjected to uniform remote in-plane stresses.The original constant mean stress(or the first invariant of the stress tensor)in the matrix remains undisturbed in the presence of the two harmonic liquid inclusions.The two non-elliptical liquid-solid interfaces are described by a fourparameter conformal mapping function that maps the doubly connected domain occupied by the matrix onto an annulus in the image plane.The closed-form expressions for the internal uniform hydrostatic stress fields within the two liquid inclusions are obtained.The hoop stresses are uniformly distributed along the two liquid-solid interfaces on the matrix side.展开更多
Decision-making of connected and automated vehicles(CAV)includes a sequence of driving maneuvers that improve safety and efficiency,characterized by complex scenarios,strong uncertainty,and high real-time requirements...Decision-making of connected and automated vehicles(CAV)includes a sequence of driving maneuvers that improve safety and efficiency,characterized by complex scenarios,strong uncertainty,and high real-time requirements.Deep reinforcement learning(DRL)exhibits excellent capability of real-time decision-making and adaptability to complex scenarios,and generalization abilities.However,it is arduous to guarantee complete driving safety and efficiency under the constraints of training samples and costs.This paper proposes a Mixture of Expert method(MoE)based on Soft Actor-Critic(SAC),where the upper-level discriminator dynamically decides whether to activate the lower-level DRL expert or the heuristic expert based on the features of the input state.To further enhance the performance of the DRL expert,a buffer zone is introduced in the reward function,preemptively applying penalties before insecure situations occur.In order to minimize collision and off-road rates,the Intelligent Driver Model(IDM)and Minimizing Overall Braking Induced by Lane changes(MOBIL)strategy are designed by heuristic experts.Finally,tested in typical simulation scenarios,MOE shows a 13.75%improvement in driving efficiency compared with the traditional DRL method with continuous action space.It ensures high safety with zero collision and zero off-road rates while maintaining high adaptability.展开更多
To meet the increasing demands for wideband communications of the vehicle’s infotainment applications such as the virtual reality(VR),the millimeter wave(mmWave)enabled connected automated vehicles(CAVs)is of great d...To meet the increasing demands for wideband communications of the vehicle’s infotainment applications such as the virtual reality(VR),the millimeter wave(mmWave)enabled connected automated vehicles(CAVs)is of great demand.However,the mmWave vehicular communication brings new challenges on the content distribution efficiency in terms of the differentiated VR’s service requirements and the dynamic interference among vehicleto-infrastructure(V2I)and vehicle-to-vehicle(V2V)links.Therefore,this paper proposes an interference cognition basedmmWave beam resource allocation algorithm for CAVs to maximize the content distribution efficiency and minimize the interference among CAVs.In the V2I stage,the interference prediction assisted V2I vehicle selection algorithm is proposed,which can aware the intra base station(BS)interference dynamically.Moreover,the coalition game based V2V content distribution algorithm is proposed,where a novel cache-hit and interference aware utility function is designed.Simulation results prove that the average successful transmission probability of the proposed algorithm can reach 72.63%,which is 53.4%higher than the conventional algorithms.展开更多
Connected and autonomous vehicle formation(CAVF)technology is considerably important for improving transportation efficiency,optimizing traffic flow,and reduc-ing energy consumption.Despite the extensive research con-...Connected and autonomous vehicle formation(CAVF)technology is considerably important for improving transportation efficiency,optimizing traffic flow,and reduc-ing energy consumption.Despite the extensive research con-ducted on trajectory tracking control and other aspects of CAVF,the quality of the extant literature varies consider-ably,and research content remains scattered.To better pro-mote the sustainable and healthy development of the CAVF field,this paper employs the mapping knowledge domain(MKD)methodology to comprehensively review and visual-ize the current research status in this domain.Based on this review,research themes,hotspots,research challenges,and future development directions are proposed.The findings suggest that the research on CAVF can be categorized into three primary developmental stages.China and the United States are the primary countries conducting CAVF research.There is a positive correlation between economic develop-ment and the generation of scientific research outcomes.Re-search institutions are predominantly concentrated in univer-sities.The field exhibits significant interdisciplinary and inte-gration characteristics,forming key research personnel and teams.It is expected that future research will concentrate on topics such as deep learning,trajectory optimization,energy management strategy,mixed vehicle platoon,and other re-lated subjects.Research on cognition-driven intelligent for-mation decision-making mechanisms,resilience-oriented for-mation safety assurance systems,multiobjective collabora-tive formation optimization strategies,and digital twin-driven formation system validation platforms represents key future development directions.展开更多
