Search Results (4,056)

Accurate and energy-efficient localization of autonomous underwater vehicles (AUVs) remains a fundamental challenge due to the complex, bandwidth-limited, and highly dynamic nature of underwater acoustic environments. This paper proposes a fully adaptive deep reinforcement learning (DRL)-driven localization framework for AUVs operating in Underwater Acoustic Sensor Networks (UAWSNs). The localization problem is formulated as a Markov Decision Process (MDP) in which an intelligent agent jointly optimizes beacon selection and transmit power allocation to minimize long-term localization error and energy consumption. A hierarchical learning architecture is developed by integrating four actor–critic algorithms, which are (i) Twin Delayed Deep Deterministic Policy Gradient (TD3), (ii) Soft Actor–Critic (SAC), (iii) Multi-Agent Deep Deterministic Policy Gradient (MADDPG), and (iv) Distributed DDPG (D2DPG), enabling robust learning under non-stationary channels, cooperative multi-AUV scenarios, and large-scale deployments. A round-trip time (RTT)-based geometric localization model incorporating a depth-dependent sound speed gradient is employed to accurately capture realistic underwater acoustic propagation effects. A multi-objective reward function jointly balances localization accuracy, energy efficiency, and ranging reliability through a risk-aware metric. Furthermore, the Cramér–Rao Lower Bound (CRLB) is derived to characterize the theoretical performance limits, and a comprehensive complexity analysis is performed to demonstrate the scalability of the proposed framework. Extensive Monte Carlo simulations show that the proposed DRL-based methods achieve significantly lower localization error, lower energy consumption, faster convergence, and higher overall system utility than classical TD3. These results confirm the effectiveness and robustness of DRL for next-generation adaptive underwater localization systems. Full article

Research on road transport infrastructures is rapidly evolving as electrification, automation, and digital connectivity reshape how systems are designed, operated, and managed. This study presents a combined bibliometric, scientometric, and network analysis of 2755 publications published between 2021 and 2025 to map the intellectual structure, main contributors, and dominant technological themes shaping contemporary road transport research. Using data from the Web of Science Core Collection, co-occurrence mapping, thematic analysis, and collaboration networks were generated using Bibliometrix and VOSviewer. The results reveal strong growth in research output, with China, the United States, and Europe forming the core of high-impact publication and collaboration networks. Six bibliometric clusters were identified and consolidated into three overarching domains: road transport systems, emphasizing vehicle dynamics, control, and real-time computational frameworks; energy and efficiency-oriented mobility research, focusing on electrification, optimization, and infrastructure integration; and emerging digital technologies, including IoT, AI, and autonomous vehicles. The analysis highlights persistent research gaps related to interoperability, cybersecurity, large-scale deployment, and governance of intelligent transport infrastructures. Overall, the findings provide a data-driven overview of current research priorities and structural patterns shaping next-generation road transport systems. Full article

The rapid integration of unmanned aerial vehicles (UAVs) into next-generation wireless systems demands seamless and reliable handover (HO) mechanisms to ensure continuous connectivity. However, frequent topology changes, high mobility, and dynamic channel variations make traditional HO schemes inadequate for UAV-assisted 6G networks. This paper presents a comprehensive review of existing HO optimization studies, emphasizing artificial intelligence (AI) and machine learning (ML) approaches as enablers of intelligent mobility management. The surveyed works are categorized into three main scenarios: non-UAV HOs, UAVs acting as aerial base stations, and UAVs operating as user equipment, each examined under traditional rule-based and AI/ML-based paradigms. Comparative insights reveal that while conventional methods remain effective for static or low-mobility environments, AI- and ML-driven approaches significantly enhance adaptability, prediction accuracy, and overall network robustness. Emerging techniques such as deep reinforcement learning and federated learning (FL) demonstrate strong potential for proactive, scalable, and energy-efficient HO decisions in future 6G ecosystems. The paper concludes by outlining key open issues and identifying future directions toward hybrid, distributed, and context-aware learning frameworks for resilient UAV-enabled HO management. Full article

