Browse Topic: Education and training

Items (6,265)
Nowadays, cognitive distraction in the process of driving has become a frequent phenomenon, which has led to a certain proportion of traffic accidents, causing a lot of property losses and casualties. Since the fact that cognitive distraction is mostly reflected in the driver's reception and thinking of information unrelated to driving, it is difficult to recognize it from the driver's facial features. As a result, the accuracy of prediction is usually lower relying solely on facial performance to detect cognitive distraction. In this research, fifty participants took part in our simulated driving experiment. And each participant conducted the experiment in four different traffic scenarios using a high-fidelity driving simulator, including three cognitive distraction scenarios and one normal driving scenarios. Firstly, we identified the facial performance indicators and vehicle performance indicators that had a significant effect on cognitive distraction through one-way ANOVA. Then we
Qu, ChixiongBao, QiongQu, QikaiShen, Yongjun
Technology for lane line semantic segmentation is crucial for ensuring the safe operation of intelligent cars. Intelligent cars can now comprehend the distribution and meaning of scenes in an image more precisely thanks to semantic segmentation, which calls for a certain degree of accuracy and real-time network performance. A lightweight module is selected, and two previous models are improved and fused to create the lane line detection model. Finally, experiments are conducted to confirm the model's efficacy. This paper proposes a lightweight replacement program with the aim of addressing the issue of large parameterization in the generative adversarial network (GAN) model and difficult training convergence. The overall network structure is selected from the Pix2Pix network in the conditional generative adversarial network, and the U-net network of the generator is cut and replaced by the Ghost Module, which consists of a modified downsampling module that enhances the global fusion
Yang, KunWang, Jian
The performance differences of multiple sensors lead to inconsistencies, incompleteness, and distortion in the perception data of multi-source vehicle information in highway scenarios. Optimizing data fusion methods is important for intelligent toll collection systems on highways. First, this paper constructs a dataset for matching and fusing multi-source vehicle information in highway gantry scenarios. Second, it develops convolutional neural network models, Match-Pyramid-MVIMF-EGS and CDSSM-MVIMF-EGS, for this purpose. Finally, comparative experiments are conducted based on the constructed dataset to assess the performance of the Match-Pyramid-MVIMF-EGS and CDSSM-MVIMF-EGS models. The experimental results indicate that the Match-Pyramid-MVIMF-EGS model performs better than the CDSSM-MVIMF-EGS model, achieving matching and fusion accuracy of 93.07%, precision of 95.71%, recall of 89.17%, F1 scores of 92.32%, and 186 of training throughput respectively.
Wang, JunjunZhao, Chihang
Tunnel linings are an important safeguard for the integrity and stability of tunnels. However, cracks in the tunnel lining may have extremely unfavourable consequences. With the acceleration of urbanisation and the increasing construction of tunnels, the problem of cracks in the concrete lining is becoming more and more prominent. These cracks not only seriously affect the stability of the structure, but also pose a serious threat to the safety of tunnel operation. If left unchecked, the cracks may expand further and cause various safety hazards, such as water leakage and falling blocks. This in turn will undermine the normal function of the tunnel and endanger the lives of tunnel users. It has been proved that the traditional manual method of detecting cracks in tunnels has problems such as low accuracy and low efficiency. In order to solve this problem, it is very necessary for this study to pioneer an intelligent method for identifying tunnel lining cracks using the YOLOv11
Zhang, YalinNiu, PeiGuo, FengYan, WeiLiu, JianKou, Lei
