Search Results (7,630)

In scattered-point spherical harmonic synthesis, repeated pointwise evaluation of associated Legendre functions is a major computational kernel. Recurrence methods use little memory but contain sequential degree dependence, whereas phase-function evaluators provide high accuracy using larger resident tables. We present a guarded hybrid evaluator that moves the high-accuracy reference evaluator to offline precomputation and replaces it at runtime with a compact three-dimensional Chebyshev table of smooth phase-related quantities. The surrogate path is used in numerically safe regions, and a C++ scaled recurrence with exponent tracking provides the fallback near turning points and in exponentially decaying regions. For bandlimits up to 4096, the runtime table is 3.66 MiB, about 60 times smaller than the public ALegendreEval table set used here. Across sparse scattered-point benchmarks, the method agrees with the reference evaluator at 10⁻¹¹-level output differences and achieves representative speedups of 2–5 over an optimized C++ recurrence baseline; even unfavorable cases fall back to recurrence-level runtime. These results show that reference-level Legendre evaluation can be reorganized into a low-resident-memory kernel suitable for repeated scattered-point synthesis. Full article

Grid-forming converters are essential for low-inertia power systems. However, their semiconductor current and junction-temperature limits make them vulnerable to protection-triggered blocking and thermal overstress during severe voltage sags. Existing control methods still struggle to suppress fault-instant current surges and the associated transient thermal stress induced by rapid overcurrent, which restricts the continuous operation of grid-forming converters during fault ride-through. This paper proposes a two-stage coordinated electro-thermal control strategy centered on fractional-order reactive power regulation. First, a fractional-order operator is embedded into the reactive-power loop of the virtual synchronous generator to smooth the transient response of the internal electromotive force and directly suppress the amplitude of fault-induced inrush current. By introducing a continuously tunable order, the proposed controller enhances transient damping, mitigates the waterbed effect trade-off associated with integer-order control, and reduces the initial junction-temperature shock at the fault instant. A constraint-based feasible-region identification procedure is then developed, in which a full-range parameter sweep is performed to screen fractional orders under stability, current, and thermal constraints. Second, generalized discontinuous pulse-width modulation and switching-frequency reduction are jointly applied to mitigate post-transient thermal accumulation. Electro-thermal co-simulation results indicate that, under the specified voltage-sag condition, the proposed strategy reduces transient overcurrent and thermal stress and improves the simulated fault-ride-through performance of grid-forming converters without additional hardware thermal margins. Full article

Existing evaluations of sandstone-hosted uranium deposits commonly describe ore-controlling troughs and faults qualitatively, which limits the quantitative characterization of structural ore-controlling elements and the prediction of favorable mineralization areas. This study proposes a relative geological time (RGT)-constrained method for quantitatively computing intra-stratal trough attributes and a 3D seismic-based workflow for trough–fault coupled structural favorability prediction in sandstone-hosted uranium exploration and applies this framework to the Qianjiadian uranium mining area. An interpreted-horizon-constrained RGT volume was constructed to extract stratal two-way travel time (TWT) slices, and multi-scale trough attributes were calculated from local TWT relief differences. Fault attributes were constructed from laterally smoothed fault-scan results to improve fault-response continuity. Known mineralized wells were used to calibrate trough and fault responses and to construct trough ore-controlling index (TOI) and fault ore-controlling index (FOI) volumes. The calibrated TOI and FOI volumes were then integrated to generate a coupled favorability index (CFI) volume for structural favorability prediction. High CFI values occur mainly near major fault zones and in areas with moderate trough responses, indicating favorable trough–fault coupled structural conditions for uranium mineralization. Blind-well validation shows that 15 of 20 blind wells agree with the CFI prediction, yielding an overall accuracy of 75.0% and an AUC of 0.85. The proposed method converts qualitative trough and fault ore-control knowledge into computable, mineralization-calibrated, and blind-well-validated 3D structural favorability results, supporting favorable-area selection and drilling deployment in sandstone-hosted uranium exploration. Full article

