The interaction between the airflow and train influences the aerodynamic characteristics and dynamic performance of high-speed trains.This study focused on the fluid-solid coupling effect of airflow and HST,and propos...The interaction between the airflow and train influences the aerodynamic characteristics and dynamic performance of high-speed trains.This study focused on the fluid-solid coupling effect of airflow and HST,and proposed a co-simulation(CS)approach between computational fluid dynamics and multi-body dynamics.Firstly,the aerodynamic model was developed by employing overset mesh technology and the finite volume method,and the detailed train-track coupled dynamic model was established.Then the User Data Protocol was adopted to build data communication channels.Moreover,the proposed CS method was validated by comparison with a reported field test result.Finally,a case study of the HST exiting a tunnel subjected to crosswind was conducted to compare differences between CS and offline simulation(OS)methods.In terms of the presented case,the changing trends of aerodynamic forces and car-body displacements calculated by the two methods were similar.Differences mainly lie in aerodynamic moments and transient wheel-rail impacts.Maximum pitching and yawing moments on the head vehicle in the two methods differ by 21.1 kN∙m and 29.6 kN∙m,respectively.And wheel-rail impacts caused by sudden changes in aerodynamic loads are significantly severer in CS.Wheel-rail safety indices obtained by CS are slightly greater than those by OS.This research proposes a CS method for aerodynamic characteristics and dynamic performance of the HST in complex scenarios,which has superiority in computational efficiency and stability.展开更多
With the continuous progress of automatic driving technology,automatic driving technology standards are gradually affecting the determination of criminal responsibility for traffic accidents in China.At present,the ch...With the continuous progress of automatic driving technology,automatic driving technology standards are gradually affecting the determination of criminal responsibility for traffic accidents in China.At present,the characteristics and tendency of China's automatic driving technology standards present the situation of high policy relevance coexisting with low normative binding,professionalism coexist with barriers,forefront coexist with ambiguity.Therefore,challenges are presented both theoretically and practically on the determination of criminal responsibility based on automatic driving technology standard..In this regard,the misunderstanding should be clarified in theory:The legal order under the automatic driving technology standard has constitutionality and systematic,and there is a balance between the frontier of automatic driving technology development and the lagging of criminal law.The automatic driving technology risk level system should be built to clarify the boundary of the effectiveness of criminal law norms,seeking fora breakthrough in the application of the establishment of a comprehensive judgment system of the risks and accidents and the system of evidence to prove the system,which clarifies the determination of criminal responsibility under the automatic driving technology standard.This essay hopes to pursue breakthroughs in the application-to establish a comprehensive judgment system of risks and accidents as well as an evidence proof system,so as to clarify the determination of criminal responsibility under automatic driving technology standards.展开更多
This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential g...This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential growth of mobile devices and data traffic has substantially increased network congestion,particularly in urban areas and regions with limited terrestrial infrastructure.Our approach jointly optimizes unmanned aerial vehicle(UAV)trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput,minimize energy consumption,and maintain equitable resource distribution.The proposed RMAPPO framework incorporates recurrent neural networks(RNNs)to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent reinforcement learning architecture to reduce communication overhead while improving system robustness.The proposed RMAPPO algorithm was evaluated through simulation experiments,with the results indicating that it significantly enhances the cumulative traffic offloading rate of nodes and reduces the energy consumption of UAVs.展开更多
Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakt...Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakthroughs in this field,in the face of complex scenes,such as image blur and target occlusion,the traffic sign detection continues to exhibit limited accuracy,accompanied by false positives and missed detections.To address the above problems,a traffic sign detection algorithm,You Only Look Once-based Skip Dynamic Way(YOLO-SDW)based on You Only Look Once version 8 small(YOLOv8s),is proposed.Firstly,a Skip Connection Reconstruction(SCR)module is introduced to efficiently integrate fine-grained feature information and enhance the detection accuracy of the algorithm in complex scenes.Secondly,a C2f module based on Dynamic Snake Convolution(C2f-DySnake)is proposed to dynamically adjust the receptive field information,improve the algorithm’s feature extraction ability for blurred or occluded targets,and reduce the occurrence of false detections and missed detections.Finally,the Wise Powerful IoU v2(WPIoUv2)loss function is proposed to further improve the detection accuracy of the algorithm.Experimental results show that the average precision mAP@0.5 of YOLO-SDW on the TT100K dataset is 89.2%,and mAP@0.5:0.95 is 68.5%,which is 4%and 3.3%higher than the YOLOv8s baseline,respectively.YOLO-SDW ensures real-time performance while having higher accuracy.展开更多
