Search Results (87)

All current transportation systems (vehicles, trucks, planes, etc.) rely on the Global Positioning System (GPS) as their main navigation technology. GPS receivers collect signals from multiple satellites and are able to provide more or less accurate positioning. For civilian applications, GPS signals are sent without any encryption system. For this reason, they are vulnerable to various attacks, and the most prevalent one is known as GPS spoofing. The main consequence is the loss of position monitoring, which may increase damage risks in terms of crashes or hijacking. In this study, we focus on UAV (unmanned aerial vehicle) positioning attacks. We first review numerous techniques for detecting and mitigating GPS spoofing attacks, finding that various types of attacks may occur. In the literature, many studies have focused on only one type of attack. We believe that targeting the study of many attacks is crucial for developing efficient mitigation mechanisms. Thus, we have explored a well-known datasetcontaining authentic UAV signals along with spoofed signals (with three types of attacked signals). As a main contribution, we propose a more interpretable approach to exploit the dataset by extracting individual mission sequences, handling non-stationary features, and converting the GPS raw data into a simplified structured format. Then, we design tree-based machine learning algorithms, namely decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost), for the purpose of classifying signal types and to recognize spoofing attacks. Our main findings are as follows: (a) random forest has significant capability in detecting and classifying GPS spoofing attacks, outperforming the other models. (b) We have been able to detect most types of attacks and distinguish them. Full article

Proton exchange membrane fuel cells are a clean energy technology with wide application in transportation and stationary energy systems. Due to the problem of voltage degradation under long-term dynamic loads, predicting their performance degradation trend is of great significance for extending the life of proton exchange membrane fuel cells and improving system reliability. This study adopts a data-driven approach to construct a degradation prediction model. In view of the problem of many input parameters and complex distribution of degradation features, a neural network model based on a multi-head attention mechanism and class token is first proposed to analyze the impact of different operating parameters on the output voltage prediction. The importance of each input variable is quantified by the attention weight matrix to assist feature screening. Subsequently, a prediction model is constructed based on Transformer to characterize the voltage degradation trend of fuel cells under dynamic conditions. The experimental results show that the root mean square error and mean absolute error of the model in the test phase are 0.008954 and 0.006590, showing strong prediction performance. Based on the importance evaluation provided by the first model, 11 key parameters were selected as inputs. After this input simplification, the model still maintained a prediction accuracy comparable to that of the full-feature model. This result verifies the effectiveness of the feature screening strategy and demonstrates its contribution to improved generalization and robustness. Full article

Effective incident detection is essential for emergency response and transportation management. Traditional methods relying on stationary technologies are often costly and provide limited coverage, prompting the exploration of crowdsourced data such as Waze. While Waze offers extensive coverage, its data can be unverified and unreliable. This study aims to identify factors affecting the reliability of Waze alerts and develop a predictive model to distinguish true incidents from false alerts using real-time Waze data, thereby improving emergency response times. Real crash data from the New Jersey Department of Transportation (NJDOT) and crowdsourced data from Waze were matched using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to differentiate true and false alerts. A binary logit model was constructed to reveal significant predictors such as time categories around peak hours, road type, report ratings, and crash type. Findings indicate that the likelihood of accurate Waze alerts increases during peak hours, on streets, and with higher report ratings and major crashes. Additionally, multiple machine learning-based predictive models were developed and evaluated to forecast in real time whether Waze alerts correspond to actual incidents. Among those models, the Random Forest model achieved the highest overall accuracy (82.5%) and F1-score (82.8%), and an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.90, demonstrating its robustness and reliability for real-time incident detection. Gradient Boosting, with an AUC-ROC of 0.90 and Area Under the Precision–Recall Curve (AUC-PR) of 0.90, also performed strongly, particularly excelling at predicting true alerts. The analysis further emphasized the importance of key predictors such as time of day, report ratings, and road type. These findings provide actionable insights for enhancing the accuracy of incident detection and improving the reliability of crowdsourced traffic alerts, supporting more effective traffic management and emergency response systems. Full article

