Search Results (10,860)

This study focuses on improving the control system of vehicle suspension, which is critical for optimizing driving dynamics and enhancing passenger comfort. Traditional passive suspension systems are limited in their ability to effectively mitigate road-induced vibrations, often resulting in compromised ride quality and vehicle handling. To overcome these limitations, this work explores the application of active suspension control strategies aimed at improving both comfort and performance. Type-I and Type-II Fuzzy Logic Control (FLC) methods were designed and implemented to enhance vehicle stability and ride quality. The Harris Hawks Optimization (HHO) algorithm was employed to optimize the membership function parameters of both fuzzy control types. The system was tested under two distinct road disturbance inputs to evaluate performance. The designed control methods were evaluated in simulations where results demonstrated that the proposed active control approaches significantly outperformed the passive suspension system in terms of vibration reduction. Specifically, the Type-II FLC achieved a 54.7% reduction in vehicle body displacement and a 76.8% reduction in acceleration for the first road input, while improvements of 75.2% and 72.8% were recorded, respectively, for the second input. Performance was assessed using percentage-based metrics and Root Mean Square Error (RMSE) criteria. Numerical and graphical analyses of suspension deflection and tire deformation further confirm that the proposed control strategies substantially enhance both ride comfort and vehicle handling. Full article

Hydrogen-powered unmanned aerial vehicles (UAVs) offer significant advantages, such as environmental sustainability and extended endurance, demonstrating broad application prospects. However, the hydrogen fuel cells face prominent thermal management challenges during flight operations. This study established a numerical model of the fuel cell thermal management system (TMS) for a hydrogen-powered UAV. Computational fluid dynamics (CFD) simulations were subsequently performed to investigate the impact of various design parameters on cooling performance. First, the cooling performance of different fan density configurations was investigated. It was found that dispersed fan placement ensures substantial airflow through the peripheral flow channels, significantly enhancing temperature uniformity. Specifically, the nine-fan configuration achieves an 18.5% reduction in the temperature difference compared to the four-fan layout. Additionally, inlets were integrated with the fan-based cooling system. While increased external airflow lowers the minimum fuel cell temperature, its impact on high-temperature zones remains limited, with a temperature difference increase of more than 19% compared to configurations without inlets. Furthermore, the middle inlet exhibits minimal vortex interference, delivering superior thermal performance. This configuration reduces the maximum temperature and average temperature by 9.1% and 22.2% compared to the back configuration. Full article

While insulator integrity is critical for power grid stability, prevailing detection algorithms often rely on computationally intensive models incompatible with resource-constrained edge devices like unmanned aerial vehicles (UAVs). Key limitations—including redundant feature interference, inadequate sensitivity to small targets, rigid fusion weights, and sample imbalance—further restrict practical deployment. To address those problems, this study presents a lightweight insulator anomaly detection algorithm, LAI-YOLO. First, the SqueezeGate-C3k2 (SG-C3k2) module, equipped with an adaptive gating mechanism, is incorporated into the Backbone network to reduce redundant information during feature extraction. Secondly, we propose a High-level Screening–Feature Weighted Feature Pyramid Network (HS-WFPN) to replace FPN+PAN via selective weighted feature fusion, enabling dynamic cross-scale integration and enhanced small-target detection. Then, a reconstructed lightweight detection head coupled with Slide Weighted Focaler Loss (SWFocalerLoss) mitigates performance degradation from sample imbalance. Ultimately, the layer adaptation for the magnitude-based pruning (LAMP) technique slashes computational demands without sacrificing detection prowess. Experimental results on our insulator anomaly dataset demonstrate that the improved model achieves higher efficacy in identifying insulator anomalies, with mAP@0.5 increasing from 88.2% to 91.1%, while model parameters and FLOPs are diminished to 45.7% and 53.9% of the baseline, respectively. This efficiency facilitates the deployment of edge devices and highlights the method’s considerable application potential. Full article

Vehicular ad hoc networks (VANETs) demand authentication mechanisms that are both secure and privacy-preserving, particularly in light of emerging quantum-era threats. In this work, we propose a lattice-based identity authentication protocol that leverages pseudo-IDs to safeguard user privacy, while allowing the Trusted Authority (TA) to trace misbehaving vehicles when necessary. Compared with existing approaches, the proposed scheme strengthens accountability, improves scalability, and offers resistance against quantum attacks. A comprehensive complexity analysis is presented, addressing computational, communication, and storage overhead. Analysis results under practical parameter settings demonstrate that the protocol delivers robust security with manageable overhead, maintaining authentication latency within the real-time requirements of VANET applications. Full article