Urban traffic generates massive and diverse data,yet most systems remain fragmented.Current approaches to congestion management suffer from weak data consistency and poor scalability.This study addresses this gap by p...Urban traffic generates massive and diverse data,yet most systems remain fragmented.Current approaches to congestion management suffer from weak data consistency and poor scalability.This study addresses this gap by proposing the Urban Traffic Congestion Unified Metadata Model(UTC-UMM).The goal is to provide a standardized and extensible framework for describing,extracting,and storing multisource traffic data in smart cities.The model defines a two-tier specification that organizes nine core traffic resource classes.It employs an eXtensible Markup Language(XML)Schema that connects general elements with resource-specific elements.This design ensures both syntactic and semantic interoperability across siloed datasets.Extension principles allow new elements or constraints to be introducedwithout breaking backward compatibility.Adistributed pipeline is implemented usingHadoop Distributed File System(HDFS)and HBase.It integrates computer vision for video and natural language processing for text to automate metadata extraction.Optimized row-key designs enable low-latency queries.Performance is tested with the Yahoo!Cloud Serving Benchmark(YCSB),which shows linear scalability and high throughput.The results demonstrate that UTC-UMM can unify heterogeneous traffic data while supporting real-time analytics.The discussion highlights its potential to improve data reuse,portability,and scalability in urban congestion studies.Future research will explore integration with association rulemining and advanced knowledge representation to capture richer spatiotemporal traffic patterns.展开更多
With network attack technology continuing to develop,traditional anomaly traffic detection methods that rely on feature engineering are increasingly insufficient in efficiency and accuracy.Graph Neural Network(GNN),a ...With network attack technology continuing to develop,traditional anomaly traffic detection methods that rely on feature engineering are increasingly insufficient in efficiency and accuracy.Graph Neural Network(GNN),a promising Deep Learning(DL)approach,has proven to be highly effective in identifying intricate patterns in graph⁃structured data and has already found wide applications in the field of network security.In this paper,we propose a hybrid Graph Convolutional Network(GCN)⁃GraphSAGE model for Anomaly Traffic Detection,namely HGS⁃ATD,which aims to improve the accuracy of anomaly traffic detection by leveraging edge feature learning to better capture the relationships between network entities.We validate the HGS⁃ATD model on four publicly available datasets,including NF⁃UNSW⁃NB15⁃v2.The experimental results show that the enhanced hybrid model is 5.71%to 10.25%higher than the baseline model in terms of accuracy,and the F1⁃score is 5.53%to 11.63%higher than the baseline model,proving that the model can effectively distinguish normal traffic from attack traffic and accurately classify various types of attacks.展开更多
This paper presents a unified Unmanned Aerial Vehicle-based(UAV-based)traffic monitoring framework that integrates vehicle detection,tracking,counting,motion prediction,and classification in a modular and co-optimized...This paper presents a unified Unmanned Aerial Vehicle-based(UAV-based)traffic monitoring framework that integrates vehicle detection,tracking,counting,motion prediction,and classification in a modular and co-optimized pipeline.Unlike prior works that address these tasks in isolation,our approach combines You Only Look Once(YOLO)v10 detection,ByteTrack tracking,optical-flow density estimation,Long Short-Term Memory-based(LSTM-based)trajectory forecasting,and hybrid Speeded-Up Robust Feature(SURF)+Gray-Level Co-occurrence Matrix(GLCM)feature engineering with VGG16 classification.Upon the validation across datasets(UAVDT and UAVID)our framework achieved a detection accuracy of 94.2%,and 92.3%detection accuracy when conducting a real-time UAV field validation.Our comprehensive evaluations,including multi-metric analyses,ablation studies,and cross-dataset validations,confirm the framework’s accuracy,efficiency,and generalizability.These results highlight the novelty of integrating complementary methods into a single framework,offering a practical solution for accurate and efficient UAV-based traffic monitoring.展开更多
With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comp...With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices,spanning a range of devices from non-encrypted ones to fully encrypted ones.Given the limited visibility into payloads in this context,this study investigates AI-based attack detection methods that leverage encrypted traffic metadata,eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices.Using the UNSW-NB15 and CICIoT-2023 dataset,encrypted and unencrypted traffic were categorized according to security protocol,and AI-based intrusion detection experiments were conducted for each traffic type based on metadata.To mitigate the problem of class imbalance,eight different data sampling techniques were applied.The effectiveness of these sampling techniques was then comparatively analyzed using two ensemble models and three Deep Learning(DL)models from various perspectives.The experimental results confirmed that metadata-based attack detection is feasible using only encrypted traffic.In the UNSW-NB15 dataset,the f1-score of encrypted traffic was approximately 0.98,which is 4.3%higher than that of unencrypted traffic(approximately 0.94).In addition,analysis of the encrypted traffic in the CICIoT-2023 dataset using the same method showed a significantly lower f1-score of roughly 0.43,indicating that the quality of the dataset and the preprocessing approach have a substantial impact on detection performance.Furthermore,when data sampling techniques were applied to encrypted traffic,the recall in the UNSWNB15(Encrypted)dataset improved by up to 23.0%,and in the CICIoT-2023(Encrypted)dataset by 20.26%,showing a similar level of improvement.Notably,in CICIoT-2023,f1-score and Receiver Operation Characteristic-Area Under the Curve(ROC-AUC)increased by 59.0%and 55.94%,respectively.These results suggest that data sampling can have a positive effect even in encrypted environments.However,the extent of the improvement may vary depending on data quality,model architecture,and sampling strategy.展开更多
Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems(ITS),playing a pivotal role in mitigating congestion,enhancing route optimization,and improving the utilization efficie...Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems(ITS),playing a pivotal role in mitigating congestion,enhancing route optimization,and improving the utilization efficiency of roadway infrastructure.However,existingmethods struggle in complex traffic scenarios due to static spatio-temporal embedding,restricted multi-scale temporal modeling,and weak representation of local spatial interactions.This study proposes Bi-STAT+,an enhanced bidirectional spatio-temporal attention framework to address existing limitations through three principal contributions:(1)an adaptive spatio-temporal embedding module that dynamically adjusts embeddings to capture complex traffic variations;(2)frequency-domain analysis in the temporal dimension for simultaneous high-frequency details and low-frequency trend extraction;and(3)an agent attention mechanism in the spatial dimension that enhances local feature extraction through dynamic weight allocation.Extensive experiments were performed on four distinct datasets,including two publicly benchmark datasets(PEMS04 and PEMS08)and two private datasets collected from Baotou and Chengdu,China.The results demonstrate that Bi-STAT+consistently outperforms existing methods in terms of MAE,RMSE,and MAPE,while maintaining strong robustness against missing data and noise.Furthermore,the results highlight that prediction accuracy improves significantly with higher sampling rates,providing crucial insights for optimizing real-world deployment scenarios.展开更多
Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection mo...Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.展开更多
Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating In...Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.展开更多
Accurate and real-time traffic-sign detection is a cornerstone of Advanced Driver-Assistance Systems(ADAS)and autonomous vehicles.However,existing one-stage detectors miss distant signs,and two-stage pipelines are imp...Accurate and real-time traffic-sign detection is a cornerstone of Advanced Driver-Assistance Systems(ADAS)and autonomous vehicles.However,existing one-stage detectors miss distant signs,and two-stage pipelines are impractical for embedded deployment.To address this issue,we present YOLO-SMM,a lightweight two-stage framework.This framework is designed to augment the YOLOv8 baseline with three targeted modules.(1)SlimNeck replaces PAN/FPN with a CSP-OSA/GSConv fusion block,reducing parameters and FLOPs without compromising multi-scale detail.(2)The MCA model introduces row-and column-aware weights to selectively amplify small sign regions in cluttered scenes.(3)MPDIoU augments CIoU loss with a corner-distance term,supplying stable gradients for sub-20-pixel boxes and tightening localization.An evaluation of YOLO-SMMon the German Traffic Sign Recognition Benchmark(GTSRB)revealed that it attained 96.3% mAP50 and 93.1% mAP50-90 at a rate of 90.6 frames per second(FPS).This represents an improvement of+1.0% over previous performance benchmarks.Them APat 64×64 resolution was found to be 50% of the maximum attainable value,with an FPS of+8.3 when compared to YOLOv8.This result indicates superior performance in terms of accuracy and speed compared to YOLOv7,YOLOv5,RetinaNet,EfficientDet,and Faster R-CNN,all of which were operated under equivalent conditions.展开更多
Long-term traffic flow prediction is a crucial component of intelligent transportation systems within intelligent networks,requiring predictive models that balance accuracy with low-latency and lightweight computation...Long-term traffic flow prediction is a crucial component of intelligent transportation systems within intelligent networks,requiring predictive models that balance accuracy with low-latency and lightweight computation to optimize trafficmanagement and enhance urban mobility and sustainability.However,traditional predictivemodels struggle to capture long-term temporal dependencies and are computationally intensive,limiting their practicality in real-time.Moreover,many approaches overlook the periodic characteristics inherent in traffic data,further impacting performance.To address these challenges,we introduce ST-MambaGCN,a State-Space-Based Spatio-Temporal Graph Convolution Network.Unlike conventionalmodels,ST-MambaGCN replaces the temporal attention layer withMamba,a state-space model that efficiently captures long-term dependencies with near-linear computational complexity.The model combines Chebyshev polynomial-based graph convolutional networks(GCN)to explore spatial correlations.Additionally,we incorporate a multi-temporal feature capture mechanism,where the final integrated features are generated through the Hadamard product based on learnable parameters.This mechanism explicitly models shortterm,daily,and weekly traffic patterns to enhance the network’s awareness of traffic periodicity.Extensive experiments on the PeMS04 and PeMS08 datasets demonstrate that ST-MambaGCN significantly outperforms existing benchmarks,offering substantial improvements in both prediction accuracy and computational efficiency for long-term traffic flow prediction.展开更多
A kind of construction truck model is built in Adams based on multi-body dynamic theory. The rigid and elastic wheels of tire-soil contact models are proposed based on the Bekker pressure model and the Jonasi shear so...A kind of construction truck model is built in Adams based on multi-body dynamic theory. The rigid and elastic wheels of tire-soil contact models are proposed based on the Bekker pressure model and the Jonasi shear soil model, and they are described in the form of S-function to enhance the calculation efficiency and simulation accuracy. Finally, the interaction of truck and soil is simulated by Adams-Maflab co-simulation to study the influence of soft terrain on the ride comfort of vehicles. The co-simulation results reveal that the terrain properties have a great influence on the ride comfort of vehicles as well as driving speed, road roughness and cargo weight. This co-simulation model is convenient for adding the factor of terrain deformation to the analysis of vehicle ride comfort. It can also be used to optimize suspension system parameters especially for off-road vehicles.展开更多
In order to design an effective hydraulic motor speed control system, Matlab_Simiulink and AMESim co-simulation technology is adopted to establish more accurate model and reflect the actual system. The neural...In order to design an effective hydraulic motor speed control system, Matlab_Simiulink and AMESim co-simulation technology is adopted to establish more accurate model and reflect the actual system. The neural network proportion-integration-differentiation （PID） control parameters on-line adjustment is utilized to improve system accuracy, celerity and stability. Simulation results indicate that with the control system proposed in this paper, the system deviation is reduced, therefore accuracy is improved; response speed for step signal and sinusoidal signal gets faster, thus acceleration is rapidly improved; and the system can be restored to the control value in case of interfering, so stability is improved.展开更多
