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.

Effect of Epoxy Resin on the Properties of Recycled Asphalt

To promote the recycling of reclaimed asphalt pavement(RAP),epoxy resin was used to prepare the epoxy-recycled asphalt mixtures.The effect of epoxy resin on the properties of aged asphalt binder was investigated based on the tensile test,flexural creep test,and laser scanning confocal microscopy.The curing characteristics and the mechanical performance of recycled asphalt with different epoxy contents were explored.The results show that the low-temperature performance,ductility,and strength of the aged asphalt binder were significantly improved when the epoxy content reached 40%.The curing time of epoxy-recycled asphalt should be at least 4 d to ensure the formation of good internal spatial network structure.

Numerical Analysis of Steady Smoldering of Biomass Rods

Understanding the steady mechanism of biomass smoldering plays a great role in the utilization of smoldering technology.In this study numerical analysis of steady smoldering of biomass rods was performed.A two-dimensional(2D)steady model taking into account both char oxidation and pyrolysis was developed on the basis of a calculated propagation velocity according to empirical correlation.The model was validated against the smoldering experiment of biomass rods under natural conditions,and the maximum error was smaller than 31%.Parameter sensitivity analysis found that propagation velocity decreases significantly while oxidation area and pyrolysis zone increase significantly with the increasing diameter of rod fuel.

Dynamic Response of A Group of Cylindrical Storage Tanks with Baffles Considering the Effect of Soil Foundation

The sloshing in a group of rigid cylindrical tanks with baffles and on soil foundation under horizontal excitation is studied analytically.The solutions for the velocity potential are derived out by the liquid subdomain method.Equivalent models with mass-spring oscillators are established to replace continuous fluid.Combined with the least square technique,Chebyshev polynomials are employed to fit horizontal,rocking and horizontal-rocking coupling impedances of soil,respectively.A lumped parameter model for impedance is presented to describe the effects of soil on tank structures.A mechanical model for the soil-foundation-tank-liquid-baffle system with small amount of calculation and high accuracy is proposed using the substructure technique.The analytical solutions are in comparison with data from reported literature and numerical codes to validate the effectiveness and correctness of the model.Detailed dynamic properties and seismic responses of the soil-tank system are given for the baffle number,size and location as well as soil parameter.

Mechanical properties and energy evolution of Beishan shallow-layer granite under different unloading paths

Rock has mechanical characteristics and a fracture damage mechanism that are closely related to its loading history and loading path. The mechanical properties, fracture damage features, acoustic emission(AE) characteristics, and strain energy evolution of the Beishan shallow-layer granite used in triaxial unloading tests were investigated in this study. Three groups of triaxial tests, namely, conventional triaxial compression test(Group Ⅰ), maintaining deviatoric stress synchronously unloading confining pressure test(Group Ⅱ), and loading axial pressure synchronously unloading confining pressure test(Group Ⅲ), were carried out for the cylindrical granite specimens. AE monitoring device was utilized in these tests to determine the degree to which the AE waves and AE events reflected the degree of rock damage. In addition, the crack stress thresholds of the specimens were determined by volumetric strain method and AE parameter method, and strain energy evolution of the rock was explored in different damage stages. The results show that the shallow-layer granite experiences brittle failure during the triaxial loading test and unloading test, and the rock has a greater damage degree during the unloading test. The crack stress thresholds of these samples vary greatly between tests, but the threshold ratios of all samples are similar in the same crack damage stage. The Mogi-Coulomb strength criterion can better describe the unloading failure strength of the rock. The evolution of the AE parameter characteristics and strain energy differs between the specimens used in different stress path tests. The dissipative strain energy is the largest in Group Ⅱ and the smallest in Group Ⅰ.

