Search Results (411)

Microscopic traffic simulation has a wide range of applications due to its high precision. However, the accuracy of such simulation is influenced by many factors during the simulation establishment process. This paper explores the impact of various factors on simulation results by comparing real-world traffic data, simulated data and simulations configured with different factors. The impact of these factors on simulation accuracy is evaluated by analyzing the traffic volume passing through a congested intersection in each direction. The results indicate that map correction, route iteration, and the inclusion of bus routes significantly affect simulation accuracy. An inaccurate map reduces traffic by 42%, while not-iterated routes prevent 6.6% of vehicles from using their original routes. Omitting bus routes increases the number of trips for private cars by 47%. Conversely, the inclusion of school zones has minimal impact, omitting them only reduces trips by 0.37%. Interestingly, integrating real traffic light data did not enhance simulation accuracy, likely due to discrepancies in junction turning percentages between the simulation and reality. This paper provides guidance for building accurate simulation maps using public data, enabling the creation of relatively precise models with minimal data and effort. Full article

Modern machining industries require high precision and efficiency in machine tools, where thermal deformations significantly impact accuracy. This study focuses on optimizing the structural parameters of a vertical turning center to minimize thermal displacements affecting machining precision. The optimization process is divided into parametric and topological methodologies. The parametric approach targets three primary objectives: minimizing mass (q₁), maximizing static stiffness (q₂), and reducing thermal displacement (q₃). Multi-criteria optimization techniques, including Pareto-based and scalarization methods, are applied to balance these conflicting factors. Finite Element Analysis (FEA) models assist in evaluating machine stiffness and displacement, with constraints imposed on structural mass and stiffness to maintain performance. Parametric optimization, using iterative computational algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), refines rib and wall thicknesses of the lathe table to achieve displacement reductions. The optimization process successfully lowers displacement at critical measurement points while maintaining structural integrity. Hybrid PSO (hPSO) outperforms other algorithms in achieving optimal parameter sets with minimal computational effort. Topological optimization, based on the Solid Isotropic Microstructure with Penalization (SIMP) method, further enhances structural efficiency by refining material distribution. The iterative process identifies optimal energy flow paths while ensuring compliance with mechanical constraints. A hybrid approach integrating parametric adjustments with topological refinement leads to superior performance, achieving a 43% reduction in displacement at key measurement points compared to the initial design. The final optimized design reduces mass by 1 ton compared to the original model and 2.5 tons compared to the best rib–wall optimization results. The study’s findings establish a foundation for implementing active deformation compensation systems in machine tools, enhancing machining precision. The integration of parametric and topological optimization presents a robust framework for designing machine tool structures with improved thermal stability and structural efficiency. Full article

Small unmanned aerial vehicles are now commonly equipped with integrated navigation systems to obtain high-precision navigation parameters. However, affected by the dual impacts of multipath effects and dynamic environmental changes, their state estimation process is vulnerable to interference from measurement outliers, which in turn leads to the degradation of navigation accuracy and poses a threat to flight safety. To address this issue, this research presents an adaptive minimum error entropy cubature Kalman filter. Firstly, the cubature Kalman filter is introduced to solve the problem of model nonlinear errors; secondly, the cubature Kalman filter based on minimum error entropy is derived to effectively curb the interference that measurement outliers impose on filtering results; finally, a kernel bandwidth adjustment factor is designed, and the kernel bandwidth is estimated adaptively to further improve navigation accuracy. Through numerical simulation experiments, the robustness of the proposed method with respect to measurement outliers is validated; further flight experiment results show that compared with existing related filters, this proposed filter can achieve more accurate navigation and positioning. 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