Under high-level earthquakes,bridge piers and bearings are prone to be damaged and the elastoplastic state of bridge structural components is easily accessible in the train-track-bridge interaction(TTBI)system.Conside...Under high-level earthquakes,bridge piers and bearings are prone to be damaged and the elastoplastic state of bridge structural components is easily accessible in the train-track-bridge interaction(TTBI)system.Considering the complexity and structural non-linearity of the TTBI system under earthquakes,a single software is not adequate for the coupling analysis.Therefore,in this paper,an interactive method for the TTBI system is proposed by combining the multi-body dynamics software Simpack and the seismic simulation software OpenSees based on the Client-Server architecture,which takes full advantages of the powerful wheel-track contact analysis capabilities of Simpack and the sophisticated nonlinear analysis capabilities of OpenSees.Based on the proposed Simpack and OpenSees co-simulating train-track-bridge(SOTTB)method,a single-span bridge analysis under the earthquake was conducted and the accuracy of co-simulation method was verified by comparing it with results of the finite element model.Finally,the TTBI model is built utilizing the SOTTB method to further discuss the running safety of HST on multi-span simply supported bridges under earthquakes.The results show that the SOTTB method has the advantages of usability,high versatility and accuracy which can be further used to study the running safety of HST under earthquakes with high intensities.展开更多
In order to observe the change and fluctuation in flow and pressure of a hydraulic quadruped robot＇s hydraulic system when the robot walks on trot gait,a co-simulation method based on ADAMS and AMESim is proposed. Fi...In order to observe the change and fluctuation in flow and pressure of a hydraulic quadruped robot＇s hydraulic system when the robot walks on trot gait,a co-simulation method based on ADAMS and AMESim is proposed. Firstly,the change rule in each swing angle of the hydraulic quadruped robot＇s four legs is analyzed and converted to the displacement change of the hydraulic cylinder by calculating their geometric relationship.Secondly,the robot＇s dynamic model is built in ADAMS and its hydraulic and control system models are built in AMESim. The displacement change of the hydraulic cylinder in the hydraulic system is used as the driving function of the dynamics model in ADAMS,and the driving force of the dynamics model is used as the loads of the hydraulic system in AMESim. By introducing the PID closed-loop control in the control system,the co-simulation between hydraulic system and mechanical system is implemented. Finally,the curve of hydraulic cylinders＇ loads,flow and pressure are analyzed and the results show that they fluctuate highly in accordance with the real situation. The study provides data support for the development of a hydraulic quadruped robot＇s physical prototype.展开更多
To study the durability of a passenger car, this work investigates numerical simulation techniques. The investigations are based on an explicit implicit approach in which substructure techniques are used to reduce the...To study the durability of a passenger car, this work investigates numerical simulation techniques. The investigations are based on an explicit implicit approach in which substructure techniques are used to reduce the simulation time, allowing full vehicle dynamic analyses to be performed on a timescale that is dif cult or impossible with the conventional nite element model (FEM). The model used here includes all necessary nonlinearities in order to maintain accuracy. All key components of the car structure are modeled with deformable materials. Tire road interactions are modeled in the explicit package with contact-impact interfaces with arbitrary frictional and geometric properties. Key parameters of the responses of the car driven on six different kinds of test road surfaces are examined and compared with experimental values. It can be concluded that the explicit implicit co-simulation techniques used here are ef cient and accurate enough for engineering purposes. This paper also discusses the limitations of the proposed method and outlines possible improvements for future work.展开更多
Various distributed cooperative control schemes have been widely utilized for cyber-physical power system(CPPS),which only require local communications among geographic neighbors to fulfill certain goals.However,the p...Various distributed cooperative control schemes have been widely utilized for cyber-physical power system(CPPS),which only require local communications among geographic neighbors to fulfill certain goals.However,the process of evaluating the performance of an algorithm for a CPPS can be affected by the physical target characteristics and real communication conditions.To address this potential problem,a testbed with controller hardware-in-the-loop(CHIL)is proposed in this paper.On the basis of a power grid simulation conducted using the real-time simulator RT-LAB developed by the company OPAL-RT,along with a communication network simulation developed with OPNET,multiple distributed controllers were developed with hardware devices to directly collect the real-time operating data of the power system model in RT-LAB and provide local control.Furthermore,the communication between neighboring controllers was realized using the cyber system modelin OPNET with an Ethernet interface.The hardware controllers produced a real-world control behavior instead of a digital simulation,and precisely simulated the dynamic features of a CPPS with high speed.A classic cooperative control case for active power output was studied to explain the integrated simulation process and validate the effectiveness of the co-simulation testbed.展开更多
Based on the characteristics of integrated virtual prototype technology, the mechanical system sub-model, the hydraulic system sub-model and the control system sub-model of a forging manipula- tor system have been bui...Based on the characteristics of integrated virtual prototype technology, the mechanical system sub-model, the hydraulic system sub-model and the control system sub-model of a forging manipula- tor system have been built using a variety of software, and a forging manipulator mtrltidisciplinary co- simulation model has been also built using a method of simulation models interface. Then the simu- lation and experiment are finished, and the result of the experiment is in good agreement with the re- sult of the simulation. It shows that the co-simulation model established can simulate accurately pa- rameter changes in real time during the moving of the forging manipulator such as displacement, ve- locity and pressure flow, which is of important significance for the optimized design of the forging manipulator system to establish the models.展开更多
基金Supported by the Sichuan Science and Technology Program(Grant No.2023ZDZX0008)the National Natural Science Foundation of China(Grant No.52388102)the New Cornerstone Science Foundation through the XPLORER PRIZE.
