Search Results (1,277)

This study presents a sensor-fused field investigation and simulation-based analysis of four horizontal and vertical traffic-calming devices—two raised speed tables, a speed hump, and a raised crosswalk—installed along a 5250-ft two-lane residential collector in Nashville, TN, USA. A dual-sensor architecture combining a Miovision Scout video-based vehicle counter and WAAS/EGNOS-augmented GPS probe-vehicle logging (5 m 3-D RMS horizontal accuracy, 1 Hz sampling) was used to reconstruct 30 quality-controlled free-flow vehicle trajectories and 12-h per-lane volume counts. A spatial kinematic transform (a = v·dv/dx) was applied to extract device-specific approach-deceleration and post-device recovery-acceleration rates, and a three-parameter log-logistic cumulative-distribution function was fitted to the field-observed desired-speed percentiles (root-mean-square error below 0.043 for both speed-table devices). The camera- and GPS-derived observations were used to calibrate and statistically validate a PTV VISSIM microsimulation replica of the corridor, achieving a mean-speed calibration error of 0.71% or better at every device, a GEH statistic below 1.5 at all four analysis turning movements, and independent travel-time validation errors of 5.7–12.1%, within the accepted 15% threshold. The validated model was then used to reconstruct device- and spacing-specific May–Keller macroscopic speed–density–flow relationships, calibrated against simulated capacities of 650–775 vehicles per hour per lane at 350-, 700-, and 1050-ft device spacing. Results show capacity reductions of 20–33% relative to free-flow conditions and yield kinematically derived maximum recommended spacings of 265–630 ft to maintain crossing speeds at or below 15 mph, depending on device geometry. The findings demonstrate a reproducible, low-cost sensor-fusion workflow for quantifying the safety–capacity trade-off of traffic-calming corridors and for informing the design of sensor-in-the-loop adaptive-calming infrastructure. Full article

High-precision long-baseline coordinate transfer is essential for maintaining spatial reference frames, and the modernized multi-frequency signals of BDS-3 provide new opportunities for this task. However, existing long-baseline network solutions still rely mainly on legacy frequency combinations, and quantitative evidence for pure new-frequency BDS-3 combinations in large-scale coordinate transfer remains limited. This study evaluates the applicability of BDS-3 high- and low-frequency signal combinations for long-baseline position datum transfer and investigates frequency-combination selection. Seven continuous stations were used to form 21 long baselines. Five dual-frequency schemes were tested, including four BDS-3 combinations, namely B1I/B3I, B1I/B2a, B1C/B2a, and B1C/B3I, and one GPS reference combination, L1/L5. Double-differenced ionosphere-free baseline processing and three-dimensional constrained network adjustment were applied. Performance was assessed using carrier-phase precision, normalized root mean square (NRMS), baseline vector quality, and point-transfer differences. The results show that the BDS-3 B1C/B2a new-frequency combination achieved the best overall consistency among the BDS-3 schemes, with an average high-frequency carrier-phase precision of 6.3 mm, a mean NRMS of 0.23, millimeter-level baseline vector formal-error RMS, and a 19.7 mm point difference at the unknown station DCMS. Given that the evaluation is based on seven consecutive days of observations, the long-term applicability of the proposed strategy requires further validation. Full article

Atmospheric water vapor is a major driver of Earth’s climate, yet despite its vital role in driving extreme weather and informing Numerical Weather Prediction (NWP) models, precise quantification of Precipitable Water Vapor (PWV) remains a challenge due to its high spatial and temporal variability. To provide an upgraded evaluation reflecting the most recent data and next-generation instrumentation, this study evaluates the accuracy, relative to radiosonde measurements, in calculating PWV values across six different instruments: Microwave Radiometer (MWR), NOAA-21, TROPOMI, Pandora spectrometer, AERONET sun-photometer, and GNSS/GPS against the bias-corrected Vaisala RS-92 and RS-41 radiosondes over Beltsville, Maryland, utilizing an updated 2024–2025 database. As a certified GRUAN site, HUBC adheres to strict international observation standards designed specifically to provide reference-quality data and comprehensive corrections for systematic errors. This rigorous framework justifies their application as the definitive ‘referent truth’ benchmark for remote sensing validation. RMSE and bias were the primary metrics used to assess relative accuracy. GPS measurements provided the highest level of relative accuracy, yielding the lowest RMSE (1.50 mm) and a near-unity linear fit (y = 0.98x). NOAA-21 and TROPOMI exhibit higher random noise when compared to ground-based instrumentation, yet both obtain high relative accuracy retrievals with negligible biases. AERONET and Pandora also showed strong performance with low RMSEs and R² values of 0.986 and 0.988, respectively, while slightly underestimating PWV. In contrast, the Radiometer performed with the lowest relative accuracy, characterized by the highest RMSE (6.19 mm) and a significant negative bias (−5.55 mm). Although all instruments maintained high correlation coefficients (R² ≥ 0.904), these results indicate that satellite and ground-based remote sensing provide reliable PWV retrievals, while GPS presents the most robust benchmark for high-accuracy PWV retrievals relative to radiosonde measurements. The findings also underscore the need for instrument-specific calibration constants to better align remote sensing retrievals with in situ observations. Full article