Developing models for predicting the low-temperature cracking resistance of asphalt mixtures is a complex process with a wide variety and complex influence mechanisms of variables, leading to higher uncertainty in the prediction results. Several models have been developed in this regard. This study developed a Bayesian neural network (BNN) model for predicting the fracture energy of low-temperature semi-circular bending (SCB) tests based on pavement condition measurements, traffic, climate, and basic parameters of the material. The model was trained and evaluated using low-temperature SCB test data from in-situ pavement core samples, and the results showed that the coefficient of determination (R2) of the BNN model was greater than 0.8 for both the training and testing sets. The variable importance scores showed that the decrease of transverse crack rating index (TCEI) and gradation were the most important factor affecting low-temperature fracture energy and that the ambient
Song, ZiyuNi, FujianHuang, JiaqiJiang, Jiwang
The introduction of autonomous vehicles (AVs) promises significant improvements to road safety and traffic congestion. However, mixed-autonomy traffic remains a major challenge as AVs are ill-suited to cooperate with human drivers in complex scenarios like intersection navigation. Specifically, human drivers use social cooperation and cues to navigate intersections while AVs rely on conservative driving behaviors that can lead to rear-end collisions, frustration from other road users, and inefficient travel. Using a virtual driving simulator, this study investigates the use of a human factors-informed cooperation model to reduce AV reliance on conservative driving behaviors. Four intersection scenarios, each involving a left-turning AV and a human driver proceeding straight, were designed to obfuscate the right-of-way. The classification models were trained to predict the future priority-taking behavior of the human driver. Results indicate that AVs employing the human factors-informed
Ziraldo, ErikaOliver, Michele
Noise, Vibration, and Harshness (NVH) simulations of vehicle bodies are crucial for assessing performance during the design phase. However, these simulations typically require detailed computer-aided design (CAD) models and are time-consuming. In the early stages of vehicle development, when only high-level vehicle sections are available, designing the body-in-white (BIW) structure to meet target values for bending and torsional stiffness is challenging and often requires multiple iterations. To address these challenges, this study deploys a reduced-order beam modelling approach. This method involves identifying the beam-like sections and major joints within the BIW and calculating their sectional properties (area, area moments of inertia along the plane’s independent axes, and torsion constant). These components form a simplified skeleton model of the BIW. Load and boundary conditions are applied to the suspension mount locations at the front and rear of the vehicle, and torsional and
Khan, Mohd Zishan AliThanapati, AlokDeshmukh, Chandrakant
As a clean energy, low carbon and pollution-free, hydrogen is the preferred alternative fuel for traditional internal combustion engines. However, how to use hydrogen internal combustion engine to achieve satisfactory performance under vehicle conditions is still a challenge.In this paper, a vehicle simulation model is established based on a modified 25-ton hydrogen internal combustion engine truck, and the model is designed as a hybrid model by selecting a suitable motor. The two models are used to simulate the CHTC (China Heavy-duty Commercial Vehicle Test Cycle) cycle conditions. According to the simulation results, compared with the original vehicle's power performance and economy, the results show that the power performance is increased by 100%, and the economy is increased by 20%. Hybrid technology can effectively improve the performance of the vehicle.
Bai, Xueyan
Monitoring the rotor temperature of drive machines is crucial for the safety and performance of electric vehicles. However, due to the complex operating conditions of electric vehicles, the thermal parameters of vehicular induction machines (IMs) vary significantly and are difficult to identify accurately. This article first establishes a concise but effective thermal network for IMs and analyzes the influencing factors of thermal parameters. Then, a parameter identification network (PIN) with multiple parallel branches is constructed to learn the mapping relationship between electromechanical variables and thermal parameters. Afterward, temperature datasets for network training are built through bench testing. Finally, the effectiveness of identified parameters for rotor temperature estimation application is verified, demonstrating improved interpretability, generalization ability, and accuracy compared to an end-to-end neural network.