In complex 3D maneuvering tasks (e.g., post-disaster rescue, urban operations, and infrastructure inspection), the trajectories that quadrotors need to track are often complex—containing both gentle flight phases and highly maneuverable trajectory segments. Under such trajectory tracking tasks with the composite characteristics of “gentle-high maneuvering”, quadrotors face challenges of limited onboard computing resources and short endurance, requiring a balance between trajectory tracking accuracy, computational efficiency, and energy consumption. To address this problem, this paper proposes a lightweight trajectory tracking control method based on hierarchical decision-making and dual-threshold adaptive switching. Inspired by the biological “prediction–reflection” mechanism, this method designs a dual-threshold collaborative early warning switching architecture of “prediction layer–confirmation layer”: The prediction layer dynamically assesses potential risks based on trajectory curvature and jerk, while the confirmation layer confirms in real time the stability risks through an attitude-angular velocity composite index. Only when both exceed the thresholds, it switches from low-energy-consuming Euler angle control to high-precision geometric control. Simulation experiments show that in four typical trajectories (straight-line rapid turn, high-speed S-shaped, anti-interference composite, and narrow space figure-eight), compared with pure geometric control, this method reduces position error by 19.5%, decreases energy consumption by 45.9%, and shortens CPU time by 28%. This study not only optimizes device performance by improving trajectory tracking accuracy while reducing onboard computational load, but also reduces energy consumption to extend UAV endurance, and simultaneously enhances anti-disturbance capability, thereby improving its operational capability to respond to emergencies in complex environments. Overall, this study provides a feasible solution for the efficient and safe flight of resource-constrained onboard platforms in multi-scenario complex environments in the future and has broad application and expansion potential. Full article

Multi-modal knowledge graph completion (MMKGC) aims to complete knowledge graphs by integrating structural information with multi-modal (e.g., visual, textual, and numerical) features and leveraging cross-modal reasoning within a unified semantic space to infer and supplement missing factual knowledge. Current MMKGC methods have advanced in terms of integrating multi-modal information but have overlooked the imbalance in modality importance for target entities. Treating all modalities equally dilutes critical semantics and amplifies irrelevant information, which in turn limits the semantic understanding and predictive performance of the model. To address these limitations, we proposed a modality information aggregation graph attention network with adversarial training for multi-modal knowledge graph completion (MIAGAT-AT). MIAGAT-AT focuses on hierarchically modeling complex cross-modal interactions. By combining the multi-head attention mechanism with modality-specific projection methods, it precisely captures global semantic dependencies and dynamically adjusts the weight of modality embeddings according to the importance of each modality, thereby optimizing cross-modal information fusion capabilities. Moreover, through the use of random noise and multi-layer residual blocks, the adversarial training generates high-quality multi-modal feature representations, thereby effectively enhancing information from imbalanced modalities. Experimental results demonstrate that our approach significantly outperforms 18 existing baselines and establishes a strong performance baseline across three distinct datasets. Full article

There has been significant interest in deploying unmanned aerial vehicles (UAVs) for their ability to perform precise and rapid remote mapping and inspection of critical environmental assets for structural health monitoring. This case study investigates the use of UAV-based LiDAR and photogrammetry at Melbourne Water’s Western Treatment Plant (WTP) to routinely monitor high-density polyethylene floating covers on anaerobic lagoons. The proposed approach integrates LiDAR and photogrammetry data to enhance the accuracy and efficiency of generating digital elevation models (DEMs) and orthomosaics by leveraging the strengths of both methods. Specifically, the photogrammetric images were orthorectified onto LiDAR-derived DEMs as the projection plane to construct the corresponding orthomosaic. This method captures precise elevation points directly from LiDAR, forming a robust foundation dataset for DEM construction. This streamlines the workflow without compromising detail, as it eliminates the need for time-intensive photogrammetry processes, such as dense cloud and depth map generation. This integration accelerates dataset production by up to four times compared to photogrammetry alone, while achieving centimetre-level accuracy. The LiDAR-derived DEM achieved higher elevation accuracy with a root mean square error (RMSE) of 56.1 mm, while the photogrammetry-derived DEM achieved higher in-plane accuracy with an RMSE of up to 35.4 mm. An analysis of cover deformation revealed that the floating cover had elevated rapidly within the first two years post-installation before showing lateral displacement around the sixth year, which was also evident from a significant increase in wrinkling. This approach delivers valuable insights into cover condition that, in turn, clarifies scum accumulation and movement, thereby enhancing structural integrity management and supporting environmental sustainability at WTP by safeguarding methane-rich biogas for renewable-energy generation and controlling odours. The findings support the ongoing collaborative industry research between Monash University and Melbourne Water, aimed at achieving comprehensive structural and prognostic health assessments of these high-value assets. Full article