Application of deep learning for informatics aided design of electrode materials in metal-ion batteries

To develop emerging electrode materials and improve the performances of batteries,the machine learning techniques can provide insights to discover,design and develop battery new materials in high-throughput way.In this paper,two deep learning models are developed and trained with two feature groups extracted from the Materials Project datasets to predict the battery electrochemical performances including average voltage,specific capacity and specific energy.The deep learning models are trained with the multilayer perceptron as the core.The Bayesian optimization and Monte Carlo methods are applied to improve the prediction accuracy of models.Based on 10 types of ion batteries,the correlation coefficients are maintained above 0.9 compared to DFT calculation results and the mean absolute error of the prediction results for voltages of two models can reach 0.41 V and 0.20 V,respectively.The electrochemical performance prediction times for the two trained models on thousands of batteries are only 72.9 ms and 75.7 ms.Besides,the two deep learning models are applied to approach the screening of emerging electrode materials for sodium-ion and potassium-ion batteries.This work can contribute to a high-throughput computational method to accelerate the rational and fast materials discovery and design.

Effect of Nozzle Inclination Angle on Fuel-Air Mixing and Combustion in a Heavy Fuel Engine

Heavy-fuel engines are widely used in UAVs(Unmanned Autonomous Vehicles)because of their reliability and high-power density.In this study,a combustion model for an in-cylinder direct injection engine has been imple-mented using the AVL FIRE software.The effects of the angle of nozzle inclination on fuel evaporation,mixture distribution,and combustion in the engine cylinder have been systematically studied at 5500 r/min and consider-ing full load cruise conditions.According to the results,as the angle of nozzle inclination increases,the maximum combustion explosion pressure in the cylinderfirst increases and then it decreases.When the angle of nozzle incli-nation is less than 45°,the quality of the mixture in the cylinder and the combustion performance can be improved by increasing the angle.When the angle of nozzle inclination is greater than 45°,however,the mixture unevenness increases slightly with the angle,leading to a deterioration of the combustion performances.When the angle of nozzle inclination is between 35°and 55°,the overall combustion performance of the engine is rela-tively good.When the angle of nozzle inclination is 45°,the combustion chamber’s geometry and the cylinder’s airflow are well matched with the fuel spray,and the mixture quality is the best.Compared with 25°,the peak heat release rate increases by 20%,and the maximum combustion burst pressure increases by 5.5%.

Dynamic analysis of axle box bearings on the high-speed train caused by wheel-rail excitation

To explore the impact of wheel-rail excitation on the dynamic performance of axle box bearings,a dynamic model of the high-speed train including axle box bearings is developed.Subsequently,the dynamic response characteristics of the axle box bearing are examined.The investigation focuses on the acceleration characteristics of bearing vibration under excitation of track irregularities and wheel flats.In addition,experiments on both normal and faulty bearings are conducted separately,and the correctness of the model and some conclusions are verified.According to the research,track irregularity is unfavorable for bearing fault detection based on resonance demodulation.Under the same speed conditions,the acceleration peak of bearing is inversely proportional to the length of the wheel flat and directly proportional to its depth.The paper will contribute to a deeper understanding of the dynamic performance of axle box bearings.

Study on the pattern of train arrival headway time in high-speed railway

Purpose-The design goal for the tracking interval of high-speed railway trains in China is 3 min,but it is difficult to achieve,and it is widely believed that it is mainly limited by the tracking interval of train arrivals.If the train arrival tracking interval can be compressed,it will be beneficial for China's high-speed railway to achieve a 3-min train tracking interval.The goal of this article is to study how to compress the train arrival tracking interval.Design/methodologylapproach-By simulating the process of dense train groups arriving at the station and stopping,the headway between train arrivals at the station was calculated,and the pattern of train arrival headway was obtained,changing the traditional understanding that the train arrival headway is considered the main factor limiting the headway of trains.Findings-When the running speed of trains is high,the headway between trains is short,the length of the station approach throat area is considerable and frequent train arrivals at the station,the arrival headway for the first group or several groups of trains will exceed the headway,but the subsequent sets of trains will havea headway equal to the arrival headway.This convergence characteristic is obtained by appropriately increasing the running time.Originality/value-According to this pattern,there is no need to overly emphasize the impact of train arrival headway on the headway.This plays an important role in compressing train headway and improving high-speedrailwaycapacity.

