Search Results (2,170)

Timely and accurate identification of surface damage in hydraulic structures is essential for maintaining structural integrity and ensuring operational safety. Traditional manual inspections are time-consuming, labor-intensive, and prone to subjectivity, especially for large-scale or inaccessible infrastructure. Leveraging advancements in aerial imaging, unmanned aerial vehicles (UAVs) enable efficient acquisition of high-resolution visual data across expansive hydraulic environments. However, existing deep learning (DL) models often lack architectural adaptations for the visual complexities of UAV imagery, including low-texture contrast, noise interference, and irregular crack patterns. To address these challenges, this study proposes a lightweight, robust, and high-precision segmentation framework, called LFPA-EAM-Fast-SCNN, specifically designed for pixel-level damage detection in UAV-captured images of hydraulic concrete surfaces. The developed DL-based model integrates an enhanced Fast-SCNN backbone for efficient feature extraction, a Lightweight Feature Pyramid Attention (LFPA) module for multi-scale context enhancement, and an Edge Attention Module (EAM) for refined boundary localization. The experimental results on a custom UAV-based dataset show that the proposed damage detection method achieves superior performance, with a precision of 0.949, a recall of 0.892, an F1 score of 0.906, and an IoU of 87.92%, outperforming U-Net, Attention U-Net, SegNet, DeepLab v3+, I-ST-UNet, and SegFormer. Additionally, it reaches a real-time inference speed of 56.31 FPS, significantly surpassing other models. The experimental results demonstrate the proposed framework’s strong generalization capability and robustness under varying noise levels and damage scenarios, underscoring its suitability for scalable, automated surface damage assessment in UAV-based remote sensing of civil infrastructure. Full article

Inspection, maintenance, and repair (IMR) operations on underwater infrastructure remain costly and time-intensive because fully teleoperated remote operated vehicle s(ROVs) lack the range and dexterity necessary for precise cooperative underwater manipulation, and the alternative of using professional divers is ruled out due to the risk involved. This work presents and experimentally validates an autonomous, dual-I-AUV (Intervention–Autonomous Underwater Vehicle) system capable of assembling rigid pipeline segments through coordinated actions in a confined underwater workspace. The first I-AUV is a Girona 500 (4-DoF vehicle motion, pitch and roll stable) fitted with multiple payload cameras and a 6-DoF Reach Bravo 7 arm, giving the vehicle 10 total DoF. The second I-AUV is a BlueROV2 Heavy equipped with a Reach Alpha 5 arm, likewise yielding 10 DoF. The workflow comprises (i) detection and grasping of a coupler pipe section, (ii) synchronized teleoperation to an assembly start pose, and (iii) assembly using a kinematic controller that exploits the Girona 500’s full 10 DoF, while the BlueROV2 holds position and orientation to stabilize the workspace. Validation took place in a 12 m × 8 m × 5 m water tank. Results show that the paired I-AUVs can autonomously perform precision pipeline assembly in real water conditions, representing a significant step toward fully automated subsea construction and maintenance. Full article

Monitoring crop damage caused by wildlife remains a significant challenge in agricultural management, particularly in the case of large-scale monocultures such as maize. The given study presents a semi-automated process for detecting wildlife-induced damage using RGB imagery acquired from unmanned aerial vehicles (UAVs). The method is designed for non-specialist users and is fully integrated within the QGIS platform. The proposed approach involves calculating three vegetation indices—Excess Green (ExG), Green Leaf Index (GLI), and Modified Green-Red Vegetation Index (MGRVI)—based on a standardized orthomosaic generated from RGB images collected via UAV. Subsequently, an unsupervised k-means clustering algorithm was applied to divide the field into five vegetation vigor classes. Within each class, 25% of the pixels with the lowest average index values were preliminarily classified as damaged. A dedicated QGIS plugin enables drone data analysts (Drone Data Analysts—DDAs) to adjust index thresholds, based on visual interpretation, interactively. The method was validated on a 50-hectare maize field, where 7 hectares of damage (15% of the area) were identified. The results indicate a high level of agreement between the automated and manual classifications, with an overall accuracy of 81%. The highest concentration of damage occurred in the “moderate” and “low” vigor zones. Final products included vigor classification maps, binary damage masks, and summary reports in HTML and DOCX formats with visualizations and statistical data. The results confirm the effectiveness and scalability of the proposed RGB-based procedure for crop damage assessment. The method offers a repeatable, cost-effective, and field-operable alternative to multispectral or AI-based approaches, making it suitable for integration with precision agriculture practices and wildlife population management. Full article