This study focuses on the development of a sensor-informed motion-continuity control method for shared-return electro-hydraulic actuator networks subject to neighboring-branch disturbances. The objective is to reduce the local velocity fluctuations induced by return-line pressure transients while retaining explicit hydraulic and valve constraints. A control-oriented shared-return disturbance model is established to map neighboring-valve action, T-port replenishment, accumulator buffering, common return-line pressure, net driving pressure difference, and local actuator motion. On this basis, sensor-derived motion and pressure states together with neighboring-action prior information are used to reconstruct the objective of a constrained predictive controller according to the disturbance stage. Soft Actor–Critic is restricted to bounded objective-weight inference, whereas the valve command remains generated by locally linearized receding-horizon optimization; bounded mapping, smoothing update, and soft pressure constraints preserve positive weighting matrices and online quadratic programming solvability. Co-simulation, simulation-based ablation and baseline comparisons, timing evaluation, and scaled dual-branch experiments show that the proposed framework improves motion continuity, reduces disturbance-induced pressure-difference excursions, maintains smoother valve execution, and completes each tested online update within the sampling period. These findings support feasibility-preserving sensor-driven objective reconstruction under the investigated shared-return disturbance scenarios. Full article

This paper presents a distributed closed-form control strategy for multi-unmanned aerial vehicle (multi-UAV) formation flight in cluttered environments. We propose a virtual-centroid-based architecture that reduces leader dependency while maintaining precise geometric configuration. A virtual-centroid navigation strategy generates reference trajectories for formation coordination. For collision avoidance, a generalized p-norm distance function accurately assesses collision risks for diverse obstacle geometries. An event-triggered mechanism incorporating velocity-dependent conditions produces amplitude-modulated, continuity-preserving avoidance vectors. A virtual navigation trajectory dynamically fuses avoidance information with reference tracking, enabling seamless coordination of tracking and collision avoidance. Closed-form controllers are derived for three-degree-of-freedom (3-DoF) dynamic formation control. Safety guarantees and asymptotic convergence in the nominal case are established via generalized Lyapunov analysis for non-smooth dynamical systems. Comparative simulations demonstrate that the proposed method achieves trajectory smoothness comparable to artificial potential field (APF). Full article

Differentially driven unmanned tracked agricultural vehicles operating on unpaved field roads are vulnerable to track micro-slip, lateral disturbances, and actuation lag, while manually tuned super-twisting sliding mode control often cannot simultaneously ensure tracking accuracy and steering smoothness. This study proposes a Crested Porcupine Optimizer-based super-twisting sliding mode controller (CPO-STSMC) for robust path tracking. Track slip and unmodeled dynamics are represented as bounded lumped disturbances in a coupled lateral–heading error model. A yaw-rate compensation law is developed with reference yaw-rate feedforward, a saturation boundary layer, and constraints on yaw-rate magnitude and rate. CPO is used offline to optimize key parameters through a fitness function that combines lateral and heading errors, peak errors, yaw-rate increments, and constraint penalties. Simulations and field experiments on unpaved farm roads are conducted using S-shaped and double-lane-change paths at 0.5, 0.8, and 1.0 m/s, with PP, SMC, and manually tuned STSMC as benchmarks. Compared with STSMC, CPO-STSMC reduces the root mean square lateral error by 23.9–30.0%, heading error by 23.9–28.0%, and yaw-rate increment by 11.8–22.3% in field experiments, while generally outperforming PP and SMC. The proposed method provides a practical balance among tracking accuracy, robustness, and steering smoothness for low-speed autonomous tracked agricultural vehicles. Full article

