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
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
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
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
This study aims to construct a virtual twin testing framework for the safety of the intended functionality of intelligent connected vehicles to address the safety requirements of intelligent driving and transportation...This study aims to construct a virtual twin testing framework for the safety of the intended functionality of intelligent connected vehicles to address the safety requirements of intelligent driving and transportation systems.The research methods include the construction of a theoretical model of safety for intelligent connected vehicles based on the concept of virtual twins,the correlation study between key concepts and functional safety,and the application research of virtual twin technology in the safety testing of intelligent connected vehicles.The results reveal that the virtual twin testing framework can effectively enhance the functional safety of intelligent connected vehicles,reduce development costs,and shorten the product launch cycle.The conclusion suggests that this framework provides strong support for the healthy development of the intelligent connected vehicle industry and has a positive impact on the safety and efficiency of intelligent transportation systems.展开更多
With the advancement of connected vehicle(CV)technology,an increasing number of CVs will appear on urban roads.Data collected by CVs can be used to optimize signal parameters at intersections,thus improving traffic ef...With the advancement of connected vehicle(CV)technology,an increasing number of CVs will appear on urban roads.Data collected by CVs can be used to optimize signal parameters at intersections,thus improving traffic efficiency.In this study,we design a real-time adaptive signal control method for an arterial road with multiple intersections with low penetration rates.By utilizing vehicle arrival information collected by CVs,our method rapidly determines optimal signal phasing and timing(SPaT).The proposed adaptive signal control method was tested with the Simulation of Urban Mobility(SUMO)software,and was found to reduce total travel delay in the network better than a fixed coordination control method.The performance of the proposed method in reducing travel delay is expected to improve as CV detection range increases.展开更多
Based on market integration theory,we investigate the static and dynamic connectedness between nonfungible tokens(NFTs)and the Association of Southeast Asian Nations(ASEAN)equity markets using the Quantile Vector Auto...Based on market integration theory,we investigate the static and dynamic connectedness between nonfungible tokens(NFTs)and the Association of Southeast Asian Nations(ASEAN)equity markets using the Quantile Vector Auto Regressive model.We also compute optimal weights and hedge ratios for our variable of interest to establish their diversification and hedging potential.Our analysis infers a moderate level of return transmission at the median quantile,where equity markets evolved as the net recipients of return spillover from the system,while NFTs emerge as key transmitters.In extreme market conditions,transmission between variables is amplified,but the increase is symmetrical across extreme quantiles,suggesting a similar impact.However,the interlinkage among assets is symmetric across conditional quantiles.The dynamic analysis demonstrates that the system integration amplifies during uncertain times(e.g.,COVID-19 and the Russia–Ukraine conflict).Our portfolio analysis shows that NFTs provide diversification and hedging in all market conditions.However,the period of turmoil dampened the diversification potential,and hedging became expensive.Our study offers detailed and insightful information about the transmission mechanism and enables the participants of financial markets to diversify and hedge their portfolio.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
OBJECTIVE:To explore the relationship between colorectal polyps and pulmonary nodules from the perspective of the lung and the large intestine being internally and externally connected,aiming to provide a theoretical ...OBJECTIVE:To explore the relationship between colorectal polyps and pulmonary nodules from the perspective of the lung and the large intestine being internally and externally connected,aiming to provide a theoretical basis for clinical diagnosis and treatment.METHODS:We retrospectively analyzed the data of patients who underwent electronic colonoscopy and were found to have colorectal polyps at the Gastrointestinal Endoscopy Center of Dongfang Hospital,Beijing University of Chinese Medicine,from January 1,2017,to December 31,2023.We also reviewed their lung CT results and used statistical software to analyze the recurrence,location,size,and pathology of colorectal polyps in relation to the presence,number,and size of pulmonary nodules.RESULTS:Both colorectal polyps and pulmonary nodules are more common in elderly males.Patients with recurrent colorectal polyps are more likely to have pulmonary nodules,which tend to be located in the left colon and are more likely to be adenomatous in nature;those without