The rapid growth of the automotive industry has raised significant concerns about the security of connected vehicles and their integrated supply chains,which are increasingly vulnerable to advanced cyber threats.Tradi...The rapid growth of the automotive industry has raised significant concerns about the security of connected vehicles and their integrated supply chains,which are increasingly vulnerable to advanced cyber threats.Traditional authentication methods have proven insufficient,exposing systems to risks such as Sybil,Denial of Service(DoS),and Eclipse attacks.This study critically examines the limitations of current security protocols,focusing on authentication and data exchange vulnerabilities,and explores blockchain technology as a potential solution.Blockchain’s decentralized and cryptographically secure framework can significantly enhance Vehicle-to-Vehicle(V2V)communication,ensure data integrity,and enable transparent,immutable transactions within the supply chain.Additionally,blockchain strengthens authentication,secures digital identities,and improves data sharing,reducing the risk of unauthorized access and data breaches.Our contribution lies in the proposal to integrate Artificial Intelligence(AI)with blockchain technology to further improve security by refining cryptographic methods,automating key management,and bolstering anomaly detection.Despite challenges related to computational complexity,latency,scalability,and regulatory concerns,the combination of blockchain,AI offers the transformative potential to enhance the security,transparency,and efficiency of connected vehicle systems and their supply chains.展开更多
基金supported by the Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research(C)23K03898.
文摘Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination system for Connected and Automated Vehicles(CAVs)at single-lane intersections,particularly in the context of left-hand side driving on roads.The goal is to facilitate smooth right turns for certain vehicles without creating bottlenecks.We consider that all approaching vehicles share relevant information through vehicular communications.The Intersection Coordination Unit(ICU)processes this information and communicates the optimal crossing or turning times to the vehicles.The primary objective of this coordination is to minimize overall traffic delays,which also helps improve the fuel consumption of vehicles.By considering information from upcoming vehicles at the intersection,the coordination system solves an optimization problem to determine the best timing for executing right turns,ultimately minimizing the total delay for all vehicles.The proposed coordination system is evaluated at a typical urban intersection,and its performance is compared to traditional traffic systems.Numerical simulation results indicate that the proposed coordination system significantly enhances the average traffic speed and fuel consumption compared to the traditional traffic system in various scenarios.
基金supported by the Research Grants Council of the Hong Kong Special Administration Region under the Grant No.14201621。
文摘In this paper,we investigate the distributed Nash equilibrium(NE)seeking problem for aggregative games with multiple uncertain Euler–Lagrange(EL)systems over jointly connected and weight-balanced switching networks.The designed distributed controller consists of two parts:a dynamic average consensus part that asymptotically reproduces the unknown NE,and an adaptive reference-tracking module responsible for steering EL systems’positions to track a desired trajectory.The generalized Barbalat’s Lemma is used to overcome the discontinuity of the closed-loop system caused by the switching networks.The proposed algorithm is illustrated by a sensor network deployment problem.
文摘With the development of fast communication technology between ego vehicle and other traffic participants,and automated driving technology,there is a big potential in the improvement of energy efficiency of hybrid electric vehicles(HEVs).Moreover,the terrain along the driving route is a non-ignorable factor for energy efficiency of HEV running on the hilly streets.This paper proposes a look-ahead horizon-based optimal energy management strategy to jointly improve the efficiencies of powertrain and vehicle for connected and automated HEVs on the road with slope.Firstly,a rule-based framework is developed to guarantee the success of automated driving in the traffic scenario.Then a constrained optimal control problem is formulated to minimize the fuel consumption and the electricity consumption under the satisfaction of inter-vehicular distance constraint between ego vehicle and preceding vehicle.Both speed planning and torque split of hybrid powertrain are provided by the proposed approach.Moreover,the preceding vehicle speed in the look-ahead horizon is predicted by extreme learning machine with real-time data obtained from communication of vehicle-to-everything.The optimal solution is derived through the Pontryagin’s maximum principle.Finally,to verify the effectiveness of the proposed algorithm,a traffic-in-the-loop powertrain platform with data from real world traffic environment is built.It is found that the fuel economy for the proposed energy management strategy improves in average 17.0%in scenarios of different traffic densities,compared to the energy management strategy without prediction of preceding vehicle speed.
