Search Results (132)

Accurate identification of visually similar targets in Unmanned Aerial Vehicle (UAV) imagery is hindered by significant inter-class ambiguity and viewpoint variability. While hierarchical deep learning mitigates these challenges, existing architectures relieve manual design, introducing subjectivity and limiting cross-domain scalability. In this work, we propose an objective, data-driven method for the automated synthesis of hierarchical classification structures. Our approach uses a hybrid inter-class proximity metric that integrates geometric distances between latent-feature-space centroids with empirical misclassification probabilities. Using a hierarchical agglomerative clustering algorithm optimized via an inconsistency coefficient, we synthesize a coarse-to-fine cascade that deploys YOLOv11 for feature extraction and FT-Transformers for specialized identification. Experimental validation on the VisDrone2019 and UAV123 datasets demonstrates that the automatically generated hierarchy achieves a peak F1-score of 94.9%, outperforming the monolithic YOLOv11 model by 0.8% and matching human-designed cascades. Sensitivity analysis indicates an optimal hybrid weight range of 0.4–0.6. The findings confirm that our automated synthesis provides high adaptability and reliability for real-time edge AI deployments, ensuring robust performance in dynamic monitoring environments without requiring manual redesign. Full article

Magnitude alone fails to capture the full complexity of pavement roughness; its spatial distribution along a road is equally vital for effective maintenance planning. While traditional assessment has long relied on specialized survey vehicles, the rise of mobile crowdsensing now allows for massive data acquisition via smartphone sensors. This study investigates the spatial structure of pavement roughness using crowdsensed data from the SmartRoadSense platform. Roughness is quantified through the Power of Prediction Error (PPE) indicator derived from smartphone accelerometer signals. The dataset consists of 475 observations sampled at 20 m intervals over approximately 9.5 km of the A3/E45 motorway in southern Italy. A multi-scale spatial–statistical framework is adopted to analyse the roughness signal. The analysis includes the evaluation of scale-dependent statistical descriptors (mean and coefficient of variation), as well as spatial correlation, spectral, and entropy-based measures. The results indicate a short spatial correlation length (approximately 60–100 m) and the absence of a dominant spatial wavelength, suggesting that pavement roughness behaves as a localized multiscale process. A complementary segmentation analysis based on Classification and Regression Trees (CART) is performed to explore the spatial partitioning of the roughness signal. Our analysis indicates that segmentation complexity spikes once the minimum node size drops below roughly 10 observations. This trend points to the existence of localized irregularities that coarser scales simply overlook. Ultimately, these results suggest that mean roughness values alone are insufficient for describing pavement condition and that hybrid spatial–statistical approaches may support more scalable, data-driven, and spatially targeted pavement monitoring strategies for sustainable transportation infrastructure management. Full article

Laser beam welding (LBW) of aluminium busbar-to-terminal connections for electric-vehicle battery packs requires precise in-process monitoring. Membrane-free optical microphones provide a high-bandwidth (DC–MHz) acoustic channel that captures keyhole, melt-pool, and plume dynamics. This study proposes Acoustic Signal Intelligence (ASI), a deep learning framework for LBW state classification from a single optical microphone, evaluated on an open dataset (183 AA1050 welds, $f_s$ = 2.5 MHz) under a five-class taxonomy: lack of fusion, lack of connection, sound, marginal, and piercing. The contributions are: (i) a compact 1-D CNN encoder on a mel-scale STFT spectrogram, reaching the highest macro-F1 (0.72 mean across three-fold replicate-out cross-validation) and 100% piercing recall in every fold—a multi-representation fusion variant adding a wavelet-packet decomposition and a 24-feature library targeting the 8, 63 and 110 kHz keyhole-resonance peaks was evaluated as an ablation arm and did not survive cross-validation, so the proposed model is mel-only; (ii) a systematic benchmark against six classical-ML and four deep learning baselines in which Transformer-hybrid ablations and ACGAN-style augmentation underperform compared to the compact CNN on the 122-sample training set, with the Transformer underperformance confirmed by a 30-configuration grid search over learning rate, weight decay, and dropout (best tuned macro-F1 = 0.441 vs. CNN 0.724); and (iii) a Grad-CAM analysis that recovers the keyhole-resonance bands without prior knowledge. A single optical microphone is thus a viable real-time alternative to multi-sensor stacks for battery-pack laser welding. Full article