pulmonary nodules show no clear pattern in polyp distribution,with a tendency towards inflammatory and hyperplastic pathology;the data from this study suggests that the proportion of lung nodules larger than 0.5 cm in the recurrent group is higher than in the non-recurrent group,and the proportion of colorectal polyps larger than 1 cm in the recurrent group is also higher than in the non-recurrent group.CONCLUSION:There is a certain connection between the pathogenesis and treatment of colorectal polyps and pulmonary nodules.Cold,phlegm,dampness,blood stasis,and toxic coagulation are common pathogenic factors of the two diseases.Patients with larger colorectal polyps should be advised to undergo regular colonoscopy.Patients with recurrent polyps or those with left colon necrosis or cancer indicated by colonoscopy should be advised to complete lung related examinations to rule out the possibility of pulmonary nodules.展开更多
We examine technology ETF and uncertainty index(VIX,GVZ,and OVZ)spillover dynamics and quantile frequency interconnectedness across market states.This study is the first to use quantile-frequency spillover,quadruple w...We examine technology ETF and uncertainty index(VIX,GVZ,and OVZ)spillover dynamics and quantile frequency interconnectedness across market states.This study is the first to use quantile-frequency spillover,quadruple wavelet coherence,and wavelet quantile correlation methodologies to facilitate these analyses.The total connectedness index value is 70%,which is much higher in both the upper and lower quantiles.Under normal market conditions,short-term connectedness significantly exceeds long-term connectedness.Levels of ETF-uncertainty indicator connectedness increase under extreme market conditions;most technology ETFs are net spillover transmitters and uncertainty indices net spillover receivers,indicating the contagion risk of ETF investments.We show that while greater ETF-uncertainty index connectedness may benefit portfolio diversification,large fluctuations in technology EFTs can result in financial instability due to high market volatility.In the long term,the joint effects of uncertainty indices on ETFs are significant,with negative correlations between ETFs and uncertainties at different frequencies,supporting the potential role of uncertainty indices in hedging technology ETF portfolio risks.Dynamic portfolio rebalancing,scenario analysis,and stress testing may help to manage the effects of high connectedness.展开更多
This study evaluates the predictive accuracy of traditional time series(TS)models versus machine learning(ML)methods in forecasting realized volatility across major cryptocurrencies—Bitcoin(BTC),Ethereum(ETH),Litecoi...This study evaluates the predictive accuracy of traditional time series(TS)models versus machine learning(ML)methods in forecasting realized volatility across major cryptocurrencies—Bitcoin(BTC),Ethereum(ETH),Litecoin(LTC),and Ripple(XRP).Employing high-frequency data,we analyze cross-cryptocurrency volatility dynamics through two complementary approaches:volatility forecasting and connectedness analysis.Our findings reveal three key insights:(i)TS models,particularly the heterogeneous autoregressive(HAR)model,exhibit superior predictive performance over their ML counterparts,with the long short-term memory(LSTM)model providing competitive yet inconsistent results due to overfitting and short-term volatility challenges;(ii)including lagged realized volatility of large-cap coins improves predictive accuracy for mid-cap coins,especially XRP,whereas forecasts for largecap coins remain stable,indicating more resilient volatility patterns;and(iii)volatility connectedness analysis reveals substantial spillover effects,particularly pronounced during market turmoil,with large-cap assets(BTC and ETH)acting as primary volatility transmitters and mid-cap assets(XRP and LTC)serving as volatility receivers.These results contribute to the understanding of volatility forecasting and risk management in cryptocurrency markets,offering implications for investors and policymakers in managing market risk and interdependencies in digital asset portfolios.展开更多
This study assessed the connectedness between oil shocks and industry stock indexes in the United States(US).We consider the normal and extreme conditions across different frequency horizons,and the quantile time–fre...This study assessed the connectedness between oil shocks and industry stock indexes in the United States(US).We consider the normal and extreme conditions across different frequency horizons,and the quantile time–frequency connectedness method is used to determine the tail risk contagion under different frequency horizons.Our results reveal that the short-term frequency connectedness significantly exceeds the long-term frequency connectedness.We also indicate that the connectedness in the lower and upper quantiles is greater than at the conditional mean.Importantly,oil risk shock is the biggest net transmitter of shocks to the US sectors in normal and extreme conditions,highlighting that oil risk shocks cause substantial variations in US sector stock returns in the short,medium,and long term.Finally,QAR(3)model demonstrates the significant impact of oil risk shocks on US sector stock returns during extreme and normal conditions.Therefore,our study underscores the role of asymmetry in the reaction of US sector stock returns to oil-related shocks,and we suggest that policies aimed at overcoming the adverse effects of oil shocks on stock markets and promoting financial stability should incorporate asymmetric features.展开更多