文摘Pavement condition monitoring and its timely maintenance is necessary to ensure the safety and quality of the roadway infrastructure. The International Roughness Index (IRI) is a commonly used measure to quantify road surface roughness and is a critical input to asset management. In Indiana, the IRI statistic contributes to roughly half of the pavement quality index computation used for asset management. Most agencies inventory IRI once a year, however, pavement conditions vary much more frequently. The objective of this paper is to develop a framework using crowdsourced connected vehicle data to identify and detect temporal changes in IRI. Over 3 billion connected vehicle records in Indiana were analyzed across 30 months between 2022 and 2024 to understand the spatiotemporal variations in roughness. Annual comparisons across all major interstates in Indiana showed the miles of interstates classified as “Good” decreased from 1896 to 1661 miles between 2022 and 2024. The miles of interstate classified as “Needs Maintenance” increased from 82 to 120 miles. A detailed case study showing monthly and daily changes of estimated IRI on I-65 are presented along with supporting dashcam images. Although the crowdsourced IRI estimates are not as robust as traditional specialized pavement profilers, they can be obtained on a monthly, weekly, or even daily basis. The paper concludes by suggesting a combination of frequent crowdsourced IRI and commercially available dashcam imagery of roadway can provide an agile and responsive mechanism for agencies to implement pavement asset management programs that can complement existing annual programs.
基金supported by Forestry discipline innovation team of Fujian Agriculture and Forestry University(72202200205)Laboratory of Virtual Teaching and Research on Forest Therapy Specialty of Taiwan Strait of Fujian Agriculture and Forestry University(111TD2104).
文摘In recent years,forest therapy has become a popular method for improving human health.However,guided forest therapy is not always easily accessible,and forest walking is a more convenient and feasible alterna-tive.Therefore,it is important to determine whether forest walking has the same effect as guided forest therapy.To investigate this,we conducted a campus forest-based study in which 247 university students were randomly assigned to participate in either forest walking or guided forest therapy activities.The study measured physical and psychological interventions in participants,while controlling for the inten-sity of physical activity.The findings indicated that both approaches were effective in promoting stress relief and physical and mental recovery among university students.No significant difference in effectiveness was observed between the two approaches.Furthermore,we constructed a mediation model that combines the biophilia hypothesis,stress reduction theory,and attention restoration theory to investigate the psychological mechanisms underlying the restorative effects of forest activities.Our findings indi-cate that an increase in nature connectedness significantly predicts a reduction in state anxiety.This effect is medi-ated by perceived restorativeness and a combination chain of mediators from perceived restorativeness to mood.This study presents a justification for selecting forest walking as a means of stress relief when guided forest therapy is unavail-able.Additionally,it enhances our comprehension of how forests contribute to the restorative effects experienced by individuals.
基金the National Natural Science Foundation of China(Grant No.52374051)the Joint Fund for Enterprise Innovation and Development of NSFC(Grant No.U24B2037).
文摘During oilfield development,a comprehensive model for assessing inter-well connectivity and connected volume within reservoirs is crucial.Traditional capacitance(TC)models,widely used in inter-well data analysis,face challenges when dealing with rapidly changing reservoir conditions over time.Additionally,TC models struggle with complex,random noise primarily caused by measurement errors in production and injection rates.To address these challenges,this study introduces a dynamic capacitance(SV-DC)model based on state variables.By integrating the extended Kalman filter(EKF)algorithm,the SV-DC model provides more flexible predictions of inter-well connectivity and time-lag efficiency compared to the TC model.The robustness of the SV-DC model is verified by comparing relative errors between preset and calculated values through Monte Carlo simulations.Sensitivity analysis was performed to compare the model performance with the benchmark,using the Qinhuangdao Oilfield as a case study.The results show that the SV-DC model accurately predicts water breakthrough times.Increases in the liquid production index and water cut in two typical wells indicate the development time of ineffective circulation channels,further confirming the accuracy and reliability of the model.The SV-DC model offers significant advantages in addressing complex,dynamic oilfield production scenarios and serves as a valuable tool for the efficient and precise planning and management of future oilfield developments.
基金funded and supported by the UCSI University Research Excellence&Innovation Grant(REIG),REIG-ICSDI-2024/044.