The increasing demand for noninvasive inspection (NII) of complex civil infrastructures requires overcoming the limitations of traditional ground-penetrating radar (GPR) systems in addressing diverse and large-scale applications. The solution proposed in this study focuses on an initial design that integrates a low-SWaP (Size, Weight, and Power) ultra-wideband (UWB) impulse radar with realistic electromagnetic modeling for deployment on unmanned aerial vehicles (UAVs). The system incorporates ultra-realistic antenna and propagation models, utilizing Finite Difference Time Domain (FDTD) solvers and multilayered media, to replicate realistic airborne sensing geometries. Verification and calibration are performed by comparing simulation outputs with laboratory measurements using varied material samples and target models. Custom signal processing algorithms are developed to extract meaningful features from complex electromagnetic environments and support anomaly detection. Additionally, machine learning (ML) techniques are trained on synthetic data to automate the identification of structural characteristics. The results demonstrate accurate agreement between simulations and measurements, as well as the potential for deploying this design in flight tests within realistic environments featuring complex electromagnetic interference. Full article

As a new generation of mobile communication networks, 6G security faces many new security challenges. Vehicle to Everything (V2X) will be an important part of 6G. In V2X, connected and automated vehicles (CAVs) need to frequently share data with other vehicles and infrastructures. Therefore, identity revocation technology in the authentication is an important way to secure CAVs and other 6G scenario applications. This paper proposes an efficient credential revocation scheme with a four-layer architecture. First, a rapid pre-filtration layer is constructed based on the cuckoo filter, responsible for the initial screening of credentials. Secondly, a directed routing layer and the precision judgement layer are designed based on the consistency hash and the dynamic RSA accumulator. By proposing the dynamic expansion of the RSA accumulator and load-balancing algorithm, a smaller and more stable revocation delay can be achieved when many users and terminal devices access 6G. Finally, a trusted storage layer is built based on the blockchain, and the key revocation parameters are uploaded to the blockchain to achieve a tamper-proof revocation mechanism and trusted data traceability. Based on this architecture, this paper also proposes a detailed identity credential revocation and verification process. Compared to existing solutions, this paper’s solution has a combined average improvement of 59.14% in the performance of the latency of the cancellation of the inspection, and the system has excellent load balancing, with a standard deviation of only 11.62, and the maximum deviation is controlled within the range of ±4%. Full article

Teleoperation emerged as a promising fallback for situations beyond the capabilities of automated vehicles. Nevertheless, teleoperation still faces challenges, such as reduced situational awareness. Since situational awareness is primarily built through the remote operator’s visual perception, the graphical user interface (GUI) design is critical. In addition to video feed, supplemental informational elements are crucial—not only for the predominantly studied remote driving, but also for emerging desk-based remote assistance concepts. This work develops a GUI for different teleoperation concepts by identifying key informational elements during the teleoperation process through expert interviews (N = 9). Following this, a static and dynamic GUI prototype was developed and evaluated in a click dummy study (N = 36). Thereby, the dynamic GUI adapts the number of displayed elements according to the teleoperation phase. Results show that both GUIs achieve good system usability scale (SUS) ratings, with the dynamic GUI significantly outperforming the static version in both usability and task completion time. However, the results might be attributable to a learning effect due to the lack of randomization. The user experience questionnaire (UEQ) score shows potential for improvement. To enhance the user experience, the GUI should be evaluated in a follow-up study that includes interaction with a real vehicle. Full article