Accurate bearing fault diagnosis in electric drive systems is crucial for ensuring the safety and reliability of new energy vehicles. Aiming at the problem of inaccurate bearing fault diagnosis caused by the failure to fully utilize the time-frequency feature information of bearing faults and the lack of an adaptive selection mechanism for features, an intelligent bearing fault diagnosis method based on wavelet packet decomposition (WPD) time-frequency feature enhancement is proposed in this paper. First, the collected vibration signals are enhanced using WPD to obtain the full-frequency-band time-frequency information, which provides input for the bearing fault diagnosis model. Second, a hybrid neural network CNN-BiLSTM-AM for bearing fault diagnosis is constructed. On the basis of using the convolutional neural network (CNN) improved with cross-convolutional layers to extract multiscale spatial features of the input data and the bidirectional long short-term memory network (BiLSTM) to capture the bidirectional temporal dependence between features, the attention mechanism (AM) is introduced to adaptively weight and enhance key global features. Finally, a fully connected layer is employed to achieve intelligent classification of bearing fault states. Validation on a laboratory test dataset shows that the proposed method achieves an average diagnostic accuracy of 98.67%, outperforming existing benchmark models and exhibiting strong generalization ability. This study provides an effective and practical intelligent fault diagnosis scheme for bearings in electric drive systems. Full article

The rise of Industry 4.0 has accelerated the adoption of intelligent automation in high-throughput manufacturing environments. Automated guided vehicles (AGVs) rely heavily on magnetic guidance tracks, which are susceptible to wear, contamination, and structural degradation. These defects frequently cause AGV misalignment, emergency stops, and production downtime. This paper presents a lightweight, embedded, vision-based framework for real-time monitoring of AGV magnetic tracks using Raspberry Pi 4 cameras and Python-based computer vision algorithms. The system integrates grayscale intensity modeling, histogram-based MeanShift tracking, contour continuity analysis, and machine learning-assisted classification to detect missing segments, wear, and foreign object interference. Experimental validation on a 30 m test track and five years of industrial data (>3000 samples) demonstrate robust tracking, reliable anomaly detection, and zero false positives under nominal conditions. The proposed hybrid deterministic, ML architecture supports predictive maintenance, reduces downtime risk, and contributes to resilient Industry 4.0 material-handling systems. Full article

This study presents a novel methodology for optimizing energy management strategies in heavy-duty Fuel Cell Hybrid Electric Vehicles (FCHEVs) with pantograph charging systems. The approach integrates machine learning (ML) techniques to predict energy demand, optimize the power distribution between the battery and fuel cell, and enhance overall efficiency. The methodology involves clustering vehicle and road data, supervised ML classification, and zonification of routes for adaptive energy management. The proposed system was validated using real-world driving data from five different routes in Germany. The results indicate a significant improvement in hydrogen consumption and fuel cell degradation compared to conventional control strategies. This research establishes a framework for advanced energy management in heavy-duty hydrogen-powered electric vehicles. Full article