文摘In the rapidly evolving landscape of intelligent transportation systems,the security and authenticity of vehicular communication have emerged as critical challenges.As vehicles become increasingly interconnected,the need for robust authentication mechanisms to safeguard against cyber threats and ensure trust in an autonomous ecosystem becomes essential.On the other hand,using intelligence in the authentication system is a significant attraction.While existing surveys broadly address vehicular security,a critical gap remains in the systematic exploration of Deep Learning(DL)-based authentication methods tailored to these communication paradigms.This survey fills that gap by offering a comprehensive analysis of DL techniques—including supervised,unsupervised,reinforcement,and hybrid learning—for vehicular authentication.This survey highlights novel contributions,such as a taxonomy of DL-driven authentication protocols,real-world case studies,and a critical evaluation of scalability and privacy-preserving techniques.Additionally,this paper identifies unresolved challenges,such as adversarial resilience and real-time processing constraints,and proposes actionable future directions,including lightweight model optimization and blockchain integration.By grounding the discussion in concrete applications,such as biometric authentication for driver safety and adaptive key management for infrastructure security,this survey bridges theoretical advancements with practical deployment needs,offering a roadmap for next-generation secure intelligent vehicular ecosystems for the modern world.
文摘Generative artificial intelligence(AI),as an emerging paradigm in content generation,has demonstrated its great potentials in creating high-fidelity data including images,texts,and videos.Nowadays wireless networks and applications have been rapidly evolving from achieving“connected things”to embracing“connected intelligence”.
文摘Vehicle overtaking poses significant risks and leads to injuries and losses on Malaysia’s roads.In most scenarios,insufficient and untimely information available to drivers for accessing road conditions and their surrounding environment is the primary factor that causes these incidents.To address these issues,a comprehensive system is required to provide real-time assistance to drivers.Building upon our previous research on a LoRa-based lane change decision-aid system,this study proposes an enhanced Vehicle Overtaking System(VOS).This system utilizes long-range(LoRa)communication for reliable real-time data exchange between vehicles(V2V)and the cloud(V2C).By providing drivers with critical information,including surrounding vehicle movements,through visual and audible warnings,the VOS aims to support vehicle overtaking decisions by calculating the safe distance between vehicles as per the Association of State Highway and Transportation Officials(AASHTO)guidelines.This study also examines the performance of LoRa communication strength and data transmission at various distances using a cloud monitoring tool or dashboard.
基金supported by the National Natural Science Foundation of China(No.U19B6003)。
文摘The connectivity of shale pores and the occurrence of movable oil in shales have long been the focus of research.In this study,samples from wells BX7 and BYY2 in the Eq3^4-10 cyclothem of Qianjiang Formation in the Qianjiang depression,were analyzed.A double mercury injection method was used to distinguish between invalid and effective connected pores.The pore characteristics for occurrence of retained hydrocarbons and movable shale oil were identified by comparing pore changes in low temperature nitrogen adsorption and high pressure mercury injection experiments before and after extraction and the change in the mercury injection amounts in the pores between two separate mercury injections.The results show that less than 50%of the total connected pores in the Eq34-10 cyclothem samples are effective.The development of effective connected pores affects the mobility of shale oil but varies with different lithofacies.The main factor limiting shale oil mobility in Well BX7 is the presence of pores with throat sizes less than 15 nm.In Well BYY2,residual mercury in injection testing of lamellar dolomitic mudstone facies was mainly concentrated in pores with throats of 10-200 nm,and in bulk argillaceous dolomite facies,it was mainly concentrated at 60-300 nm.The throats of hydrocarbon-retaining pores can be 5 nm or even smaller,but pores with movable shale oil in the well were found to have throat sizes greater than 40 nm.Excluding the influence of differences in wettability,the movability of shale oil is mainly affected by differences in lithofacies,the degree of pore deformation caused by diagenesis,the complexity of pore structures,and the connectivity of pore throats.Dissolution and reprecipitation of halite also inhibit the mobility of shale oil.
