Macroscopic Fundamental Diagrams According to the Different Flood Depths on Probe Vehicle Data in Urban Bangkok

Impact of flood depth on traffic volume in two different zones of the Bangkok road network was investigated using traffic data obtained from probe vehicle trajectories.A Macroscopic Fundamental Diagram(MFD)was utilized to compare traffic flow rates across two road network zones in the city for a variety of flood depths,namely 0-5 cm,5-10 cm,10-15 cm,15-30 cm,and more than 30 cm.Results of empirical analysis over an observation period of one year as 2019 showed that flood depths had a strong correlation with the MFD parameters of free-flow speed,maximum flow,and traffic jam density.In particular,road floods greatly reduced average maximum flow across the inner city road network in Bangkok.Road floods had a significant impact on traffic characteristics of urban road networks.

Evaluation of Effective Vehicle Probe Information Delivery with Multiple Communication Methods

Vehicle probe information delivery systems can be broadly divided into the center type and center-less type. Since conventional center-type information delivery systems generate a large load on the communications infrastructure and data center, research efforts have come to be focused on the centerless type. However, existing vehicle probe information delivery systems suffer from various problems including a limited service area, low delivery efficiency, and lack of immediacy in delivery. Our objective in this study is efficient delivery of vehicle probe information as needed. We propose a delivery scheme that uses vehicle-to-vehicle communication, infrastructure-to-vehicle communication, and mobile communication as well as Geo cast. This combined use of multiple communication methods achieves efficient information delivery by changing the communication method to fit the current situation. The results of an evaluation by simulation showed that the proposed scheme could deliver information efficiently in a variety of environments.

The Future Trend of E-Mobility in Terms of Battery Electric Vehicles and Their Impact on Climate Change: A Case Study Applied in Hungary

The transportation sector is responsible for 25% of the total Carbon dioxide (CO2) emissions, whereas 60.6% of this sector represents small and medium passenger cars. However, as noted by the European Union Long-term strategy, there are two ways to reduce the amount of CO2 emissions in the transportation sector. The first way is characterized by creating more efficient vehicles. In contrast, the second way is characterized by changing the fuel used. The current study addressed the second way, changing the fuel type. The study examined the potential of battery electric vehicles (BEVs) as an alternative fuel type to reduce CO2 emissions in Hungarys transportation sector. The study used secondary data retrieved from Statista and stata.com to analyze the future trends of BEVs in Hungary. The results showed that the percentage of BEVs in Hungary in 2022 was 0.4% compared to the total number of registered passenger cars, which is 3.8 million. The simple exponential smoothing (SES) time series forecast revealed that the number of BEVs is expected to reach 84,192 in 2030, indicating a percentage increase of 2.21% in the next eight years. The study suggests that increasing the number of BEVs is necessary to address the negative impact of CO2 emissions on society. The Hungarian Ministry of Innovation and Technologys strategy to reduce the cost of BEVs may increase the percentage of BEVs by 10%, resulting in a potential average reduction of 76,957,600 g/km of CO2 compared to gasoline, diesel, hybrid electric vehicles (HEVs), and plug-in hybrid vehicles (PHEVs).

Lift-Off Effect of Koch and Circular Differential Pickup Eddy Current Probes

A flexible or planar eddy current probe with a differential structure can suppress the lift-off noise during the inspection of defects.However,the extent of the lift-off effect on differential probes,including different coil structures,varies.In this study,two planar eddy current probes with differential pickup structures and the same size,Koch and circular probes,were used to compare lift-off effects.The eddy current distributions of the probes perturbed by 0°and 90°cracks were obtained by finite element analysis.The analysis results show that the 90°crack can impede the eddy current induced by the Koch probe even further at relatively low lift-off distance.The peak-to-peak values of the signal output from the two probes were compared at different lift-off distances using finite element analysis and experimental methods.In addition,the effects of different frequencies on the lift-off were studied experimentally.The results show that the signal peak-to-peak value of the Koch probe for the inspection of cracks in 90°orientation is larger than that of the circular probe when the lift-off distance is smaller than 1.2 mm.In addition,the influence of the lift-off distance on the peak-to-peak signal value of the two probes was studied via normalization.This indicates that the influence becomes more evident with an increase in excitation frequency.This research discloses the lift-off effect of differential planar eddy current probes with different coil shapes and proves the detection merit of the Koch probe for 90°cracks at low lift-off distances.

Integrated Active Suspension and Anti-Lock Braking Control for Four-Wheel-Independent-Drive Electric Vehicles

This paper presents an integrated control scheme for enhancing the ride comfort and handling performance of a four-wheel-independent-drive electric vehicle through the coordination of active suspension system(ASS)and anti-lock braking system(ABS).First,a longitudinal-vertical coupled vehicle dynamics model is established by integrating a road input model.Then the coupling mechanisms between longitudinal and vertical vehicle dynamics are analyzed.An ASS-ABS integrated control system is proposed,utilizing an H∞controller for ASS to optimize load transfer effect and a neural network sliding mode control for ABS implementation.Finally,the effectiveness of the proposed control scheme is evaluated through comprehensive tests conducted on a hardware-in-loop(HIL)test platform.The HIL test results demonstrate that the proposed control scheme can significantly improve the braking performance and ride comfort compared to conventional ABS control methods.