Infrastructure and energy are two important areas for African countries to achieve sustainable development,as well as are among the priorities in the African Union’s Agenda 2063,the continent’s ambitious development...Infrastructure and energy are two important areas for African countries to achieve sustainable development,as well as are among the priorities in the African Union’s Agenda 2063,the continent’s ambitious development blueprint.In February,Lerato Mataboge was elected as the African Union Commissioner for Infrastructure and Energy.She is a global policy and trade and investment facilitation expert and was the deputy director general in the South African Department of Trade,Industry and Competition when she was elected.展开更多
Reliable electricity infrastructure is critical for modern society,highlighting the importance of securing the stability of fundamental power electronic systems.However,as such systems frequently involve high-current ...Reliable electricity infrastructure is critical for modern society,highlighting the importance of securing the stability of fundamental power electronic systems.However,as such systems frequently involve high-current and high-voltage conditions,there is a greater likelihood of failures.Consequently,anomaly detection of power electronic systems holds great significance,which is a task that properly-designed neural networks can well undertake,as proven in various scenarios.Transformer-like networks are promising for such application,yet with its structure initially designed for different tasks,features extracted by beginning layers are often lost,decreasing detection performance.Also,such data-driven methods typically require sufficient anomalous data for training,which could be difficult to obtain in practice.Therefore,to improve feature utilization while achieving efficient unsupervised learning,a novel model,Densely-connected Decoder Transformer(DDformer),is proposed for unsupervised anomaly detection of power electronic systems in this paper.First,efficient labelfree training is achieved based on the concept of autoencoder with recursive-free output.An encoder-decoder structure with densely-connected decoder is then adopted,merging features from all encoder layers to avoid possible loss of mined features while reducing training difficulty.Both simulation and real-world experiments are conducted to validate the capabilities of DDformer,and the average FDR has surpassed baseline models,reaching 89.39%,93.91%,95.98%in different experiment setups respectively.展开更多
To improve the fault diagnosis accuracy of a PV grid-connected inverter,a PV grid-connected inverter data diagnosis method based on MPA-VMD-PSO-BiLSTM is proposed.Firstly,unlike the traditional VMD algorithm which rel...To improve the fault diagnosis accuracy of a PV grid-connected inverter,a PV grid-connected inverter data diagnosis method based on MPA-VMD-PSO-BiLSTM is proposed.Firstly,unlike the traditional VMD algorithm which relies on manual experience to set parameters(e.g.,noise tolerance,penalty parameter,number of decompositions),this paper achieves adaptive optimization of parameters through MPA algorithmto avoid the problemof feature information loss caused by manual parameter tuning,and adopts the improved VMD algorithm for feature extraction of DC-side voltage data signals of PV-grid-connected inverters;and then,adopts the PSO algorithm for theThen,the PSO algorithm is used to optimize the optimal batch size,the number of nodes in the hidden layer and the learning rate of the BiLSTM network,which significantly improves the model’s ability to capture the long-term dependent features of the PV inverter’s timing signals,to construct the PV grid-connected inverter prediction model of PSO-BiLSTM,and predict the capacitance value of the PVgrid-connected inverter.Finally,diagnostic experiments are carried out based on the expected capacitance value and the capacitance failure criterion.The results showthat compared with the traditional VMD algorithm,the MPA-optimised VMD improves the signal-to-noise ratio(SNR)of the signal decomposition from 28.5 to 33.2 dB(16.5%improvement).After combining with the PSO-BiLSTM model,the mean absolute percentage error(MAPE)of the fault diagnosis is reduced to 1.31%,and the coefficient of determination(R2)is up to 0.99.It is concluded that the present method has excellent diagnostic performance of PV grid-connected inverter data signals and effectively improves the accuracy of PV grid-connected inverter diagnosis.展开更多
