Search Results (399)

Zr alloy-shaped charge liners (SCLs) offer broad application prospects due to their multiple post-penetration damage effects. However, research on these liners is still in its early stages. The mechanisms of jet formation and penetration for Zr alloys SCL remain unclear, and the specific contribution of different liner regions to the penetration process is not yet understood. This gap in knowledge has limited their structural design to a black-box correlation between global structural parameters and macroscopic penetration efficiency. To address this gap, a region-tracing Smoothed Particle Hydrodynamics (SPH) simulation was employed. Following a strategy of “wall thickness layering + axial segmentation,” the Zr alloy liner was partitioned into ten characteristic regions. This methodology facilitated the tracking of material transport from each region during jet formation and penetration into an AISI 1045 steel target. The contribution of each region to the penetration depth was then quantitatively assessed via post-processing. For the first time, the “critical region” contributing most to penetration depth was identified, and the influence of the liner’s cone angle and wall thickness on the contribution of each region was revealed. This study enhances the theoretical framework for understanding the damage effects of Zr alloy shaped charge liners. It not only advances the fundamental understanding of jet penetration mechanisms but also provides a theoretical basis for the refined design and performance optimization of these liners. Full article

In Time Division Duplex (TDD) Direct-Sequence Code Division Multiple Access (DS/CDMA) architectures, Pre-Rake filtering serves as a powerful transmitter-side strategy to alleviate receiver hardware constraints by leveraging channel reciprocity. Nevertheless, rapid channel fluctuations induced by high Doppler spreads critically undermine this reciprocity assumption. This failure is primarily driven by the unavoidable latency between uplink reception and downlink transmission, leading to severe performance deterioration. To address these challenges and enhance system robustness in modern high-speed scenarios, we propose an improved hybrid transceiver architecture. This scheme integrates multiplexed Pre-Rake processing with a Matched Filter-based Rake receiver and employs a Minimum Mean Square Error (MMSE) equalizer to suppress the severe Inter-Symbol Interference (ISI) and Multi-User Interference (MUI). Furthermore, we conduct a comparative analysis of channel estimation methods tailored for a 10 Mbps high-speed transmission environment.Our investigation reveals that while complex quadratic interpolation is often prioritized in low-data-rate studies, simple averaging is sufficient and even superior in high-speed communications. This is because the shortened slot duration allows simple averaging to effectively track channel variations while avoiding the noise overfitting associated with higher-order interpolation. The simulation results demonstrate that the proposed MMSE-optimized architecture achieves superior Bit Error Rate (BER) performance, providing a practical and computationally efficient solution for next-generation mobile networks. Full article

Accurate temperature regulation is essential for ensuring product quality, operational safety, and energy efficiency in industrial electric furnace systems. However, the inherent thermal inertia, time-delay effects, and nonlinear dynamics of furnace processes often make precise temperature control a challenging task. Motivated by these challenges, this study proposes an optimization-based control framework aimed at improving the temperature regulation performance of electric furnace systems. The proposed approach integrates a proportional–integral–derivative (PID) controller with the recently developed gazelle optimization algorithm (GOA) for automatic tuning of the controller parameters. First, a mathematical model of the electric furnace is established to describe the dynamic relationship between the control input and the furnace temperature output. Based on this model, a PID controller is implemented to regulate the furnace temperature. The parameters of the PID controller are then optimized using GOA, a nature-inspired metaheuristic algorithm that mimics the adaptive predator–prey survival strategies observed in gazelle herds. In order to achieve a balanced improvement in both steady-state and transient performance, a composite objective function is introduced. The proposed performance index combines the integral of absolute error with additional transient performance indicators related to maximum overshoot and settling time. The effectiveness of the proposed GOA-based tuning framework is evaluated through extensive simulation studies and statistical analyses conducted over multiple independent optimization runs. The results demonstrate stable convergence behavior, with the optimization process achieving a minimum objective value of 2.4251, a maximum value of 2.5347, and an average value of 2.4674 across 25 runs. The optimized control system exhibits improved dynamic characteristics, including a rise time of 1.8509 s, a settling time of 3.6834 s, and a low overshoot of 1.5104%. To further assess its effectiveness, the proposed GOA–PID control strategy is compared with several widely used controller tuning methods reported in the literature, including genetic algorithm, Ziegler–Nichols, Cohen–Coon, Nelder–Mead, and direct synthesis approaches. Comparative results indicate that the proposed method achieves a superior balance between response speed, stability, and temperature tracking accuracy. Full article

