Browse Topic: Education and training

Items (6,318)
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Xie, DongxuanLi, DongyangZhang, YoukangZhao, YingjieHong, BaofengWang, Nan
With more 5G base stations coming into play, making an accurate assessment of RF-EMF exposure currently faces increasing demand to check if they meet regulatory requirements and ensure people’s safety. We present here PSF-Net, a novel deep learning network by uniting TabPFN’s meta-learned prior knowledge and SAINT’s dual attention structure; its use makes it particularly suitable to deal with applications like prediction of downlink power density and radiation level classification under different conditions within various kinds of 5G cell. A major component in the design of this approach is an uncertainty-aware gating block that determines the optimal weighting for each model output—TabPFN or SAINT—based on the estimated prediction variance as quantified via Monte Carlo sampling during training or the prediction variance calculated using inference-time dropout. In addition, a residual multi-layer perceptron (MLP) is also included to extract refined fused features and maintain a steady
Zhang, YanjinYu, Zefeng
With the rapid development of Internet of Vehicles (IoV) and cyber-physical systems (CPS), connected autonomous vehicles (CAVs) have also developed rapidly. However, at the same time, in-vehicle networks also face more security challenges, mainly in terms of resource constraints, dynamic attacks, protocol heterogeneity, and high real-time requirements. Firstly, the trade-offs between lightweight encryption primitives and their software and hardware collaborative design in terms of performance, resource overhead, and security strength are analyzed. Secondly, the resource efficiency of AI-based intrusion detection system (IDS) is evaluated at the edge. Finally, we propose a dynamic adaptive collaborative defense framework (DACDF), which integrates federated learning with dynamic weight distillation, blockchain authentication with lightweight verifiable delay function (Light-VDF) and cross-domain IDS with hierarchical attention feature fusion to deal with collaborative attacks in resource
Zhou, YouZhang, JiguiDing, KaniYang, Guozhi
In contemporary society, where Global Navigation Satellite Systems (GNSS) are utilised extensively, their inherent fragility gives rise to potential hazards with respect to the safety of ship navigation. In order to address this issue, the present study focuses on an ASM signal delay measurement system based on software defined radio peripherals. The system comprises two distinct components: a transmitting end and a receiving end. At the transmitting end, a signal generator, a first time-frequency synchronisation device, and a VHF transmitting antenna are employed to transmit ASM signals comprising dual Barker 13 code training sequences. At the receiving end, signals are received via software-defined radio equipment, a second time-frequency synchronisation device, a computing host, and a VHF receiving antenna. Utilising sliding correlation algorithms enables accurate time delay estimation. The present study leverages the high performance and low cost advantages of the universal
Li, HaoSun, XiaowenWang, TianqiZhou, ZeliangWang, Xiaoye
Recent advancements in energy efficient wireless communication protocols and low powered digital sensor technologies have led to the development of wireless sensor network (WSN) applications in diverse industries. These WSNs are generally designed using Bluetooth Low Energy (BLE), ZigBee and Wi-Fi communication protocol depending on the range and reliability requirements of the application. Designing these WSN applications also depends on the following factors. First, the environment under which devices operate varies with the industries and products they are employed in. Second, the energy availability for these devices is limited so higher signal strength for transmission and retransmission reduces the lifetime of these nodes significantly and finally, the size of networks is increasing hence scheduling and routing of messages becomes critical as well. These factors make simulation for these applications essential for evaluating the performance of WSNs before physical deployment of
Periwal, GarvitKoparde, PrashantSewalkar, Swarupanand