To cope with the constraint problem of power consumption and transmission delay in the virtual backbone of wireless sensor network, a distributed connected dominating set （CDS） algorithm with （α,β）-constraints i...To cope with the constraint problem of power consumption and transmission delay in the virtual backbone of wireless sensor network, a distributed connected dominating set （CDS） algorithm with （α,β）-constraints is proposed. Based on the （α, β）-tree concept, a new connected dominating tree with bounded transmission delay problem（CDTT） is defined and a corresponding algorithm is designed to construct a CDT-tree which can trade off limited total power and bounded transmission delay from source to destination nodes. The CDT algorithm consists of two phases： The first phase constructs a maximum independent set（MIS）in a unit disk graph model. The second phase estimates the distance and calculates the transmission power to construct a spanning tree in an undirected graph with different weights for MST and SPF, respectively. The theoretical analysis and simulation results show that the CDT algorithm gives a correct solution to the CDTF problem and forms a virtual backbone with（ α,β）-constraints balancing the requirements of power consumption and transmission delay.展开更多
Connected vehicle(CV)is regarded as a typical feature of the future road transportation system.One core benefit of promoting CV is to improve traffic safety,and to achieve that,accurate driving risk assessment under V...Connected vehicle(CV)is regarded as a typical feature of the future road transportation system.One core benefit of promoting CV is to improve traffic safety,and to achieve that,accurate driving risk assessment under Vehicle-to-Vehicle(V2V)communications is critical.There are two main differences concluded by comparing driving risk assessment under the CV environment with traditional ones:(1)the CV environment provides high-resolution and multi-dimensional data,e.g.,vehicle trajectory data,(2)Rare existing studies can comprehensively address the heterogeneity of the vehicle operating environment,e.g.,the multiple interacting objects and the time-series variability.Hence,this study proposes a driving risk assessment framework under the CV environment.Specifically,first,a set of time-series top views was proposed to describe the CV environment data,expressing the detailed information on the vehicles surrounding the subject vehicle.Then,a hybrid CNN-LSTM model was established with the CNN component extracting the spatial interaction with multiple interacting vehicles and the LSTM component solving the time-series variability of the driving environment.It is proved that this model can reach an AUC of 0.997,outperforming the existing machine learning algorithms.This study contributes to the improvement of driving risk assessment under the CV environment.展开更多
In this paper, we study the connectedness of the invariant sets of a graph-directed iterated function system（IFS）. For a graph-directed IFS with N states, we construct N graphs. We prove that all the invariant sets ...In this paper, we study the connectedness of the invariant sets of a graph-directed iterated function system（IFS）. For a graph-directed IFS with N states, we construct N graphs. We prove that all the invariant sets are connected, if and only if all the N graphs are connected; in this case, the invariant sets are all locally connected and path connected. Our result extends the results on the connectedness of the self-similar sets.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.
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 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.
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This study aims to construct a virtual twin testing framework for the safety of the intended functionality of intelligent connected vehicles to address the safety requirements of intelligent driving and transportation systems.The research methods include the construction of a theoretical model of safety for intelligent connected vehicles based on the concept of virtual twins,the correlation study between key concepts and functional safety,and the application research of virtual twin technology in the safety testing of intelligent connected vehicles.The results reveal that the virtual twin testing framework can effectively enhance the functional safety of intelligent connected vehicles,reduce development costs,and shorten the product launch cycle.The conclusion suggests that this framework provides strong support for the healthy development of the intelligent connected vehicle industry and has a positive impact on the safety and efficiency of intelligent transportation systems.
基金supported by the Program of Humanities and Social Science of the Ministry of Education of China(No.24YJA630013)the Natural Science Foundation of Ningbo of China(No.2024J125)the“Innovation Yongjiang 2035”Key R&D Programme(No.2024H032),China。
文摘With the advancement of connected vehicle(CV)technology,an increasing number of CVs will appear on urban roads.Data collected by CVs can be used to optimize signal parameters at intersections,thus improving traffic efficiency.In this study,we design a real-time adaptive signal control method for an arterial road with multiple intersections with low penetration rates.By utilizing vehicle arrival information collected by CVs,our method rapidly determines optimal signal phasing and timing(SPaT).The proposed adaptive signal control method was tested with the Simulation of Urban Mobility(SUMO)software,and was found to reduce total travel delay in the network better than a fixed coordination control method.The performance of the proposed method in reducing travel delay is expected to improve as CV detection range increases.