Near-field direct illumination from extended luminaires presents a fundamental modeling challenge in automated visual inspection: standard far-field IES (Illuminating Engineering Society) photometric data treat luminaires as point sources, an assumption that breaks down when source-to-target distances are comparable to luminaire dimensions. This paper addresses this breakdown by establishing, both analytically and experimentally, the conditions under which IES-based point-source discretization remains valid and the minimum discretization required when it does not. A solid-angle-based illuminance formulation on triangular meshes is coupled with a controlled virtual-emitter discretization of elongated luminaires, and a reproducible selection workflow is derived that relates emitter count to the source–target distance ratio and a specified error criterion. The dependence of discretization requirements on source–target distance is characterized by an angular-subtense argument, yielding the scaling relation $N\sim (L/H)·C$, where L is the luminaire length, H the working distance, and C a constant determined by the IES angular gradient and the required accuracy. Validation experiments with one and two industrial linear luminaires (1.2 m, Banner Engineering WLS15xDW1200Dx) include single-luminaire heights of $H=20\mathrm{cm}$, $25\mathrm{cm}$, $35\mathrm{cm}$, $50\mathrm{cm}$, and $60\mathrm{cm}$, as well as effective-length reduction cases produced by opaque end masking at $H=35\mathrm{cm}$. The single-luminaire validation set compares $N=1$–7 virtual emitters for all cases, with the $H=20\mathrm{cm}$ case extended to $N=9$. The selected emitter counts are chosen as the best-performing candidate simulations after considering scalar error, smoothness, and profile consistency. The selected uncovered cases are $N=9$ at $H=20\mathrm{cm}$, $N=7$ at $H=25\mathrm{cm}$, $N=5$ at $H=35\mathrm{cm}$, and $N=3$ at $H=50$–$60\mathrm{cm}$. The masked-source experiments confirm that reducing the effective source length at fixed height reduces the required emitter count. The fitted selection relation developed from these cases indicates that the working distance H has the stronger influence within the tested range. The method provides a computationally efficient, practitioner-ready tool for inspection-lighting design when near-field goniophotometry or optical ray tracing is unavailable. Full article

Liquid crystal (LC) is a key enabling technology for continuously phase-reconfigurable microwave devices, offering analogue-tuning capabilities distinct from discrete alternatives such as MEMS and p-i-n diodes. However, the insertion loss of LC-based phase shifters is inevitably influenced by conductor surface roughness—a factor often neglected in idealised simulations. This paper presents, for the first time, a rigorous numerical quantification of how metal surface roughness affects the insertion loss and phase shift of a 60 GHz LC-filled coaxial delay line (0–180° phase shifter) with radial conductor surfaces instead of conventional planar ones. Using full-wave finite-element simulations incorporating Groisse’s phenomenological model and Huray’s snowball model, four surface configurations are analysed at 54–66 GHz: perfectly smooth conductors, roughness on both inner and outer conductors simultaneously, and roughness applied to each conductor individually. Results show that roughness induces a measurable increase in insertion loss—worst when both conductors are rough—but its impact on differential phase shift remains minimal (<0.32°). Huray’s model predicts conductor losses 1.77 times higher than Groisse’s model, yielding more conservative metrics. For the insertion loss evaluation in Case 2 at 60 GHz under the reference isotropic LC state, Groisse’s model predicts 1.90921 dB, while Huray’s model predicts 2.16319 dB, a 0.25 dB discrepancy (12% uncertainty relative to the mean). The inner conductor dominates roughness-induced losses due to concentrated current density, suggesting prioritised surface finishing of the core line. This study isolates loss mechanisms in a coaxial LC structure, providing insights into low-loss reconfigurable devices. Practical PCB copper foil fabrication methods are also evaluated with quantitative analysis of non-ideal cylindrical geometries. Full article

Surface quality is a critical indicator of product performance, creating a growing demand for real-time surface inspection in industrial manufacturing. However, conventional surface normal measurement techniques require sequential measurements with varying illumination or observation angles, making them unsuitable for high-speed online inspection. To overcome this limitation, this paper proposes a novel imaging technique, termed normalography, for visualizing pixel-wise surface normal distributions. Analogous to thermography, normalography visualizes the spatial distribution of surface normal directions over a material surface. The proposed method targets highly glossy and smooth surfaces and is based on reflectance measurements. A multi-angle collimator was developed to simultaneously illuminate the target surface from multiple incident directions, while multispectral illumination was employed to distinguish the reflected light corresponding to each direction. An imaging system incorporating red, green, and blue illumination sources enables single-shot acquisition of surface normal information over a 1024 × 1024-pixel field of view. The proposed normalography enables camera-like real-time visualization of surface normal distributions without sequential image acquisition, demonstrating its potential for online surface inspection and quality monitoring in industrial manufacturing. Full article

