Theory and practice for assessing structural integrity and dynamical integrity of high-speed trains

Purpose–The safety and reliability of high-speed trains rely on the structural integrity of their components and the dynamic performance of the entire vehicle system.This paper aims to define and substantiate the assessment of the structural integrity and dynamical integrity of high-speed trains in both theory and practice.The key principles and approacheswill be proposed,and their applications to high-speed trains in Chinawill be presented.Design/methodology/approach–First,the structural integrity and dynamical integrity of high-speed trains are defined,and their relationship is introduced.Then,the principles for assessing the structural integrity of structural and dynamical components are presented and practical examples of gearboxes and dampers are provided.Finally,the principles and approaches for assessing the dynamical integrity of highspeed trains are presented and a novel operational assessment method is further presented.Findings–Vehicle system dynamics is the core of the proposed framework that provides the loads and vibrations on train components and the dynamic performance of the entire vehicle system.For assessing the structural integrity of structural components,an open-loop analysis considering both normal and abnormal vehicle conditions is needed.For assessing the structural integrity of dynamical components,a closed-loop analysis involving the influence of wear and degradation on vehicle system dynamics is needed.The analysis of vehicle system dynamics should follow the principles of complete objects,conditions and indices.Numerical,experimental and operational approaches should be combined to achieve effective assessments.Originality/value–The practical applications demonstrate that assessing the structural integrity and dynamical integrity of high-speed trains can support better control of critical defects,better lifespan management of train components and better maintenance decision-making for high-speed trains.

An Optimal Distribution of RSU for Improving Self-Driving Vehicle Connectivity

Self-driving and semi-self-driving cars play an important role in our daily lives.The effectiveness of these cars is based heavily on the use of their surrounding areas to collect sensitive and vital information.However,external infrastructures also play significant roles in the transmission and reception of control data,cooperative awareness messages,and caution notifications.In this case,roadside units are considered one of themost important communication peripherals.Random distribution of these infrastructures will overburden the spread of self-driving vehicles in terms of cost,bandwidth,connectivity,and radio coverage area.In this paper,a new distributed roadside unit is proposed to enhance the performance and connectivity of these cars.Therefore,this approach is based primarily on k-means to find the optimal location of each roadside unit.In addition,this approach supports dynamicmobility with a long period of connectivity for each car.Further,this system can adapt to various locations(e.g.,highways,rural areas,urban environments).The simulation results of the proposed system are reflected in its efficiency and effectively.Thus,the system can achieve a high connectivity rate with a low error rate while reducing costs.

Parametric analysis of wheel wear in high-speed vehicles

In order to reduce the wheel profile wear of highspeed trains and extend the service life of wheels, a dynamic model for a high-speed vehicle was set up, in which the wheelset was regarded as flexible body, and the actual measured track irregularities and line conditions were considered. The wear depth of the wheel profile was calculated by the well-known Archard wear law. Through this model, the influence of the wheel profile, primary suspension stiffness, track gage, and rail cant on the wear of wheel profile were studied through multiple iterafive calculations. Numerical simulation results show that the type XP55 wheel profile has the smallest cumulative wear depth, and the type LM wheel profile has the largest wear depth. To reduce the wear of the wheel profile, the equivalent conicity of the wheel should not be too large or too small. On the other hand, a small primary vertical stiffness, a track gage around 1,435-1,438 mm, and a rail cant around 1：35-1：40 are beneficial for dynamic performance improvement and wheel wear alleviation.

Using discriminant analysis to detect intrusions in external communication for self-driving vehicles

Security systems are a necessity for the deployment of smart vehicles in our society. Security in vehicular ad hoe networks is crucial to the reliable exchange of information and control data. In this paper, we propose an intelligent Intrusion Detection System （IDS） to protect the external communication of self-driving and semi self-driving vehicles. This technology has the ability to detect Denial of Service （DOS） and black hole attacks on vehicular ad hoe networks （VANETs）. The advantage of the proposed IDS over existing security systems is that it detects attacks before they causes significant damage. The intrusion prediction technique is based on Linear Discriminant Analysis （LDA） and Quadratic Diseriminant Analysis （QDA） which are used to predict attacks based on observed vehicle behavior. We perform simulations using Network Simulator 2 to demonstrate that the IDS achieves a low rate of false alarms and high accuracy in detection.

Should Self-Driving Vehicles Be Encouraged?

Late this March.China＇s Internet giant Baidu became the first self-driving car developer to obtain temporary license plates to carry out self driving tests on public roads in Beijing.