Reliable altitude and vertical speed estimation are fundamental for unmanned aerial vehicle (UAV) autonomous flight, especially during low-altitude operations such as takeoff and landing. Barometric altimeters are widely used due to their low cost, high availability, and good long-term stability, providing smooth altitude trends over a wide operating range. However, barometric measurements are indirectly inferred from static pressure and are therefore sensitive to local airflow disturbances. In particular, rotor downwash and ground effect-induced pressure perturbations near the surface can introduce significant biases and short-term fluctuations in barometric altitude, which propagate into erroneous vertical speed estimates during critical flight phases. Time-of-flight (TOF) altimeters, such as radar or laser sensors, provide direct above-ground-level (AGL) measurements and are largely insensitive to ground effect-related pressure disturbances. Within their limited operational range, TOF altimeters typically offer higher accuracy and lower short-term noise compared with barometric altitude. Nevertheless, TOF sensors are characterized by a restricted valid measurement range and frequently exhibit non-ideal behaviors in real-world UAV operations, including out-of-range outputs, frozen measurements, and in-range biased readings. These anomalies violate the nominal sensor assumptions used in conventional Kalman filter-based fusion and can significantly degrade estimation performance if not properly handled. This paper proposes a hybrid Kalman–rule-based altitude estimation framework that fuses barometric and TOF altitude measurements to exploit their complementary characteristics while mitigating their respective limitations. A vertical dynamic state-space model is formulated to jointly estimate altitude, vertical velocity, accelerometer bias, and ground height offset. A rule-based anomaly detection and classification module is developed to identify multiple TOF altimeter failure modes observed in operational UAV flights. The detected anomaly states are incorporated into the Kalman filter to adaptively weight, accept, or reject TOF measurements, thereby improving robustness against sensor non-idealities. The proposed approach is validated using 39 real UAV flight logs covering diverse flight regimes, including low-altitude maneuvers, cruise, and autonomous landing. Experimental results show that the proposed framework provides more stable and robust altitude and vertical speed estimation under practical sensor anomaly conditions compared with conventional barometer-only and standard Kalman fusion configurations. These results demonstrate the practical effectiveness of the proposed method for fault-aware altitude estimation in UAV autonomous flight. Full article

Reliable perception of roadway signals is critical for autonomous vehicles operating in complex urban environments, particularly when traffic lights convey safety-critical instructions through flashing and arrow indications that extend beyond conventional red, yellow, and green states. However, most existing vision-based approaches focus primarily on static traffic-light recognition and lack robust mechanisms for interpreting temporal behaviors such as flashing signals. To address this limitation, this paper proposes a unified real-time perception framework, termed HybridSignalNet, for multi-class recognition of traffic lights, road signs, and lane-related roadway elements. The framework combines spatial detection with temporal state reasoning to interpret both steady and flashing signal patterns in video streams. Experimental evaluation demonstrates strong performance across multiple object classes, achieving an average detection F1-score of 91.3%, while traffic-light state classification reaches 96.7%, including reliable identification of flashing and arrow-based signals. The proposed system operates in real-time and provides an interpretable and deployable solution for intelligent transportation systems and autonomous driving applications, particularly at signalized intersections where temporal signal understanding is essential for safe decision-making. Full article

Ports are complex infrastructure systems operating under adverse marine environments, diverse loading regimes, and significant economic pressures. Among their critical assets are pavement infrastructures that serve multiple functional domains, including container handling and storage areas, internal circulation corridors, passenger–vehicle interfaces, and auxiliary parking zones. However, existing port pavement research remains predominantly concentrated on heavy-duty container applications, while other functional categories are comparatively underexplored. This study develops a structured engineering synthesis of port pavement infrastructure assets by integrating bibliometric mapping, conducted using Scopus-indexed publications, with a functional–structural analysis of worldwide practices. Following the identification of research trends, additional insights from engineering-oriented studies and technical guidance documents were incorporated to strengthen the practical relevance of the investigation. These findings indicate that functional classification should precede structural design decisions, enabling the systematic identification of loading conditions, serviceability requirements, and transition demands across port environments. Heavy-duty operational zones require high-stiffness systems capable of resisting concentrated and repetitive loads, while circulation areas are particularly sensitive to low-speed traffic effects. In contrast, passenger and mixed-use zones necessitate hybrid design strategies that balance structural adequacy with serviceability and long-term durability under marine exposure, whereas auxiliary areas are primarily governed by cost-efficiency and maintenance considerations. The overall research provides a rational basis for investment prioritization, material selection, lifecycle planning, and performance-based pavement management within multifunctional port environments. Full article