Tunnel lining defects pose significant threats to structural integrity and operational safety. Traditional image processing and machine learning methods often suffer from limited accuracy and poor generalization under complex backgrounds. To address these limitations, this study proposes a lightweight intelligent recognition method based on You Only Look Once version 11 nano (YOLOv11n) for Ground Penetrating Radar (GPR) images of tunnel linings. The backbone is replaced with Mobile Network Version 3 (MobileNetV3) to reduce parameters and Floating Point Operations (FLOPs), while depthwise separable convolution and a streamlined Compressed 2-Stage Fused-Lite (C2f-Lite) structure are integrated into the Neck to further decrease computational overhead. Channel mapping layers are employed to ensure smooth feature transfer, and selective use of Squeeze-and-Excitation (SE) attention and Hard-Swish (H-swish) activation balances detection accuracy with efficiency. Evaluated on a low-power mobile workstation acting as an edge-precursor proxy platform, experimental results demonstrate that the improved YOLOv11n_MobileNetV3 model achieves high accuracy with a mean Average Precision (mAP) at 0.5 of 94.4% and mAP@0.5:0.95 of 62.4%, low computational cost of 4.7 Giga Floating Point Operations (GFLOPs), and fast inference speed of 45 Frames Per Second (FPS). Comparative analysis further confirms its superior balance of detection performance and efficiency over YOLO version 5 (YOLOv5) and YOLO version 8 (YOLOv8) baselines. The proposed approach provides a highly optimized, edge-oriented engineering solution for real-time tunnel lining defect inspection, establishing strong structural and theoretical feasibility for future deployment in embedded systems. Full article

Unauthorized unmanned aerial vehicle (UAV) intrusions in sensitive areas such as airports have made accurate UAV detection and localization a pressing need. Acoustic sensing is passive and weather-independent, but conventional microphone arrays require many elements and a large aperture. This paper proposes an acoustic vector sensor (AVS)-based method, termed Covariance Enhancement and Confidence-guided Tracking for 3D Acoustic Localization (CECT-3DAL). A single AVS measures the sound pressure and three-axis particle velocity at one point. Adaptive diagonal loading improves the robustness of the covariance matrix at a low signal-to-noise ratio (SNR). An exponential spectral enhancement strategy sharpens the spatial spectrum peaks for direction estimation, and an eigenvalue-ratio-based confidence drives confidence-weighted smoothing of the angle sequences. Meanwhile, a dual-sensor geometric model provides a closed-form three-dimensional solution. In simulations, the azimuth and elevation root-mean-square errors (RMSEs) were below 1.5° for SNR above 4 dB. In an anechoic chamber, confidence-weighted smoothing reduced the azimuth and elevation standard deviations from 4.34° and 2.63° to 1.46° and 0.86°. In field experiments, the hovering azimuth stayed within a 90% span of 2–3.5°, with an average horizontal RMSE of 0.209 m against a GPS reference, and trajectories under various flight modes remained continuous and smooth. The proposed method offers a compact, passive, and low-cost solution for counter-UAV acoustic surveillance. Full article

Low-frequency base vibration transmitted through supporting platforms can degrade the stability of precision payloads, optical instruments, and inertial measurement systems. In velocity-sensor-based active isolation platforms, displacement-related feedback states are unavailable. Direct velocity integration can drift under sensor bias, while raw lower-platform velocity feedforward can introduce measurement noise and out-of-band components. This paper proposes a dual extended state observer (ESO) reconstruction method combining equivalent-displacement feedback and lower-platform feedforward. The upper-platform ESO reconstructs feedback velocity and a bounded equivalent-displacement state, while the lower-platform ESO provides a smoothed velocity reference for feedforward compensation. The method was implemented on a plate-type active vibration isolation platform and evaluated using lower-to-upper-platform acceleration transmissibility over 0.1–10 Hz. Across three repeated 200 s records, the proposed ESO feedback–feedforward condition achieved an integrated input–output suppression ratio of $38.67\pm 0.46$ dB, reduced the upper-platform output acceleration RMS to $(1.31\pm 0.09)\times {10}^{-7}$ g, and provided a $48.92\pm 2.12$ dB RMS reduction relative to the passive baseline. Direct nominal–measured comparisons further showed that the reduced model captured the dominant passive and feedback-controlled dynamics. These results demonstrate that low-frequency active micro-vibration isolation can be achieved using only velocity measurements, without additional displacement sensors. Full article