Aerodynamic/control integrated optimization method for unpowered high-speed vehicle configuration design

The unpowered high-speed vehicle experiences a significant coupling between the disciplines of aerodynamics and control due to its characteristics of high flight speed and extensive maneuverability within large airspace.The conventional aircraft conceptual design process follows a sequential design approach,and there is an artificial separation between the disciplines of aerodynamics and control,neglecting the coupling effects arising from their interaction.As a result,this design process often requires extensive iterations over long periods when applied to high-speed vehicles,and may not be able to effectively achieve the desired design objectives.To enhance the overall performance and design efficiency of high-speed vehicles,this study integrates the concept of Active Control Technology(ACT)from modern aircraft into the philosophy of aerodynamic/control integrated optimization.Two integrated optimization strategies,with differences in coupling granularity,have been developed.Subsequently,these strategies are put into action on a biconical vehicle that operates at Mach 5.The results reveal that the comprehensive performance of the synthesis optimal model derived from the aerodynamic/control integrated optimization strategy is improved by 31.76%and 28.29%respectively compared to the base model under high-speed conditions,demonstrating the feasibility and effectiveness of the method and optimization strategies employed.Moreover,in comparison to the single-stage strategy,the multi-stage strategy takes into deeper consideration the impact of control capacity.As a result,the control performance of the synthesis opti-mal model derived from the multi-stage strategy improves by 13.99%,whereas the single-stage strategy only achieves a 5.79%improvement.This method enables a fruitful interaction between aerodynamic configuration design and control system design,leading to enhanced overall performance and design efficiency.Furthermore,it improves the controllability of high-speed vehicles,mitigating the risk of mission failure resulting from an ineffective control system.

Hierarchical model updating for high-speed maglev vehicle/guideway coupled system based on multiobjective optimization

The high-speed maglev vehicle/guideway coupled model is an essential simulation tool for investigating vehicle dynamics and mitigating coupled vibration.To improve its accuracy efficiently,this study investigated a hierarchical model updating method integrated with field measurements.First,a high-speed maglev vehicle/guideway coupled model,taking into account the real effect of guideway material properties and elastic restraint of bearings,was developed by integrating the finite element method,multi-body dynamics,and electromagnetic levitation control.Subsequently,simultaneous in-site measurements of the vehicle/guideway were conducted on a high-speed maglev test line to analyze the system response and structural modal parameters.During the hierarchical updating,an Elman neural network with the optimal Latin hypercube sampling method was used to substitute the FE guideway model,thus improving the computational efficiency.The multi-objective particle swarm optimization algorithm with the gray relational projection method was applied to hierarchically update the parameters of the guideway layer and magnetic force layer based on the measured modal parameters and the electromagnet vibration,respectively.Finally,the updated coupled model was compared with the field measurements,and the results demonstrated the model’s accuracy in simulating the actual dynamic response,validating the effectiveness of the updating method.

Influence of Vehicle Parameters on Critical Hunting Speed Based on Ruzicka Model

While introducing foreign advanced technology and cooperating with Chinese famous research institutes,the high-speed vehicles are designed and take the major task of passenger transport in China.In high-speed vehicle,the characteristic of shock absorber is an important parameter which determines overall behavior of the vehicle.The most existing researches neglect the influence of the series stiffness of the shock absorber on the vehicle dynamic behavior and have one-sided views on the equivalent conicity of wheel tread.In this paper,a high speed passenger vehicle in China is modeled to investigate the effect of the parameters taking series hydraulic shock absorber stiffness into consideration on Ruzicka model.Using the vehicle dynamic model,the effect of main suspension parameters on critical speed is studied.In order to verify the reasonableness of shock absorber parameter settings,vibration isolation characteristics are calculated and the relationship between suspension parameters and the vehicle critical hunting speed is studied.To study the influence of equivalent conicity on vehicle dynamic behavior,a series of wheel treads with different conicities are set and the vehicle critical hunting speeds with different wheel treads are calculated.The discipline between the equivalent conicity of wheel tread and critical speed are obtained in vehicle nonlinear system.The research results show that the critical speed of vehicle much depends on wheelset positioning stiffness and anti-hunting motion damper,and the series stiffness produces notable effect on the vehicle dynamic behavior.The critical speed has a peak value with the equivalent conicity increasing,which is different from the traditional opinion in which the critical speed will decrease with the conicity increasing.The relationship between critical speed and conicity of wheel tread is effected by the suspension parameters of the vehicle.The study results obtained offer a method and useful data to designing the parameters of the high speed vehicle and simulation study.