Intelligent pavement condition survey:Overview of current researches and practices

Automated pavement condition survey is of critical importance to road network management.There are three primary tasks involved in pavement condition surveys,namely data collection,data processing and condition evaluation.Artificial intelligence(AI)has achieved many breakthroughs in almost every aspect of modern technology over the past decade,and undoubtedly offers a more robust approach to automated pavement condition survey.This article aims to provide a comprehensive review on data collection systems,data processing algorithms and condition evaluation methods proposed between 2010 and 2023 for intelligent pavement condition survey.In particular,the data collection system includes AI-driven hardware devices and automated pavement data collection vehicles.The AI-driven hardware devices including right-of-way(ROW)cameras,ground penetrating radar(GPR)devices,light detection and ranging(LiDAR)devices,and advanced laser imaging systems,etc.These different hardware components can be selectively mounted on a vehicle to simultaneously collect multimedia information about the pavement.In addition,this article pays close attention to the application of artificial intelligence methods in detecting pavement distresses,measuring pavement roughness,identifying pavement rutting,analyzing skid resistance and evaluating structural strength of pavements.Based upon the analysis of a variety of the state-of-the-art artificial intelligence methodologies,remaining challenges and future needs with respect to intelligent pavement condition survey are discussed eventually.

Bird’s-eye view of recycled solid wastes in road engineering

Recent trends in road engineering have explored the potential of incorporating recycled solid wastes into infrastructures that including pavements,bridges,tunnels,and accessory structures.The utilization of solid wastes is expected to offer sustainable solutions to waste recycling while enhancing the performance of roads.This review provides an extensive analysis of the recycling of three main types of solid wastes for road engineering purposes:industrial solid waste,infrastructure solid waste,and municipal life solid waste.Industrial solid wastes suitable for road engineering generally include coal gangue,fly ash,blast furnace slag,silica fume,and steel slag,etc.Infrastructure solid wastes recycled in road engineering primarily consist of construction&demolition waste,reclaimed asphalt pavements,and recycled cement concrete.Furthermore,recent exploration has extended to the utilization of municipal life solid wastes,such as incinerated bottom ash,glass waste,electronics waste,plastic waste,and rubber waste in road engineering applications.These recycled solid wastes are categorized into solid waste aggregates,solid waste cements,and solid waste fillers,each playing distinct roles in road infrastructure.Roles of solid waste acting aggregates,cements,and fillers in road infrastructures were fully investigated,including their pozzolanic properties,integration effects to virgin materials,modification or enhancement solutions,engineering performances.Utilization of these materials not only addresses the challenge of waste management but also offers environmental benefits aiming carbon neutral and contributes to sustainable infrastructure development.However,challenges such as variability in material properties,environmental impact mitigation,secondary pollution to environment by leaching,and concerns regarding long-term performance need to be further addressed.Despite these challenges,the recycled solid wastes hold immense potential in revolutionizing road construction practices and fostering environmental stewardship.This review delves into a bird’seye view of the utilization of recycled solid wastes in road engineering,highlighting advances,benefits,challenges,and future prospects.

Building trust for traffic flow forecasting components in intelligent transportation systems via interpretable ensemble learning

Traffic flow forecasting constitutes a crucial component of intelligent transportation systems(ITSs).Numerous studies have been conducted for traffic flow forecasting during the past decades.However,most existing studies have concentrated on developing advanced algorithms or models to attain state-of-the-art forecasting accuracy.For real-world ITS applications,the interpretability of the developed models is extremely important but has largely been ignored.This study presents an interpretable traffic flow forecasting framework based on popular tree-ensemble algorithms.The framework comprises multiple key components integrated into a highly flexible and customizable multi-stage pipeline,enabling the seamless incorporation of various algorithms and tools.To evaluate the effectiveness of the framework,the developed tree-ensemble models and another three typical categories of baseline models,including statistical time series,shallow learning,and deep learning,were compared on three datasets collected from different types of roads(i.e.,arterial,expressway,and freeway).Further,the study delves into an in-depth interpretability analysis of the most competitive tree-ensemble models using six categories of interpretable machine learning methods.Experimental results highlight the potential of the proposed framework.The tree-ensemble models developed within this framework achieve competitive accuracy while maintaining high inference efficiency similar to statistical time series and shallow learning models.Meanwhile,these tree-ensemble models offer interpretability from multiple perspectives via interpretable machine-learning techniques.The proposed framework is anticipated to provide reliable and trustworthy decision support across various ITS applications.