英汉笔译课程PROBE教学模式探究

作为本科翻译专业的一门核心课程,英汉笔译课程在培养学生英汉双语运用能力、翻译能力和跨文化能力,尤其是学生英译汉翻译实践能力作用明显。文章对“PROBE教学模式”进行概念诠释,从教学目标、课程设计、教学过程和学习评价四个方面探讨了“PROBE教学模式”在英汉笔译课程中的应用,最后提出几点建议,以期提高英汉笔译课程的教学效果。

Communication Resource-Efficient Vehicle Platooning Control With Various Spacing Policies

Platooning represents one of the key features that connected automated vehicles may possess as it allows multiple automated vehicles to be maneuvered cooperatively with small headways on roads. However, a critical challenge in accomplishing automated vehicle platoons is to deal with the effects of intermittent and sporadic vehicle-to-vehicle data transmissions caused by limited wireless communication resources. This paper addresses the co-design problem of dynamic event-triggered communication scheduling and cooperative adaptive cruise control for a convoy of automated vehicles with diverse spacing policies. The central aim is to achieve automated vehicle platooning under various gap references with desired platoon stability and spacing performance requirements, while simultaneously improving communication efficiency. Toward this aim, a dynamic event-triggered scheduling mechanism is developed such that the intervehicle data transmissions are scheduled dynamically and efficiently over time. Then, a tractable co-design criterion on the existence of both the admissible event-driven cooperative adaptive cruise control law and the desired scheduling mechanism is derived. Finally, comparative simulation results are presented to substantiate the effectiveness and merits of the obtained results.

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

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

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

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

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

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

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

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

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

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

A method to interpret fracture aperture of rock slope using adaptive shape and unmanned aerial vehicle multi-angle nap-of-the-object photogrammetry

The aperture of natural rock fractures significantly affects the deformation and strength properties of rock masses,as well as the hydrodynamic properties of fractured rock masses.The conventional measurement methods are inadequate for collecting data on high-steep rock slopes in complex mountainous regions.This study establishes a high-resolution three-dimensional model of a rock slope using unmanned aerial vehicle(UAV)multi-angle nap-of-the-object photogrammetry to obtain edge feature points of fractures.Fracture opening morphology is characterized using coordinate projection and transformation.Fracture central axis is determined using vertical measuring lines,allowing for the interpretation of aperture of adaptive fracture shape.The feasibility and reliability of the new method are verified at a construction site of a railway in southeast Tibet,China.The study shows that the fracture aperture has a significant interval effect and size effect.The optimal sampling length for fractures is approximately 0.5e1 m,and the optimal aperture interpretation results can be achieved when the measuring line spacing is 1%of the sampling length.Tensile fractures in the study area generally have larger apertures than shear fractures,and their tendency to increase with slope height is also greater than that of shear fractures.The aperture of tensile fractures is generally positively correlated with their trace length,while the correlation between the aperture of shear fractures and their trace length appears to be weak.Fractures of different orientations exhibit certain differences in their distribution of aperture,but generally follow the forms of normal,log-normal,and gamma distributions.This study provides essential data support for rock and slope stability evaluation,which is of significant practical importance.

An exo Probe-based Reverse Transcription and Recombinase Polymerase Amplification for Rapid Detection of Feline Calicivirus in Cats

Feline calicivirus(FCV)was a highly prevalent RNA virus causing upper respiratory tract infections in cats through the mouth and nose around the world,result in the death of cats especially under 1 year old with upper respiratory tract disease(URTD)and virulent systemic disease(FCV-VSD)[1].Infected cats usually presented with mouth ulcers,salivation and gingivitis-stomatitis.Furthermore,both recovered cats and asymptomatic carriers can still carry and spread the FCV virus for extended periods of time,contributing to the potential for outbreak.This presented a significant threat to the health and lives of cats,and rare wildlife and took a heavy financial loss on the pet industry,making the prevention and control of the virus difficult.Hence,rapid identification of FCV in clinical samples was necessary to prevent the spread of FCV and avoid financial loss.

Activatable fluorescent probes for imaging and diagnosis of rheumatoid arthritis

Rheumatoid arthritis(RA)is a systemic autoimmune disease that is primarily manifested as synovitis and polyarticular opacity and typically leads to serious joint damage and irreversible disability,thus adversely affecting locomotion ability and life quality.Consequently,good prognosis heavily relies on the early diagnosis and effective therapeutic monitoring of RA.Activatable fluorescent probes play vital roles in the detection and imaging of biomarkers for disease diagnosis and in vivo imaging.Herein,we review the fluorescent probes developed for the detection and imaging of RA biomarkers,namely reactive oxygen/nitrogen species(hypochlorous acid,peroxynitrite,hydroxyl radical,nitroxyl),pH,and cysteine,and address the related challenges and prospects to inspire the design of novel fluorescent probes and the improvement of their performance in RA studies.