Aileron fault diagnosis in fixed-wing unmanned aerial vehicles (UAVs) faces significant challenges due to strong noise, multi-modal coupling, and limited fault samples. This paper presents a hybrid fault diagnosis framework that integrates variational mode decomposition (VMD) with a cross-attention-based feature fusion mechanism. First, residual signals are generated from UAV kinematic models and decomposed into multi-scale intrinsic mode functions (IMFs) using VMD to extract multiscale frequency-localized features. An integrated framework is then constructed, where Transformer encoders capture the global features and bidirectional long short-term memory (BiLSTM) networks extract local temporal dynamics. To effectively combine these complementary features, a cross-attention fusion module is designed to focus on the discriminative time-frequency features. Furthermore, a hybrid pooling strategy integrating max pooling and attention pooling is introduced to enhance classification robustness. Experiments on the AirLab failure and anomaly (ALFA) dataset demonstrate that the proposed method achieves 95.12% accuracy with improved fault separability, outperforming VMD + BiLSTM (87.66%), VMD + Transformer (86.89%), Transformer + BiLSTM (84.83%), Transformer (72.24%), CNN + LSTM (94.05%), and HDMTL (94.86%). Full article

This study analyzes the factors associated with vehicle technology classification in Ecuador, using fuel category (electric, hybrid, and internal combustion) as the dependent variable under an Explainable Artificial Intelligence (XAI) approach. Following the CRISP-DM methodology, we compared the performance of XGBoost and LightGBM algorithms using a dataset of 482,754 administrative records from the Internal Revenue Service (SRI). Both models achieved outstanding predictive performance with a Macro F1-score of 0.987, demonstrating robustness despite the severe class imbalance (electric vehicles represent only 1.3% of the total). The integration of SHAP (SHapley Additive exPlanations) values identified tax appraisal and engine displacement as the most influential features in the model predictions in the adoption of electric vehicles. In contrast, territorial factors exert a more significant influence on the acquisition of hybrid vehicles. Finally, the findings demonstrate that boosting models, combined with XAI techniques, provide transparent analytical tools that can support evidence-based transport decarbonization strategies in emerging economies. Full article

Unmanned aerial vehicles (UAVs) have been extensively deployed across a range of applications thank to their flexibility and low cost. While this expansion has significantly improved their operational efficiency and service capacity, it has also posed challenges for UAV supervision and management systems. To address these issues, this paper proposes a three-dimensional (3D) localization method that integrates time difference of arrival (TDOA) and angle of arrival (AOA) measurements based on the extended Kalman filter (EKF). Specifically, for AOA-based positioning, a uniform circular array (UCA) is employed to capture spatial signal characteristics, and the multiple-signal classification (MUSIC) algorithm is applied to precisely estimate the azimuth and elevation angles of incoming signals. In TDOA-based localization, a multipath signal separation and identification algorithm is implemented to enhance robustness against multipath propagation in complex environments. Subsequently, the TDOA and AOA measurements are fused using the EKF, where nonlinear measurement models are linearized via Jacobian matrices to improve computational efficiency and estimation accuracy. Finally, simulation results demonstrate that the proposed hybrid localization method outperforms existing positioning methods that rely solely on AOA or TDOA, achieving a positioning accuracy of approximately 5 m and an angular error within $3°$, which is suitable for applications in multipath environments such as urban areas. Full article