Urban rail transit networks in developing countries are rapidly expanding, entering a networked operational phase where accurate passenger flow forecasting is crucial for optimizing vehicle scheduling, resource allocation, and transportation efficiency. In the short term, accurate real-time forecasting enables the dynamic adjustment of train headways and crew deployment, reducing average passenger waiting times during peak hours and alleviating platform overcrowding; in the long term, reliable trend predictions support strategic planning, including capacity expansion, station retrofitting, and energy management. This paper proposes a novel hybrid forecasting model, EEMD-TiDE, that combines improved Ensemble Empirical Mode Decomposition (EEMD) with a Time Series Dense Encoder (TiDE) to enhance prediction accuracy. The EEMD algorithm effectively overcomes mode mixing issues in traditional EMD by incorporating white noise perturbations, decomposing raw passenger flow data into physically meaningful Intrinsic Mode Functions (IMFs). At the same time, the TiDE model, a linear encoder–decoder architecture, efficiently handles multi-scale features and covariates without the computational overhead of self-attention mechanisms. Experimental results using Xi’an Metro passenger flow data (2017–2019) demonstrate that EEMD-TiDE significantly outperforms baseline models. This study provides a robust solution for urban rail transit passenger flow forecasting, supporting sustainable urban development. Full article

This paper presents a hierarchical model architecture for the analysis and optimization of Fuel Cell Electric Vehicles (FCEVs). The model encompasses the levels of cell, stack, and complete vehicle, which are interconnected through clearly defined transfer parameters. At the cell level, electrochemical and thermodynamic processes are mapped, the results of which are aggregated at the stack level into characteristic maps such as current–voltage curves and efficiency profiles. These maps serve as interfaces to the vehicle level, where the electric powertrain—comprising the fuel cell, energy storage, electric motor, and auxiliary consumers—is integrated. Special attention is given to the trade-off between the lifetime and dynamics of the fuel cell, which is methodically captured through variable parameter vectors. The transfer parameters enable consistent and scalable modelling that considers both detailed cell and stack information as well as vehicle-side requirements. On this basis, various vehicle configurations can be evaluated and optimized with regard to efficiency, lifetime, and drivability. Full article

The growing demand for electric vehicles, renewable energy systems, and portable electronics has led to the widespread adoption of power conversion systems. Although advanced structures like the superjunction MOSFET have prolonged the viability of silicon in power applications, maintaining its dominance through cost efficiency, Si-based technology is ultimately constrained by its intrinsic limitations in critical electric fields. To address these constraints, research into wide bandgap semiconductors aims to minimize system footprint while maximizing efficiency. This study reviews the semiconductor landscape, demonstrating why Gallium Nitride (GaN) has emerged as the most promising technology for next-generation power applications. With a critical electric field of $3.75\mathrm{MV}/\mathrm{cm}$ ($12.5\times$ higher than Si), GaN facilitates power devices with lower conduction loss and higher frequency capability when compared to their Si counterpart. Furthermore, this paper surveys the GaN ecosystem, ranging from device modeling and packaging to monolithic ICs and switching converter implementations based on discrete transistors. While existing literature primarily focuses on discrete devices, this work addresses the critical gap regarding GaN monolithic integration. It synthesizes key challenges and achievements in the design of GaN integrated circuits, providing a comprehensive review that spans semiconductor technology, monolithic circuit architectures, and system-level applications. Reported data demonstrate monolithic stages reaching $30\mathrm{m}\Omega$ and $25\mathrm{MHz}$, exceeding Si performance limits. Additionally, the study reports on high-density hybrid implementations, such as a space-grade POL converter achieving $123.3\mathrm{kW}/\mathrm{L}$ with $90.9\%$ efficiency. Full article

