Search Results (5,961)

Multi-UAV cooperative encirclement tasks have attracted considerable attention in areas such as military defense and target interception. Fixed-wing UAVs face substantial challenges due to intrinsic dynamic limits, including their minimum velocity and turning radius, particularly when engaging evasive target and navigating in obstacle environments. This paper presents a hybrid deep reinforcement learning approach, in which a cooperative task environment is developed for fixed-wing UAVs that jointly integrates encirclement and obstacle avoidance. A composite MADDPG framework enhanced with curriculum learning is designed, employing progressive task staging and reward optimization to accelerate convergence and improve policy stability. Simulation results demonstrate that the proposed method achieves single-step encirclement success rates exceeding 80% in complex environments, while maintaining 10-step success rates around 70%, thereby strengthening both encirclement capability and obstacle avoidance safety in fixed-wing UAV swarm. This study provides new insights into the intelligent cooperative control of fixed-wing UAVs in high-risk missions. Full article

In order to study the impact on the shock effect when a high-speed train passes over a concrete-filled steel tube (CFST) tied-arch bridge, a dynamic load test was carried out in the background of the Qinjiang River Bridge in Qinzhou, Guangxi Province, to test the bridge displacements, accelerations, and dynamic stresses. The bridge finite element model was coupled with a CRH2 train model developed in SIMPACK to perform ANSYS–SIMPACK co-simulation of vehicle–bridge interactions. Model reliability was verified by comparing simulated results with field measurements under matched operating conditions. On this basis, a parametric study was conducted for single-line operation with a mainline spacing of 4.2–5.4 m (0.4 m increments) and train speeds of 80–270 km/h (10 km/h increments), yielding 80 working conditions to evaluate hanger impact responses. The results indicate that the ANSYS–SIMPACK co-simulation provides reliable predictions. Compared with long hangers, short hangers exhibit larger stress impact coefficients. As train speed increases, the hanger impact effect shows a wavelike increasing trend. When the speed approaches 180–200 km/h, the excitation nears the bridge’s dominant natural frequency, and impact effects on bridge components peak, identifying a critical speed range that is more prone to inducing vehicle–bridge resonance; the impact coefficient of the shock effect on both sides of the train is different: the coefficient on the far side of the bridge is about 2 times of that on the near side of the bridge, so when the impact coefficient is regulated, the unevenness of the impact of the shock effect on both sides can be taken into account. Single-line operation can introduce a lateral load bias on the train, and the distance of the train from the center line is positively correlated with the impact size of the shock effect, with the stress impact coefficient of the shock effect on both sides of the bridge and span deflection increasing as the spacing of the main line increases. Full article

Understanding vehicle acceleration behavior during intersection departures is critical for advancing traffic safety, sustainable mobility, and intelligent transport systems. This study presents a high-resolution kinematic analysis of 714 vehicle departures from signalized intersections, encompassing straight crossings, left turns, and right turns, and involving a diverse sample of internal combustion engine (ICE), hybrid electric (HEV), and battery electric vehicles (BEV). Using synchronized Micro Electro-Mechanical Systems (MEMS) accelerometers and Real-Time Kinematic (RTK)-GPS systems, the study captures longitudinal acceleration and velocity profiles over fixed distances. Results indicate that BEVs exhibit significantly higher acceleration and final speeds than ICE and HEV vehicles, particularly during straight crossings and longer left-turn maneuvers. Several mathematical models—including polynomial, arctangent, and Akçelik functions—were calibrated to describe acceleration and velocity dynamics. Findings contribute by modeling jerk and delay propagation, supporting better calibration of AV acceleration profiles and the optimization of intersection control strategies. Moreover, the study provides validated acceleration benchmarks that enhance the accuracy of forensic engineering and road accident reconstruction, particularly in scenarios involving intersection dynamics, and demonstrates that BEVs accelerate more rapidly than ICE and HEV vehicles, especially in straight crossings, with direct implications for traffic simulation, ADAS calibration, and urban crash analysis. Full article