Artificial Intelligence Applications in the Development of Autonomous Vehicles:A Survey

The advancement of artificial intelligence(AI)has truly stimulated the development and deployment of autonomous vehicles(AVs)in the transportation industry.Fueled by big data from various sensing devices and advanced computing resources,AI has become an essential component of AVs for perceiving the surrounding environment and making appropriate decision in motion.To achieve goal of full automation(i.e.,self-driving),it is important to know how AI works in AV systems.Existing research have made great efforts in investigating different aspects of applying AI in AV development.However,few studies have offered the research community a thorough examination of current practices in implementing AI in AVs.Thus,this paper aims to shorten the gap by providing a comprehensive survey of key studies in this research avenue.Specifically,it intends to analyze their use of AIs in supporting the primary applications in AVs:1)perception;2)localization and mapping;and 3)decision making.It investigates the current practices to understand how AI can be used and what are the challenges and issues associated with their implementation.Based on the exploration of current practices and technology advances,this paper further provides insights into potential opportunities regarding the use of AI in conjunction with other emerging technologies:1)high definition maps,big data,and high performance computing;2)augmented reality(AR)/virtual reality(VR)enhanced simulation platform;and 3)5G communication for connected AVs.This paper is expected to offer a quick reference for researchers interested in understanding the use of AI in AV research.

Progress in high-speed train technology around the world

This is a review of high-speed train development in the sense of technology advances all over the world. Three generations of high-speed trains are classified according to their technical characteristics and maximum operating speed. Emphasis is given to the newly developed high-speed train in China, CRH380. The theoretical foundations and future development of CRH380 are briefly discussed.

Damage Tolerance Assessment of a Brake Unit Bracket for High-Speed Railway Welded Bogie Frames

The brake unit bracket of a bogie frame is an important load-carrying component, particularly under emergency start/stop conditions. Conventional infinite/safe life approaches provide an over-conservative recommendation for the allowable strength and lifetime, which hinders the lightweight design of modern railway vehicles. In this study, to ensure the reliability and durability of a brake unit bracket, an attempt was made to integrate the nominal stress method and an advanced damage tolerance method. First, a complex bogie frame was modelled using solid elements instead of plate and beam elements. A hot spot stress region on the bracket was found under an eight-stage load spectrum obtained from the Wuhan–Guangzhou high-speed railway line. Based on the probability of foreign damage, a semi-elliptical surface crack was then assumed for residual life assessment. The results obtained by the cumulative damage and damage tolerance methods show that the brake unit bracket can operate for over 30 years. Moreover, even if a 2-mm depth crack exists, the brake unit bracket can be safely operated for more than 2.27 years, with the hope that the crack can be detected in subsequent maintenance procedures. Finally, an appropriate safety margin was suggested which provides a basis for the life prediction and durability assessment of brake unit brackets of high-speed railways.

Development of new electromagnetic suspension-based high-speed Maglev vehicles in China:Historical and recent progress in the field of dynamical simulation

High-speed Maglev is a cutting-edge technology brought back into the focus of research by plans of the Chinese government for the development of a new 600 km/h Maglev train.A Chinese‐German cooperation with industrial and academic partners has been established to pursue this ambitious goal and bring together experts from multiple disciplines.This contribution presents the joint work and achievements of CRRC Qingdao Sifang,thyssenkrupp Transrapid,CDFEB,and the ITM of the University of Stuttgart,regarding research and development in the field of high‐speed Maglev systems.Furthermore,an overview is given of the historical development of the Transrapid in Germany,the associated development of dynamical simulation models,and recent developments regarding high-speed Maglev trains in China.

Wear characteristics and prediction of wheel profiles in high-speed trains

Wheel/rail relationship is a fundamental problem of railway system. Wear of wheel profiles has great effect on vehicle performance. Thus, it is important not just for the analysis of wear characteristics but for its prediction. Actual wheel profiles of the high-speed trains on service were measured in the high-speed line and the wear characteristics were analyzed which came to the following results. The wear location was centralized from-15 mm to 25 mm. The maximum wear value appeared at the area of 5 mm from tread center far from wheel flange and it was less than 1.5 mm. Then, wheel wear was fitted to get the polynomial functions on different locations and operation mileages. A binary numerical prediction model was raised to predict wheel wear. The prediction model was proved by vehicle system dynamics and wheel/rail contact geometry. The results show that the prediction model can reflect wear characteristics of measured profiles and vehicle performances.