This paper offers recent ideas and its implementation on leveraging AI for off highway Autonomous vehicle Simulations in SIL and HIL frameworks. Our objective is to enhance software quality and reliability while reducing costs and efforts through advanced simulation techniques. We employed multiple innovative solutions to build a System of Systems Simulation. Physics based models are a prerequisite for detailed and accurate representation of the real-world system, but it poses challenges due to its computational complexity and storage requirements. Machine learning algorithms were used to create surrogate/reduced order models to optimize by preserving the expected fidelity of models. It helped to speed up simulation and compile model code for SIL & HIL Targets. Built AI driven interfaces to bridge windows, Linux and Mobile Operating systems. Time synchronization was the key challenge as multiple environments were needed for end-to-end solutions. This was resolved by reinforcement
Karegaonkar, Rohit P.Aole, SumitDasnurkar, SwapnilSingh, VishwajeetSaha, Soumyadeep
This paper presents a novel approach to automated robot programming and robot integration in manufacturing domain and minimizing the dependency on manual online/offline programming. Traditional industrial robots programming is typically done by online programing via teach pendants or by offline programming tools. This presents a major challenge as it requires skilled professionals and is a time-consuming process. In today’s competitive market, factories need to harness their full potential through smart and adaptive thinking to keep pace with evolving technology, customer demand, and manufacturing processes. This requires ability to manufacture multiple products on the same production line, minimum time for changeovers and implement robotic automation for efficiency enhancement. But each custom automation piece also demands significant human efforts for development and maintenance. By integrating the Robot Operating System (ROS) with vision-based 3D model generation systems, we address
Hepat, Abhijeet
Single motorcycle accidents are common in Nagano Prefecture where is mountainous areas in Japan. In a previous study, analysis of traffic accident statistics data suggested that the fatality and serious injury rates for uphill right curves and downhill left curves are high, however the true causes of these accidents remain unclear. In this study, a motorcycle simulator was used to evaluate the driving characteristics due to these road alignments. Evaluation courses based on combinations of uphill/downhill slopes and left/right curves were created, and experiments were conducted. The subjects of the study were expert riders and novice riders. The results showed that right curves are even more difficult to see near the entrance of the curve when accompanied by an uphill slope, making it easier to delay recognition and judgment of the curve. Expert riders recognized curves faster than novice riders. Additionally, expert riders take a large lean of the vehicle body, actively attempted to
Kuniyuki, HiroshiKatayama, YutaKitagawa, TaiseiNumao, Yusuke
In Automobile AC system, HVAC is one of major component as it controls the air flow and air distribution based on cabin requirement. HVAC kinematics mechanism is used for controlling the air flow based on passenger requirement inside the cabin. The air flow movement inside HVAC has a severe impact on servo motor/cable torque which is controlling the mechanism. Simulation driven design method is widely used in world due to highly competitive automotive industry. Launching the product at the market within short span of time, with good quality and less cost is more challenging. Hence CAE/MBD based approach is more significant as it will reduce number of prototypes as well as the cost of testing. The objective of the analysis is to predict the HVAC servomotor torque required to operate the HAVC linkages under operating conditions. The air pressure load will have significant impact on damper face which will cause torque at CAM as well as servo lever center. The torque values at servo lever
Parayil, Paulson
Mobile air conditioning (MAC) systems play a critical role in ensuring occupant thermal comfort, particularly under extreme ambient conditions. Any delay in compressor engagement directly affects cabin cooldown performance, impacting both perceived and measured comfort levels. This study assesses the thermal comfort risks associated with compressor engagement delays of 6.5 seconds and 13 seconds under varying ambient conditions. A comprehensive frontloading approach was employed, integrating 1D CAE simulations with objective and subjective experimental testing. Initial simulations provided insights into transient cabin heat load behavior and air distribution effectiveness, enabling efficient test case selection. Physical testing was conducted in a controlled climatic chamber under severe (>40°C) ambient condition, replicating real-world scenarios. Objective metrics, including cabin air temperature, vent temperature and cooldown rates, were measured to quantify thermal performance