Vertical axis wind turbines (VAWTs) have significant potential for renewable energy generation, yet their operational efficiency is often limited by reduced aerodynamic performance and difficulties during start-up. This study investigates the effect of passive flow control and material selection on the performance of H-Darrieus VAWT blades, with the aim of identifying design solutions that enhance start-up dynamics and overall efficiency. Two-dimensional numerical simulations were conducted using the Dynamic Mesh method with six degrees of freedom (6DOF) in ANSYS 19.2 Fluent, enabling a time-resolved assessment of rotor behavior under constant wind velocities. Two blade configurations were analyzed: a baseline NACA0012 geometry and a modified profile with inclined cavities on the extrados. In addition, the influence of blade material was examined by comparing 3D-printed resin blades with lighter 3D-printed polycarbonate blades. The results demonstrate that cavity-modified blades provide superior performance compared to the baseline, showing faster acceleration, higher tip speed ratios, and improved power coefficients, particularly at higher wind velocities. Furthermore, polycarbonate blades achieved more efficient energy conversion than resin blades, highlighting the importance of material properties in turbine optimization. These findings confirm that combining passive flow control strategies with advanced lightweight materials can significantly improve the aerodynamic and dynamic performance of VAWTs, offering valuable insights for future experimental validation and prototype development. Full article

Aiming at the speed chattering problem caused by high-frequency square wave injection in permanent magnet synchronous motors (PMSMs) during low-speed operation (200–500 r/min), this study intends to improve the rotor position estimation accuracy of sensorless control systems as well as the system’s ability to resist harmonic interference and sudden load changes. The goal is to enhance the control performance of traditional control schemes in this scenario and meet the requirement of stable low-speed operation of the motor. First, the study analyzes the harmonic error propagation mechanism of high-frequency square wave injection and finds that the traditional PI phase-locked loop (PI-PLL) is susceptible to high-order harmonic interference during demodulation, which in turn leads to position estimation errors and periodic speed fluctuations. Therefore, the extended state observer phase-locked loop (ESO-PLL) is adopted to replace the traditional PI-PLL. A third-order extended state observer (ESO) is used to uniformly regard the system’s unmodeled dynamics, external load disturbances, and harmonic interference as “total disturbances”, realizing real-time estimation and compensation of disturbances, and quickly suppressing the impacts of harmonic errors and sudden load changes. Meanwhile, a dynamic pole placement strategy for the speed loop is designed to adaptively adjust the controller’s damping ratio and bandwidth parameters according to the motor’s operating states (loaded/unloaded, steady-state/transient): large poles are used in the start-up phase to accelerate response, small poles are switched in the steady-state phase to reduce errors, and a smooth attenuation function is used in the transition phase to achieve stable parameter transition, balancing the system’s dynamic response and steady-state accuracy. In addition, high-frequency square wave voltage signals are injected into the dq axes of the rotating coordinate system, and effective rotor position information is extracted by combining signal demodulation with ESO-PLL to realize decoupling of high-frequency response currents. Verification through MATLAB/Simulink simulation experiments shows that the improved strategy exhibits significant advantages in the low-speed range of 200–300 r/min: in the scenario where the speed transitions from 200 r/min to 300 r/min with sudden load changes, the position estimation curve of ESO-PLL basically overlaps with the actual curve, while the PI-PLL shows obvious deviations; in the start-up and speed switching phases, dynamic pole placement enables the motor to respond quickly without overshoot and no obvious speed fluctuations, whereas the traditional fixed-pole PI control has problems of response lag or overshoot. In conclusion, the “ESO-PLL + dynamic pole placement” cooperative control strategy proposed in this study effectively solves the problems of harmonic interference and load disturbance caused by high-frequency square wave injection in the low-speed range and significantly improves the accuracy and robustness of PMSM sensorless control. This strategy requires no additional hardware cost and achieves performance improvement only through algorithm optimization. It can be directly applied to PMSM control systems that require stable low-speed operation, providing a reliable solution for the promotion of sensorless control technology in low-speed precision fields. Full article