Research of inverse mathematical model to high-speed trains

Operation safety and stability of the train mainly depend on the interaction between the wheel and rail.Knowledge of wheel/rail contact force is important for vehicle control systems that aim to enhance vehicle stability and passenger safety.Since wheel/rail contact forces of high-speed train are very difficult to measure directly,a new estimation process for wheel/rail contact forces was introduced in this work.Based on the state space equation,dynamic programming methods and the Bellman principle of optimality,the main theoretical derivation of the inversion mathematical model was given.The new method overcomes the weakness of large fluctuations which exist in current inverse techniques.High-speed vehicle was chosen as the research object,accelerations of axle box as input conditions,10 degrees of freedom vertical vibration model and 17 degrees of freedom lateral vibration model were established,respectively.Under 250 km/h,the vertical and lateral wheel/rail forces were identified.From the time domain and frequency domain,the comparison of the results between inverse and SIMPACK models were given.The results show that the inverse mathematical model has high precision for inversing the wheel/rail contact forces of an operation high-speed vehicle.

Laws and Ethics Policy of Self-driving Cars in Taiwan

Countries have invested considerable sums of human capital and material resources in the practical application of self-driving cars demonstrating the impressive market opportunity.In light of this trend,Taiwan does not want to fall behind either.As on-road testing and technological development for self-driving cars continue to develop in different countries,the controversial issues of safety,ethics,liability,and the invasion of privacy continue to emerge.In order to resolve these issues,the government of Taiwan seeks to provide a good environment for AI(artificial intelligence)innovation and applications.This article summarizes and highlights relevant content and key points of Unmanned Vehicles Technology Innovative Experimentation Act,which was legislated in Taiwan in 2018.In addition,it points out the fundamental ethics regulation of AI,which has influenced Taiwan legal policy.

Small-scale self-driving cars:A systematic literature review

The autonomous vehicle(AV)technology has the potential to significantly improve safety and efficiency of the transportation and logistics industry.Full-scale AV testing is limited by time,space,and cost,while simulation-based testing often lacks the necessary accuracy of AV and environmental modeling.In recent years,several initiatives have emerged to test autonomous software and hardware on scaled vehicles.This systematic literature review provides an overview of the literature surrounding small-scale self-driving cars,summarizing the current autonomous platforms deployed and focusing on the software and hardware developments in this field.The studies published in English-language journals or conference papers that present small-scale testing of self-driving cars were included.Web of Science,Scopus,Springer Link,Wiley,ACM Digital Library,and TRID databases were used for the literature search.The systematic literature search found 38 eligible studies.Research gaps in the reviewed papers were identified to provide guidance for future research.Some key takeaway emerging from this manuscript are:(i)there is a need to improve the models and neural network architectures used in autonomous driving systems,as most papers present only preliminary results;(ii)increasing datasets and sharing databases can help in developing more reliable control policies and reducing bias and variance in the training process;(iii)small-scaled vehicles to ensure safety is a major benefit,and incorporating data about unsafe driving behaviors and infrastructure problems can improve the accuracy of predictive models.

Gas-surface interaction features under effects of gas-gas molecules interaction in high-speed flows

Understanding interactions between gas molecules and solid surface is key to the aerodynamic design of high-speed,high-altitude aerospace vehicles,but there is a large dispersion of gas-surface interaction parameters or namely accommodation coefficients.The uncertainty results partly from different considerations of the interaction between gas molecules in various experimental and numerical methods.In this study,effects of gas-gas molecules interaction are systematically discussed by comparing two different approaches of molecular dynamics simulation of high-speed argon molecules scattering on a graphite surface.The popularly-used“single scattering”approach repeats the scattering process of a single gas molecule without considering the gas-gas molecules interaction.The newly-developed“continual scattering”approach continually shoots gas molecules at the surface,considering collisions between gas molecules in addition to gas molecules’collisions with surface.Gas-surface interaction features in the two approaches are compared and discussed under various affecting factors including rarefaction degree,gas-surface interaction strength,surface temperature and incident velocity.It is shown that these two approaches usually produce different accommodation coefficients,and the corresponding mechanisms are explained.This study could help clarify some doubts about the selection of accommodation coefficients in engineering practice,and also provide an instruction on design of an appropriate molecular dynamics simulation approach.