文摘The interaction between the airflow and train influences the aerodynamic characteristics and dynamic performance of high-speed trains.This study focused on the fluid-solid coupling effect of airflow and HST,and proposed a co-simulation(CS)approach between computational fluid dynamics and multi-body dynamics.Firstly,the aerodynamic model was developed by employing overset mesh technology and the finite volume method,and the detailed train-track coupled dynamic model was established.Then the User Data Protocol was adopted to build data communication channels.Moreover,the proposed CS method was validated by comparison with a reported field test result.Finally,a case study of the HST exiting a tunnel subjected to crosswind was conducted to compare differences between CS and offline simulation(OS)methods.In terms of the presented case,the changing trends of aerodynamic forces and car-body displacements calculated by the two methods were similar.Differences mainly lie in aerodynamic moments and transient wheel-rail impacts.Maximum pitching and yawing moments on the head vehicle in the two methods differ by 21.1 kN∙m and 29.6 kN∙m,respectively.And wheel-rail impacts caused by sudden changes in aerodynamic loads are significantly severer in CS.Wheel-rail safety indices obtained by CS are slightly greater than those by OS.This research proposes a CS method for aerodynamic characteristics and dynamic performance of the HST in complex scenarios,which has superiority in computational efficiency and stability.
基金The National Social Science Foundation Youth Project of China:Research on the collaborative govemance path of administrative law and criminal law against dangerous driving behaviors in the digital-intelligent society(25CFX108)。
文摘With the continuous progress of automatic driving technology,automatic driving technology standards are gradually affecting the determination of criminal responsibility for traffic accidents in China.At present,the characteristics and tendency of China's automatic driving technology standards present the situation of high policy relevance coexisting with low normative binding,professionalism coexist with barriers,forefront coexist with ambiguity.Therefore,challenges are presented both theoretically and practically on the determination of criminal responsibility based on automatic driving technology standard..In this regard,the misunderstanding should be clarified in theory:The legal order under the automatic driving technology standard has constitutionality and systematic,and there is a balance between the frontier of automatic driving technology development and the lagging of criminal law.The automatic driving technology risk level system should be built to clarify the boundary of the effectiveness of criminal law norms,seeking fora breakthrough in the application of the establishment of a comprehensive judgment system of the risks and accidents and the system of evidence to prove the system,which clarifies the determination of criminal responsibility under the automatic driving technology standard.This essay hopes to pursue breakthroughs in the application-to establish a comprehensive judgment system of risks and accidents as well as an evidence proof system,so as to clarify the determination of criminal responsibility under automatic driving technology standards.
文摘This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential growth of mobile devices and data traffic has substantially increased network congestion,particularly in urban areas and regions with limited terrestrial infrastructure.Our approach jointly optimizes unmanned aerial vehicle(UAV)trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput,minimize energy consumption,and maintain equitable resource distribution.The proposed RMAPPO framework incorporates recurrent neural networks(RNNs)to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent reinforcement learning architecture to reduce communication overhead while improving system robustness.The proposed RMAPPO algorithm was evaluated through simulation experiments,with the results indicating that it significantly enhances the cumulative traffic offloading rate of nodes and reduces the energy consumption of UAVs.
基金funded by Key research and development Program of Henan Province(No.251111211200)National Natural Science Foundation of China(Grant No.U2004163).
文摘Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakthroughs in this field,in the face of complex scenes,such as image blur and target occlusion,the traffic sign detection continues to exhibit limited accuracy,accompanied by false positives and missed detections.To address the above problems,a traffic sign detection algorithm,You Only Look Once-based Skip Dynamic Way(YOLO-SDW)based on You Only Look Once version 8 small(YOLOv8s),is proposed.Firstly,a Skip Connection Reconstruction(SCR)module is introduced to efficiently integrate fine-grained feature information and enhance the detection accuracy of the algorithm in complex scenes.Secondly,a C2f module based on Dynamic Snake Convolution(C2f-DySnake)is proposed to dynamically adjust the receptive field information,improve the algorithm’s feature extraction ability for blurred or occluded targets,and reduce the occurrence of false detections and missed detections.Finally,the Wise Powerful IoU v2(WPIoUv2)loss function is proposed to further improve the detection accuracy of the algorithm.Experimental results show that the average precision mAP@0.5 of YOLO-SDW on the TT100K dataset is 89.2%,and mAP@0.5:0.95 is 68.5%,which is 4%and 3.3%higher than the YOLOv8s baseline,respectively.YOLO-SDW ensures real-time performance while having higher accuracy.
基金supported by the National Natural Science Foundation of China(Grant No.62172033).