文摘Based on market integration theory,we investigate the static and dynamic connectedness between nonfungible tokens(NFTs)and the Association of Southeast Asian Nations(ASEAN)equity markets using the Quantile Vector Auto Regressive model.We also compute optimal weights and hedge ratios for our variable of interest to establish their diversification and hedging potential.Our analysis infers a moderate level of return transmission at the median quantile,where equity markets evolved as the net recipients of return spillover from the system,while NFTs emerge as key transmitters.In extreme market conditions,transmission between variables is amplified,but the increase is symmetrical across extreme quantiles,suggesting a similar impact.However,the interlinkage among assets is symmetric across conditional quantiles.The dynamic analysis demonstrates that the system integration amplifies during uncertain times(e.g.,COVID-19 and the Russia–Ukraine conflict).Our portfolio analysis shows that NFTs provide diversification and hedging in all market conditions.However,the period of turmoil dampened the diversification potential,and hedging became expensive.Our study offers detailed and insightful information about the transmission mechanism and enables the participants of financial markets to diversify and hedge their portfolio.
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Beijing Natural Science Foundation:Research on the Mechanism of Chaiqi Decoction in Improving Vascular Endothelial Injury in Metabolic Syndrome by Regulating Pyroptosis via miR-21(No.7212181)。
文摘OBJECTIVE:To explore the relationship between colorectal polyps and pulmonary nodules from the perspective of the lung and the large intestine being internally and externally connected,aiming to provide a theoretical basis for clinical diagnosis and treatment.METHODS:We retrospectively analyzed the data of patients who underwent electronic colonoscopy and were found to have colorectal polyps at the Gastrointestinal Endoscopy Center of Dongfang Hospital,Beijing University of Chinese Medicine,from January 1,2017,to December 31,2023.We also reviewed their lung CT results and used statistical software to analyze the recurrence,location,size,and pathology of colorectal polyps in relation to the presence,number,and size of pulmonary nodules.RESULTS:Both colorectal polyps and pulmonary nodules are more common in elderly males.Patients with recurrent colorectal polyps are more likely to have pulmonary nodules,which tend to be located in the left colon and are more likely to be adenomatous in nature;those without pulmonary nodules show no clear pattern in polyp distribution,with a tendency towards inflammatory and hyperplastic pathology;the data from this study suggests that the proportion of lung nodules larger than 0.5 cm in the recurrent group is higher than in the non-recurrent group,and the proportion of colorectal polyps larger than 1 cm in the recurrent group is also higher than in the non-recurrent group.CONCLUSION:There is a certain connection between the pathogenesis and treatment of colorectal polyps and pulmonary nodules.Cold,phlegm,dampness,blood stasis,and toxic coagulation are common pathogenic factors of the two diseases.Patients with larger colorectal polyps should be advised to undergo regular colonoscopy.Patients with recurrent polyps or those with left colon necrosis or cancer indicated by colonoscopy should be advised to complete lung related examinations to rule out the possibility of pulmonary nodules.
基金supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2024S1A5A2A01028034).
文摘We examine technology ETF and uncertainty index(VIX,GVZ,and OVZ)spillover dynamics and quantile frequency interconnectedness across market states.This study is the first to use quantile-frequency spillover,quadruple wavelet coherence,and wavelet quantile correlation methodologies to facilitate these analyses.The total connectedness index value is 70%,which is much higher in both the upper and lower quantiles.Under normal market conditions,short-term connectedness significantly exceeds long-term connectedness.Levels of ETF-uncertainty indicator connectedness increase under extreme market conditions;most technology ETFs are net spillover transmitters and uncertainty indices net spillover receivers,indicating the contagion risk of ETF investments.We show that while greater ETF-uncertainty index connectedness may benefit portfolio diversification,large fluctuations in technology EFTs can result in financial instability due to high market volatility.In the long term,the joint effects of uncertainty indices on ETFs are significant,with negative correlations between ETFs and uncertainties at different frequencies,supporting the potential role of uncertainty indices in hedging technology ETF portfolio risks.Dynamic portfolio rebalancing,scenario analysis,and stress testing may help to manage the effects of high connectedness.