Kulkarni, ShridharDeshmukh, GaneshJoshi, GauravShah, GeetJaybhay, Sambhaji
This document establishes training guidelines applicable to fiber optic safety training, technical training and fiber awareness for individuals involved in the manufacturing, installation, support, integration and testing of fiber optic systems. Applicable personnel include: Managers Engineers Technicians Logisticians Trainers/Instructors Third Party Maintenance Agencies Quality Assurance Shipping Receiving Production Purchasing
AS-3 Fiber Optics and Applied Photonics Committee
This document establishes re-certification guidelines applicable to fiber optic fabricator technical training for individuals involved in the manufacturing, installation, support, integration and testing of fiber optic systems. Applicable personnel include: Managers Engineers Technicians Trainers/Instructors Third Party Maintenance Agencies Quality Assurance Production
AS-3 Fiber Optics and Applied Photonics Committee
This document establishes training guidelines applicable to fiber optic fabricator technical training for individuals involved in the manufacturing, installation, support, integration and testing of fiber optic systems. Applicable personnel include: Managers Engineers Technicians Trainers/Instructors Third Party Maintenance Agencies Quality Assurance Production
AS-3 Fiber Optics and Applied Photonics Committee
With the rapid development of autonomous driving technologies, intelligent ports, particularly autonomous logistics, have become the focus of industry attention. Ensuring safe and efficient operations require port management systems to perceive and predict the behaviors of people and vehicles. In the filed of behavior perception, research efforts have primarily focused on the detection and tracking of vehicles, pedestrians, and obstacles under various sensor configurations. Common approaches include vision-based, LiDAR-based, and multi-sensor fusion methods. In terms of behavior prediction, existing approaches can be broadly categorized into four paradigms: model-driven, data-driven, environment-assisted, and anomaly prediction methods. Model-driven approaches rely on physical and motion models, while data-driven approaches utilize deep learning techniques. Environment-assisted approaches integrate prior knowledge such as maps, while anomaly prediction focus on identifying unexpected
Lu, ZhiyongWang, XiyuanLiu, ShiquYang, ZhengLi, HaoHe, Xiaofei
Urban road traffic state classification is essential for identifying early-stage deterioration and enabling proactive traffic management. This study presents a novel method to accurately assess the traffic state of urban roads while addressing the limitations of existing methods in spatial generalization performance. The approach consists of three key components. First, several indicators are designed to capture the spatial-temporal evolution mechanisms of traffic state, speed freedom, flow saturation, and their variations over time and space. Then, a feature learning module based on an AutoEncoder network is introduced to reduce the dimensionality of the constructed feature set. This enhances feature distinction while mitigating noise effects on classification results. Third, k-means clustering is applied to analyze significant features extracted from the AutoEncoder latent space, categorizing road traffic states into fluent, basic fluent, moderate congested and severe congested
Wang, XiaocongHuang, MinGuo, XinlingXie, JieminZhang, Xiaolan
Recent policies have set ambitious goals for reducing greenhouse gas (GHG) emissions to mitigate climate change and achieve climate neutrality by 2050. In this context, the feasibility of hydrogen applications is under investigation in various sectors and promoted by government funding. The transport sector is one of the most investigated sectors in terms of emission mitigation strategies, as it contributes to about one-fifth of the total GHG emissions. This study proposes an integrated numerical approach, using a simulation framework, to analyze potential powertrain alternatives in the road transport sector. Non-causal point parametric vehicle models have been developed for various vehicle classes to evaluate key environmental, energy, and economic performance indicators. The modular architecture of the simulation framework allows the analysis of different vehicle classes. The developed framework has been used to compare powertrain alternatives based on hydrogen and electricity energy