Historically, the Mediterranean Sea has been an area of cultural exchange and maritime commerce. One out of many submerged archaeological sites is the Roman shipwreck that was discovered in 2006 off the coast of Santo Stefano al Mare, in the Ligurian Sea, Italy. The wreck was dated to the 1st century B.C. and consists of a well-preserved cargo ship of Roman amphorae that were likely used for transporting wine. In this study, we present the results of the first underwater survey of the wreck using an Autonomous Underwater Vehicle (AUV) industrialized by Graal Tech. The AUV was equipped with a NORBIT WBMS multibeam sonar, a 450 kHz side-scan sonar, and inertial navigation systems. The AUV conducted multiple high-resolution surveys on the wreck site and the collected data were processed using geospatial analysis methods to highlight local anomalies directly related to the presence of the Roman shipwreck. The main feature was an accumulation of amphorae, covering an area of approximately 10 × 7 m with a maximum height of 1 m above the seabed. The results of this interdisciplinary work demonstrated the effectiveness of integrating AUV technologies with spatial analysis techniques for underwater archaeological applications. Furthermore, the success of this mission highlighted the potential for broader applications of AUVs in the study of the seafloor, such as monitoring seabed movements related to offshore underground energy storage or the identification of objects lying on the seabed, such as cables or pipelines. Full article

Thrust vectoring technology significantly improves the manoeuvrability and environmental adaptability of unmanned aerial vehicles by dynamically regulating the direction and magnitude of thrust. In this paper, the principles and applications of mechanical thrust vectoring technology, fluidic thrust vectoring technology and the distributed electric propulsion system are systematically reviewed. It is shown that the mechanical vector nozzle can achieve high-precision control but has structural burdens, the fluidic thrust vectoring technology improves the response speed through the design of no moving parts but is accompanied by the loss of thrust, and the distributed electric propulsion system improves the hovering efficiency compared with the traditional helicopter. Addressing multi-physics coupling and non-linear control challenges in unmanned aerial vehicles, this paper elucidates the disturbance compensation advantages of self-disturbance rejection control technology and the optimal path generation capabilities of an enhanced path planning algorithm. These two approaches offer complementary technical benefits: the former ensures stable flight attitude, while the latter optimises flight trajectory efficiency. Through case studies such as the Skate demonstrator, the practical value of these technologies in enhancing UAV manoeuvrability and adaptability is further demonstrated. However, thermal management in extreme environments, energy efficiency and lack of standards are still bottlenecks in engineering. In the future, breakthroughs in high-temperature-resistant materials and intelligent control architectures are needed to promote the development of UAVs towards ultra-autonomous operation. This paper provides a systematic reference for the theory and application of thrust vectoring technology. Full article

In response to the inability to quickly assess wind noise performance during the early stages of automotive styling design, this paper proposes a method for predicting interior wind noise by integrating automotive point cloud models with the Gray Wolf Optimization Residual Network model (GWO-ResNet). Based on wind tunnel test data under typical operating conditions, the point cloud model of the test vehicle is compressed using an auto-encoder and used as input features to construct a nonlinear mapping model between the whole vehicle point cloud and the wind noise level at the driver’s left ear. Through adaptive optimization of key hyperparameters of the ResNet model using the gray wolf optimization algorithm, the accuracy and generalization of the prediction model are improved. The prediction results on the test set indicate that the proposed GWO-ResNet model achieves prediction results that are consistent with the actual measured values for the test samples, thereby validating the effectiveness of the proposed method. A comparative analysis with traditional ResNet models, GWO-LSTM models, and LSTM models revealed that the GWO-ResNet model achieved Mean Absolute Percentage Error (MAPE) and mean squared error (MSE) of 9.72% and 20.96, and 9.88% and 19.69, respectively, on the sedan and SUV test sets, significantly outperforming the other comparison models. The prediction results on the independent validation set also demonstrate good generalization ability and stability (MAPE of 10.14% and 10.15%, MSE of 23.97 and 29.15), further proving the reliability of this model in practical applications. The research results provide an efficient and feasible technical approach for the rapid evaluation of wind noise performance in vehicles and provide a reference for wind noise control in the early design stage of vehicles. At the same time, due to the limitations of the current test data, it is impossible to predict the wind noise during the actual driving of the vehicle. Subsequently, the wind noise during actual driving can be predicted by the test data of multiple working conditions. Full article