文摘Urban traffic generates massive and diverse data,yet most systems remain fragmented.Current approaches to congestion management suffer from weak data consistency and poor scalability.This study addresses this gap by proposing the Urban Traffic Congestion Unified Metadata Model(UTC-UMM).The goal is to provide a standardized and extensible framework for describing,extracting,and storing multisource traffic data in smart cities.The model defines a two-tier specification that organizes nine core traffic resource classes.It employs an eXtensible Markup Language(XML)Schema that connects general elements with resource-specific elements.This design ensures both syntactic and semantic interoperability across siloed datasets.Extension principles allow new elements or constraints to be introducedwithout breaking backward compatibility.Adistributed pipeline is implemented usingHadoop Distributed File System(HDFS)and HBase.It integrates computer vision for video and natural language processing for text to automate metadata extraction.Optimized row-key designs enable low-latency queries.Performance is tested with the Yahoo!Cloud Serving Benchmark(YCSB),which shows linear scalability and high throughput.The results demonstrate that UTC-UMM can unify heterogeneous traffic data while supporting real-time analytics.The discussion highlights its potential to improve data reuse,portability,and scalability in urban congestion studies.Future research will explore integration with association rulemining and advanced knowledge representation to capture richer spatiotemporal traffic patterns.
基金National Natural Science Foundation of China(Grant No.62103434)National Science Fund for Distinguished Young Scholars(Grant No.62176263).
文摘With network attack technology continuing to develop,traditional anomaly traffic detection methods that rely on feature engineering are increasingly insufficient in efficiency and accuracy.Graph Neural Network(GNN),a promising Deep Learning(DL)approach,has proven to be highly effective in identifying intricate patterns in graph⁃structured data and has already found wide applications in the field of network security.In this paper,we propose a hybrid Graph Convolutional Network(GCN)⁃GraphSAGE model for Anomaly Traffic Detection,namely HGS⁃ATD,which aims to improve the accuracy of anomaly traffic detection by leveraging edge feature learning to better capture the relationships between network entities.We validate the HGS⁃ATD model on four publicly available datasets,including NF⁃UNSW⁃NB15⁃v2.The experimental results show that the enhanced hybrid model is 5.71%to 10.25%higher than the baseline model in terms of accuracy,and the F1⁃score is 5.53%to 11.63%higher than the baseline model,proving that the model can effectively distinguish normal traffic from attack traffic and accurately classify various types of attacks.
基金supported by the IITP(Institute of Information&Communications Technology Planning&Evaluation)-ICAN(ICT Challenge and Advanced Network of HRD)(IITP-2025-RS-2022-00156326,50)grant funded by theKorea government(Ministry of Science and ICT)supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R410)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia。
文摘This paper presents a unified Unmanned Aerial Vehicle-based(UAV-based)traffic monitoring framework that integrates vehicle detection,tracking,counting,motion prediction,and classification in a modular and co-optimized pipeline.Unlike prior works that address these tasks in isolation,our approach combines You Only Look Once(YOLO)v10 detection,ByteTrack tracking,optical-flow density estimation,Long Short-Term Memory-based(LSTM-based)trajectory forecasting,and hybrid Speeded-Up Robust Feature(SURF)+Gray-Level Co-occurrence Matrix(GLCM)feature engineering with VGG16 classification.Upon the validation across datasets(UAVDT and UAVID)our framework achieved a detection accuracy of 94.2%,and 92.3%detection accuracy when conducting a real-time UAV field validation.Our comprehensive evaluations,including multi-metric analyses,ablation studies,and cross-dataset validations,confirm the framework’s accuracy,efficiency,and generalizability.These results highlight the novelty of integrating complementary methods into a single framework,offering a practical solution for accurate and efficient UAV-based traffic monitoring.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2023-00235509Development of security monitoring technology based network behavior against encrypted cyber threats in ICT convergence environment).
文摘With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices,spanning a range of devices from non-encrypted ones to fully encrypted ones.Given the limited visibility into payloads in this context,this study investigates AI-based attack detection methods that leverage encrypted traffic metadata,eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices.Using the UNSW-NB15 and CICIoT-2023 dataset,encrypted and unencrypted traffic were categorized according to security protocol,and AI-based intrusion detection experiments were conducted for each traffic type based on metadata.To mitigate the problem of class imbalance,eight different data sampling techniques were applied.The effectiveness of these sampling techniques was then comparatively analyzed using two ensemble models and three Deep Learning(DL)models from various perspectives.The experimental results confirmed that metadata-based attack detection is feasible using only encrypted traffic.In the UNSW-NB15 dataset,the f1-score of encrypted traffic was approximately 0.98,which is 4.3%higher than that of unencrypted traffic(approximately 0.94).In addition,analysis of the encrypted traffic in the CICIoT-2023 dataset using the same method showed a significantly lower f1-score of roughly 0.43,indicating that the quality of the dataset and the preprocessing approach have a substantial impact on detection performance.Furthermore,when data sampling techniques were applied to encrypted traffic,the recall in the UNSWNB15(Encrypted)dataset improved by up to 23.0%,and in the CICIoT-2023(Encrypted)dataset by 20.26%,showing a similar level of improvement.Notably,in CICIoT-2023,f1-score and Receiver Operation Characteristic-Area Under the Curve(ROC-AUC)increased by 59.0%and 55.94%,respectively.These results suggest that data sampling can have a positive effect even in encrypted environments.However,the extent of the improvement may vary depending on data quality,model architecture,and sampling strategy.
基金partly supported by the Youth Foundation of the Inner Mongolia Natural Science Foundation[grant number 2024QN06017 and 2025MS06022]the Basic Scientific Research Business Fee Project for Universities in Inner Mongolia[grant numbers 2023XKJX019 and 2023XKJX024]the Central Guidance on Local Science and Technology Development Fund through[grant number 2024ZY0084].