Pipicelli, MicheleSedarsky, DavidDi Blasio, Gabriele
With the ongoing electrification of vehicles, components contributing a minor share of overall drivetrain losses are coming into focus. Analyzing these losses is crucial for enhancing the energy efficiency of modern vehicles and meeting the increasing demands for sustainability and extended driving range. These components include wheel bearings, whose friction losses are influenced by parameters such as temperature, mechanical loads, and mounting situation. Therefore, it is essential to analyze the resulting friction losses and their dependence on the mentioned influencing parameters at an early stage of development, both through test bench measurements and with the help of simulation models. To achieve these objectives, this submission presents a methodology that combines test bench measurements with a measurement-based simulation of the friction losses of wheel bearings occurring in the vehicle as a complete system under varying driving cycles and parameters. For this purpose, an
Hartmann, LukasSturm, AxelHenze, RomanNotz, Fabian
Designing the gear shift control for an automotive transmission is a complex task because it involves handling nonlinear behaviors like changes in friction between clutch plates and fluctuations in oil temperature. While deep reinforcement learning (DRL) has recently been used to reduce shift shock, most existing methods don’t account for real-world changes such as transmission aging. One major issue that becomes worse with aging is clutch judder—a type of vibration caused by wear. Traditional reinforcement learning assumes that the environment stays the same, which can lead to unstable learning when conditions change, making it hard to consistently reduce shift shock. To address this, we propose a new algorithm that adapts to aging transmissions by adjusting the discount factor—a key parameter in reinforcement learning that balances short-term and long-term rewards. Instead of keeping this factor fixed, our method starts with a lower value to ensure stable learning and gradually
Ogawa, KazukiAihara, TatsuhitoGoto, TakeruMinorikawa, Gaku
An important characteristic of battery electric vehicles (BEVs) is their noise signature. Besides tire and wind noise, noise from auxiliaries as pumps, the electric drive unit (EDU) is one of the major contributors. The dynamic and acoustic behavior of EDUs can be significantly affected by production tolerances. The effects that lead to these scatter bands must be understood to be able to control them better and thus guarantee a consistently high quality of the products and a silent and pleasant drive. The paper discusses a simulation driven approach to investigate production tolerances and their effect on the NVH behavior of the EDU, using high precision transient multi-body dynamic analysis. This approach considers the main effects, influences, and the interaction from elastic structures of electric motor and transmission with accurate gear contact models in a fully coupled way. It serves as virtual end of line test, applicable in all steps of a new EDU development, by increasing
Klarin, BorislavSchweiger, ChristophResch, Thomas
The United States Marine Corps enlists the JCB 4CX backhoe loader as its latest recruit. JCB recently announced that it has secured a contract to provide 4CX backhoe loaders to the United States Marine Corps. According to JCB, the agreement includes not only machines but also attachments testing and hands-on operator training. “The 4CX is the direct result of more than 70 years of continuous improvement,” said Chris Giorgianni, vice president of government and defense for JCB North America. “It's built to perform in the most demanding environments, whether that's military engineering missions or high-pressure construction jobsites.”
Wolfe, Matt
Like those in many other industries, truck and off-highway vehicle manufacturers face the challenge of producing quality components and maintaining productive processes while also generating a better bottom line. Improving employee training, simplifying complex operations and implementing better workflows can all help generate efficiencies. While not a new concept, lightweighting - in this case, reducing the weight of parts through the substitution of traditional steel with high-strength, thinner steels - can also be a viable answer to a better vehicle. As a rule of thumb, when manufacturers double the strength of the material through lightweighting, it is possible to reduce the weight of the part by one-third. That weight reduction can then lower the cost per part for greater profitability per piece of equipment and greater annual savings.
