Search Results (10,499)

Aiming at the problems of difficult hard-constraint enforcement and weak environmental generalization ability in the safe trajectory planning of manipulators in complex environments, a Policy-Guided Model Predictive Path Integral (PG-MPPI) planning framework is proposed. This framework integrates the advantages of reinforcement learning and model predictive control to construct a global prior guidance, local real-time optimization and hard-constraint safety assurance: a Constraint-Discounted Soft Actor–Critic (CD-SAC) offline learning policy is designed, which incorporates the configuration-space distance field as a safety guidance term to realize the learning of obstacle avoidance behavior; the offline policy is used to guide the online sampling and optimization of MPPI, improving sampling efficiency and planning quality; and a Control Barrier Function (CBF) safety filter is introduced to revise control commands in real time, ensuring the strict satisfaction of constraints. Taking the SIASUN T12B manipulator as the research object, simulation comparison experiments are carried out in multi-obstacle scenarios. The results show that the PG-MPPI algorithm outperforms the comparison algorithms in the success rate of collision-free target reaching, ensures the smoothness and feasibility of the trajectory, and has a good adaptive capacity to complex environments with unknown obstacle configurations, thus providing an efficient solution for the autonomous and safe operation of manipulators. Full article

The push for higher energy density in electric vehicles has resulted in large-sized lithium-ion batteries, but their geometric upscaling exacts a heavy thermal price. Under high-rate discharge, these massive cells become heat traps, risking thermal runaway. To tame this instability, this paper engineered a hybrid management strategy fusing liquid cooling, Phase Change Materials (PCMs), and flow deflectors. With a primary focus on the structural optimization of the cooling channel, a three-dimensional numerical model, calibrated using experimentally determined thermophysical properties, was developed to overcome the thermal bottlenecks of conventional cooling architectures. Results indicated that the initial channel optimization effectively reduced the maximum temperature to 327.7 K, but it still remained near the safety threshold. Integrating PCM radically altered the thermal landscape, slashing the outlet temperature differential by 41.67% (from 2.76 K to 1.61 K) compared to pure liquid cooling and blunting peak thermal spikes. Furthermore, to overcome laminar stagnation, strategic deflector baffles were introduced to agitate the coolant, enhancing heat dissipation. Specifically, the optimal half-coverage (L = 1/2) baffle configuration successfully lowered the maximum temperature to 322.42 K while substantially reducing the system pressure drop from 948.16 Pa to 627.57 Pa, achieving a 33.33% reduction compared to the full-coverage scheme. Finally, a multi-variable sensitivity analysis confirmed the extraordinary engineering robustness of the optimized configuration, demonstrating a negligible maximum temperature fluctuation of less than 0.5% despite ±10% operational and material uncertainties. This synergistic system actively stabilizes the thermal envelope, offering a robust engineering blueprint for next-generation high-power battery packs. Full article

The paper investigates the applicability of time series models for processing data obtained from a customized crack-propagation sensor. Because the sensor records a variable and noise-affected waveform, the study focuses on models capable of forecasting signals composed of both trend and stochastic components. Adaptive, analytical, and autoregressive approaches were examined, with particular attention to their suitability for short, non-stationary sequences typical of fatigue-related measurements. Based on the statistical characteristics of the sensor output during crack growth, the ARIMA model was selected for further analysis and algorithm development. The forecasting performance of ARIMA was evaluated for different parameter configurations by comparing the range and variability of the base and predicted data. Initial tests using first-order parameters produced unsatisfactory results, with high variance observed in both raw and modeled signals. Therefore, model parameters were optimized using the aicbic function, and the analyses were repeated. For the selected datasets, variance reduction by 3–4 orders of magnitude was achieved, demonstrating a substantial improvement in prediction stability. The presented results confirm that the proposed methodology is effective for processing complex sensor signals and highlight the broader significance of applying statistically grounded time series models in structural health monitoring. The study introduces an innovative framework for evaluating fatigue-related sensor data and establishes a reliable baseline for future predictive methods. Full article