As the intelligent transformation of manufacturing accelerates, Unmanned Aerial Vehicles are increasingly being deployed for workshop operations, making efficient obstacle avoidance path planning a critical requirement. This paper introduces a parameter-optimized path planning method for the Unmanned Aerial Vehicle, termed the Artificial Potential Field A* algorithm, which enhances the standard A* approach through the integration of an artificial potential field and a variable step size strategy. The variable step size mechanism allows dynamic adjustment of the search step size, while potential field values from the artificial potential field are embedded into the cost function to improve planning accuracy. Key parameters of the hybrid algorithm are subsequently optimized using response surface methodology, with a regression model built to analyze parameter interactions and determine the optimal configuration. Simulation results across multiple performance indicators confirm that the proposed Artificial Potential Field A* algorithm delivers superior outcomes in path length, attitude angle variation, and flight altitude stability. This approach provides an effective solution for enhancing Unmanned Aerial Vehicle operational efficiency in production workshops. Full article

Molecular simulation is central to modern drug discovery but is often limited by high computational cost and the complexity of molecular interactions. Deep-learning drug–target interaction (DTI) prediction can accelerate screening; however, many models underuse the local functional structure features—binding motifs, reactive groups, and residue-level fragments—that drive recognition. We present LoF-DTI, a framework that explicitly represents and couples such local features. Drugs are converted from SMILES into molecular graphs and targets from sequences into feature representations. On the drug side, a Jumping Knowledge (JK) enhanced Graph Isomorphism Network (GIN) extracts atom- and neighborhood-level patterns; on the target side, residual CNN blocks with progressively enlarged receptive fields, augmented by N-mer substructural statistics, capture multi-scale local motifs. A Gated Cross-Attention (GCA) module then performs atom-to-residue interaction learning, highlighting decisive local pairs and providing token-level interpretability through attention scores. By prioritizing locality during both encoding and interaction, LoF-DTI delivers competitive results across multiple benchmarks and improves early retrieval relevant to virtual screening. Case analyses show that the model recovers known functional binding sites, suggesting strong potential to provide mechanism-aware guidance for molecular simulation and to streamline the drug design pipeline. Full article

Prefabricated building projects represent industrialized and intelligent construction through factory production, standardized design, and mechanized assembly. This study presents a multi-agent simulation approach to model the prefabricated construction process, allowing for the concurrent optimization of the prefabricated component (PC) splitting design and the construction organization plan through iterative simulation. (1) Employing a questionnaire survey, it identifies critical factors affecting schedule and cost from a design–construction coordination perspective. (2) Based on these findings, an agent-based model was developed incorporating PC installation, crane operations, and storage yard spatial constraints, along with interaction rules governing these agents. (3) Data interoperability was achieved among Revit, NetLogo3D and Navisworks. This integrated environment offers project managers digital management of design and construction plans, simulation support, and visualization tools. Simulation results confirm that a hybrid resource allocation strategy utilizing both tower cranes and mobile cranes enhances resource leveling, accelerates schedule performance, and improves cost efficiency. Full article

This paper presents a sensorless control strategy for permanent magnet synchronous motor (PMSM) drives in industrial and automotive air compressor applications. The strategy utilizes an adaptive-gain sliding mode observer integrated with a refined back-EMF model to suppress chattering and improve convergence. The proposed approach achieves precise rotor position and speed estimation across a wide operational range without mechanical sensors. It directly addresses the critical needs of reliability, compactness, and resilience in automotive environments. Unlike conventional observers, its originality lies in the enhanced gain structure, enabling accurate and robust sensorless control validated through both simulation and hardware tests. Comprehensive simulation results demonstrate effective performance from 2000 to 8500 rpm, with steady-state speed tracking errors maintained below 0.4% at 2000 rpm and 0.035% at 8500 rpm under rated load. The control methodology exhibits excellent disturbance rejection capabilities, maintaining speed regulation within ±5 rpm under an 80% load disturbance at 8500 rpm while limiting q-axis current ripple to 2.5% of rated values. Experimental validation on a 2.2 kW PMSM-driven compressor test platform confirms stable operation at 4000 rpm with speed fluctuations constrained to 20 rpm (0.5% error) and precise current regulation, maintaining the d-axis current within ±0.07 A. The system demonstrates rapid dynamic response, achieving acceleration from 1320 rpm to 2365 rpm within one second during testing. The results confirm the method’s practical viability for enhancing reliability and reducing maintenance in industrial and automotive compressors systems. Full article