文摘Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems(ITS),playing a pivotal role in mitigating congestion,enhancing route optimization,and improving the utilization efficiency of roadway infrastructure.However,existingmethods struggle in complex traffic scenarios due to static spatio-temporal embedding,restricted multi-scale temporal modeling,and weak representation of local spatial interactions.This study proposes Bi-STAT+,an enhanced bidirectional spatio-temporal attention framework to address existing limitations through three principal contributions:(1)an adaptive spatio-temporal embedding module that dynamically adjusts embeddings to capture complex traffic variations;(2)frequency-domain analysis in the temporal dimension for simultaneous high-frequency details and low-frequency trend extraction;and(3)an agent attention mechanism in the spatial dimension that enhances local feature extraction through dynamic weight allocation.Extensive experiments were performed on four distinct datasets,including two publicly benchmark datasets(PEMS04 and PEMS08)and two private datasets collected from Baotou and Chengdu,China.The results demonstrate that Bi-STAT+consistently outperforms existing methods in terms of MAE,RMSE,and MAPE,while maintaining strong robustness against missing data and noise.Furthermore,the results highlight that prediction accuracy improves significantly with higher sampling rates,providing crucial insights for optimizing real-world deployment scenarios.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.IPP:172-830-2025.
文摘Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.
文摘Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.
基金supported by University of Malaya and Ministry of High Education-Malaysia via Fundamental Research Grant Scheme No.FRGS/1/2023/TK10/UM/02/3.
文摘Accurate and real-time traffic-sign detection is a cornerstone of Advanced Driver-Assistance Systems(ADAS)and autonomous vehicles.However,existing one-stage detectors miss distant signs,and two-stage pipelines are impractical for embedded deployment.To address this issue,we present YOLO-SMM,a lightweight two-stage framework.This framework is designed to augment the YOLOv8 baseline with three targeted modules.(1)SlimNeck replaces PAN/FPN with a CSP-OSA/GSConv fusion block,reducing parameters and FLOPs without compromising multi-scale detail.(2)The MCA model introduces row-and column-aware weights to selectively amplify small sign regions in cluttered scenes.(3)MPDIoU augments CIoU loss with a corner-distance term,supplying stable gradients for sub-20-pixel boxes and tightening localization.An evaluation of YOLO-SMMon the German Traffic Sign Recognition Benchmark(GTSRB)revealed that it attained 96.3% mAP50 and 93.1% mAP50-90 at a rate of 90.6 frames per second(FPS).This represents an improvement of+1.0% over previous performance benchmarks.Them APat 64×64 resolution was found to be 50% of the maximum attainable value,with an FPS of+8.3 when compared to YOLOv8.This result indicates superior performance in terms of accuracy and speed compared to YOLOv7,YOLOv5,RetinaNet,EfficientDet,and Faster R-CNN,all of which were operated under equivalent conditions.
基金supported byNationalNatural Science Foundation of China,GrantNo.62402046the Beijing Forestry University Science and Technology Innovation Project under Grant No.BLX202358.
文摘Long-term traffic flow prediction is a crucial component of intelligent transportation systems within intelligent networks,requiring predictive models that balance accuracy with low-latency and lightweight computation to optimize trafficmanagement and enhance urban mobility and sustainability.However,traditional predictivemodels struggle to capture long-term temporal dependencies and are computationally intensive,limiting their practicality in real-time.Moreover,many approaches overlook the periodic characteristics inherent in traffic data,further impacting performance.To address these challenges,we introduce ST-MambaGCN,a State-Space-Based Spatio-Temporal Graph Convolution Network.Unlike conventionalmodels,ST-MambaGCN replaces the temporal attention layer withMamba,a state-space model that efficiently captures long-term dependencies with near-linear computational complexity.The model combines Chebyshev polynomial-based graph convolutional networks(GCN)to explore spatial correlations.Additionally,we incorporate a multi-temporal feature capture mechanism,where the final integrated features are generated through the Hadamard product based on learnable parameters.This mechanism explicitly models shortterm,daily,and weekly traffic patterns to enhance the network’s awareness of traffic periodicity.Extensive experiments on the PeMS04 and PeMS08 datasets demonstrate that ST-MambaGCN significantly outperforms existing benchmarks,offering substantial improvements in both prediction accuracy and computational efficiency for long-term traffic flow prediction.
基金The National Natural Science Foundation of China(No.50575040)the Natural Science Foundation of Jiangsu Province(No.BK2007112)
文摘A kind of construction truck model is built in Adams based on multi-body dynamic theory. The rigid and elastic wheels of tire-soil contact models are proposed based on the Bekker pressure model and the Jonasi shear soil model, and they are described in the form of S-function to enhance the calculation efficiency and simulation accuracy. Finally, the interaction of truck and soil is simulated by Adams-Maflab co-simulation to study the influence of soft terrain on the ride comfort of vehicles. The co-simulation results reveal that the terrain properties have a great influence on the ride comfort of vehicles as well as driving speed, road roughness and cargo weight. This co-simulation model is convenient for adding the factor of terrain deformation to the analysis of vehicle ride comfort. It can also be used to optimize suspension system parameters especially for off-road vehicles.