The global rise in electronic waste (e-waste), especially in semiconductor components such as circuit boards and microchips, underscores a critical need for improved recycling technology. Current industrial sorters often miss small, high-value components. This leads to the loss of precious metals and inefficient recycling processes. This paper introduces an automated detection framework for detecting semiconductor components in e-waste. It assesses ensemble learning methods that leverage the strengths of multiple YOLO (You Only Look Once) object detection models, including YOLOv5, YOLOv8, YOLOv9, YOLOv10, YOLOv11, and YOLOv12. Three ensemble fusion strategies are systematically compared: standard Non-Maximum Suppression (NMS), voting-based strategies (Affirmative, Consensus, Unanimous), and Weighted Box Fusion (WBF) with both static and dynamic weight optimization. Our simulations demonstrate that using multiple models together is far more effective than a single model for the following reasons. 1. Higher Accuracy: The best configuration, Top-4 Consensus Voting ensemble strategy, achieved an mAP@0.5 of 59.63%, a 10.3% improvement over the best individual model (YOLOv8s, 54.04%); 2. Greater Reliability: It significantly reduced “false negatives” (missed detections), even in cluttered or crowded e-waste scenarios; 3. Enhanced Detection: While the individual YOLOv8 model is fast (taking only 62.6 ms), supporting real-time detection, the best ensemble configuration (Consensus Top-4) takes 384.9 ms, creating a trade-off between detection accuracy and speed; 4. Well-Balanced Performance: Some fusion strategies showed slight trade-offs in mAP for certain parts, but collectively achieved a 7% rise in F1-score, indicating a better balance between precision and recall. This research marks significant progress in smart recycling. Improved component identification allows for more efficient recovery of high-purity materials. This promotes a circular economy by ensuring that rare and strategic materials in electronics are reused instead of discarded. Full article

This paper addresses the low-carbon transformation needs of the high-energy-consuming industry of cement and proposes a planning method that integrates photovoltaic capacity planning and multi-time-scale demand response. The aim of this method is to minimize the total system cost throughout the entire life cycle, including the investment cost of photovoltaic and the expected operating cost considering demand response. A multi-time-scale demand response model that precisely describes the temporal coupling of the cement production process, inventory dynamics, and hourly/weekly scenarios was constructed. By establishing a two-layer stochastic optimization framework and using the typical scenario method to handle the uncertainties of photovoltaic output and market demand, the coordinated optimization of photovoltaic configuration and load flexibility was achieved. Based on a case study of a typical cement plant in China, it is shown that, compared with traditional planning methods, the proposed method can significantly increase the photovoltaic consumption rate, reduce electricity costs, and effectively quantify the system’s demand response capability, providing a theoretical basis and practical tools for industrial users to achieve “source-load” coordinated low-carbon planning. Full article

The remaining useful life (RUL) of lithium-ion batteries plays a crucial role in fault prognosis and health management. Therefore, accurate RUL prediction can effectively improve equipment safety and mitigate operational risks. However, existing RUL prediction methods often exhibit limited accuracy caused by capacity regeneration and excessively long training times due to model complexity. In this study, Pearson and Spearman correlation analyses are employed to effectively extract health features that are highly correlated with battery capacity to characterize capacity degradation, and a lithium-ion battery RUL prediction model based on stochastic configuration networks (SCNs) optimized by sparrow search algorithm (SSA) is proposed. The battery datasets from NASA and CALCE are used for validation and testing. Experimental results demonstrate that the proposed SSA-SCN achieves a root mean squared error of 0.0036 and a mean absolute error of 0.0030, while exhibiting faster training time compared with other hybrid methods. The results verify that the proposed method can provide more accurate battery RUL predictions and effectively improve the accuracy and reliability of lithium-ion battery RUL estimation. Full article