Experimental Study on Clogging Characteristics of Silt Soft Soil in Nansha District of Guangzhou

The silt soft soil in Nansha District of Guangzhou was the softest soft soil in China. It had the characteristics of high natural water content, high compressibility, long consolidation time, and complex layered distribution of soil layers. These characteristics formed the clogging characteristics of silt soft soil, which greatly increased the construction difficulty and hindered the construction progress. Therefore, based on the basic physical and mechanical properties of silt soft soil in Nansha District of Guangzhou, this paper evaluated the clogging characteristics of three silt soft soil areas in Nansha District of Guangzhou through long-term permeability test, and carried out scanning electron microscope test to explore the influence of different parameters and microstructure on the clogging difficulty of silt soft soil. The results showed that the silt soft soil Zone I and Zone II (shallow layer) in Nansha District of Guangzhou were divided into slight siltation levels, and the silt soft soil Zone III (deep layer) was mild siltation level. Large pores were widely distributed in shallow silt soft soil, while the continuity of large pores in deep silt soft soil was poor. The migration of fine particles that failed to establish contact with surrounding particles in the soil blocks the small pores of seepage and thus produces siltation.

Research on visual differences of exits of different grades of tunnels based on machine learning

Tunnels are vital in connecting crucial transportation hubs as transportation infrastructure evolves.Variations in tunnel design standards and driving conditions across different levels directly impact driver visual perception and traffic safety.This study employs a Gaussian hybrid clustering machine learning model to explore driver gaze patterns in highway tunnels and exits.By utilizing contour coefficients,the optimal number of classification clusters is determined.Analysis of driver visual behavior across tunnel levels,focusing on gaze point distribution,gaze duration,and sweep speed,was conducted.Findings indicate freeway tunnel exits exhibit three distinct fixation point categories aligning with Gaussian distribution,while highway tunnels display four such characteristics.Notably,in both tunnel types,65%of driver gaze is concentrated on the near area ahead of their lane.Differences emerge in highway tunnels due to oncoming traffic,leading to 13.47%more fixation points and 0.9%increased fixation time in the right lane compared to regular highway tunnel conditions.Moreover,scanning speeds predominantly fall within the 0.25-0.3 range,accounting for 75.47%and 31.14%of the total sweep speed.

Social Media Based Transportation Research: the State of the Work and the Networking

Recently, there has been an increased interest in the use of social media data as important traffic information sources. In this paper, we review social media based transportation research with social network analysis methods.We summarize main research topics in this field, and report collaboration patterns at levels of researchers, institutions, and countries, respectively. Finally, some future research directions are identified. © 2014 Chinese Association of Automation.

An experimental analysis on the turnover time of railway freight transportation based on the impact strength of factors

Based on the factors impact strength model(FISM), we studied on calculation formulas of influence strength and key elements of FISM, and analyzed the turnover time of railway freight transportation of China. The results show that wagon transfer time is the most critical factor among the three subjective factors of wagons turnover time. The FISM based analysis of wagon transfer time show that the wagon turnover time is significantly correlated with transit time with resorting. Among the seven factors of detention time of transit time with resorting, the time of waiting to departing, converging, and waiting to break-up are key factors, while the time of make-up, break-up, arrival and departure are general factors. We carried out one empirical research based on the data of Baoji East Railway Station in 2015. The results of empirical research and FISM are consistent completely.