High-performance electrical contact materials are crucial for electric power systems, new energy vehicles, and rail transportation, as their properties directly impact the reliability and safety of electronic devices. Enhancing these materials not only improves energy efficiency but also offers notable environmental and economic advantages. However, traditional composite contact materials often suffer from poor dispersion of the reinforcing phase, which restricts further performance improvement. Graphene (G), with its unique two-dimensional structure and exceptional electrical, mechanical, and tribological properties, is considered an ideal reinforcement for metal matrix composites. Yet, its tendency to agglomerate poses a significant challenge to achieving uniform dispersion. To overcome this, the study introduces a dual approach: modulation of the zeta potential (ζ) in the silver-plated liquid to enhance G’s dispersion stability, and concurrent optimization of the composite electrodeposition process. Experimental results demonstrate that this synergistic strategy enables the uniform distribution of G within the silver matrix. The resulting silver–graphene (Ag-G) composite coatings exhibit outstanding overall performance at both micro and macro levels. This work offers a novel and effective pathway for the design of advanced electrical contact materials with promising application potential. Full article

(1) Background: Structural Health Monitoring Systems (SHMSs) can reduce maintenance costs and aircraft downtime. However, their economic impact remains underexplored, particularly in cost–benefit terms. (2) Methods: This study conducted a targeted literature review on all the existing studies consisting of seventeen economic analyses of SHMS applications. Key features—such as SHMS type, structural material, vehicle type, integration stage, and cost elements—were classified to identify prevailing trends and gaps. (3) Results: The analysis revealed a predominance of piezoelectric-based SHMS applied to metallic fixed-wing aircraft, with limited attention to composite structures and e-VTOLs. Most studies focused on maintenance phase impacts, overlooking integration costs during manufacturing. Potential benefits like operational life extension, prognostic capabilities, and safety margin reduction were rarely explored, while critical drawbacks such as detection performance, reliability, and power consumption were underrepresented. Maintenance and fuel costs were the most frequently considered economic drivers; downtime costs were often neglected. (4) Conclusions: Although the majority of reviewed studies suggest a positive economic impact from SHMS implementation, significant gaps remain. Future research should address SHMS reliability, integration during early design stages, and applications to emerging aircraft like e-VTOLs to fully realize SHMS economic advantages. Full article

This paper explores the development and implementation of a brain–computer interface (BCI) system that utilizes electroencephalogram (EEG) signals for real-time monitoring of attention levels to control unmanned aerial vehicles (UAVs). We propose an innovative approach that combines spectral power analysis and machine learning classification techniques to translate cognitive states into precise UAV command signals. This method overcomes the limitations of traditional threshold-based approaches by adapting to individual differences and improving classification accuracy. Through comprehensive testing with 20 participants in both controlled laboratory environments and real-world scenarios, our system achieved an 85% accuracy rate in distinguishing between high and low attention states and successfully mapped these cognitive states to vertical UAV movements. Experimental results demonstrate that our machine learning-based classification method significantly enhances system robustness and adaptability in noisy environments. This research not only advances UAV operability through neural interfaces but also broadens the practical applications of BCI technology in aviation. Our findings contribute to the expanding field of neurotechnology and underscore the potential for neural signal processing and machine learning integration to revolutionize human–machine interaction in industries where dynamic relationships between cognitive states and automated systems are beneficial. Full article

Over the past decade, digitalization in the logistics sector has heightened the significance of autonomous systems and AI-based applications, while the integration of advanced deep learning technologies with air cargo carriers has ushered in a new era in the logistics industry. This study systematically addresses the current applications of these technological advances in logistics planning, the challenges faced, and perspectives for the future. These developments are transforming the role of UAVs and autonomous systems in logistics operations by improving last-mile efficiency and reducing costs. Key functions of autonomous vehicles, including environmental perception, decision-making, and route optimization, have shown notable progress through deep learning algorithms. However, major obstacles remain to their widespread adoption, particularly in terms of energy efficiency, data security, and the absence of a mature regulatory framework. Accordingly, this paper discusses these issues in detail and highlights areas for further research. This systematic literature review reveals the disruptive potential of AACV for the logistics industry and presents findings that can guide both academic inquiry and industrial practice. The results underscore that establishing a sustainable and efficient logistics ecosystem is essential in the context of these emerging technologies. Full article

Accurate prediction of the RUL of electric drive bearings over the entire service life cycle for new energy vehicles optimizes maintenance strategies and reduces costs, addressing clear application needs. Full life data of electric drive bearings exhibit long time spans and abrupt degradation, complicating the modeling of time dependent relationships and degradation states; therefore, a piecewise linear degradation model is appropriate. An RUL prediction method is proposed based on degradation assessment and spatiotemporal feature fusion, which extracts strongly time correlated features from bearing vibration data, evaluates sensitive indicators, constructs weighted fused degradation features, and identifies abrupt degradation points. On this basis, a piecewise linear degradation model is constructed that uses a path graph structure to represent temporal dependencies and a temporal observation window to embed temporal features. By incorporating GAT-LSTM, RUL prediction for bearings is performed. The method is validated on the XJTU-SY dataset and on a loaded ball bearing test rig for electric vehicle drive motors, yielding comprehensive vibration measurements for life prediction. The results show that the method captures deep degradation information across the full bearing life cycle and delivers accurate, robust predictions, providing guidance for the health assessment of electric drive bearings in new energy vehicles. Full article