文摘Using satellites to complete spectrum monitoring tasks can effectively receive and process electromagnetic spectrum signals emitted by radiation sources.However,due to the shortage of satellite storage,computing and network resources,the intersatellite coordination is weak,and with the massive growth of spectrum data,the traditional cloud computing mode cannot meet the requirements of electromagnetic spectrum monitoring in terms of real-time,bandwidth,and security.We apply edge computing technology and deep learning technology to the satellite.Aiming at the problems of distributed satellite management and control,we propose a space-based distributed electromagnetic spectrum monitoring intelligent connected cloud-edge collaborative architecture SpaceEdge.SpaceEdge applies edge computing and artificial intelligence technology to space-based spectrum monitoring.SpaceEdge deploys intelligent monitoring algorithms to edge nodes to form edge intelligent satellite,and uses the cloud to uniformly manage and control heterogeneous edge satellite and monitor satellite resources.In addition,SpaceEdge can also adjust edge intelligent spectrum monitoring applications as needed to achieve effective coordination of inter-satellite algorithms and data to achieve the purpose of collaborative monitoring.Finally,SpaceEdge was experimentally verified,and the results proved the feasibility of SpaceEdge and can improve the timeliness and autonomy of the distributed satellite’s coordinated signal monitoring.
文摘The ITU Global Youth Celebration(GYC-25)is more than just a celebration.It reaffirms our commitment to youth as essential partners in building a connected,inclusive,and sustainable digital future.This is where bold ideas meet real action.Get ready for an electrifying mix of inspiration,learning,and co-creation all fueled by youth leadership and digital innovation.
文摘New materials reshape daily life through safer,smarter technology From connected clothing to domestic objects and smart medical devices,new materials are permeating every aspect of our daily lives,transforming our relationship with comfort,health and technology.
基金supported in part by the Natural Science Foundation of Heilongjiang Province under Grant LH2023E084by the National Natural Science Foundation of China under Grant 51777048。
文摘The coupling effect of dual-parallel rotor connected stator permanent magnet synchronous motor not only affects the magnetic field in the coupling area, but also generates an additional magnetic field in the uncoupled area.The characteristics of the additional magnetic field and its influence on electromagnetic torque are studied in this paper.The topology and parameters of motor are described briefly.The existence of additional magnetic field is proved by the simulation models under two boundary conditions, and its characteristics and source are analyzed. The analytical model is established, and the influence of key parameters on the additional magnetic field is discussed. On this basis, the influence of the additional magnetic field on the electromagnetic torque of the motor is studied, and the analytical expression of the additional torque is constructed.The fluctuation rule is analyzed, and the additional magnetic field separation model is proposed. The theoretical analysis and simulation results reveal and improve the internal mechanism of reducing motor torque ripple by optimizing the duty angle and coupling distance. Finally, a prototype test platform is built to verify the correctness of the proposed theory and the accuracy of the simulation model.
文摘Last year,China Standardization Press interviewed Mr.Jo Cops,IEC President,during the IEC Global Impact Fund Forum in Nanjing city,East China’s Jiangsu province.He talked about IEC’s contribution to an all-electric,connected,and sustainable world,and further expounded on the vision and practice of the newly established IEC Global Impact Fund.
基金supported by the Natural Sciences and Engineering Research Council of Canada(No.RGPIN-2023-03227 Schiavo)。
文摘We present the design of two interacting harmonic non-elliptical compressible liquid inclusions embedded in an infinite isotropic elastic matrix subjected to uniform remote in-plane stresses.The original constant mean stress(or the first invariant of the stress tensor)in the matrix remains undisturbed in the presence of the two harmonic liquid inclusions.The two non-elliptical liquid-solid interfaces are described by a fourparameter conformal mapping function that maps the doubly connected domain occupied by the matrix onto an annulus in the image plane.The closed-form expressions for the internal uniform hydrostatic stress fields within the two liquid inclusions are obtained.The hoop stresses are uniformly distributed along the two liquid-solid interfaces on the matrix side.
基金Supported by National Key R&D Program of China(Grant No.2022YFB2503203)National Natural Science Foundation of China(Grant No.U1964206).
文摘Decision-making of connected and automated vehicles(CAV)includes a sequence of driving maneuvers that improve safety and efficiency,characterized by complex scenarios,strong uncertainty,and high real-time requirements.Deep reinforcement learning(DRL)exhibits excellent capability of real-time decision-making and adaptability to complex scenarios,and generalization abilities.However,it is arduous to guarantee complete driving safety and efficiency under the constraints of training samples and costs.This paper proposes a Mixture of Expert method(MoE)based on Soft Actor-Critic(SAC),where the upper-level discriminator dynamically decides whether to activate the lower-level DRL expert or the heuristic expert based on the features of the input state.To further enhance the performance of the DRL expert,a buffer zone is introduced in the reward function,preemptively applying penalties before insecure situations occur.In order to minimize collision and off-road rates,the Intelligent Driver Model(IDM)and Minimizing Overall Braking Induced by Lane changes(MOBIL)strategy are designed by heuristic experts.Finally,tested in typical simulation scenarios,MOE shows a 13.75%improvement in driving efficiency compared with the traditional DRL method with continuous action space.It ensures high safety with zero collision and zero off-road rates while maintaining high adaptability.