Real-time, precise monitoring of the number and distribution of indoor personnel is crucial for building safety management, operational optimization, and personnel scheduling. However, narrow entrances and high-density passageways often lead to missed detections, false positives, and tracking failures in pedestrian detection, thereby reducing cross-line counting accuracy. Additionally, edge devices deployed in practical scenarios frequently process multiple video streams simultaneously, resulting in computational resource constraints. To address these challenges, this paper proposes a lightweight, enhanced multi-object pedestrian tracking and counting method tailored for indoor scenarios by optimizing deep learning models. Firstly, modular optimizations are applied to the YOLOv8n model to construct a more lightweight detector, RL_YOLOv8, reducing computational overhead while maintaining accuracy. Secondly, correlated pedestrian auxiliary prediction and pedestrian position change constraints are employed to mitigate ID switching, tracking interruptions, and trajectory jumps in dense scenes. Finally, a buffer zone auxiliary counting strategy is designed to further reduce missed detections of pedestrians crossing lines. Experimental results demonstrate that compared to the original detection-and-tracking-based line-crossing counting method, the improved approach effectively enhances counting accuracy and real-time performance, better meeting the requirements of practical intelligent security and crowd monitoring systems. Full article

Ocean turbulence, a fundamental process influencing marine hydrodynamics, holds significant guiding implications for the development of multiple disciplines and has emerged as a research hotspot in ocean science in recent years. However, constrained by traditional oceanographic instruments limited to single-point measurements, current observations and analyses of oceanic turbulence still experience considerable shortcomings. To advance oceanic turbulence observations beyond single-point measurements toward comprehensive three-dimensional (3D) field characterization, this study introduces an innovative Holographic Astigmatic Particle Tracking Velocimetry (HAPTV) technology combined with an integrated in situ underwater measurement and processing system. For the first time, this system has successfully acquired 3D two-component (u, v components) ocean flow fields in natural environments. The measured flow velocities reach up to 15 cm/s, with turbulence dissipation rates on the order of 10⁻⁴ m²/s³, which is consistent with the hydrodynamic conditions in coastal marine environments. These results demonstrate the feasibility of using HAPTV for field-scale turbulence observations, offering a novel volumetric alternative to conventional single-point techniques. Nevertheless, due to factors such as excessively high concentrations of suspended matter in nearshore waters and z-axis positioning limitations, the accuracy of the flow field results obtained from the initial sea trials still needs to be improved. Full article

Full-penetration laser welding (FPLW) is increasingly adopted in manufacturing pipelines, yet its industrial scalability is constrained by in-process defect formation, particularly incomplete penetration. To address this, we propose a sensor-driven framework for non-destructive monitoring and automated defect detection that uses infrared (IR) thermography as the primary in situ sensing modality and applies deep learning to the acquired thermal signals. High-speed IR camera recordings were processed to track spatter and the weld zone, yielding a time series of physically interpretable spatiotemporal features (mean spatter area, mean spatter temperature, number of spatters, and mean welding zone temperature). Defect recognition is formulated as a multi-label classification problem targeting incomplete penetration, sagging, shrinkage groove, and linear misalignment, and multiple temporal models were evaluated on the same sensor-derived feature sequences. Experimental validation on 09G2S pipeline steel demonstrates that the proposed time series pipeline based on a hybrid CNN–transformer achieves a mean Average Precision (mAP) of 0.85 while preserving near-real-time inference on a CPU. The results indicate that IR thermography-based spatter dynamics provide actionable sensing signatures for automated defect prediction and can serve as a foundation for closed-loop quality control in industrial laser pipeline welding. Full article