Automated vehicles controlled by artificial intelligence are becoming capable of making moral decisions independently. This study investigates the differences in participants’ perceptions of the moral decision-maker’s permissibility when viewing scenarios (pre-test) and after witnessing the outcomes of moral decisions (post-test). It also investigates how permissibility, ten typical moral emotions, and perceived moral agency fluctuate when AI and the human driver make deontological or utilitarian decisions in a pedestrian-sacrificing dilemma (Experiment 1, N = 254) and a driver-sacrificing dilemma (Experiment 2, N = 269) from a third-person perspective. Moreover, by conducting binary logistic regression, this study examined whether these factors could predict the non-decrease in permissibility ratings. In both experiments, participants preferred to delegate decisions to human drivers rather than to AI, and they generally preferred utilitarianism over deontology. The results of perceived moral emotions and moral agency provide evidence. Moreover, Experiment 2 elicited greater variations in permissibility, moral emotions, and perceived moral agency compared to Experiment 1. Moreover, deontology and gratitude could positively predict the non-decrease in permissibility ratings in Experiment 1, while contempt had a negative influence. In Experiment 2, the human driver and disgust were significant negative predictor factors, while perceived moral agency had a positive influence. These findings deepen the comprehension of the dynamic processes of autonomous driving’s moral decision-making and facilitate understanding of people’s attitudes toward moral machines and their underlying reasons, providing a reference for developing more sophisticated moral machines. Full article

Visual inspection of civil infrastructure for structural health assessment, as performed by structural engineers, is expensive and time-consuming. Therefore, automating this process is highly attractive, which has received significant attention in recent years. With the increasing capabilities of computers, deep neural networks have become a standard tool and can be used for structural health inspections. A key challenge, however, is the availability of reliable datasets. In this work, the U-net and DeepLab v3+ convolutional neural networks are trained on a synthetic Tokaido dataset. This dataset comprises images representative of data acquired by unmanned aerial vehicle (UAV) imagery and corresponding ground truth data. The data includes semantic segmentation masks for both categorizing structural elements (slabs, beams, and columns) and assessing structural damage (concrete spalling or exposed rebars). Data augmentation, including both image quality degradation (e.g., brightness modification, added noise) and image transformations (e.g., image flipping), is applied to the synthetic dataset. The selected neural network architectures achieve excellent performance, reaching values of 97% for accuracy and 87% for Mean Intersection over Union (mIoU) on the validation data. It also demonstrates promising results in the semantic segmentation of real-world structures captured in photographs, despite being trained solely on synthetic data. Additionally, based on the obtained results of semantic segmentation, it can be concluded that DeepLabV3+ outperforms U-net in structural component identification. However, this is not the case in the damage identification task. Full article

Inter-turn short-circuit (ITSC) faults in permanent magnet synchronous machines (PMSMs) present a significant reliability challenge in electric vehicle (EV) drivetrains, particularly under non-stationary operating conditions characterized by inverter-driven transients, variable loads, and magnetic saturation. Existing diagnostic approaches, including motor current signature analysis (MCSA) and wavelet-based methods, are primarily designed for steady-state conditions and rely on manual feature selection, limiting their applicability in real-time embedded systems. Furthermore, the lack of publicly available, high-fidelity datasets capturing the transient dynamics and nonlinear flux-linkage behaviors of PMSMs under fault conditions poses an additional barrier to developing data-driven diagnostic solutions. To address these challenges, this study introduces a simulation framework that generates a comprehensive dataset using finite element method (FEM) models, incorporating magnetic saturation effects and inverter-driven transients across diverse EV operating scenarios. Time-frequency features extracted via Discrete Wavelet Transform (DWT) from stator current signals are used to train a Transformer model for automated ITSC fault detection. The Transformer model, leveraging self-attention mechanisms, captures both local transient patterns and long-range dependencies within the time-frequency feature space. This architecture operates without sequential processing, in contrast to recurrent models such as LSTM or RNN models, enabling efficient inference with a relatively low parameter count, which is advantageous for embedded applications. The proposed model achieves 97% validation accuracy on simulated data, demonstrating its potential for real-time PMSM fault detection. Additionally, the provided dataset and methodology contribute to the facilitation of reproducible research in ITSC diagnostics under realistic EV operating conditions. Full article