Gugel, Mick
The Gatik Arena platform integrates NVIDIA Cosmos models to create closed-loop, ultra-realistic digital environments that address real-world limitations. Gatik Arena is a next-generation simulation platform designed to accelerate the development and validation of autonomous vehicle (AV) systems. Gatik, which targets autonomous middle-mile logistics, built and fine-tuned Arena in-house to meet specific operational and technical needs. Unveiled in July 2025, the platform is said to produce photorealistic, structured and controllable synthetic data that addresses the limitations of traditional real-world data collection. Founded in 2017, Gatik plans to scale its freight-only, driverless operations in 2025, and the Arena platform is central to this endeavor. Gatik collaborated with NVIDIA to integrate its Cosmos world foundation models (WFMs), which enable the creation of ultra-high-fidelity, physics-informed digital environments for robust AV training and validation, said Norm Marks, VP
Gehm, Ryan
Analyzing and accurately estimating the energy consumption of battery electric buses (BEBs) is essential as it directly impacts battery aging. As fleet electrification of transit agencies (TAs) is on the rise, they must take into account battery aging, since the battery accounts for nearly a quarter of the total bus cost. Understanding the strain placed on batteries during day-to-day operations will allow TAs to implement best-use practices, continue successful fleet electrification, and prolong battery life. The main objective of this research is to estimate and analyze the energy consumption of BEBs based on ambient conditions, geographical location, and driver behavior. This article presents a model for estimating the battery energy consumption of BEBs, which is validated using the data on federal transit bus performance tests performed by Penn State University and experimental aggregated trip data provided by the Central Ohio Transit Authority (COTA). The developed simulator aims
Shiledar, AnkurShanker, AnirudhPulvirenti, LucaDi Luca, GiuseppeAkintade, RebeccahRizzoni, Giorgio
The use of modeling and simulation (M&S) to enable aggressive training, testing, analysis, and experimentation of capabilities has risen in recent years. An increase in M&S demand to enable Force Readiness necessitates the use of modular and reusable simulation software. To meet this need, the U.S. Army Combat Capabilities Development Command Ground Vehicle Systems Center (DEVCOM GVSC) has developed a modular simulation software framework called Project Great Lakes (ProjectGL). The software supports complex simulation requirements for multiple vehicles, terrains, sensors and other technologies, while using a common, internal framework to support extensive configuration. The paper presents the framework’s core design philosophy, architecture and common use cases. The paper concludes with a discussion on possible areas of framework expansion and development guidelines for partners interested in extending the framework.
Stanko, ThomasJoyce, JonathanBarry, JamesFlores, DavidHogan, JasonMiller, DavidBanoon, HawraaBostick, WilliamCampbell, CaleGangl, JoshuaHideg, ChristopherKlein, PhilipMacAfee, AndrewMalinowski, BenjaminMatthews, JeffreyMorton, StuartMontague, JoshuaThompson, ChristopherTily, ConorTrombley, AlexanderMikulski, Christopher
Navigation in off-road terrains is a well-studied problem for self-driving and autonomous vehicles. Frequently cited concerns include features like soft soil, rough terrain, and steep slopes. In this paper, we present the important but less studied aspect of negotiating vegetation in off-road terrain. Using recent field measurements, we develop a fast running model for the resistance on a ground vehicle overriding both small vegetation like grass and larger vegetation like bamboo and trees. We implement of our override model into a 3D simulation environment, the MSU Autonomous Vehicle Simulator (MAVS), and demonstrate how this model can be incorporated into real-time simulation of autonomous ground vehicles (AGV) operating in off-road terrain. Finally, we show how this model can be used to simulate autonomous navigation through a variety of vegetation with a PID speed controller and measuring the effect of navigation through vegetation on the vehicle speed.