To continuously improve the efficiency of the construction project delivery process, various innovative methods and technologies have been developed and adopted in the past decades. Among these methods, modular construction has become a popular option due to its short on-site installation time generated by off-site prefabrication. However, the process of modular construction requires a highly integrated system to accurately connect multiple phases, including material packaging, transportation logistics, locating and tracking, and on-site installation. Accordingly, this process typically poses a significant challenge for contractors to efficiently manage the materials needed for daily tasks. This paper introduces a construction material management system that integrates every phase from off-site packaging to on-site installation. The integrated system was developed based on Logistic Chain and Building Information Modeling (BIM) using a three-layer framework, namely material packaging, inventory management, and material locating and tracking. The new system utilizes recent innovative technologies for transparent consolidation and highly efficient operation of off-site inventory management and on-site visualization. The developed system was further examined in a real-world case study project. The material handling time was then analyzed and compared with benchmark data without using the integrated system. The results indicated that the newly developed system was able to effectively reduce the time of locating materials and the rate of missing materials during on-site installation. In addition, this case study project added value to the verification of the broader system’s capabilities for inventorying, tracking, and visualizing construction materials. The findings of this project provide valuable knowledge and insight into improving construction efficiency through an integrated material management system. Future research is needed to expand the applicability of multiple framework designs and assess the cost–benefit analysis for production-scale and commercial use. Full article

Accurate one-step forecasting of scenic-spot visitor demand is challenging due to strong non-stationarity, holiday-induced peaks, and abrupt reputation-driven shocks. We propose a symmetry-aware dynamic graph learning framework that fuses social–physical sensing streams for robust demand prediction. Online reviews are treated as social sensing, transformed into daily sentiment indicators, and aligned with demand using a delay-aware aggregation scheme. To capture evolving inter-spot dependencies, we construct a time-varying adjacency matrix that is updated over time and integrated into a lightweight spatio-temporal forecasting model, Dynamic Spatio-temporal Graph Attention LSTM (DSGAT-LSTM). The model preserves the permutation-invariant property of graph learning while introducing sentiment-guided feature reweighting and sentiment-gated temporal updates to better track volatility. Experiments on multi-year daily data from multiple A-level scenic spots with holiday and weather context demonstrate consistent error reductions over representative temporal and graph-based baselines, together with improved stability under peak and shock conditions. We will release the processed feature-level dataset and implementation scripts to support reproducibility. Full article

Precise object tracking of space objects is an image acquisition method that uses the mount of the telescope to orient the instrument in real time towards the target to be tracked, compensating for the target’s motion. Using this method, the object of interest will appear as a circular or point-like shape in the acquired image, while the background stars will appear as streaks. Using precise object tracking, the light from a faint object accumulates in the same region of the image, increasing the chance of observation, but longer exposures also increase the length of the background star streaks and makes the astrometric calibration difficult. This paper presents a method for the automatic processing of image sequences acquired in precise object tracking mode. Our proposed method includes a filtering mechanism that will ensure local maxima in the center of star streaks in order to allow for a publicly available astrometric calibration software to work even if the stars are not point-like, a weighted stacking mechanism to increase the signal-to-noise ratio for faint targets while excluding the stars, an automatic object detection and astrometric reduction mechanism and a constraint-based filtering of outliers for the final generation of the tracklet. The method was tested on multiple observation sessions for surveying the CLUSTER II highly eccentric orbit satellites, including the CLUSTER II FM5 satellite (Rumba) on its final passes before reentry, and the accuracy of the measurements was estimated based on ground truth from ESA’s reentry team. The method was also tested on lower orbit objects and found to be accurate for objects with ranges of more than 1300 km from the observer. Full article

This paper presents a novel bio-inspired fluid dynamics framework that unifies path planning and control within a single continuous navigation process. Unlike conventional approaches that separate trajectory generation and execution, the proposed method models the robot as a particle immersed in an artificial fluid field, where the goal acts as a sink and obstacles modify the flow to produce collision-free motion. To ensure global optimality and eliminate local minima traps, the framework incorporates a sampling-based enhancement that evaluates multiple trajectories within high-flow regions and selects the optimal path using graph-based optimization. A fluid-based control law directly converts the velocity field into robot motion commands, enabling seamless integration between planning and execution. Theoretical stability is established using Lyapunov analysis, guaranteeing convergence to the goal. Extensive experiments on a Pioneer P3-DX robot demonstrate that the proposed approach achieves execution speeds 1.5 to 9.7 times faster than A*, PRM, and RRT*, while producing paths 3.6% to 29.5% shorter. Furthermore, the unified framework provides smooth and accurate motion with tracking errors within ±0.1 m. These results confirm that the proposed method improves path quality, computational efficiency, and real-time navigation performance. Full article