This study examines the extended dissipativity analysis for newly designed mixed traffic systems (MTSs) utilizing the coupling memory sampled-data control (CMSDC) approach. The traffic flow creates a platoon, and the behavior of human-driven vehicles (HDVs) is presumed to adhere to the optimal velocity model, with the acceleration of a single-linked automated vehicle regulated directly by a suggested CMSDC. The ultimate objective of this work is to present a CMSDC approach for optimizing traffic flow amidst disruptions. The primary emphasis is on the proper design of the CMSDC to ensure that the closed-loop MTS is extended dissipative and quadratically stable. A more generalized CMSDC methodology incorporating a time delay effect is created using a Bernoulli-distributed sequence. The existing Lyapunov–Krasovskii functional (LKF) and enhanced integral inequality methods offer sufficient conditions for the suggested system to achieve an extended dissipative performance index. The suggested criteria provide a comprehensive dissipative study, evaluating $L_2-L_{\infty }$, $H_{\infty }$, passivity, and dissipativity performance. A simulation example illustrates the accuracy and superiority of the proposed controller architecture for the MTS. Full article

Modern vehicles are rapidly transforming into interconnected cyber–physical systems that rely on advanced sensor technologies and pervasive connectivity to support autonomous functionality. Yet, despite this evolution, standardized methods for quantifying cybersecurity vulnerabilities across critical automotive components remain scarce. This paper introduces a novel hybrid model that integrates expert-driven insights with generative AI tools to adapt and extend the Common Vulnerability Scoring System (CVSS) specifically for autonomous vehicle sensor systems. Following a three-phase methodology, the study conducted a systematic review of 16 peer-reviewed sources (2018–2024), applied CVSS version 4.0 scoring to 15 representative attack types, and evaluated four free source generative AI models—ChatGPT, DeepSeek, Gemini, and Copilot—on a dataset of 117 annotated automotive-related vulnerabilities. Expert validation from 10 domain professionals reveals that Light Detection and Ranging (LiDAR) sensors are the most vulnerable (9 distinct attack types), followed by Radio Detection And Ranging (radar) (8) and ultrasonic (6). Network-based attacks dominate (104 of 117 cases), with 92.3% of the dataset exhibiting low attack complexity and 82.9% requiring no user interaction. The most severe attack vectors, as scored by experts using CVSS, include eavesdropping (7.19), Sybil attacks (6.76), and replay attacks (6.35). Evaluation of large language models (LLMs) showed that DeepSeek achieved an F1 score of 99.07% on network-based attacks, while all models struggled with minority classes such as high complexity (e.g., ChatGPT F1 = 0%, Gemini F1 = 15.38%). The findings highlight the potential of integrating expert insight with AI efficiency to deliver more scalable and accurate vulnerability assessments for modern vehicular systems.This study offers actionable insights for vehicle manufacturers and cybersecurity practitioners, aiming to inform strategic efforts to fortify sensor integrity, optimize network resilience, and ultimately enhance the cybersecurity posture of next-generation autonomous vehicles. Full article

Remote sensing tools have been proposed to assist with rice crop monitoring but have been developed and validated on ponded rice. This two-year study was conducted on a commercial rice farm with irrigation automation technology aimed to (i) understand how canopy reflectance differs between high-yielding ponded and aerobic rice, (ii) validate the feasibility of using the squared simplified canopy chlorophyll content index (SCCCI²) for N uptake estimates, and (iii) explore the SCCCI² and similar chlorophyll-sensitive indices for grain quality monitoring. Multispectral images were collected from an unmanned aerial vehicle during both rice-growing seasons. Above-ground biomass and nitrogen (N) uptake were measured at panicle initiation (PI). The performance of single-vegetation-index models in estimating rice N uptake, as previously published, was assessed. Yield and grain quality were determined at harvest. Results showed that canopy reflectance in the visible and near-infrared regions differed between aerobic and ponded rice early in the growing season. Chlorophyll-sensitive indices showed lower values in aerobic rice than in the ponded rice at PI, despite having similar yields at harvest. The SCCCI² model (RMSE = 20.52, Bias = −6.21 Kg N ha⁻¹, and MAPE = 11.95%) outperformed other models assessed. The SCCCI², squared normalized difference red edge index, and chlorophyll green index correlated at PI with the percentage of cracked grain, immature grain, and quality score, suggesting that grain milling quality parameters could be associated with N uptake at PI. This study highlights canopy reflectance differences between high-yielding aerobic (averaging 15 Mg ha⁻¹) and ponded rice at key phenological stages and confirms the validity of a single-vegetation-index model based on the SCCCI² for N uptake estimates in ponded and non-ponded rice crops. Full article