Jiang, ShangHu, Zhishuo
At present, due to the complexity and nonlinearity, the thermal safety and economic feasibility assessment and optimization of the Solid Oxide Fuel Cell-Gas Turbine (SOFC-GT) system under variable loads is important to extend the service life and reduce the cost. To solve these problems, this paper proposes a top-level cyclic SOFC-GT system, which considers the design of two-stage preheaters, as well as the impact of material reaction kinetics and thermoelectric coupling characteristics on system performance. Furthermore, the multi-criteria evaluation of the SOFC-GT system under variable loads has been studied, with evaluation indicators primarily including thermodynamic and economic indicators. Afterwards, a Spearman-based parametric sensitivity analysis is used to explore the response trends of performance indicators within the SOFC-GT system. Additionally, an intelligent learning method based on convolutional neural network is designed to determine the dynamic behavior between
Fan, LiyunKui, XuChen, ChenShen, ChongchongLi, BoWei, Yunpeng
The degradation of vehicle performance resulting from powertrain degradation throughout the lifecycle of alternative energy vehicles (AEVs) has consistently been a focal issue among scholars and consumers. The purpose of this paper is to utilize a one-dimensional vehicle simulation model to analyze the changes in power performance and economy of fuel cell vehicles as the Proton Exchange Membrane Fuel Cell (PEMFC) stack degrades. In this study, a simulation model was developed based on the design parameters and vehicle architecture of a 45kW fuel cell vehicle. The 1D model was validated for accuracy using experimental data. The results indicate that as the stack performance degrades, the attenuation rate of the fuel cell engine is further amplified, with a degradation of up to 13.6% in the system's peak output power at the End of Life (EOL) state after 5000 hours. Furthermore, the level of economic performance degradation of the complete vehicle in the EOL state is dependent on the
Li, YouDu, JingGuo, DonglaiWang, KaiWang, Yupeng
This research, path planning optimization of the deep Q-network (DQN) algorithm is enhanced through integration with the enhanced deep Q-network (EDQN) for mobile robot (MR) navigation in specific scenarios. This approach involves multiple objectives, such as minimizing path distance, energy consumption, and obstacle avoidance. The proposed algorithm has been adapted to operate MRs in both 10 × 10 and 15 × 15 grid-mapped environments, accommodating both static and dynamic settings. The main objective of the algorithm is to determine the most efficient, optimized path to the target destination. A learning-based MR was utilized to experimentally validate the EDQN methodology, confirming its effectiveness. For robot trajectory tasks, this research demonstrates that the EDQN approach enables collision avoidance, optimizes path efficiency, and achieves practical applicability. Training episodes were implemented over 3000 iterations. In comparison to traditional algorithms such as A*, GA
Arumugam, VengatesanAlagumalai, VasudevanRajendran, Sundarakannan
Aerospace engineering programmes typically cover airworthiness philosophies, principles, structures, processes, and procedures. The industry has recently recognized the need to enhance the graduate engineers’ skills around airworthiness. This has led to introduction of standards acting as guides for developing curricula and content for university airworthiness courses. Concept maps, a visual mapping of concepts in a hierarchical way, enjoy wide use in engineering education (teaching and assessment). Airworthiness courses are both technical and legalistic, presenting challenges to students when it comes to understanding complex and intertwined regulations. Schematic representations of concepts can foster the cognitive processes of learning. Concept maps can assess efficiently and comprehensively a multitude of airworthiness topics. This study examines the feasibility of applying concept maps in airworthiness education. Fill-in-a-map concept maps were developed as assessment tools for an
Kourousis, KyriakosChatzi, Anna
The SAE Formula prototypes are developed by students, where in the competition, various aspects of project definitions are evaluated. Among the factors evaluated for scoring is the braking system, in which the present work aims to present the development and design of the braking system of a vehicle, prototype of Formula SAE student competition. As it is a project manufactured mostly by students, where the chassis, suspension system, electrical, transmission and powertrain are developed, it is important to first pass the static and safety tests, where the brakes of the four wheels are tested during deceleration at a certain distance from the track. To enable such approval and also to demonstrate, for the competition judges, the veracity of the system’s sizing, all the parameters and assumptions of the choice of the vehicle’s braking system are presented, thus ensuring their reliability, efficiency and safety. Using drawing and simulation software such as SolidWorks and Excel for
Gomes, Lucas OlenskiGrandinetti, Francisco JoséMartins, Marcelo SampaioSouza Soares, Alvaro ManoelReis de Faria Neto, AntônioCastro, Thais SantosAlmeida, Luís Fernando