This review aims to help researchers, designers, and engineering staff extend operational times and elevate robots’ efficiency. The study represents an up-to-date summary of power electronic converters, their classification, and solutions found by leading robot manufacturers. While some advances have not yet become commonplace in mainstream robotics, their crucial role and promise are evident for expanding automation capabilities in various stationary and mobile applications. The work demonstrates two interconnected directions that are currently applied or are planned to be employed in the future as key factors contributing to reducing losses and accelerating energy transformation. The former direction relates to the implementation of wide bandgap devices that are superior to silicon-based electronics. The second trend concerns the advancements of converter topologies. In this way, the article presents how rectifiers, inverters, and their combinations provide voltage control, current management, and waveform shaping, thereby revealing their potential in improving energy utilisation in industry, transport, agriculture, households, and other sectors of vital activity. Full article

This paper investigates the design and performance of a tunable pendulum energy harvester (TPEH) integrated with cone continuously variable transmission (CVT) to enhance energy harvesting efficiency in broadband and non-stationary vibrational environments. The cone CVT mechanism enables the tunability of the harvester’s natural frequency, allowing it to dynamically adapt and maintain resonance across varying excitation frequencies. A specific focus is placed on the system’s behavior under chirp signal base excitation, which simulates a time-varying frequency environment. Experimental and analytical approaches are employed to evaluate the system’s dynamic response, energy output, and frequency adaptation capabilities. The results demonstrate that the proposed TPEH system achieves significant energy harvesting performance improvements by leveraging the cone CVT to optimize power generation under resonance conditions. The system is also shown to be effective in maintaining stable operation over a wide range of frequencies, demonstrating its versatility for real-world vibrational energy harvesting applications. This research highlights the importance of tunability in energy harvesting systems and the role of mechanical transmission mechanisms in improving adaptability. The proposed design has strong potential for applications in environments with non-stationary vibrations, such as transportation systems, industrial machinery, and infrastructure monitoring. Full article

The geometric parameters of the Null-Flux coil (NFC) are crucial to the load capacity and economic viability of electrodynamic suspension (EDS) systems. This study investigates the influence of NFC geometry on the electromagnetic force characteristics in EDS systems. Through the electromagnetic modeling of EDS mechanisms, an analytical model for EDS systems is established. Systematic experiments compare electromagnetic forces under varying NFC lengths and gaps, supported by a self-developed stationary EDS dynamic simulation platform. The results demonstrate that the average levitation force is positively correlated with the coil length, and it is larger when the coil length is close to its width. Meanwhile, the NFC length has a significant impact on the lift-to-drag ratio, while the NFC gap has a relatively smaller effect on it. This work provides a complete methodology integrating analytical modeling and experimental validation, offering practical guidelines for NFC design in maglev actuators. The findings advance EDS system optimization through quantifiable geometric criteria, particularly for transportation applications requiring precision electromagnetic force control. Full article

Proton-Exchange Membrane Fuel Cells (PEMFCs), as efficient and environmentally friendly energy conversion devices, have wide application potential in areas such as transportation, mobile power, and distributed energy. However, the remaining useful life (RUL) issue of PEMFCs has been one of the main challenges limiting their commercialization. The RUL prediction problem of PEMFCs exhibits characteristics of time series forecasting, but its data possess multidimensional features and non-stationarity, which limits the applicability of classical time series forecasting models like the Transformer in solving the RUL prediction problem. In this paper, we propose a PEMFC RUL prediction model based on the Crossformer for non-stationary time series (De-stationary-Crossformer). Firstly, the overall architecture adopts the Crossformer model to extract dependencies between different features and temporal dependencies. Secondly, adaptive normalization is applied to the data to mitigate the non-stationarity in the original data, thereby increasing their predictability. Subsequently, a non-stationary attention mechanism is introduced in the model to simultaneously utilize the non-stationarity in the original data when extracting deep information. Additionally, manual features are introduced through mathematical statistics to enhance the predictive performance of the model. During the training process, the TILDE-Q loss function is used to focus on the similarity between the predicted sequence and the true sequence. The model proposed in this paper improves the MSE by 31% compared to the Transformer and 23% compared to the Crossformer in the experimental prediction of the RUL of PEMFCs in actual vehicles. Full article