Cementation using Ordinary Portland Cement (OPC) remains the standard method for conditioning low- and intermediate-level radioactive waste, including Spent Ion Exchange Resins (SIERs). This work presents an integrated strategy involving thermal pretreatment to minimize waste volume and eliminate organic constituents, followed by encapsulation within three distinct binders: CEM I, CEM III, and a novel one-part geopolymer. The one-part geopolymer system represents a significant operational innovation, enabling safe and simple “just-add-water” processing and avoiding the need to handle alkaline solutions. The proposed geopolymer, synthesized from metakaolin, blast furnace slag, and solid sodium silicate, was systematically benchmarked against conventional OPC matrices (CEM I, CEM III) by assessing their capacity to immobilize thermally treated SIER ashes under accelerated leaching conditions. For benchmarking, leaching indices for Cs and Sr were determined following the ANSI/ANS 16.9 standard protocol in three representative environments simulating operational and long-term repository scenarios, providing a quantitative evaluation of radionuclide retention and matrix durability. Results indicate that the one-part geopolymer improved leaching indices for Cs and Sr compared to both cementitious binders and complied with regulatory waste acceptance criteria. The comparative results highlight the potential of geopolymer technology to increase waste loading efficiencies and improve long-term safety, establishing a robust framework for future radioactive waste management approaches. Full article

To address the issues of excessive current ripple and poor dynamic response in conventional angle position control (APC) for high-speed switched reluctance generator (SRG), this paper proposes an online parameter identification-based model-free predictive control (MFPC) strategy. First, the system dynamics are represented as an ultra-local model (ULM), enabling the design of an extended state observer (ESO) for two-step current prediction to compensate for control delays. Second, an improved Recursive Least Squares (RLS) algorithm with covariance resetting and error clearance is implemented to accurately identify dynamic inductance online, thereby enhancing the prediction accuracy of the ESO. Third, a bus current estimation-based adaptive feedforward compensation (AFC) technique is introduced to accelerate DC-bus voltage regulation and system dynamic response. Finally, simulations conducted on a 250 kW SRG platform demonstrate that the proposed method achieves superior dynamic performance and significantly reduced current ripple compared to conventional APC method. Full article

The objective of this study was to develop a stochastic simulation model to evaluate the impact of accelerated lambing on income over feed cost (IOFC) in Lacaune ewes managed under an intensive production system in Greece. The economic comparison of two lambing intervals (LI) was performed by varying the voluntary waiting period to allow for an 8-month LI (3 lambings in 2 years) versus a 12-month LI (1 lambing per year). Milk production per year was greater (p < 0.01) for the 8-month compared to the 12-month LI (777.4 ± 2.8 kg vs. 661.9 ± 2.1 kg, respectively), and the income from the sale of milk was greater for the 8-month compared to the 12-month LI (EUR 1166.0 ± 4.1 vs. EUR 992.9 ± 3.1, respectively). Feed cost per year was greater for the 8-month compared to the 12-month LI (EUR 255.9 ± 0.5 vs. EUR 227.8 ± 0.5, respectively). Therefore, IOFC per year was greater for the 8-month compared to the 12-month LI (EUR 989.4 ± 3.2 vs. EUR 817.1 ± 2.1, respectively). The cost per additional day open in the 12-month LI was estimated at EUR 1.08 ± 0.06 and demonstrates the importance of reproductive performance in the profitability of dairy sheep production. Therefore, reproductive management strategies that result in reduced LI are expected to increase IOFC in Lacaune dairy ewes. Future research is needed to develop practical strategies to successfully reduce the lambing interval in Lacaune ewes. Full article