This paper proposes a theoretical drive cycle for the competition, considering the battery pack project under design. The vehicle has a non-reversible, double-stage gear train, created without a dynamic investigation. To evaluate the effect on performance, several ratios were analyzed. Dynamic model uses Eksergian’s Equation of Motion to evaluate car equivalent mass (generalized inertia), and external forces acting on the vehicle. The circuit is divided into key locations where the driver is likely to accelerate or brake, based on a predicted behavior. MATLAB ODE Solver executed the numerical integration, evaluating time forward coordinates, creating the drive cycle. Linear gear train results provided data as boundary conditions for a second round of simulations performed with epicyclic gear trains. Model is updated to include their nonlinearity by differential algebraic equation employment with Lagrange multipliers. All data undergoes evaluation to ascertain the mechanical and
Rodrigues, Patrícia Mainardi TortorelliSilveira, Henrique Leandro
The SAE Formula, a national stage of the international competition, consists of a student project at universities in Brazil that seeks to encourage engineering students to apply the theoretical knowledge obtained in the classroom to practice, dealing with real problems and difficulties in order to prepare them for the job market. The SAE Formula prototype is developed with the intention of competing in the SAE national competition, where teams from various universities in Brazil meet to compete and demonstrate the projects developed during the year. Focusing on the vehicle dynamics subsystem, which can be divided into the braking, suspension, and steering systems of a prototype, the steering system includes main mechanical components such as the front axle sleeves, wheel hub, steering arm, steering column, rack, wheel, and tire. All these components work together with the suspension systems, including suspension arms, “bell crank,” and spring/shock absorber assembly. These components
Rigo, Cristiano Shuji ShimadaNeto, Antonio Dos Reis De FariaGrandinetti, Francisco JoseCastro, Thais SantosDias, Erica XimenesMartins, Marcelo Sampaio
Autonomous driving technology plays a crucial role in enhancing driving safety and efficiency, with the decision-making module being at its core. To achieve more human-like decision-making and accommodate drivers with diverse styles, we propose a method based on deep reinforcement learning. A driving simulator is utilized to collect driver data, which is then classified into three driving styles—aggressive, moderate, and conservative—using the K-means algorithm. A driving style recognition model is developed using the labeled data. We then design distinct reward functions for the Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC) algorithms based on the driving data of the three styles. Through comparative analysis, the SAC algorithm is selected for its superior performance in balancing comfort and driving efficiency. The decision-making models for different styles are trained and evaluated in the SUMO simulation environment. The results indicate that
Shen, ChuanliangZhang, LongxuShi, BowenMa, XiaoyuanLi, YiHu, Hongyu
The planning of mountain campus bus routes needs to take into account user demand, convenience, and other factors. This study adopts a comprehensive research method that combines quantitative and qualitative viewpoints. From the perspective of university students, this article studies the demand of campus public transportation and proposes the layout of campus bus routes in mountainous universities to meet the needs of users. The psychological needs questionnaire was used to investigate college students’ expectation of bus station service function. Taking three mountain universities as examples, the integration and selectivity of campus road networks are evaluated by using space syntax analysis, which provides valuable insights into the quality of bus stop areas. This article discusses the correlation between psychological needs assessment of college students and objective conditions of campus road network. The study concludes with the following findings: (1) The pedestrian environment
Duan, RanTang, RuiWang, ZhigangZhao, YixueWang, QidaYang, JiyiSu, Jiafu
Recent advancements in electric vertical take-off and landing (eVTOL) aircraft and the broader advanced air mobility (AAM) movement have generated significant interest within and beyond the traditional aviation industry. Many new applications have been identified and are under development, with considerable potential for market growth and exciting potential. However, talent resources are the most critical parameters to make or break the AAM vision, and significantly more talent is needed than the traditional aviation industry is able to currently generate. One possible solution—leverage rapid advancements of artificial intelligence (AI) technology and the gaming industry to help attract, identify, educate, and encourage current and future generations to engage in various aspects of the AAM industry. Beyond Aviation: Embedded Gaming, Artificial Intelligence, Training, and Recruitment for the Advanced Air Mobility Industry discusses how the modern gaming population of 3.3 million