基金supported by National Natural Science Foundation of China(NSFC)(62341101,62321001,62401077)China Telecom Research Institute Project(Grants No.HQBYG2100321GGN00)+1 种基金Beijing Natural Science Foundation(L232003)Fundamental Research Funds for the Central Universities(2024RC02).
文摘To meet the increasing demands for wideband communications of the vehicle’s infotainment applications such as the virtual reality(VR),the millimeter wave(mmWave)enabled connected automated vehicles(CAVs)is of great demand.However,the mmWave vehicular communication brings new challenges on the content distribution efficiency in terms of the differentiated VR’s service requirements and the dynamic interference among vehicleto-infrastructure(V2I)and vehicle-to-vehicle(V2V)links.Therefore,this paper proposes an interference cognition basedmmWave beam resource allocation algorithm for CAVs to maximize the content distribution efficiency and minimize the interference among CAVs.In the V2I stage,the interference prediction assisted V2I vehicle selection algorithm is proposed,which can aware the intra base station(BS)interference dynamically.Moreover,the coalition game based V2V content distribution algorithm is proposed,where a novel cache-hit and interference aware utility function is designed.Simulation results prove that the average successful transmission probability of the proposed algorithm can reach 72.63%,which is 53.4%higher than the conventional algorithms.
基金The National Natural Science Foundation of China (No. 52302373, 52472317)the Natural Science Foundation of Beijing (No. L231023)the Beijing Nova Program (No. 20230484443)。
文摘Connected and autonomous vehicle formation(CAVF)technology is considerably important for improving transportation efficiency,optimizing traffic flow,and reduc-ing energy consumption.Despite the extensive research con-ducted on trajectory tracking control and other aspects of CAVF,the quality of the extant literature varies consider-ably,and research content remains scattered.To better pro-mote the sustainable and healthy development of the CAVF field,this paper employs the mapping knowledge domain(MKD)methodology to comprehensively review and visual-ize the current research status in this domain.Based on this review,research themes,hotspots,research challenges,and future development directions are proposed.The findings suggest that the research on CAVF can be categorized into three primary developmental stages.China and the United States are the primary countries conducting CAVF research.There is a positive correlation between economic develop-ment and the generation of scientific research outcomes.Re-search institutions are predominantly concentrated in univer-sities.The field exhibits significant interdisciplinary and inte-gration characteristics,forming key research personnel and teams.It is expected that future research will concentrate on topics such as deep learning,trajectory optimization,energy management strategy,mixed vehicle platoon,and other re-lated subjects.Research on cognition-driven intelligent for-mation decision-making mechanisms,resilience-oriented for-mation safety assurance systems,multiobjective collabora-tive formation optimization strategies,and digital twin-driven formation system validation platforms represents key future development directions.
文摘The rapid growth of the automotive industry has raised significant concerns about the security of connected vehicles and their integrated supply chains,which are increasingly vulnerable to advanced cyber threats.Traditional authentication methods have proven insufficient,exposing systems to risks such as Sybil,Denial of Service(DoS),and Eclipse attacks.This study critically examines the limitations of current security protocols,focusing on authentication and data exchange vulnerabilities,and explores blockchain technology as a potential solution.Blockchain’s decentralized and cryptographically secure framework can significantly enhance Vehicle-to-Vehicle(V2V)communication,ensure data integrity,and enable transparent,immutable transactions within the supply chain.Additionally,blockchain strengthens authentication,secures digital identities,and improves data sharing,reducing the risk of unauthorized access and data breaches.Our contribution lies in the proposal to integrate Artificial Intelligence(AI)with blockchain technology to further improve security by refining cryptographic methods,automating key management,and bolstering anomaly detection.Despite challenges related to computational complexity,latency,scalability,and regulatory concerns,the combination of blockchain,AI offers the transformative potential to enhance the security,transparency,and efficiency of connected vehicle systems and their supply chains.