Accurately determining actual energy consumption is essential for guiding technological developments in the transport sector, assessing vehicle development outcomes, and designing effective energy and climate policies. Although laboratory driving cycles such as the WLTP provide standardized benchmarks, they do not reflect the complex interactions between human behavior, environmental conditions, and vehicle dynamics under real-world operating conditions. This article presents an integrated framework for assessing long-term, actual energy carrier consumption in four main vehicle categories: internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), hydrogen fuel cell electric vehicles (H2EVs), and battery electric vehicles (BEVs). The entire discussion here is based on the results of data analysis from natural operation using the so-called vehicle energy footprint. This framework provides a method for determining the average energy carrier consumption for each group of vehicles with the specified drivetrains. This information formed the basis for assessing the total energy demand for the operation of the analyzed vehicle types in normal operation. The simulations show that among mid-range passenger vehicles, ICEVs are the most energy-intensive in normal operation, followed by H2EVs and HEVs, and BEVs are the least. This study highlights the methodological challenges and implications of accurately quantifying energy consumption. The presented method for assessing energy demand in vehicle operation can be useful for manufacturers, consumers, fleet operators, and policymakers, particularly in terms of energy efficiency, emission reduction, and public health protection. Full article

The traditional ultrasonic bonding technique for IGBT T2 copper terminals often causes physical damage to ceramic substrates, severely compromising the reliability of power modules. Meanwhile, T2 copper laser welding faces inherent challenges including low laser absorption efficiency and unstable molten pool dynamics. To address these issues, this study targets the high-quality connection of IGBT T2 copper terminals and proposes a welding solution integrating a Fiber-Diode Hybrid Laser system with galvo-scanning technology. Comparative experiments between galvo-scanning and traditional oscillation methods CNC scanning were conducted under sinusoidal and circular trajectories to explore the regulation mechanism of welding quality. The results demonstrate that CNC scanning lacks precision in thermal input control, resulting in inconsistent welding quality. Galvo-scanning enables precise modulation of laser energy distribution and molten pool behavior, effectively reducing spatter and porosity defects. It also promotes the transition from columnar grains to equiaxed grains, significantly refining the weld microstructure. Under the sinusoidal trajectory with a welding speed of 20 mm/s, the Lap-shear strength of the galvo-scanned joint reaches 277 N/mm², outperforming all CNC-scanned joints. This research proposes a non-contact welding strategy targeted at eliminating the mechanical failure mechanism associated with conventional ultrasonic bonding of ceramic substrates. It establishes the superiority of galvo-scanning for precision welding of high-reflectivity materials and lays a foundation for its potential application in new energy vehicle power modules and microelectronic packaging. Full article

Fuel consumption in heavy-duty off-road machinery depends on a wide range of interacting factors related to the operating environment, the technical characteristics and condition of the machine, and the behaviour, experience and state of the operator. Existing studies typically address only fragments of this relationship, focusing on vehicle parameters, selected environmental factors or individual aspects of driving style. The method proposed in this work provides a general and transferable framework for assessing fuel consumption in any type of machine or vehicle. The Integrated Fuel Consumption Assessment Model (IFCAM) combines environmental, vehicle and human domains into a coherent structured formula that can be used across different operational contexts. The model was developed using continuous short-term measurements and long-term operational data collected during real industrial work. Its universal structure makes it applicable not only to mining equipment, but also to construction machinery and transport vehicles, as well as conventional passenger cars, where it offers a systematic procedure for estimating fuel demand under variable operating conditions. The results demonstrate that integrating multi-domain data improves predictive accuracy and opens new possibilities for analysing operator influence and overall energy efficiency. Full article