In the evaluation of vehicle noise, vibration and harshness (NVH) performance, interior noise control is the core consideration. In the early stage of automobile research and development, accurate prediction of interior noise caused by road surface is very important for optimizing NVH performance and shortening the development cycle. Although the data-driven machine learning method has been widely used in automobile noise research due to its advantages of no need for accurate physical modeling, data learning and generalization ability, it still faces the challenge of insufficient accuracy in capturing key local features, such as peaks, in practical NVH engineering. Aiming at this challenge, this paper introduces a forecast approach that utilizes an empirical-informed neural network, which aims to integrate a physical mechanism and a data-driven method. By deeply analyzing the transmission path of interior noise, this method embeds the acoustic mechanism features such as local peak and noise correlation into the deep neural network as physical constraints; therefore, this approach significantly enhances the model’s predictive performance. Experimental findings indicate that, in contrast to conventional deep learning techniques, this method is able to develop better generalization capabilities with limited samples, while still maintaining prediction accuracy. In the verification of specific models, this method shows obvious advantages in prediction accuracy and computational efficiency, which verifies its application value in practical engineering. The main contributions of this study are the proposal of an empirical-informed neural network that embeds vibro-acoustic mechanisms into the loss function and the introduction of an adaptive weight strategy to enhance model robustness. Full article

All over the world, the problem of ensuring the safety of pedestrians, who are the most vulnerable road users, is becoming more acute due to urbanization and the growth of micromobility. In 2013, according to WHO data, more than 270 thousand pedestrians were dying each year worldwide (accounting for 22% of all traffic accidents). Currently, experts report that around 1.3 million people die every year globally from road crashes. The roads in developing countries are particularly hazardous, according to experts, because the increase in the number of vehicles far exceeds the development of road infrastructure and safety systems. Since the risk of hitting a pedestrian depends on many factors that can have different natures, and the severity of the consequences can be determined by a set of other factors, the risk of an accident can only be reduced by influencing all these factors in a comprehensive manner. The novelty of our approach is to create an intelligent system that will gradually accumulate all the best practices into a single complex aimed at reducing the risk of an accident with pedestrians and the severity of the consequences if an accident does occur. The distinction lies in offering an integrated system where each module addresses a particular task, so by mitigating risks at every stage, one achieves a synergistic outcome. From the analysis of existing and applied developments, it is known that many specialists mainly solve a narrowly focused problem aimed at ensuring the one subsystems sustainability in the “vehicle-infrastructure-driver-pedestrian” system. Some of these ideas are given as practical examples. The relevance of the designated problem increases with the emergence of autonomous vehicles and smart cities, the sustainability of which depends on the sustainable interaction between all road users. As experience shows, only the implementation of comprehensive solutions allows us to solve strategic problems, including improving road safety. Here, by complex solutions we mean solutions that combine technical issues, as well as environmental, social, and managerial aspects. To account for different kinds of effects, indicator systems are developed and composite indices are computed to choose the most rational solution. The novelty of our approach consists in combining within a unified DSS algorithms for assessing the efficiency of the proposed solution with respect to technological soundness, environmental sustainability, economic viability, social acceptability, as well as administrative rationality and computation of interrelated effects resulting from implementing any given project. In our opinion, the proposed system will lead to a synergistic effect due to the integrated application of various developments, which will ensure increased sustainability and safety of the transport system of smart cities. Our paper proposes a conceptual approach to addressing pedestrian safety, and the examples provided illustrate how the same model or algorithm can lead to positive changes from different perspectives. Full article

Flapping-Wing Aerial Vehicles (FWAVs), which take inspiration from the flight of birds and insects, have gained increasing attention over the past decades due to advantages such as low noise, biomimicry and safety, enabled by the absence of propellers. These features make them particularly suitable for applications in natural environments and operations near humans. However, their complexity introduces significant challenges, including difficulties in take-off and landing as well as limited endurance. Perching represents a promising solution to address these limitations. By equipping these drones with a perching mechanism, they could land on branches to save energy and later exploit the altitude to resume flight without requiring human intervention. Specifically, this review focuses on perching mechanisms based on grasping. It presents designs developed for flapping-wing platforms and complements them with systems originally intended for other types of aerial robots, evaluating their applicability to FWAV applications. The purpose of this work is to provide a structured overview of the existing strategies to support the development of new, effective solutions that could enhance the use of FWAVs in real-world applications. Full article