As the demand for marine resource development escalates, underwater robots have gained prominence as a technological alternative to human participation in deep-sea exploration, resource assessments, and other intricate tasks, underscoring their academic and engineering importance. Traditional underwater robots, however, typically exhibit limited resilience to environmental disturbances and are readily obstructed or interfered with by aquatic vegetation, sediments, and other physical impediments. This paper examines the biological locomotion mechanisms of black ghostfish, which utilize undulatory fins and flapping wings, and presents a coupled undulatory-flapping propulsion strategy to facilitate rapid movement and precise posture adjustment in underwater robots. A multimodal undulatory-flapping bio-inspired underwater robotic platform is proposed, with a systematic explanation of its structure and motion principles. Additionally, kinematic and dynamic models for coordinated propulsion with multiple actuators are developed, and the robot’s performance under various driving modes is evaluated using computational fluid dynamics simulations. The simulation outcomes confirm the viability of the developed dynamic model. A prototype was constructed, and a PID-based control algorithm was developed to assess the robot’s performance in linear movement, turning, and other behaviors in both undulatory fin and flapping modes. Experimental findings indicate that the robot, functioning in undulatory fin propulsion mode at a frequency of 2.5 Hz, attains a velocity of 0.35 m/s, while maintaining attitude angle fluctuation errors within ±5°. In the flapping propulsion mode, precise posture modifications can be executed. These results validate the feasibility of the proposed multimodal bio-inspired underwater robot design and provide a new approach for the development of high-performance, autonomous bio-inspired underwater robots. Full article

Production industries create high-quality products through effective machining precision, lead times, productivity, cost benefits, and implementing sustainable manufacturing practices. This study compares the effect of cryogenic CO₂ as a cutting fluid with a flooded conventional system and dry turning on the surface roughness, early-stage tool phenomena (including adhesion, material transfer, and built-up edge (BUE) formation), and the chip morphology of aluminum 7075-T6. Taguchi’s L9 orthogonal array is applied to identify the optimal cutting parameters that minimize surface roughness (Ra). Cutting speed (Vc), feed rate (f), depth of cut (ap), and the type of cutting fluid condition were defined at three levels. The surface roughness (Ra) was determined, and the built-up edge (BUE) and chip morphology were evaluated. Moreover, SEM and energy-dispersive X-ray spectroscopy (EDX) were employed to characterize the machined surface and the cutting tools. The optimal values for the cryogenic cooling and cutting parameters are as follows: 220 m/min (Vc), 0.05 mm/rev (f), and 0.5 mm (ap). These conditions yield a surface roughness mean (Ra) of 0.736 µm, improving the surface roughness by 10.57% compared with the lowest Ra value from all of the tests. In addition, ANOVA showed the feed rate to be the most significant cutting parameter over surface roughness under the given conditions. Regarding chip morphology, snarled chip shapes are associated with low surface roughness values. The results indicate that cryogenic cutting fluid enhances the machined surface quality and reduces the built-up edge compared with dry and flooded conditions. Full article

Inconel 625 is a nickel-based superalloy widely applied in aerospace and energy sectors due to its high strength and corrosion resistance. However, its poor machinability remains a significant challenge in precision manufacturing. This study investigates the influence of tool geometry and cutting parameters on surface roughness of Inconel 625 during turning operations under the minimum quantity lubrication (MQL) conditions. Experiments were carried out using three types of cutting inserts with distinct chip breaker geometries while systematically varying the cutting speed, feed rate, and depth of cut. The results were statistically analyzed using analysis of variance (ANOVA) to determine the significance of individual factors. The findings reveal that both the type of cutting insert and the process parameters have a considerable effect on surface roughness, which is the key output examined in this study. Cutting forces and chip type were examined to provide complementary insights and improve understanding of the observed relationships. Based on the results, an optimal set of cutting data was proposed to achieve a required surface roughness during the turning of Inconel 625 with MQL. Furthermore, a practical algorithm was developed to support the selection of cutting parameters in industrial applications. Analysis of the results showed that a cutting insert with a 0.4 mm corner radius achieved the required surface finish (Rz ≤ 0.4 µm). Furthermore, the analysis revealed a significant effect of the thermal properties of Inconel 625 on machining results and chip geometry. Full article

Global agriculture is confronting unprecedented pressures from population growth, diminishing arable land, and severe rural labor scarcity, necessitating the advancement of intelligent agricultural equipment. As a core component of precision farming, unmanned agricultural tractors demand highly accurate and robust path tracking control. However, conventional methods often fail to cope with unstructured terrain and dynamic wheel slip under real field conditions. This paper proposes an extended state observer (ESO)-based improved Stanley guidance law, which incorporates real-time sideslip angle observation, adaptive preview-based path curvature compensation, and a sliding mode heading controller. The ESO estimates lateral slip caused by varying soil conditions, while the modified Stanley law utilizes look-ahead path information to proactively adjust the desired heading angle during high-curvature turns. Both co-simulation in Matlab-Carsim and field experiments demonstrate that the proposed method significantly reduces lateral tracking error and overshoot, outperforming classical algorithms such as fuzzy Stanley and sliding mode controller, especially in U-turn scenarios and under low-adhesion conditions. Full article