Doo, Johnny
To establish and validate new systems incorporated into next generation vehicles, it is important to understand actual scenarios which the autonomous vehicles will likely encounter. Consequently, to do this, it is important to run Field Operational Tests (FOT). FOT is undertaken with many vehicles and large acquisition areas ensuing the capability and suitability of a continuous function, thus guaranteeing the randomization of test conditions. FOT and Use case(a software testing technique designed to ensure that the system under test meets and exceeds the stakeholders' expectations) scenario recordings capture is very expensive, due to the amount of necessary material (vehicles, measurement equipment/objectives, headcount, data storage capacity/complexity, trained drivers/professionals) and all-time robust working vehicle setup is not always available, moreover mileage is directly proportional to time, along with that it cannot be scaled up due to physical limitations. During the early
Sehgal, VishalSekaran, Nikhil
In India, Driver Drowsiness and Attention Warning (DDAW) system-based technologies are rising due to anticipation on mandatory regulation for DDAW. However, readiness of the system to introduce to Indian market requires validations to meet standard (Automotive Industry Standard 184) for the system are complex and sometimes subjective in nature. Furthermore, the evaluation procedure to map the system accuracy with the Karolinska sleepiness scale (KSS) requirement involves manual interpretation which can lead to false reading. In certain scenarios, KSS validation may entail to fatal risks also. Currently, there is no effective mechanism so far available to compare the performance of different DDAW systems which are coming up in Indian market. This lack of comparative investigation channel can be a concerning factor for the automotive manufactures as well as for the end-customers. In this paper, a robust validation setup using motion drive simulator with 3 degree of freedom (DOF) is
Raj, Prem raj AnandSelvam, Dinesh KumarThanikachalam, GaneshSivakumar, Vishnu
A new aviation supply chain integrity coalition has offered 13 recommended actions to prevent the circulation of non-serialized aircraft parts throughout the global aviation industry. Embry-Riddle Aeronautical University, Daytona Beach, FL In the summer of 2023, a receiving clerk in the procurement department of TAP Air Portugal, a Lisbon-based airline, made a curious discovery: A $65 engine part that should have appeared brand-new showed signs of significant wear. The clerk checked the documentation from the London-based parts supplier and noticed that the submitted documentation was also suspicious. Using his safety training, the employee immediately reported the anomaly to TAP Air Portugal management, which raised the issue with the jet engine's manufacturer. Little did the procurement clerk know at the time, but this escalation led to one of the biggest investigations in the history of the aviation supply chain, as reported by Reuters and the British Broadcasting Corporation in
Sometimes, I cringe; sometimes, I just listen and wonder. These past few months have given us all a lot to think about in the automotive space, and it's clear now that the coming years will keep the foot down on the accelerator when it comes to the dramatic changes we've experienced this past decade. One thing that stood out to me in various recent conversations is that there's a widening gulf opening between Chinese automakers and the rest of the world. This isn't exactly news, and this column isn't meant to monger any fears. It's just a bit of off-the-cuff reporting that sheds a bit of light on the level of the challenges we face. As you can read in Chris Clonts' excellent report further in this issue about the warning that Voltaiq's CEO gave at The Battery Show this October, the U.S. is in serious danger of falling well behind Chinese competitors in the EV battery race (Michael Robinette tackles similar ground through a tariff lens in this month's Supplier Eye). But that message was
Blanco, Sebastian
Increased use of advanced composite structural materials on aircraft has resulted in the need to address the more demanding quality and nondestructive testing procedures. Accordingly, increased utilization of solid laminate composites is driving changes to airline NDI/NDT training requirements and greater emphasis on the application of accurate NDI/NDT methods for composite structures. Teaching modules, including an introduction to composite materials, composite NDI/NDT theory and practice, special cases and lessons learned, are included in this document as well as various hands-on NDI/NDT exercises. A set of proficiency specimens containing realistic composite structures and representative damage are available to reinforce teaching points and evaluate inspector’s proficiency. Extensive details of the guidance modules, hands-on exercises, and proficiency specimens are all presented in this document. This document does not replace OEM guidance as may be specific to material, process
AMS CACRC Commercial Aircraft Composite Repair Committee