Global Positioning System (GPS) positioning accuracy is strongly affected by inaccurate noise modeling and non-Gaussian pseudorange measurement errors, including heavy-tailed disturbances and abnormal outliers caused by multipath propagation and signal degradation. Conventional extended Kalman filters (EKFs) generally assume Gaussian measurement noise with fixed covariance matrices, which limits their robustness under degraded measurement conditions. This study proposes a Student-t robust quantum-behaved particle swarm optimization-based extended Kalman filter (ST-QPSO-EKF) for adaptive GPS state estimation. The proposed framework combines quantum-behaved particle swarm optimization (QPSO) with a Student-t-based robust measurement update, where the process-noise scaling factor, measurement-noise scaling factor, and Student-t degrees-of-freedom parameter are jointly optimized. The optimized parameters are obtained through an offline calibration stage and subsequently applied in the recursive GPS filtering process. A nonlinear GPS navigation simulation was conducted using Gaussian, Student-t heavy-tailed, and outlier-contaminated pseudorange measurement scenarios. The proposed method was compared with conventional EKF, QPSO-EKF, and Student-t EKF using 20 independent Monte Carlo realizations. The results demonstrate that QPSO-EKF provides improved accuracy under nominal Gaussian conditions, whereas ST-QPSO-EKF achieves superior performance under non-Gaussian measurement environments. Under Student-t heavy-tailed noise, ST-QPSO-EKF reduced the position RMSE to 3.814 m, while under outlier-contaminated noise it achieved a position RMSE of 3.952 m, outperforming the other compared methods. In addition, the proposed method maintained comparable online computational cost because the QPSO optimization was performed offline. The results indicate that jointly optimizing covariance parameters and Student-t robustness provides an effective strategy for improving GPS positioning reliability under complex pseudorange measurement conditions. Full article

Toward low-carbon and sustainable power systems, hydro–wind–solar complementarity provides an important pathway for enhancing renewable energy accommodation and operational flexibility, while accurate characterization of uncertainty and cross-energy dependencies is essential for system optimization, operational risk assessment, and sustainable utilization of renewable resources. This study proposes a conditional Wasserstein generative adversarial network with gradient penalty integrating one-dimensional multi-scale channel attention (MSA) and an exponential moving average (EMA) mechanism (MSA-cWGAN-GP) for joint runoff–wind–photovoltaic (PV) scenario generation. The generator employs parallel depthwise 1D convolutions with multiple temporal receptive fields to capture multi-timescale variations, while an EMA shadow generator is used for model validation and scenario generation. Conditional labels are obtained by clustering joint 24 h runoff–wind–PV profiles, enabling generation under typical resource states. Case studies using historical runoff observations from Shuibuya Hydropower Station and wind and PV power series derived from ERA5 reanalysis data show overall absolute errors of the autocorrelation function (ACF) and Kendall coefficient of 0.0113 and 0.0495, respectively. The proposed model achieves the best average performance among the evaluated models in preserving intraday temporal dependence, cross-energy dependencies, and distributional characteristics, providing representative scenarios for uncertainty analysis and subsequent optimization of hydro–wind–solar complementary systems. Full article