Semi-rigid tool polishing is widely used in the high-precision manufacturing of electroless nickel surface due to its stable material removal and high efficiency in correcting mid- and high-frequency profile errors. However, predicting mid-frequency errors remains challenging due to the complexity of their underlying sources. In this study, a theoretical model for semi-rigid tool polishing was developed based on multi-scale contact theory, incorporating a bridging model, rough surface contact, and Hertzian contact mechanics. The model accounts for the effects of tool surface roughness, polishing force, and path spacing. A series of experiments on diamond-turned electroless nickel mirrors was conducted to quantitatively evaluate the model’s feasibility and accuracy. The results demonstrate that the model can effectively predict mid-frequency errors, reveal the material removal mechanisms in semi-rigid polishing, and guide the optimization of process parameters. Ultimately, a surface with mid-frequency errors of 0.59 nm Rms (measured over a 1.26 mm × 0.94 mm window) was achieved, closely matching the predicted value of 0.64 nm. Full article

Current ringing in dual-active bridge (DAB) converters significantly degrades efficiency and reliability, particularly due to resonant interactions in the magnetic tank impedance network. We propose a novel impedance resonant channel shaping technique to suppress the ringing by systematically modifying the converter’s equivalent impedance model. The method begins with establishing a high-fidelity network representation of the magnetic tank, incorporating transformer parasitics, external inductors, and distributed capacitances, where secondary-side components are referred to the primary via the turns ratio squared. Critical damping is achieved through a rank-one modification of the coupling denominator, which is analytically normalized to a second-order form with explicit expressions for resonant frequency and damping ratio. The optimal series–RC damping network parameters are derived as functions of leakage inductance and winding capacitance, enabling precise control over the effective damping factor while accounting for core loss effects. Furthermore, the integrated network with the damping network dynamically shapes the impedance response, thereby attenuating ringing currents without compromising converter dynamics. Experimental validation confirms that the proposed approach reduces peak ringing amplitude by over 60% compared to the conventional snubber-based methods, while maintaining full soft-switching capability. Full article

This study introduces the Double-Diamond Reactor (DDR), a novel planar passive microreactor designed to overcome the following conventional limitations: inefficient mass transfer, high flow resistance, and clogging. The DDR integrates splitting–turning–impinging (STI) hydrodynamic principles via CFD-guided optimization, generating chaotic advection to enhance mixing. Experimental evaluations using Villermaux–Dushman tests showed a segregation index ($X_s$) as low as 0.027 at 100 mL·min⁻¹, indicating near-perfect mixing. In BaSO₄ nanoparticle synthesis, the DDR achieved a 46% smaller average particle size (95 nm) and narrower distribution (${\sigma }_g=1.27$) compared to reference designs (AFR-1), while maintaining low pressure drops (<20 kPa at 60 mL·min⁻¹). The DDR’s superior performance stems from its hierarchical flow division and concave-induced vortices, which eliminate stagnant zones. This work demonstrates the DDR’s potential for high-throughput nanomaterial synthesis with precise control over particle characteristics, offering a scalable and energy-efficient solution for advanced chemical processes. Full article

In the slow tool servo (STS) turning technology for optical lenses, the D-shaped toolpath can improve the quality of the optical surfaces of off-axis aspheric and cylindrical microlens arrays. However, the traditional D-shaped toolpath has the problem of excessive servo following error in the X-axis. To address this issue, the projection of the D-shaped toolpath in the XZ plane is divided into a cutting zone and a transition zone. In the transition zone, an equation system based on continuity constraints (surface height, feed-rate, acceleration) is established. By solving this system of equations, a toolpath can be obtained along which the feed-rate of the X-axis varies smoothly. An example shows that the acceleration of the X-axis of the lathe is reduced by 84% compared to the traditional D-shaped toolpath. In the XZC interpolation mode, the spindle velocity of the C-axis changes smoothly. An off-axis spherical surface and an integral mirror have been machined using the optimized D-shaped toolpath. The X-axis servo following error of the lathe during processing is within 7 nm, and the surface shape accuracy reaches 0.361λ at 632.8 nm. This method enables high-precision processing of off-axis curved surfaces and cylindrical arrays. Full article