Advancing Crowd Object Detection: A Review of YOLO, CNN and ViTs Hybrid Approach*

One of the most basic and difficult areas of computer vision and image understanding applications is still object detection. Deep neural network models and enhanced object representation have led to significant progress in object detection. This research investigates in greater detail how object detection has changed in the recent years in the deep learning age. We provide an overview of the literature on a range of cutting-edge object identification algorithms and the theoretical underpinnings of these techniques. Deep learning technologies are contributing to substantial innovations in the field of object detection. While Convolutional Neural Networks (CNN) have laid a solid foundation, new models such as You Only Look Once (YOLO) and Vision Transformers (ViTs) have expanded the possibilities even further by providing high accuracy and fast detection in a variety of settings. Even with these developments, integrating CNN, YOLO and ViTs, into a coherent framework still poses challenges with juggling computing demand, speed, and accuracy especially in dynamic contexts. Real-time processing in applications like surveillance and autonomous driving necessitates improvements that take use of each model type’s advantages. The goal of this work is to provide an object detection system that maximizes detection speed and accuracy while decreasing processing requirements by integrating YOLO, CNN, and ViTs. Improving real-time detection performance in changing weather and light exposure circumstances, as well as detecting small or partially obscured objects in crowded cities, are among the goals. We provide a hybrid architecture which leverages CNN for robust feature extraction, YOLO for rapid detection, and ViTs for remarkable global context capture via self-attention techniques. Using an innovative training regimen that prioritizes flexible learning rates and data augmentation procedures, the model is trained on an extensive dataset of urban settings. Compared to solo YOLO, CNN, or ViTs models, the suggested model exhibits an increase in detection accuracy. This improvement is especially noticeable in difficult situations such settings with high occlusion and low light. In addition, it attains a decrease in inference time in comparison to baseline models, allowing real-time object detection without performance loss. This work introduces a novel method of object identification that integrates CNN, YOLO and ViTs, in a synergistic way. The resultant framework extends the use of integrated deep learning models in practical applications while also setting a new standard for detection performance under a variety of conditions. Our research advances computer vision by providing a scalable and effective approach to object identification problems. Its possible uses include autonomous navigation, security, and other areas.

A self-driving solution for resource-constrained autonomous vehicles in parked areas

Autonomous vehicles in industrial parks can provide intelligent,efficient,and environmentally friendly transportation services,making them crucial tools for solving internal transportation issues.Considering the characteristics of industrial park scenarios and limited resources,designing and implementing autonomous driving solutions for autonomous vehicles in these areas has become a research hotspot.This paper proposes an efficient autonomous driving solution based on path planning,target recognition,and driving decision-making as its core components.Detailed designs for path planning,lane positioning,driving decision-making,and anti-collision algorithms are presented.Performance analysis and experimental validation of the proposed solution demonstrate its effectiveness in meeting the autonomous driving needs within resource-constrained environments in industrial parks.This solution provides important references for enhancing the performance of autonomous vehicles in these areas.

Shipborne Underwater Acoustic Communication System and Sea Trials with Submersible Shenhai Yongshi

The Shipborne acoustic communication system of the submersible Shenhai Yongshi works in vertical, horizontal and slant channels according to the relative positions. For ease of use, an array combined by a vertical-cone directional transducer and a horizontal-toroid one is installed on the mothership. Improved techniques are proposed to combat adverse channel conditions, such as frequency selectivity, non-stationary ship noise, and Doppler effects of the platform’s nonlinear movement. For coherent modulation, a turbo-coded single-carrier scheme is used. In the receiver, the sparse decision-directed Normalized Least-Mean-Square soft equalizer automatically adjusts the tap pattern and weights according to the multipath structure, the two receivers’ asymmetry, the signal’s frequency selectivity and the noise’s spectrum fluctuation. The use of turbo code in turbo equalization significantly suppresses the error floor and decreases the equalizer’s iteration times, which is verified by both the extrinsic information transfer charts and bit-error-rate performance. For noncoherent modulation, a concatenated error correction scheme of nonbinary convolutional code and Hadamard code is adopted to utilize full frequency diversity. Robust and lowcomplexity synchronization techniques in the time and Doppler domains are proposed. Sea trials with the submersible to a maximum depth of over 4500 m show that the shipborne communication system performs robustly during the adverse conditions. From the ten-thousand communication records in the 28 dives in 2017, the failure rate of the coherent frames and that of the noncoherent packets are both below 10%, where both synchronization errors and decoding errors are taken into account.