Background/Objectives: Family caregivers play a central role in long-term care but are frequently exposed to considerable physical, emotional, and social strain. In Germany, care counseling pursuant to §45 SGB XI aims to identify caregiver burden at an early stage and provide preventive, resource-oriented support. Structured self-assessment tools may facilitate reflective dialogue within time-limited counseling sessions. The Self-Assessment Tool for Family Caregivers (SSA-PA) was developed to support this process; however, empirical evidence regarding its applicability in statutory counseling settings remains limited. This exploratory mixed-methods study aimed to generate empirical insights into (1) the perceived usefulness and acceptance of the SSA-PA among care advisors, (2) opportunities and challenges associated with its practical implementation, and (3) its perceived integration potential within routine counseling practice. Methods: Thirteen care advisors working under §45 SGB XI applied the SSA-PA in routine counseling and subsequently completed a structured online survey combining Likert-scale items and open-ended questions. Quantitative data were analyzed descriptively using IBM SPSS Statistics (Version 29), and qualitative responses were examined using thematic analysis. Given the moderate sample size (n = 13), analyses were primarily descriptive and exploratory in nature. Results: Care advisors reported high perceived usefulness and broad acceptance of the SSA-PA as a structuring and reflective instrument in counseling sessions. The tool was described as supportive in facilitating discussion of caregiver burden across multiple life domains and enhancing transparency of stress-related issues. At the same time, participants identified practical challenges, including time constraints, emotional strain for caregivers, technical barriers, and the need for clearer evaluation outputs. Suggestions for further development included automated result processing, individualized recommendations, and longitudinal tracking functions. Conclusions: From the perspective of participating care advisors, the SSA-PA demonstrates promising feasibility and acceptance within statutory preventive counseling under §45 SGB XI. While the findings provide practice-based evidence for its applicability, conclusions regarding effectiveness or outcome improvements cannot be drawn. Further research with larger samples and outcome-oriented designs is required to evaluate its impact on caregiver burden and counseling processes. Full article

The temperature regulation of nonlinear continuous stirred tank reactor (CSTR) processes remains a challenging control problem due to strong nonlinearities, time-delay effects, and sensitivity to disturbances and parameter variations. Conventional proportional–integral–derivative (PID)-based control strategies often fail to provide the robustness and precision required under such conditions, motivating the use of more flexible controller structures and advanced optimization techniques. In this study, an enhanced joint-opposition artificial lemming algorithm (JOS-ALA) is proposed for the optimal tuning of a fractional-order PID (FOPID) controller applied to CSTR temperature control. The proposed JOS-ALA incorporates a joint opposite selection mechanism into the original ALA to improve population diversity, convergence stability, and resistance to local optima stagnation. A nonlinear CSTR model is linearized around a stable operating point, and the resulting model is employed for controller design and optimization. The FOPID controller parameters are tuned by minimizing a composite cost function that simultaneously accounts for tracking accuracy, overshoot suppression, and instantaneous error behavior. The effectiveness of the proposed approach is assessed through extensive simulation studies and benchmarked against state-of-the-art and high-performance metaheuristic optimizers, including ALA, electric eel foraging optimization (EEFO), linear population size reduction success-history based adaptive differential evolution (L-SHADE), and the improved artificial electric field algorithm (iAEFA). The benchmarking set is further extended with the success rate-based adaptive differential evolution variant (L-SRTDE) to broaden the comparative evaluation. Simulation results demonstrate that the JOS-ALA-based FOPID controller consistently achieves superior performance across multiple criteria. Specifically, it attains the lowest mean cost function value of 0.1959, eliminates overshoot, and yields a normalized steady-state error of 4.7290 × 10⁻⁴. In addition, faster transient response and improved robustness under external disturbances and measurement noise are observed when compared with competing methods. Statistical reliability of the observed performance differences is additionally examined using a Wilcoxon signed-rank test conducted over 25 independent runs. The resulting p-values confirm that the improvements achieved by the proposed approach are statistically significant at the 5% level across all pairwise algorithm comparisons. These findings indicate that the proposed JOS-ALA provides an effective and reliable optimization framework for high-precision temperature control in nonlinear CSTR systems and offers strong potential for broader application in complex process control problems. Full article