Transportation Characteristics Change under Rapid Urban Expansion:A Case Study of Shanghai

This paper focuses on the economic development and urban morphology as well as its impact on the transportation system during the urban expansion of Shanghai in the last more than 20 years(1986-2008).Based on data from 3 comprehensive transport surveys of Shanghai since 1986,the changes in residential trip demand,vehicle use,and the spatial distribution of trips were studied to understand the demands on the transportation system.The factors contributing to those changes in transportation demand were discussed,by which the conclusions were arrived:1) economic development promotes population growth and stimulates residential trip demand greatly;2) unsynchronized migration of population and job from central district to periphery district of Shanghai make trips and congestion diffuse in the same way;and 3) urban sprawl from a city center encourages the single-occupant vehicle mode,which imposes greater pressure on the roadway system.It is concluded that urban development should coordinate with the transportation system planning and expansion.

Numerical simulation and analysis of aerodynamic drag on a subsonic train in evacuated tube transportation

The aerodynamic drag on a train running in an evacuated tube varies with tube air pressure, train speed and shape, as well as blockage ratio. This paper uses numerical simulations to study the effects of different factors on the aerodynamic drag of a train running at subsonic speed in an evacuated tube. Firstly, we present the assumption of a steady state, two dimensional, incompressible viscous flow with lubricity wall conditions. Subsequently, based on the Navier-Stokes equation and the k-c turbulent models, we calculate the aerodynamic drag imposed on the column train with a 3-meter diameter running under different pressure and blockage ratio conditions in an evacuated tube transporta- tion （ETT） system. The simulation is performed with FLUENT 6.3 software package. An analyses of the simulation re- sults suggest that the blockage ratio for ETT should be in the range of 0.25-0.7, and the tube internal diameter in the range of 2-4 m, with the feasible vacuum pressure in the range of 1-10 000 Pa for the future subsonic ETT trains.

ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model

The ChatGPT,a lite and conversational variant of Generative Pretrained Transformer 4(GPT-4)developed by OpenAI,is one of the milestone Large Language Models(LLMs)with billions of parameters.LLMs have stirred up much interest among researchers and practitioners in their impressive skills in natural language processing tasks,which profoundly impact various fields.This paper mainly discusses the future applications of LLMs in dentistry.We introduce two primary LLM deployment methods in dentistry,including automated dental diagnosis and cross-modal dental diagnosis,and examine their potential applications.Especially,equipped with a cross-modal encoder,a single LLM can manage multi-source data and conduct advanced natural language reasoning to perform complex clinical operations.We also present cases to demonstrate the potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical application.While LLMs offer significant potential benefits,the challenges,such as data privacy,data quality,and model bias,need further study.Overall,LLMs have the potential to revolutionize dental diagnosis and treatment,which indicates a promising avenue for clinical application and research in dentistry.

Analysis of a landfill cover without geomembrane using varied particle sizes of recycled concrete

Previous studies have demonstrated the effectiveness of a novel three-layer landfill cover system constructed with recycled concrete aggregates(RCAs)without geomembrane in both laboratory and field.However,no systematic investigation has been carried out to optimize the combination of the particle sizes for fine-grained RCAs(FRC)and coarse-grained RCAs(CRC)that can be used for the three-layer landfill cover system.The aim of this paper is to assist engineers in designing the three-layer landfill cover system under a rainfall of 100-year return period in humid climate conditions using an easily controlled soil parameter D10 of RCAs.The numerical study reveals that when D10 of FRC increases from 0.05 mm to 0.16 mm,its saturated permeability increases by 10 times.As a result,a larger amount of rainwater infiltrates into the cover system,causing a higher lateral diversion in both the top FRC and middle CRC layers.No further changes in the lateral diversion are observed when the D10 value of FRC is larger than 0.16 mm.Both the particle sizes of FRC and CRC layers are shown to have a minor influence on the percolation under the extreme rainfall event.This implies that the selection of particle sizes for the FRC and CRC layers can be based on the availability of materials.Although it is well known that the bottom layer of the cover system should be constructed with very fine-grained soils if possible,this study provides an upper limit to the particle size that can be used in the bottom layer(D10 not larger than 0.02 mm).With this limit,the three-layer system can still minimize the water percolation to meet the design criterion(30 mm/yr)even under a 100-year return period of rainfall in humid climates.