China’s urbanisation has transitioned from an era of rapid, coarse expansion to one of refined and targeted development. In accordance with China’s “dual-carbon” strategy, the building sector—presently the third-largest source of domestic carbon emissions—is compelled to pursue emission optimisation in its forthcoming evolution. Photovoltaic-building technologies offer an effective response to this imperative. Within the context of accelerating high-rise residential construction, the architectural integration of scientifically configured photovoltaic façades has emerged as a critical challenge. Employing an integrated methodology of urban surveying and simulation, this study examines the façade characteristics of residential buildings in northern Chinese cities, selecting Xi’an as the representative case. Three PV-facade integration strategies for existing stock are presented: window retrofitting, wall retrofitting, and full-façade renovation. Utilising the EnergyPlus platform, the manuscript simulates the electrical demand profiles and clean-electricity generation of typical dwellings under varying photovoltaic materials and configuration schemes, while concurrently assessing economic performance. It demonstrates that a judicious determination of photovoltaic installation scale and layout strategy markedly amplifies energy-saving efficacy, diminishes aggregate energy consumption and carbon emissions, and simultaneously reduces the capital expenditure of photovoltaic systems. For multi-story buildings, a full façade retrofit yielded the highest annual electricity generation of 514,703.56 kWh and an annual carbon reduction of 15,521.50 kgCO₂. For high-rise buildings, installing PV modules only above the 20th floor increased the effective generation ratio from 45.24% to 87.17%, while the carbon reduction efficiency per unit investment improved from 0.05 to 0.22 kgCO₂/¥. Full article

In large-scale distributed storage simulations, disk simulation plays a critical role in evaluating system reliability, scalability, and performance. However, the existing virtual disk technologies face challenges in supporting ultra-large capacities and high-concurrency workloads under constrained physical resources. To address this limitation, we propose an overcapacity mapping (OCM) virtual disk technology that substantially reduces simulation costs while preserving functionality similar to real physical disks. OCM integrates thin provisioning and data deduplication at the Linux Device Mapper layer to construct virtual disks whose logical capacities greatly exceed their physical capacities. We further introduce an SSD-based tiered asynchronous I/O strategy to mitigate performance bottlenecks under high-concurrency random read/write workloads. Our experimental results show that OCM achieves substantial space savings in scenarios with data duplication. In high-concurrency workloads involving small-block random I/O, cache acceleration yields up to 7.8× write speedup and 248.2× read speedup. Moreover, we deploy OCM in a Kubernetes environment to construct a Ceph system with 3 PB logical capacity using only 8.8 TB of physical resources, achieving 98.36% disk cost savings. Full article

Enhancing ride comfort and vibration control performance is a critical requirement for fuel cell commercial vehicles (FCCVs). This study develops a semi-active seat suspension control strategy that integrates a fuzzy logic controller with a Modified Fireworks Algorithm (MFWA) to systematically optimize fuzzy parameters. A seven-degree-of-freedom (7-DOF) half-vehicle model, including the magnetorheological damper (MRD)-based seat suspension system, is established in MATLAB/Simulink to evaluate the methodology under both random and bump road excitations. In addition, a hardware-in-the-loop (HIL) experimental validation was conducted, confirming the real-time feasibility and effectiveness of the proposed controller. Comparative simulations are conducted against passive suspension (comprising elastic and damping elements) and conventional PID control. Results show that the proposed MFWA-FL approach significantly improves ride comfort, reducing vertical acceleration of the human body by up to 49.29% and seat suspension dynamic deflection by 12.50% under C-Class road excitation compared with the passive system. Under bump excitations, vertical acceleration is reduced by 43.03% and suspension deflection by 11.76%. These improvements effectively suppress vertical vibrations, minimize the risk of suspension bottoming, and highlight the potential of intelligent optimization-based control for enhancing FCCV reliability and passenger comfort. Full article