As the world transitions towards sustainable transportation, the advancement of electric vehicles (EVs) has become imperative. Wireless power transfer (WPT) technology presents a promising solution to enhance the convenience and efficiency of EV charging while alleviating the challenges associated with traditional wired systems. This paper conducts an in-depth exploration of WPT technologies for EVs, focusing on their theoretical foundations, practical implementation, optimization strategies, development trends, and limitations. The theoretical principles of wireless charging are first elucidated, categorizing them into near-field methods, such as inductive and capacitive charging, and far-field methods, including microwave and laser-based charging. A comparative analysis reveals the advantages and limitations inherent to each technology. The implementation section examines various charging strategies, encompassing stationary, dynamic, and quasi-dynamic wireless charging, assessing their feasibility and effectiveness in practical applications. Furthermore, optimization techniques aimed at enhancing WPT system performance are examined in depth, with particular emphasis on coil structure optimizations, anti-misalignment solutions, compensation topology optimizations, modulation strategy optimizations, and parameter identifications. The discussion section outlines current development trends in wireless charging technologies for EVs, highlighting the limitations that hinder the widespread adoption of wireless charging technologies in the EV market. Finally, potential research directions and the implications of wireless charging technology on the development of EVs are summarized. This critical review aims to provide valuable insights for researchers and practitioners dedicated to advancing the field of wireless charging for EVs. Full article

Proton Exchange Membrane Fuel Cells (PEMFCs) are widely regarded as promising clean energy technologies due to their high energy conversion efficiency, low environmental impact, and versatile application potential in transportation, stationary power, and portable devices. Central to the operation and performance of PEMFCs are advancements in materials and manufacturing processes that directly influence their efficiency, durability, and scalability. This review provides a comprehensive overview of recent progress in these areas, emphasizing the critical role of membrane electrode assembly (MEA) technology and its constituent components, including catalyst layers, membranes, and gas diffusion layers (GDLs). The MEA, as the heart of PEMFCs, has seen significant innovations in its structure and manufacturing methodologies to ensure optimal performance and durability. At the material level, catalyst layer advancements, including the development of platinum-group metal catalysts and cost-effective non-precious alternatives, have focused on improving catalytic activity, durability, and mass transport. Similarly, the evolution of membranes, particularly advancements in perfluorosulfonic acid membranes and alternative hydrocarbon-based or composite materials, has addressed challenges related to proton conductivity, mechanical stability, and operation under harsh conditions such as low humidity or high temperature. Additionally, innovations in gas diffusion layers have optimized their porosity, hydrophobicity, and structural properties, ensuring efficient reactant and product transport within the cell. By examining these interrelated aspects of PEMFC development, this review aims to provide a holistic understanding of the state of the art in PEMFC materials and manufacturing technologies, offering insights for future research and the practical implementation of high-performance fuel cells. Full article

The transition to hydrogen as a clean energy source is critical for addressing climate change and supporting environmental sustainability. This review provides an accessible summary of general research trends in hydrogen risk assessment methodologies, enabling diverse stakeholders, including researchers, policymakers, and industry professionals, to gain insights into this field. By examining representative studies across theoretical, experimental, and simulation-based approaches, the review highlights prominent trends and applications within academia and industry. The key focus is on evaluating risks in stationary and transportation applications, paying particular attention to hydrogen storage systems, transportation infrastructures, and energy systems. By offering a concise yet informative summary of hydrogen risk assessment trends, this paper aims to serve as a foundational resource for fostering safer and more sustainable hydrogen systems. Full article

Nanoporous membranes are heterogeneous structures, with heterogeneity manifesting at the microscale. In examining particle transport through such media, it has been observed that this transport deviates from classical diffusion, as described by Fick’s second law. Moreover, the classical model is physically unsustainable, as it is non-causal and predicts an infinite speed of concentration perturbation propagation through a substantial medium. In this work, we have derived two causal models as extensions of Fick’s second law, where causality is linked to the effects of inertial memory in the nanoporous membrane. The results of the derived models have been compared with each other and with those obtained from the classical model. It has been demonstrated that both causal models, one with exponentially fading inertial memory and the other with power-law fading memory, predict that the concentration perturbation propagates as a damped wave, leading to an increased time required for the cumulative amount of molecules passing through the membrane to reach a steady state compared to the classical model. The power-law fading memory model predicts a longer time required to achieve a stationary state. These findings have significant implications for understanding cell physiology, developing drug delivery systems, and designing nanoporous membranes for various applications. Full article