State Estimation of Drive-by-Wire Chassis Vehicle Based on Dual Unscented Particle Filter Algorithm

Accurate vehicle dynamic information plays an important role in vehicle driving safety.However,due to the characteristics of high mobility and multiple controllable degrees of freedom of drive-by-wire chassis vehicles,the current mature application of traditional vehicle state estimation algorithms can not meet the requirements of drive-by-wire chassis vehicle state estimation.This paper proposes a state estimation method for drive-by-wire chassis vehicle based on the dual unscented particle filter algorithm,which make full use of the known advantages of the four-wheel drive torque and steer angle parameters of the drive-by-wire chassis vehicle.In the dual unscented particle filter algorithm,two unscented particle filter transfer information to each other,observe the vehicle state information and the tire force parameter information of the four wheels respectively,which reduce the influence of parameter uncertainty and model parameter changes on the estimation accuracy during driving.The performance with the dual unscented particle filter algorithm,which is analyzed in terms of the time-average square error,is superior of the unscented Kalman filter algorithm.The effectiveness of the algorithm is further verified by driving simulator test.In this paper,a vehicle state estimator based on dual unscented particle filter algorithm was proposed for the first time to improve the estimation accuracy of vehicle parameters and states.

Plasma potential measurements using an emissive probe made of oxide cathode

A novel emissive probe consisting of an oxide cathode coating is developed to achieve a low operating temperature and long service life.The properties of the novel emissive probe are investigated in detail,in comparison with a traditional tungsten emissive probe,including the operating temperature,the electron emission capability and the plasma potential measurement.Studies of the operating temperature and electron emission capability show that the tungsten emissive probe usually works at a temperature of 1800 K-2200 K while the oxide cathode emissive probe can function at about 1200 K-1400 K.In addition,plasma potential measurements using the oxide cathode emissive probe with different techniques have been accomplished in microwave electron cyclotron resonance plasmas with different discharge powers.It is found that a reliable plasma potential can be obtained using the improved inflection point method and the hot probe with zero emission limit method,while the floating point method is invalid for the oxide cathode emissive probe.

Distal-scanning common path probe for optical coherence tomography

In this paper,we present a distal-scanning common path probe for optical coherence tomography(OCT)equipped with a hollow ultrasonic motor and a simple and specially designed beam-splitter.This novel probe proves to be able to effectively circumvent polarization and dispersion mismatch caused by fiber motion and is more robust to a variety of interfering factors during the imaging process,experimentally compared to a conventional noncommon path probe.Furthermore,our design counteracts the attenuation of backscattering with depth and the fall-off of the signal,resulting in a more balanced signal range and greater imaging depth.Spectral-domain OCT imaging of phantom and biological tissue is also demonstrated with a sensitivity of∼100dB and a lateral resolution of∼3μm.This low-cost probe offers simplified system configuration and excellent robustness,and is therefore particularly suitable for clinical diagnosis as one-off medical apparatus.

Design of compact integrated diamond nitrogen-vacancy center quantum probe

An integrated quantum probe for magnetic field imaging is proposed,where the nitrogen–vacancy(NV)center fixed at the fiber tip is located on the periphery of flexible ring resonator.Using flexible polyimide(PI)as the substrate medium,we design a circular microstrip antenna,which can achieve a bandwidth of 140 MHz at Zeeman splitting frequency of 2.87 GHz,specifically suitable for NV center experiments.Subsequently,this antenna is seamlessly fixed at a three-dimensional-printed cylindrical support,allowing the optical fiber tip to extend out of a dedicated aperture.To mitigate errors originating from processing,precise tuning within a narrow range can be achieved by adjusting the conformal amplitude.Finally,we image the microwave magnetic field around the integrated probe with high resolution,and determine the suitable area for placing the fiber tip(SAP).

Temperature-controlled DCP Fluorescent Probe Based on Hydrogel-immobilized Quantum Dots Composite

A composite was created by incorporating the quantum dot-enhanced SiO_(2)nanoparticles within this hydrogel.Based on this composite,a temperature-controlled fluorescent probe for DCP was developed.A meticulous examination of this probe revealed its attributes and factors affecting its performance.By using temperature modulation,the probe was adept at detecting DCP concentrations ranging between 1.0×10^(-6)and 9.0×10^(-6)mol/L.Such a probe offers remarkable selectivity,repeatability,and robust stability,so that the detection of DCP can be carried out at different temperatures,and a fast,reliable,sensitive and low-cost intelligent detection method is realized.