文摘In order to design an effective hydraulic motor speed control system, Matlab_Simiulink and AMESim co-simulation technology is adopted to establish more accurate model and reflect the actual system. The neural network proportion-integration-differentiation （PID） control parameters on-line adjustment is utilized to improve system accuracy, celerity and stability. Simulation results indicate that with the control system proposed in this paper, the system deviation is reduced, therefore accuracy is improved; response speed for step signal and sinusoidal signal gets faster, thus acceleration is rapidly improved; and the system can be restored to the control value in case of interfering, so stability is improved.
基金Project(2020EEEVL0403)supported by the China Earthquake AdministrationProjects(51878674,52022113)supported by the National Natural Science Foundation of ChinaProject(2022ZZTS0670)supported by the Fundamental Research Funds for the Central Universities,China。
文摘Under high-level earthquakes,bridge piers and bearings are prone to be damaged and the elastoplastic state of bridge structural components is easily accessible in the train-track-bridge interaction(TTBI)system.Considering the complexity and structural non-linearity of the TTBI system under earthquakes,a single software is not adequate for the coupling analysis.Therefore,in this paper,an interactive method for the TTBI system is proposed by combining the multi-body dynamics software Simpack and the seismic simulation software OpenSees based on the Client-Server architecture,which takes full advantages of the powerful wheel-track contact analysis capabilities of Simpack and the sophisticated nonlinear analysis capabilities of OpenSees.Based on the proposed Simpack and OpenSees co-simulating train-track-bridge(SOTTB)method,a single-span bridge analysis under the earthquake was conducted and the accuracy of co-simulation method was verified by comparing it with results of the finite element model.Finally,the TTBI model is built utilizing the SOTTB method to further discuss the running safety of HST on multi-span simply supported bridges under earthquakes.The results show that the SOTTB method has the advantages of usability,high versatility and accuracy which can be further used to study the running safety of HST under earthquakes with high intensities.
文摘In order to observe the change and fluctuation in flow and pressure of a hydraulic quadruped robot＇s hydraulic system when the robot walks on trot gait,a co-simulation method based on ADAMS and AMESim is proposed. Firstly,the change rule in each swing angle of the hydraulic quadruped robot＇s four legs is analyzed and converted to the displacement change of the hydraulic cylinder by calculating their geometric relationship.Secondly,the robot＇s dynamic model is built in ADAMS and its hydraulic and control system models are built in AMESim. The displacement change of the hydraulic cylinder in the hydraulic system is used as the driving function of the dynamics model in ADAMS,and the driving force of the dynamics model is used as the loads of the hydraulic system in AMESim. By introducing the PID closed-loop control in the control system,the co-simulation between hydraulic system and mechanical system is implemented. Finally,the curve of hydraulic cylinders＇ loads,flow and pressure are analyzed and the results show that they fluctuate highly in accordance with the real situation. The study provides data support for the development of a hydraulic quadruped robot＇s physical prototype.
文摘To study the durability of a passenger car, this work investigates numerical simulation techniques. The investigations are based on an explicit implicit approach in which substructure techniques are used to reduce the simulation time, allowing full vehicle dynamic analyses to be performed on a timescale that is dif cult or impossible with the conventional nite element model (FEM). The model used here includes all necessary nonlinearities in order to maintain accuracy. All key components of the car structure are modeled with deformable materials. Tire road interactions are modeled in the explicit package with contact-impact interfaces with arbitrary frictional and geometric properties. Key parameters of the responses of the car driven on six different kinds of test road surfaces are examined and compared with experimental values. It can be concluded that the explicit implicit co-simulation techniques used here are ef cient and accurate enough for engineering purposes. This paper also discusses the limitations of the proposed method and outlines possible improvements for future work.
基金the National Key Research and Development Program of China(Basic Research Class)(No.2017YFB0903000)the National Natural Science Foundation of China(No.U1909201).
文摘Various distributed cooperative control schemes have been widely utilized for cyber-physical power system(CPPS),which only require local communications among geographic neighbors to fulfill certain goals.However,the process of evaluating the performance of an algorithm for a CPPS can be affected by the physical target characteristics and real communication conditions.To address this potential problem,a testbed with controller hardware-in-the-loop(CHIL)is proposed in this paper.On the basis of a power grid simulation conducted using the real-time simulator RT-LAB developed by the company OPAL-RT,along with a communication network simulation developed with OPNET,multiple distributed controllers were developed with hardware devices to directly collect the real-time operating data of the power system model in RT-LAB and provide local control.Furthermore,the communication between neighboring controllers was realized using the cyber system modelin OPNET with an Ethernet interface.The hardware controllers produced a real-world control behavior instead of a digital simulation,and precisely simulated the dynamic features of a CPPS with high speed.A classic cooperative control case for active power output was studied to explain the integrated simulation process and validate the effectiveness of the co-simulation testbed.
基金Supported by the National Natural Science Foundation of China(No.51575471)Collaborative Innovation Program Topics of Heavy Machinery of Yanshan University(2011 Program,No.ZX01-20140400-01)
文摘Based on the characteristics of integrated virtual prototype technology, the mechanical system sub-model, the hydraulic system sub-model and the control system sub-model of a forging manipula- tor system have been built using a variety of software, and a forging manipulator mtrltidisciplinary co- simulation model has been also built using a method of simulation models interface. Then the simu- lation and experiment are finished, and the result of the experiment is in good agreement with the re- sult of the simulation. It shows that the co-simulation model established can simulate accurately pa- rameter changes in real time during the moving of the forging manipulator such as displacement, ve- locity and pressure flow, which is of important significance for the optimized design of the forging manipulator system to establish the models.