Autonomous navigation in mobile robots operating in dynamic and partially known environments demands the coordinated integration of perception, decision-making, and control while ensuring stability, safety, and energy efficiency. This paper presents an integrated navigation framework for differential-drive mobile robots that combines deep learning-based visual perception, reinforcement learning (RL) for high-level decision-making, and a Lyapunov-based trajectory reference generator for low-level motion execution. A convolutional neural network processes RGB-D images to classify obstacle configurations in real time, enabling navigation without prior map information. Based on this perception layer, an RL policy generates adaptive navigation subgoals in response to environmental changes. To ensure stable motion execution, a Lyapunov-based control strategy is formulated at the kinematic level to generate smooth velocity references, which are subsequently tracked by embedded PID controllers, explicitly decoupling learning-based decision-making from stability-critical control tasks. The local stability of the trajectory-tracking error is analyzed using a quadratic Lyapunov candidate function, ensuring asymptotic convergence under ideal kinematic assumptions. Experimental results demonstrate that while higher control gains provide faster convergence in simulation, an intermediate gain value (K = 0.5I) achieves a favorable trade-off between responsiveness and robustness in real-world conditions, mitigating oscillations caused by actuator dynamics, delays, and sensor noise. Validation across multiple navigation scenarios shows average tracking errors below 1.2 cm, obstacle detection accuracies above 95% for human obstacles, and a significant reduction in energy consumption compared to classical A* planners, highlighting the effectiveness of integrating learning-based navigation with analytically grounded control. Full article

Eye tracking can provide quantitative indices of visual exploration and cognitive processing during radiographic image interpretation. This study examined eye-movement patterns and pupil dynamics and their associations with task performance while fifth-year dental students interpreted a single mixed-dentition panoramic radiograph under free-viewing conditions. Task performance was defined as the number of correctly identified pre-specified items (three radiographic findings plus two interpretive items: dental age estimation and the presence/absence of congenital anomalies). Eye-movement patterns were classified into four groups: clockwise (R, 29.6%), counterclockwise (L, 44.4%), saccadic (S, 16.7%), and concentrated (C, 9.3%). Clockwise scan paths were associated with higher task scores and more globally distributed fixations than other patterns (p < 0.001). Linear mixed-effects modeling suggested that task scores increased up to 120 s of viewing time, whereas longer viewing times were not associated with further improvements. Furthermore, ordinal logistic regression analysis revealed that higher task scores were significantly associated with a smaller mean pupil area across the entire viewing time, combined with a larger pupil area specifically during fixations, suggesting more selective allocation of cognitive resources. These findings indicate associations between global scan structure, time allocation, pupil dynamics, and task performance in this single-image setting. Generalization to overall diagnostic competence or other radiographs requires replication using multiple panoramic images and a broader range of verified findings. Full article

Accurate estimation of the State of Charge (SOC) for lithium-ion batteries is a core function of the Battery Management System (BMS). However, LiFePO₄ batteries present specific challenges for SOC estimation due to the characteristic plateau in their open-circuit voltage (OCV) versus SOC relationship. Moreover, data-driven estimation approaches often face significant difficulties stemming from measurement noise and interference, the highly nonlinear internal dynamics of the battery, and the time-varying nature of key battery parameters. To address these issues, this paper proposes a Long Short-Term Memory (LSTM) model integrated with feature engineering, physical constraints, and the Extended Kalman Filter (EKF). First, the model’s temporal perception of the historical charge–discharge states of the battery is enhanced through the fusion of temporal voltage information. Second, a post-processing strategy based on physical laws is designed, utilizing the Particle Swarm Optimization (PSO) algorithm to search for optimal correction factors. Finally, the SOC obtained from the previous steps serves as the observation input to EKF filtering, enabling a probabilistically weighted fusion of the data-driven model output and the EKF to improve the model’s dynamic tracking performance. When applied to SOC estimation of LiFePO₄ batteries under various operating conditions and temperatures ranging from 0 °C to 50 °C, the proposed model achieves average Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) as low as 0.46% and 0.56%, respectively. These results demonstrate the model’s excellent robustness, adaptability, and dynamic tracking capability. Additionally, the proposed approach only requires derived features from existing input data without the need for additional sensors, and the model exhibits low memory usage, showing considerable potential for practical BMS implementation. Furthermore, this study offers an effective technical pathway for state estimation under a “physical information–data-driven–filter fusion” framework, enabling accurate SOC estimation of lithium-ion batteries across multiple operating scenarios. Full article