文摘This study evaluates the predictive accuracy of traditional time series(TS)models versus machine learning(ML)methods in forecasting realized volatility across major cryptocurrencies—Bitcoin(BTC),Ethereum(ETH),Litecoin(LTC),and Ripple(XRP).Employing high-frequency data,we analyze cross-cryptocurrency volatility dynamics through two complementary approaches:volatility forecasting and connectedness analysis.Our findings reveal three key insights:(i)TS models,particularly the heterogeneous autoregressive(HAR)model,exhibit superior predictive performance over their ML counterparts,with the long short-term memory(LSTM)model providing competitive yet inconsistent results due to overfitting and short-term volatility challenges;(ii)including lagged realized volatility of large-cap coins improves predictive accuracy for mid-cap coins,especially XRP,whereas forecasts for largecap coins remain stable,indicating more resilient volatility patterns;and(iii)volatility connectedness analysis reveals substantial spillover effects,particularly pronounced during market turmoil,with large-cap assets(BTC and ETH)acting as primary volatility transmitters and mid-cap assets(XRP and LTC)serving as volatility receivers.These results contribute to the understanding of volatility forecasting and risk management in cryptocurrency markets,offering implications for investors and policymakers in managing market risk and interdependencies in digital asset portfolios.
基金supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2024S1A5A2A01028034).
文摘This study assessed the connectedness between oil shocks and industry stock indexes in the United States(US).We consider the normal and extreme conditions across different frequency horizons,and the quantile time–frequency connectedness method is used to determine the tail risk contagion under different frequency horizons.Our results reveal that the short-term frequency connectedness significantly exceeds the long-term frequency connectedness.We also indicate that the connectedness in the lower and upper quantiles is greater than at the conditional mean.Importantly,oil risk shock is the biggest net transmitter of shocks to the US sectors in normal and extreme conditions,highlighting that oil risk shocks cause substantial variations in US sector stock returns in the short,medium,and long term.Finally,QAR(3)model demonstrates the significant impact of oil risk shocks on US sector stock returns during extreme and normal conditions.Therefore,our study underscores the role of asymmetry in the reaction of US sector stock returns to oil-related shocks,and we suggest that policies aimed at overcoming the adverse effects of oil shocks on stock markets and promoting financial stability should incorporate asymmetric features.
文摘Infrastructure and energy are two important areas for African countries to achieve sustainable development,as well as are among the priorities in the African Union’s Agenda 2063,the continent’s ambitious development blueprint.In February,Lerato Mataboge was elected as the African Union Commissioner for Infrastructure and Energy.She is a global policy and trade and investment facilitation expert and was the deputy director general in the South African Department of Trade,Industry and Competition when she was elected.
基金supported in part by the National Natural Science Foundation of China under Grant 62303090,U2330206in part by the Postdoctoral Science Foundation of China under Grant 2023M740516+1 种基金in part by the Natural Science Foundation of Sichuan Province under Grant 2024NSFSC1480in part by the New Cornerstone Science Foundation through the XPLORER PRIZE.
文摘Reliable electricity infrastructure is critical for modern society,highlighting the importance of securing the stability of fundamental power electronic systems.However,as such systems frequently involve high-current and high-voltage conditions,there is a greater likelihood of failures.Consequently,anomaly detection of power electronic systems holds great significance,which is a task that properly-designed neural networks can well undertake,as proven in various scenarios.Transformer-like networks are promising for such application,yet with its structure initially designed for different tasks,features extracted by beginning layers are often lost,decreasing detection performance.Also,such data-driven methods typically require sufficient anomalous data for training,which could be difficult to obtain in practice.Therefore,to improve feature utilization while achieving efficient unsupervised learning,a novel model,Densely-connected Decoder Transformer(DDformer),is proposed for unsupervised anomaly detection of power electronic systems in this paper.First,efficient labelfree training is achieved based on the concept of autoencoder with recursive-free output.An encoder-decoder structure with densely-connected decoder is then adopted,merging features from all encoder layers to avoid possible loss of mined features while reducing training difficulty.Both simulation and real-world experiments are conducted to validate the capabilities of DDformer,and the average FDR has surpassed baseline models,reaching 89.39%,93.91%,95.98%in different experiment setups respectively.
基金supported by Science and Technology Projects of Jiangsu Province(No.BE2022003)Science and Technology Projects of Jiangsu Province(No.BE2022003-5).