Goodin, ChristopherMoore, Marc N.Hudson, Christopher R.Carruth, Daniel W.Salmon, EthanCole, Michael P.Jayakumar, ParamsothyEnglish, Brittney
The development of cyber-physical systems necessarily involves the expertise of an interdisciplinary team – not all of whom have deep embedded software knowledge. Graphical software development environments alleviate many of these challenges but in turn create concerns for their appropriateness in a rigorous software initiative. Their tool suites further enable the creation of physics models which can be coupled in the loop with the corresponding software component’s control law in an integrated test environment. Such a methodology addresses many of the challenges that arise in trying to create suitable test cases for physics-based problems. If the test developer ensures that test development in such a methodology observes software engineering’s design-for-change paradigm, the test harness can be reused from a virtualized environment to one using a hardware-in-the-loop simulator and/or production machinery. Concerns over the lack of model-based software engineering’s rigor can be
McBain, Jordan
As unmanned vehicular networks become more prevalent in civilian and defense applications, the need for robust security solutions grows in parallel. While ROS 2 offers a flexible platform for robotic operations, its security model lacks the adaptability required for dynamic trust management and proactive threat mitigation. To address these shortcomings, we propose a novel framework that integrates containerized ROS 2 nodes with Kubernetes-based orchestration, a dynamic trust management subsystem, and integrability with simulators for real-time and protocol-flexible network simulation. By embedding trust management directly within each ROS 2 container and leveraging Kubernetes, we overcome ROS 2’s security limitations by enabling real-time monitoring and machine learning-driven anomaly detection (via an autoencoder trained on custom data), facilitating the isolation or removal of suspicious nodes. Additionally, Kubernetes policies allow seamless scaling and enforcement of trust-based
Tinker, NoahBoone, JuliaWang, Kuang-Ching
Navigation in off-road terrains is a well-studied problem for self-driving and autonomous vehicles. Frequently cited concerns include features like soft soil, rough terrain, and steep slopes. In this paper, we present the important but less studied aspect of negotiating vegetation in off-road terrain. Using recent field measurements, we develop a fast running model for the resistance on a ground vehicle overriding both small vegetation like grass and larger vegetation like bamboo and trees. We implement of our override model into a 3D simulation environment, the MSU Autonomous Vehicle Simulator (MAVS), and demonstrate how this model can be incorporated into real-time simulation of autonomous ground vehicles (AGV) operating in off-road terrain. Finally, we show how this model can be used to simulate autonomous navigation through a variety of vegetation with a PID speed controller and measuring the effect of navigation through vegetation on the vehicle speed.
Goodin, ChristopherMoore, Marc N.Hudson, Christopher R.Carruth, Daniel W.Salmon, EthanCole, Michael P.Jayakumar, ParamsothyEnglish, Brittney
Computer vision is being revolutionized by the use of transformer-based machine learning architectures. However, these models need large datasets to enable pre-training through self-supervised learning. However, there is a lack of open-source datasets of the same magnitude as standard RGB color images. This work analyzes the effect of using randomly generated fractal-based hyperspectral images versus real data to understand the effect of pre-training dataset on a Swin image encoder model performance, during supervised-training of a semantic segmentation hyperspectral dataset. Two real data datasets are used for comparison to the synthetic dataset, one RGB-based and another hyperspectral-based to understand how variability in spectral resolution during pre-training effects model performance on semantic segmentation.
Medellin, AnthonyGrabowsky, DavidMikulski, DariuszLangari, Reza
This study presents a novel approach for predicting fuel consumption in heavy-duty vehicles using a Machine Learning-based model, which is based on feedforward neural network (FFNN). The model is designed to enhance real-time vehicle monitoring, optimize route planning, and reduce both operational costs and environmental impact, making it particularly suitable for fleet management applications. Unlike traditional physics-based approaches, the FFNN relies solely on a refined selection of input variables, including vehicle speed, acceleration, altitude, road slope, ambient temperature, and engine power. Additionally, vehicle mass is estimated using a methodology presented elsewhere and is included as an input for a better generalization of the consumption model. This parameter significantly impacts fuel consumption and is particularly challenging to obtain for heavy-duty vehicles. Engine power is derived from both engine torque and speed (RPM), ensuring a direct relationship with fuel
Vicinanza, MatteoPandolfi, AlfonsoArsie, IvanGiannetti, FlavioPolverino, PierpaoloEsposito, AlfonsoPaolino, AntonioAdinolfi, Ennio AndreaPianese, CesareFrasci, Valentino
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