To accurately investigate the loss characteristics of fuel cell vehicles (FCVs) under actual operating conditions and enhance their power performance and economic efficiency, this study establishes a numerical model of the proton exchange membrane fuel cell (PEMFC) stack based on real-world data from the second-generation Mirai. The stack model incorporates leakage current losses and imposes a limit on maximum current density. Besides, this study analyzes the effects of operating parameters (PEM water content, hydrogen partial pressure, current density, oxygen partial pressure, and operating temperature) on stack power output, efficiency, and eco-performance coefficient (ECOP). Furthermore, Non-Dominated Sequential Genetic Algorithm (NSGA-II) is employed to optimize the PEMFC stack performance, yielding the optimal operating parameter set for FCV operation. Further simulations are conducted on dynamic performance characteristics of the second-generation Mirai under two typical driving cycles, evaluating the power performance and economy of the FCV before and after optimization. Results demonstrate that the established PEMFC stack model accurately analyzes the output performance of an actual FCV when compared with real-world performance test data from the second-generation Mirai. Through optimization, output power increases by 7.4%, efficiency improves by 1.95%, and ECOP rises by 3.84%, providing guidance for enhancing vehicle power performance and improving overall vehicle economy. This study provides a practical framework for enhancing the power performance and overall energy sustainability of fuel cell vehicles, contributing to the advancement of sustainable transportation. Full article

This review addresses the pivotal challenge in distributed-drive electric vehicle (DDEV) dynamics control: how to optimally distribute braking and steering forces during combined maneuvers to simultaneously enhance lateral stability, safety, and energy efficiency. The over-actuated nature of DDEVs presents a unique opportunity for precise torque vectoring but also introduces complex coupled dynamics, making vehicles prone to instability such as rollover during aggressive steering–braking scenarios. Moving beyond a simple catalog of methods, this work provides a structured synthesis and evolutionary analysis of chassis control methodologies. The problem is first deconstructed into two core control objectives: lateral stability and longitudinal braking performance. This is followed by a critical analysis of how integrated control architectures resolve the inherent conflicts between them. The analysis reveals a clear trajectory from independent control loops to intelligent, context-aware coordination. It further identifies a paradigm shift from the conventional goal of merely maintaining stability toward proactively managing stability boundaries to enhance system resilience. Furthermore, this review highlights the growing integration with high-level motion planning in automated driving. By synthesizing the current knowledge and mapping future directions toward deeply integrated, intelligent control systems, it serves as both a reference for researchers and a design guide for engineers aiming to unlock the full potential of the distributed drive paradigm. Full article

As a core support for the integration of new energy and smart grids, Vehicle-to-Grid (V2G) networks face a core contradiction between user privacy protection and transaction security traceability—a dilemma that is further exacerbated by issues such as the quantum computing vulnerability of traditional cryptography, cumbersome key management in stateful ring signatures, and conflicts between revocation mechanisms and privacy protection. To address these problems, this paper proposes a post-quantum revocable linkable ring signature scheme based on SPHINCS+, with the following core innovations: First, the scheme seamlessly integrates the pure hash-based architecture of SPHINCS+ with a stateless design, incorporating WOTS+, FORS, and XMSS technologies, which inherently resists quantum attacks and eliminates the need to track signature states, thus completely resolving the state management dilemma of traditional stateful schemes; second, the scheme introduces an innovative “real signature + pseudo-signature polynomially indistinguishable” mechanism, and by calibrating the authentication path structure and hash distribution of pseudo-signatures (satisfying the Kolmogorov–Smirnov test with $D\le 0.05$), it ensures signer anonymity and mitigates the potential risk of distinguishable pseudo-signatures; third, the scheme designs a KEK (Key Encryption Key)-sharded collaborative revocation mechanism, encrypting and storing the $(I,pk,RID)$ mapping table in fragmented form, with KEK split into $KE{K}_1$ (held by the Trusted Authority, TA) and $KE{K}_2$ (held by the regulatory node), with collaborative decryption by both parties required to locate malicious users, thereby resolving the core conflict of privacy leakage in traditional revocation mechanisms; fourth, the scheme generates forward-secure linkable tags based on one-way private key updates and one-time random factors, ensuring that past transactions cannot be traced even if the current private key is compromised; and fifth, the scheme adopts hash commitments instead of complex cryptographic commitments, simplifying computations while efficiently binding transaction amounts to signers—an approach consistent with the pure hash-based design philosophy of SPHINCS+. Security analysis demonstrates that the scheme satisfies the following six core properties: post-quantum security, unforgeability, anonymity, linkability, unframeability, and forward secrecy, thereby providing technical support for secure and anonymous payments in V2G networks in the quantum era. Full article