The proliferation of Internet of Things (IoT) devices has introduced significant security challenges. Resource-constrained devices face sophisticated threats but lack the computational capacity for advanced security analysis. This study investigates optimal security task allocation in Cognitive IoT (CIoT) networks. It specifically examines when IoT devices should process security tasks locally or offload them to Mobile Edge Computing (MEC) servers. The problem is formulated as a Continuous-Time Markov Decision Process (CTMDP). The study demonstrates that the optimal offloading policy has a threshold structure. Security tasks are offloaded to MEC servers when the offloading queue length is below a critical threshold, $k^{\ast }$. Otherwise, tasks are processed locally. This structural property is robust to changes in MEC server configurations and threat arrival patterns. It ensures an optimal and easily implementable security policy under the exponential model. Theoretical analysis establishes upper bounds on the performance of AI-based security controllers using the same models. The results also show that standard model-free Q-learning algorithms can recover optimal thresholds without any prior knowledge of the system parameters. Simulations across multiple reinforcement learning architectures, including Q-learning, State–Action–Reward–State–Action (SARSA), and Deep Q-networks (DQN), confirm that all methods converge to the predicted threshold. This empirically validates the analytical findings. The threshold structure remains effective under practical imperfections such as imperfect sensing and parameter estimation errors. Systems maintain 85% to 93% of their optimal performance. This work extends threshold Markov Decision Process (MDP) analysis from classical queuing theory to the context of CIoT security offloading. It provides optimal and practical policies and model-free algorithms for use by resource-constrained devices. Full article

Pedestrian safety at urban crosswalks remains a major public concern, as both vehicle speeds and roadway characteristics strongly influence drivers’ behaviour when approaching these locations. This study investigates driver behaviour patterns when approaching pedestrian crossings by integrating operating speed with key road-layout features derived from a naturalistic driving experiment conducted in Florence. A dataset of 401 observations was analysed using an unsupervised clustering framework specifically designed to handle mixed numerical and categorical variables. After preprocessing, the optimal number of clusters was identified using an elbow-based model selection applied to the K-Prototypes algorithm. The analysis produced four distinct clusters, primarily differentiated by operating speed and secondarily by contextual variables such as lane number, lane width, and acceleration behaviour. Lower-speed clusters were associated with single narrow-lane configurations, whereas higher-speed clusters were characterised by wider or multilane segments and more frequent acceleration near crossings. Information Gain analysis confirmed the dominant role of lane-related attributes, while the presence of crosswalks alone did not systematically reduce speeds. Complementary clustering excluding speed resulted in fewer clusters, indicating that speed adds essential granularity to behavioural segmentation. These findings highlight the interplay between road design and driver behaviour and provide evidence-based insights to support crosswalk configurations that mitigate high-speed conflicts in urban settings. Full article

Objective: The aim of this study was to determine the optimal region-of-interest (ROI) pixel size for fractal dimension analysis on panoramic radiographs that best reflects implant stability assessed by resonance frequency analysis (ISQ) and to investigate whether implant stability can be directly estimated from radiographic images. Materials and Methods: This retrospective cross-sectional study included 65 patients for whom panoramic radiographs and resonance frequency analysis measurements were available. All panoramic images were converted to TIFF format and standardized to a resolution of 2627 × 1646 pixels. All radiographic images were obtained using the same panoramic imaging device and standardized acquisition protocol. Exposure parameters were adjusted within the manufacturer’s recommended range to ensure optimal image quality while maintaining methodological consistency across patients. During ROI selection, care was taken to avoid cortical bone margins, overlapping anatomical structures, and radiographic artifacts in order to ensure that the analyzed regions represented trabecular bone adjacent to the implant surface. Fractal dimension analysis was performed in the cervical peri-implant bone region, starting from the first bone–implant contact and extending apically, using three different ROI configurations. The ROI size was defined as 30 pixels apically and 10 pixels horizontally for FMD1, 30 × 20 pixels for FMD2, and 30 × 30 pixels for FMD3. Implant stability was assessed using ISQ values. Data distribution was evaluated using the Shapiro–Wilk test. Associations between ISQ and fractal dimension measurements were analyzed using Pearson and Spearman correlation analyses. Multiple linear regression models adjusted for age and sex were constructed to assess independent associations. Results: The mean age of the participants was 50.0 ± 9.9 years, and the mean ISQ value was 78.6 ± 5.9. The mean fractal dimension values were 1.466 ± 0.055 for FMD1, 1.595 ± 0.031 for FMD2, and 1.655 ± 0.046 for FMD3. No significant association was found between ISQ and FMD1 or FMD3. A weak positive correlation was observed between ISQ and FMD2; however, this association did not remain statistically significant after correction for multiple comparisons. In multiple linear regression analysis, ISQ was identified as an independent predictor of FMD2, but not of FMD1 or FMD3. Age and sex had no significant effect on fractal dimension measurements. Conclusions: Fractal dimension measurements derived from panoramic radiographs showed a weak association with implant stability that was dependent on the selected ROI pixel size. Among the evaluated configurations, the 30 × 20-pixel ROI at the cervical peri-implant region demonstrated the strongest association with ISQ values, suggesting that this ROI configuration showed the most consistent association with ISQ values among the tested ROI sizes. Full article