The automotive industry faces significant obstacles in its efforts to improve fuel economy and reduce carbon dioxide emissions. Current conventional automotive powertrain systems are approaching their technical limits and will not be able to meet future carbon dioxide emission targets as defined by the tank-to-wheel benchmark test. As automakers transition to low-carbon transportation solutions through electrification, there are significant challenges in managing energy and improving overall vehicle efficiency, particularly in real-world driving scenarios. While electrification offers a promising path to low-carbon transportation, it also presents significant challenges in terms of energy management and vehicle efficiency, particularly in real-world scenarios. Battery electric vehicles have a favorable tank-to-wheel balance but are constrained by limited range due to the low battery energy density inherent in their technology. This limitation has led to the development of hybrid
Kraljevic, IvicaSpicher, Ulrich
The automobile industry strives to develop high-quality vehicles quickly that fulfill the buyer’s needs and stand out within the competition. Full utilization of simulation and Computer-Aided Engineering (CAE) tools can empower quick assessment of different vehicle concepts and setups without building physical models. This research focuses on optimizing vehicle ride and handling performance by utilizing a tuning specifications range. Traditional approaches to refining these aspects involve extensive physical testing, which consumes both time and resources. In contrast, our study introduces a novel methodology leveraging virtual Subjective Rating through driving simulators. This approach aims to significantly reduce tuning time and costs, consequently streamlining overall development expenditures. The core objective is to enhance vehicle ride and handling dynamics, ensuring a superior driving experience for end-users. By meticulously defining and implementing tuning specifications, we
Ganesh, Lingadalu
If you're just getting comfortable with Industry 4.0, which saw the beginnings of smart manufacturing, digitization and real-time decision-making in factories, a senior leader at Intel says the world is already moving on to Industry 5.0. What's Industry 5.0? A joint study by many researchers (link: Industry 5.0: A Survey on Enabling Technologies and Potential Applications (oulu.fi)) describes 5.0 as merging human creativity with intelligent and efficient machines to deliver customized products quickly. But it will take a lot of change and learning to get there.
Clonts, Chris
More than 80 percent of stroke survivors experience walking difficulty, significantly impacting their daily lives, independence, and overall quality of life. Now, new research from the University of Massachusetts Amherst pushes forward the bounds of stroke recovery with a unique robotic hip exoskeleton, designed as a training tool to improve walking function. This invites the possibility of new therapies that are more accessible and easier to translate from practice to daily life compared to current rehabilitation methods.
Electric Vehicles and Battery-Fuel_Cell hybrid vehicles are increasingly becoming popular in the market, especially in the commercial vehicle segment. Range estimation and control is of paramount importance as it is the main cause of anxiety among the vehicle owners. This paper discusses application of Reinforcement Learning (RL) to achieve range control. In RL, the learning agent choses actions dependent on the state of the environment and gets a reward in return. Ultimately the agent will learn the policy of choosing the actions for each state such that his long-term reward is maximized. The technique of RL has been applied for various scenarios where in a look up table (between the states of a system and actions to be taken) needs to be developed for optimal performance. In this paper, we use RL to manipulate other energy sources and sinks like Fuel Cell and HVAC (in addition to the battery which is the main energy source) for range control, and thereby achieve the optimal
Changavar, Ganesh
Recently, the increasing complexity of systems and diverse customer demands have necessitated the development of highly efficient vehicles. The ability to accurately predict vehicle performance through simulation allows for the determination of design specifications before the construction of test vehicles, leading to reduced development schedules and costs. Therefore, detailed brake thermal performance predictions are required both for the front and rear brakes. Moreover, scenarios requiring validation, such as alpine conditions that apply braking severity to xEV with the regenerative braking system, have become increasingly diverse. To address this challenge, this study proposes a co-simulation method that incorporates a machine-learned brake pad friction coefficient prediction model to enhance the accuracy of brake thermal capacity predictions within the vehicle simulation environment. This innovative method allows for the simultaneous prediction of both front and rear-wheel brakes
Cho, SunghyunBaek, SangHeumKim, Min SooHong, IncheolKim, Hyun KiKim, GwichulLee, Jounghee
Air Force Test Pilot School Edwards Air Force Base, CA 661-277-1110
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