Path planning, as a key technology in unmanned aerial vehicle (UAV) systems, affects the overall efficiency of task completion and is often limited by energy consumption, obstacles, and maneuverability in complex application environments. Traditional algorithms have insufficient performance in nonlinear, multimodal, and multiconstraints problems. Based on this, this paper proposes an improved exponential-trigonometric optimization (ETO) to solve a 3D smooth path planning model based on a spherical Bezier curve. Firstly, a fixed arc length resampling strategy is proposed to address the issue of the insufficient adaptability of existing path smoothing methods to dynamic threats. Generate a uniformly distributed set of reference points along the Bezier curve and combine it with spherical projection to improve the safety and efficiency of the flight path. On this basis, establish a total cost function that includes four types of costs. Secondly, a new ETO variant called IETO is proposed by introducing the alpha evolution strategy, noise and physical attack strategy, and opposition-based cross teaching strategy into ETO. Then, the effectiveness of IETO for addressing various optimization problems is showcased through population diversity analysis, ablation analysis, and benchmark experiments. Finally, the results of the simulation experiment indicate that IETO stably provides shorter and smoother safe paths for UAVs in three elevation maps with different terrain features. Full article

Unmanned ground vehicles (UGVs) in agriculture face challenges in navigating complex environments due to the presence of dynamic obstacles. This causes several practical problems including mission delays, higher energy consumption, and potential safety risks. This study addresses the challenge by shifting path planning from reactive local avoidance to proactive global optimization. To that end, it integrates aerial imagery from an unmanned aerial vehicle (UAV) to identify dynamic obstacles using a low-latency YOLOv8 detection pipeline. These are translated into georeferenced exclusion zones for the UGV. The UGV follows the optimized path while relying on a LiDAR-based reactive protocol to autonomously detect and respond to any missed obstacles. A farm management information system is used as the central coordinator. The system was tested in 30 real-field trials in a walnut orchard for two distinct scenarios with varying worker and vehicle loads. The system achieved high mission success, with the UGV completing all tasks safely, with four partial successes caused by worker detection failures under afternoon shadows. UAV energy consumption remained stable, while UGV energy and mission time increased during reactive maneuvers. Communication latency was low and consistent. This enabled timely execution of both proactive and reactive navigation protocols. In conclusion, the present UAV–UGV system ensured efficient and safe navigation, demonstrating practical applicability in real orchard conditions. Full article

Hierarchical federated learning (HFL) has emerged as a promising paradigm for distributed machine learning over vehicular networks. Despite recent advances in vehicle selection and resource allocation, most still adopt a fixed Edge-and-Vehicle Scheduling (EVS) configuration that keeps the number of participating edge nodes and vehicles per node constant across training rounds. However, given the diverse training tasks and dynamic vehicular environments, our experiments confirm that such static configurations struggle to efficiently meet the task-specific requirements across model accuracy, time delay, and energy consumption. To address this, we first formulate a unified, long-term training cost metric that balances these conflicting objectives. We then propose AptEVS, an adaptive scheduling framework based on deep reinforcement learning (DRL), designed to minimize this cost. The core of AptEVS is its phase-aware design, which adapts the scheduling strategy by first identifying the current training phase and then switching to specialized strategies accordingly. Extensive simulations demonstrate that AptEVS learns an effective scheduling policy online from scratch, consistently outperforming baselines and and reducing the long-term training cost by up to 66.0%. Our findings demonstrate that phase-aware DRL is both feasible and highly effective for resource scheduling over complex vehicular networks. Full article