This study proposes a tension-spring-based unidirectional rotational stiffness mechanism (TS-URM) and its implementation in a Unidirectional Series Elastic Actuator (USEA). Unlike conventional bidirectional rotary SEAs, the proposed design is structurally optimized for unidirectional torque transmission, improving deformation utilization efficiency in pulling-type applications. An analytical model was derived to establish the geometric relationship between spring elongation and rotational deformation, enabling explicit formulation of the torque–angle relationship. The influence of the installation angle on stiffness linearity was systematically analyzed, and a multilayer spring configuration was optimized to achieve a target rotational stiffness of approximately 42 Nm/rad. A preload adjustment mechanism was incorporated to eliminate nonlinear behavior in the initial operating region. Experimental results validated the analytical model and demonstrated stable unidirectional force control up to 130 N with steady-state errors within 1 N. The proposed mechanism provides predictable stiffness characteristics and an efficient structural solution for compact USEA systems. Full article

Space-deployable antennas have development requirements of an ultra-large aperture, high stiffness, and multi-frequency multiplexing. To address the challenge of stiffness characterization in the multi-closed-loop complex systems of deployable mechanisms, this paper proposes a parametric stiffness modeling method and a static stiffness model is established, ranging from components and limbs to the overall mechanism. The motion/force mapping model of the deployable mechanism is obtained using screw theory, and the stiffness mapping from joint space to workspace is achieved via the Jacobian matrix. A comprehensive stiffness model of the deployable mechanism incorporating joint effects is established based on the principle of virtual work and the superposition principle of deformations, and its validity is verified through finite element simulation. Building on this, stiffness characteristics based on structural configuration are investigated, and structural forms with excellent stiffness performance are selected through comprehensive evaluation. Six configurations of the deployable mechanism are derived topologically from this structure, and the optimal configuration is selected based on stiffness performance. The parametric stiffness modeling method proposed in this study can effectively characterize the contribution of each component to the overall system stiffness. It lays a theoretical foundation for establishing a quantitative relationship between stiffness performance and configuration, enabling performance-based configuration optimization and dimensional optimization. Full article

The thermal conductivity of thermal insulation materials is a critical parameter for assessing energy efficiency and performance in building, industrial, and aerospace applications. This study combined numerical simulation, parameter inversion optimization and experimental measurement to evaluate the transient hot-wire method for measuring the thermal conductivity of expanded polystyrene (EPS) foam. Using a nickel wire as the hot wire, the effects of various parameters—including wire length and width, heating power, Kapton film thickness and end effect—were systematically analyzed through finite element analysis and Bayesian optimization algorithm. Following the simulation and inversion conclusions, a series of hot-wire sensors with a fixed length of 30 mm and widths of 25 μm, 50 μm, 100 μm, 150 μm, and 200 μm were fabricated for experimental validation. Measurement results were compared against a reference value obtained by the guarded hot plate method. It was found that the sensor with a length of 30 mm and a width of 100 μm demonstrated optimal performance among the configurations tested, with deviations between the experimental measurements and the reference value remaining within approximately ±1.5%. Full article