With the increasing demand for heavy-haul railway freight, both the number and volume of heavy-haul freight cars continue to grow. As the core system of railway freight transportation, the reliable operation of the brake system is fundamental to ensuring train safety. The freight car braking system fault diagnosis model, which relies on historical data while failing to account for changes in braking curves when locomotives are coupled with different vehicles, is the main reason why early failures of the braking system are not diagnosed. Consequently, real-time monitoring of the freight car braking system and early fault diagnosis have emerged as a pivotal technical challenge that necessitates resolution within the framework of the railway freight maintenance reform. This paper proposes a novel hybrid-driven prediction method that effectively combines Convolutional Neural Networks, Adaptive Radial Basis Function Neural Networks, and Extreme Learning Machines (CARE). To achieve comprehensive fault feature extraction, based on CNN of the image data classification, the K-means clustering algorithm is introduced to adaptively initialize the radial basis centers of the RBF and recalculate the radial basis radii. Moreover, to improve the real-time performance and accuracy of fault diagnosis, the network layers are expanded, and the ELM algorithm is employed to construct an optimization strategy for high-dimensional data processing in the network layers. The experimental results demonstrate that when considering the coupling of different vehicles in the railway freight car, the proposed CARE model exhibits faster convergence speed and significantly improves the effectiveness and real-time performance of fault diagnosis in the railway freight car braking system. Full article

This paper proposes a unified energy management, fault detection, and fault-tolerant control (EMS–FDI–FTC) framework for Hybrid Hydrogen Electric Vehicles (HHEVs) integrating a fuel cell (FC), battery (Bat), and supercapacitor (SC). While such multi-source architectures enable high-efficiency propulsion under dynamic driving conditions, actuator and state faults such as FC voltage sag, Bat internal resistance increase, and SC capacitance degradation can compromise safety, availability, and component lifetime. The proposed framework converts real-world GPS-recorded vehicle speed profiles into route-aware traction power demand and combines interpretable model-based indicators with an AI-based fault detection and classification module. Based on the diagnosis outcome, a fault-tolerant supervisory strategy performs online power reallocation among the FC, Bat, and SC while enforcing operational constraints. Validation is conducted in a MATLAB-based software-in-the-loop (SIL) environment using three urban driving routes collected from on-road measurements in Tunisia with injected ground-truth faults. The results demonstrate reliable fault classification performance and effective service continuity during fault intervals, supplying over 94% of the demanded energy across all routes, with energy-not-served remaining below 0.02 kWh. In addition, processor-in-the-loop (PIL) implementation on an STM32F407VG controller confirms real-time feasibility with a 10 Hz supervisory sampling rate and execution time margins compatible with embedded automotive deployment. Overall, the proposed closed-loop framework provides a practical route-aware diagnosis-to-control solution for robust and fault-resilient HHEV operation under realistic driving variability. All energy and efficiency indicators reported in this study are derived from control-oriented component models and are intended for consistent comparative evaluation across routes and operating scenarios, rather than absolute representation of a specific commercial vehicle. Full article

With the rapid development of the Internet of Vehicles (IoV), the explosive growth of data traffic within the system has led to a surge in demand for spectrum resources. However, the strict limitations on spectrum supply make the construction of an efficient and reasonable resource allocation scheme crucial for IoV. To maximize social benefits and improve security in the resource allocation process under IoV spectrum scarcity, this paper proposes a dynamic spectrum allocation (DSA) scheme based on a consortium blockchain framework. In this scheme, we design a demand-based vehicle priority classification method and propose a novel hybrid consensus mechanism—PhDPoR—which integrates practical byzantine fault tolerance (PBFT) and Hierarchical Delegated Proof of Reputation. Furthermore, we construct a multi-leader, multi-follower (MLMF) Stackelberg game model and utilize smart contracts to implement an immutable on-chain record of spectrum resource allocation, thereby deriving the optimal spectrum pricing and purchase strategy. Experimental results show that the proposed scheme not only effectively optimizes the utility of both supply and demand sides and improves overall social benefits while ensuring efficiency, but also significantly outperforms baseline algorithms in identifying and mitigating malicious nodes, thus verifying its feasibility and application value in complex IoV environments. Full article