Nickel significantly increases the toughness of steel and makes it ideal for use in applications that require high impact and fracture resistance at low temperatures. These inherent advantages position nickel steel as indispensable material in various domains, with a pronounced presence in stationary Liquefied Natural Gas (LNG) tanks and in the shipbuilding industry, particularly for tanks in vessels intended for the transport of liquefied ethane and LNG. The presented study focuses on assessing the fracture toughness behaviour of nickel alloy steel 1.5662+QT640 under sub-zero and cryogenic temperatures. The fracture performance of the material was evaluated, specifically emphasizing the impact toughness and fracture toughness characteristics of the material. Moreover, it was discussed if the transferability of the experimental results to the well-known fracture mechanics-based concept of EN 1993-1-10, which relies on the master curve concept, is possible. The results show that the master curve concept is not applicable to the nickel alloy steel 1.5662+QT640 due to its exceptional fracture toughness behaviour at very low temperatures. Full article

In various applications, dry friction could induce vibrations. A well-known example is frictional braking systems in ground transportation vehicles involving a sliding contact between a rotating and a stationary part. In such scenarios, the emission of high-intensity noise, commonly known as squeal, can present human health risks based on the noise’s intensity, frequency, and occurrences. Despite the importance of squeal in the context of advancing urbanization, the parameters determining its occurrence remain uncertain due to the complexity of the involved phenomena. This study aims to identify a relevant operando indicator for predicting squeal occurrences. To this end, a pin-on-disc test rig was developed to replicate various contact conditions found in road profiles and investigate resulting squealing. Each test involves a multimodal instrumentation, complemented by surface observations. It is illustrated that the enhanced thermal indicator identified is relevant because it is sensitive to the thermomechanical and tribological phenomena involved in squealing. Full article

The development of smart cities relies on the implementation of cutting-edge technologies. Unmanned aerial vehicles (UAVs) and deep learning (DL) models are examples of such disruptive technologies with diverse industrial applications that are gaining traction. When it comes to road traffic monitoring systems (RTMs), the combination of UAVs and vision-based methods has shown great potential. Currently, most solutions focus on analyzing traffic footage captured by hovering UAVs due to the inherent georeferencing challenges in video footage from nonstationary drones. We propose an innovative method capable of estimating traffic speed using footage from both stationary and nonstationary UAVs. The process involves matching each pixel of the input frame with a georeferenced orthomosaic using a feature-matching algorithm. Subsequently, a tracking-enabled YOLOv8 object detection model is applied to the frame to detect vehicles and their trajectories. The geographic positions of these moving vehicles over time are logged in JSON format. The accuracy of this method was validated with reference measurements recorded from a laser speed gun. The results indicate that the proposed method can estimate vehicle speeds with an absolute error as low as 0.53 km/h. The study also discusses the associated problems and constraints with nonstationary drone footage as input and proposes strategies for minimizing noise and inaccuracies. Despite these challenges, the proposed framework demonstrates considerable potential and signifies another step towards automated road traffic monitoring systems. This system enables transportation modelers to realistically capture traffic behavior over a wider area, unlike existing roadside camera systems prone to blind spots and limited spatial coverage. Full article

This paper presents the development of an experimental setup for acoustic noise characterization of electric motors. It describes the sound measurement microphones utilized in the setup and discusses the application of octave bands and A-weighting in noise measurement. Various methods for acoustic noise measurement and sound power calculation, including those based on sound pressure and sound intensity, are also covered. Given the relatively noisy test environment and restricted access around the test setup, discrete point sound intensity measurement is selected for sound power calculation. Initially, a stationary probe-holding fixture is designed and fabricated for sound intensity measurements. To enhance the fixture’s flexibility and the accuracy of the measurements, a transportable fixture is subsequently designed and fabricated. The necessary hardware and software settings for acoustic noise characterization are then developed. Finally, the setup is used to conduct acoustic noise characterization of an IPM motor, validating the application of the transportable probe-holding fixture. Full article