Vertical axis wind turbines (VAWTs) suffer from dynamic stall (DS) at low tip-speed ratios (λ), where cyclic variations in angle of attack (α) dominate the blade aerodynamics, severely undermining aerodynamic performance and power extraction. The coupled influence of airfoil parameters on DS remains unexplored. To address this gap, a fully coupled parametric study using 126 incompressible URANS simulations is conducted, examining three geometric parameters of symmetric airfoils: maximum thickness (t/c), chordwise position of maximum thickness (x_t/c), and leading-edge (LE) radius index (I). The results show that coupled geometric modification fundamentally alters the stall mechanism, shifting it from abrupt, LE-driven separation toward a gradual, trailing-edge (TE)-controlled process as airfoils transition from thin, forward-x_t/c profiles to thicker configurations with aft x_t/c and reduced I. This transition enhances boundary-layer (BL) stability, delays DS onset, weakens dynamic stall vortex (DSV) formation, and mitigates unsteady aerodynamic loading. Within the investigated design space, the best-performing configuration (NACA0024–4.5/3.5) achieves a 73% increase in turbine power coefficient (C_P) relative to the baseline airfoil (NACA0018–6.0/3.0), mainly through passive control of BL separation and vortex development. These findings highlight the limitations of single-parameter optimization and establish a physics-based, coupled-design framework for mitigating DS-induced performance losses in VAWTs. Full article

Finned tubes contribute to the heat transfer performance of heat exchangers by increasing the surface area; they also modify patterns within the flow around the tubes and thus increase the likelihood of flow-induced vibrations (FIVs), which can undermine structural integrity. The tradeoff between improved heat transfer and minimized vibration risks is thus of concern in the optimization of finned tube designs. This paper examines the vibration behavior of circular finned tubes fitted in a parallel triangular configuration when subjected to crossflow conditions with particular reference to the structural response as opposed to thermal performance. In this study, two tube bundles arranged in a 30° parallel triangular layout were tested. The test tube has pitch-to-diameter (P/D) ratios of 1.16 and 1.37 and fin densities of 3, 6, and 9. In this study, experiments were conducted in a low-speed closed-loop water tunnel, which also involved the fabrication of circular finned tubes, the preparation of test bundles, and vibration response measurements. The key parameters analyzed in this experiment were the vibration amplitude, damping, pitch ratio, and fin density. Based on the free-stream velocity range of 0.13–0.28 m/s in a 300 mm × 300 mm closed-circuit water tunnel (hydraulic diameter $D_h=0.3$ m), the Reynolds number ranged from 3.9 × 10⁴ to 8.4 × 10⁴ (water at 20 °C). The results of this experiment demonstrate that by increasing the fin density, the vibration amplitudes can be reduced, which also raises the critical velocities. Reducing the pitch ratio from 1.37 to 1.16 produced an onset of instability approximately 53% earlier than the onset of instability at the ratio of 1.37. The bandwidth of the pitch ratio of 1.16 at the same fin density of 9 was almost 45% lower than that at 1.37, which confirms that the system at 1.16 is much more unstable. In general, the 1.37 pitch ratio offers 3 times higher stability margins than those of 1.16 for the fin densities under study. The development of optimal finned tube heat exchanger designs that reduce flow-induced vibrations without sacrificing thermal performance is aided by these findings, which provide information on the relationship between the fin density, pitch ratio and vibration behavior. Full article

Local Lorentz Invariance is one of the fundamental postulates of General Relativity, making its experimental verification of paramount importance. Given that various frontier theoretical models predict potential symmetry breaking, the Standard Model Extension framework has been established to systematically study such phenomena. Within the Standard Model Extension gravitational sector, the high-order Lorentz-violating terms with mass dimension $d=6$ exhibit a rapid signal decay with distance, providing a distinct detection advantage in short-range gravity experiments. This work is dedicated to optimizing the testing schemes for $d=6$ Lorentz-violating coefficients. Based on a high-precision torsion balance platform, we propose a novel scheme featuring a comb-stripe design. The improvements are twofold: first, the spatial orientation of the experimental apparatus is optimized to leverage the modulation effects of the Earth’s rotation, thereby enhancing the capability to distinguish and constrain different violation parameters; second, the test and source masses are reconfigured into specifically designed stripe patterns to significantly amplify the fringe-field signals sensitive to Lorentz-violating effects. This paper systematically elaborates on the theoretical foundation and design principles of the new scheme. By performing a detailed comparison of the constraint potentials of various stripe configurations, the five-stripe geometry is identified as the optimal experimental configuration. This study provides a new experimental methodology for exploring physics beyond the Standard Model at higher levels of precision. Full article