Background/Objectives: Korea has experienced rapid and heterogeneous shifts in cancer epidemiology since 1999. We aimed to characterize divergent incidence trajectories across the 24 major cancer types, quantify sex-specific burdens, and project case counts through 2030. Methods: Annual incidence data—including new case counts and age-standardized incidence rates (ASIRs) per 100,000 (2020 Korean standard population) stratified by sex—were extracted from the KCCR via the Korean Statistical Information Service (KOSIS) for 1999–2023. Annual percent change (APC) was estimated by log-linear regression with 95% confidence intervals (CIs). Holt–Winters damped exponential smoothing generated 2024–2030 projections with 95% prediction intervals (PIs). Changes in case ascertainment and coding over the study period were considered in interpretation. Results: Total incidence increased from 101,854 (1999) to 288,613 cases (2023), a 183.4% increase. Overall ASIR rose from 402.7 to 522.9 per 100,000. Of the 24 cancer types, 15 showed statistically significant increasing ASIR trends, 6 showed significant decreasing trends, and 3 showed no significant change. The highest-APC cancers were thyroid (+7.56%), prostate (+6.98%), testis (+5.39%), breast (+5.03%), and corpus uteri (+5.03%; all p < 0.001). The largest significant declines occurred in cervix uteri (−3.81%), larynx (−3.18%), liver (−2.90%), and stomach (−2.20%; all p < 0.001). Female ASIR increased from 294.7 to 488.9 per 100,000 (+65.9%); male ASIR increased from 573.3 to 587.0 per 100,000 (+2.4%). Total cancer incidence is projected to reach 307,091 (95% PI: 281,200–332,983) in 2026 and 330,213 (95% PI: 290,663–369,763) by 2030, with the steepest projected relative growth for prostate (+48.9%), kidney (+33.7%), and breast (+31.3%) cancers. Conclusions: Korean cancer epidemiology is undergoing a pronounced transition from infection-related toward metabolic, hormonal, and aging-related malignancies, with a marked and widening sex-specific divergence. Cancer-type-resolved projections through 2030 provide an evidence base for strategic capacity planning in Korean oncology. Full article

Learning transferable region embeddings is fundamental to urban computing, supporting applications from economic forecasting to public safety. Existing multi-view fusion methods predominantly rely on coarse-grained fusion, applying uniform weights across an entire city or task, thereby neglecting spatial heterogeneity—the fact that the dominant view driving a region’s function varies significantly across regions. To address this, we propose RAMEN, a two-stage framework for Region-adaptive Mixture of Ego-Networks learning. In the pre-training stage, a unified spatially aware Transformer distills universal urban semantics. In the fine-grained adaptation stage, a Mixture of Ego-Networks (MoEN) module employs a region-adaptive gating mechanism to dynamically allocate exclusive view weights for each region. A Region Ego-aware Spatial Transformer (REST) then aggregates these fused local subgraphs by explicitly injecting degree and physical distance priors, overcoming the over-smoothing limitations of traditional GNNs. Extensive experiments on real-world datasets for check-in, crime, service call and population prediction show that RAMEN consistently outperforms state-of-the-art baselines, achieving up to 35.3% MAE improvement. Visualizations of gating weights further suggest that RAMEN’s fusion aligns well with real-world urban physical characteristics. Full article

This study proposes a mathematical framework for predicting complete response-time distributions associated with parametric video-game configurations. Each configuration is encoded as a point in a normalized metric space, and the statistical descriptors of its response-time distribution (median, mean, selected quantiles, interquartile range, and Skewness) are treated as real-valued Lipschitz functions on that space. Predictions for unseen configurations are obtained through McShane–Whitney extension formulas, which provide geometrically controlled upper and lower bounds compatible with the empirical Lipschitz regularity of the observed data. To handle the sequential incorporation of new observations, a regularization mechanism is introduced that replaces raw descriptors violating Lipschitz continuity constraints with a convex combination of the observed value and a weighted historical estimate. In the extended version of the method, the coefficient of this combination is selected through a one-pass online Q-learning-inspired procedure that selects, for each descriptor and instability regime, a data-dependent trade-off between fidelity and geometric regularity. The final output is a continuous Log-Normal density fitted by nonlinear least squares to the predicted descriptors, together with a Wasserstein-type uncertainty band derived from the Lipschitz bounds. The framework is validated on a controlled experiment with 24 participants across 20 game levels. Results show that the predicted distributions shift systematically with the input configuration and that, in the illustrative comparison, the adaptive mechanism produces feature-specific smoothing decisions that differ from those obtained with a fixed coefficient. The method also provides interpretable predictions from small experimental datasets without requiring fully data-driven models. Full article