文摘To improve the fault diagnosis accuracy of a PV grid-connected inverter,a PV grid-connected inverter data diagnosis method based on MPA-VMD-PSO-BiLSTM is proposed.Firstly,unlike the traditional VMD algorithm which relies on manual experience to set parameters(e.g.,noise tolerance,penalty parameter,number of decompositions),this paper achieves adaptive optimization of parameters through MPA algorithmto avoid the problemof feature information loss caused by manual parameter tuning,and adopts the improved VMD algorithm for feature extraction of DC-side voltage data signals of PV-grid-connected inverters;and then,adopts the PSO algorithm for theThen,the PSO algorithm is used to optimize the optimal batch size,the number of nodes in the hidden layer and the learning rate of the BiLSTM network,which significantly improves the model’s ability to capture the long-term dependent features of the PV inverter’s timing signals,to construct the PV grid-connected inverter prediction model of PSO-BiLSTM,and predict the capacitance value of the PVgrid-connected inverter.Finally,diagnostic experiments are carried out based on the expected capacitance value and the capacitance failure criterion.The results showthat compared with the traditional VMD algorithm,the MPA-optimised VMD improves the signal-to-noise ratio(SNR)of the signal decomposition from 28.5 to 33.2 dB(16.5%improvement).After combining with the PSO-BiLSTM model,the mean absolute percentage error(MAPE)of the fault diagnosis is reduced to 1.31%,and the coefficient of determination(R2)is up to 0.99.It is concluded that the present method has excellent diagnostic performance of PV grid-connected inverter data signals and effectively improves the accuracy of PV grid-connected inverter diagnosis.
基金Major Program of the National Natural Science Foundation of China (No.70533050)High Technology Research Program ofJiangsu Province(No.BG2007012)+1 种基金China Postdoctoral Science Foundation(No.20070411065)Science Foundation of China University of Mining andTechnology(No.OC080303)
文摘To cope with the constraint problem of power consumption and transmission delay in the virtual backbone of wireless sensor network, a distributed connected dominating set （CDS） algorithm with （α,β）-constraints is proposed. Based on the （α, β）-tree concept, a new connected dominating tree with bounded transmission delay problem（CDTT） is defined and a corresponding algorithm is designed to construct a CDT-tree which can trade off limited total power and bounded transmission delay from source to destination nodes. The CDT algorithm consists of two phases： The first phase constructs a maximum independent set（MIS）in a unit disk graph model. The second phase estimates the distance and calculates the transmission power to construct a spanning tree in an undirected graph with different weights for MST and SPF, respectively. The theoretical analysis and simulation results show that the CDT algorithm gives a correct solution to the CDTF problem and forms a virtual backbone with（ α,β）-constraints balancing the requirements of power consumption and transmission delay.
基金sponsored by the Zhejiang Province Science and Technology Major Project of China(No.2021C01011)the National Natural Science Foundation of China(NSFC)(No.52172349)the China Scholarship Council(CSC).
文摘Connected vehicle(CV)is regarded as a typical feature of the future road transportation system.One core benefit of promoting CV is to improve traffic safety,and to achieve that,accurate driving risk assessment under Vehicle-to-Vehicle(V2V)communications is critical.There are two main differences concluded by comparing driving risk assessment under the CV environment with traditional ones:(1)the CV environment provides high-resolution and multi-dimensional data,e.g.,vehicle trajectory data,(2)Rare existing studies can comprehensively address the heterogeneity of the vehicle operating environment,e.g.,the multiple interacting objects and the time-series variability.Hence,this study proposes a driving risk assessment framework under the CV environment.Specifically,first,a set of time-series top views was proposed to describe the CV environment data,expressing the detailed information on the vehicles surrounding the subject vehicle.Then,a hybrid CNN-LSTM model was established with the CNN component extracting the spatial interaction with multiple interacting vehicles and the LSTM component solving the time-series variability of the driving environment.It is proved that this model can reach an AUC of 0.997,outperforming the existing machine learning algorithms.This study contributes to the improvement of driving risk assessment under the CV environment.
基金Supported by the Teaching Research Project of Hubei Province(2013469)the 12th Five-Year Project of Education Plan of Hubei Province(2014B379)
文摘In this paper, we study the connectedness of the invariant sets of a graph-directed iterated function system（IFS）. For a graph-directed IFS with N states, we construct N graphs. We prove that all the invariant sets are connected, if and only if all the N graphs are connected; in this case, the invariant sets are all locally connected and path connected. Our result extends the results on the connectedness of the self-similar sets.