This paper proposes an AI-driven automated test generation framework for vehicle control units (VCUs), integrating natural language processing (NLP) and dynamic variable binding. To address the critical limitation of traditional AI-generated test cases lacking executable variables, the framework establishes a closed-loop transformation from requirements to executable code through a five-layer architecture: (1) structured parsing of PDF requirements using domain-adaptive prompt engineering; (2) construction of a multidimensional variable knowledge graph; (3) semantic atomic decomposition of requirements and logic expression generation; (4) dynamic visualization of cause–effect graphs; (5) path-sensitization-driven optimization of test sequences. Validated on VCU software from a leading OEM, the method achieves 97.3% variable matching accuracy and 100% test case executability, reducing invalid cases by 63% compared to conventional NLP approaches. This framework provides an explainable and traceable automated solution for intelligent vehicle software validation, significantly enhancing efficiency and reliability in automotive testing. Full article

The accurate estimation and prediction of internal states in lithium-ion (Li-Ion) batteries, such as State of Charge (SoC) and Remaining Useful Life (RUL), are vital for optimizing battery performance, safety, and longevity in electric vehicles and other applications. This paper presents a unified, modular, and extensible machine learning (ML) framework designed to address the heterogeneity and complexity of battery state prediction tasks. The proposed framework supports flexible configurations across multiple dimensions, including feature engineering, model selection, and training/testing strategies. It integrates standardized data processing pipelines with a diverse set of ML models, such as a long short-term memory neural network (LSTM), a convolutional neural network (CNN), a feedforward neural network (FFNN), automated machine learning (AutoML), and classical regressors, while accommodating heterogeneous datasets. The framework’s applicability is demonstrated through five distinct use cases involving SoC estimation and RUL prediction using real-world and benchmark datasets. Experimental results highlight the framework’s adaptability, methodological transparency, and robust predictive performance across various battery chemistries, usage profiles, and degradation conditions. This work contributes to a standardized approach that facilitates the reproducibility, comparability, and practical deployment of ML-based battery analytics. Full article

Given the rising importance of cost-effective solutions in battery research, this study employs an accessible testing approach using low-cost, sensor-equipped platforms that enable broader research and educational applications. It presents a comparative evaluation of lithium-ion battery degradation under two charging strategies: static charging (constant current at 1.2 A) and dynamic charging (stepped current from 400 mA to 800 mA) over 200 charge–discharge cycles. A custom-built, low-cost test platform based on an ESP32 microcontroller was developed to provide real-time monitoring of voltage, current, temperature, and internal resistance, with automated control and cloud-based data logging. The results indicate that static charging provides greater voltage stability and a lower increase in internal resistance (9.3%) compared to dynamic charging (30.17%), suggesting reduced electrochemical stress. Discharge time decreased for both strategies, by 6.25% under static charging and 18.46% under dynamic charging, highlighting capacity fade and aging effects. Internal resistance emerged as a reliable indicator of degradation, closely correlating with reduced runtime. These findings underscore the importance of selecting charging profiles based on specific application needs, as dynamic charging, while offering potential thermal benefits, may accelerate battery aging. Furthermore, the low-cost testing platform proved effective for long-term evaluation and degradation analysis, offering an accessible alternative to commercial battery cyclers. The insights gained contribute to the development of adaptive battery management systems that optimize performance, lifespan, and safety in electric vehicle applications. Full article