This paper presents the design and simulation-based validation of an acoustic navigation system intended for operation within a swarm of underwater vehicles. The system is deployed on a mobile leader unit, while the remaining vehicles, referred to as followers, navigate relative to the leader. The proposed solution utilises two or three acoustic transmitters mounted at the front and rear, and, in the option with three transmitters, also in the middle of the leader platform. These transmitters periodically emit acoustic signals that are received by the follower vehicles. By measuring the time-of-flight of the received signals, followers estimate their distances to the transmitters. This dual(triple)-range information, combined with Kalman filter dead-reckoning, enables relative position estimation with respect to the leader, supporting coordinated swarm movement without reliance on external positioning infrastructure such as GPS, which is unavailable underwater. The system was evaluated in a simulation environment across multiple scenarios with varying levels of distance-measurement error. Rather than modelling detailed acoustic signal propagation, the study focuses on assessing the robustness of the positioning method to measurement inaccuracies. The results demonstrate that the proposed configuration provides useful relative positioning accuracy under a range of error conditions and identifies the operating conditions in which its performance deteriorates, supporting the feasibility of the proposed approach for leader–follower coordination in underwater swarms. Full article

Determining the parameters of a manipulator for optimal performance is a challenging task. This is primarily due to possible conflicting objectives, various tasks that should be considered, and the highly non-linear behavior that is involved. This work proposes an enhanced version of the concept-based design space exploration (C-DSE) approach for the design of manipulators. According to the C-DSE approach, prior to the search, the designers divide the set of feasible solutions into meaningful subsets, which are termed concepts. The design space exploration involves a simultaneous search for optimal solutions within each of the pre-defined concepts. This enhanced framework integrates the following: (1) kinematics, dynamics, and control co-design, and the simultaneous optimization of manipulator morphology and controller parameters; (2) surrogate-assisted optimization using Gaussian process (GP) and neural network (NN) models to reduce computational cost; (3) approximately 30 performance metrics spanning kinematic, dynamic, structural, control, and task performance domains; (4) task-aware feasibility verification applying a multi-level hierarchy; (5) a generative AI integration pathway using diffusion models and LLM-guided concept generation (proposed in this preliminary investigation). The results demonstrate a 95.7% reduction in high-fidelity function evaluations (50,000 to 2150), corresponding to a 23.3 times reduction in evaluation count and a 6.6 times reduction in wall-clock computation time (25 h to 3.8 h). Co-design yields up to a 35% improvement in energy efficiency and a 28% reduction in tracking error compared to sequential morphology-only optimization. Full article

Maritime navigation efficiency is commonly assessed using isolated route, speed, energy, or traffic indicators that do not fully represent voyage context. This study proposes a context-aware framework based on GPS–AIS data fusion, planned-route geofencing, metocean information, and encounter-based validation. The Navigation Efficiency Resilience Index (NERI) combines target achievement, trajectory-derived response activity, and disturbance intensity into a bounded, time-resolved diagnostic index. The framework was evaluated using a Singapore–Montevideo container-ship voyage with 30 s position data, surrounding-vessel AIS, corridor-specific cross-track limits, and collocated metocean variables. The voyage-level mean NERI was 0.679, and its 10th percentile was 0.519. Lower values occurred mainly in constrained waters, approach areas, and the metocean-intensive Cape transition, whereas the Indian Ocean and South Atlantic legs achieved higher mean values of 0.704 and 0.736, respectively. For the analysed datasets, the regular own-ship position record produced more stable trajectory-derived indicators than the less regularly sampled own-ship AIS series, without implying an inherent accuracy advantage. The full NERI formulation achieved an AUROC of 0.83 and an AUPRC of 0.41 for CPA/TCPA conflict-window classification. NERI therefore provides a decomposable, plan-relative analytical layer for retrospective voyage monitoring and diagnostics, but it is not a direct safety or collision-risk measure. Full article

Background/Objectives: Artificial intelligence (AI)-based systems enable automated bone age (BA) assessment according to the Greulich and Pyle (G&P) method with expert-level performance. Deeplasia is a recently introduced deep learning-based approach that demonstrated promising results in previous studies. This study aimed to externally validate Deeplasia for G&P-based BA and chronological age (CA) estimation. Methods: This retrospective single-center study included two independent cohorts. For BA assessment, 306 children and adolescents aged 1–18 years were analyzed using the mean rating of three expert readers as the reference standard. For CA assessment, 1653 children and adolescents undergoing hand radiography after trauma were included after exclusion of pathological findings. Deeplasia was compared with four CE-certified AI systems. Performance was evaluated using mean error, mean absolute error (MAE), root mean squared error (RMSE), and Bland–Altman limits of agreement. Results: Deeplasia achieved a very good overall agreement with the human reference standard, with the lowest RMSE (0.59 years in boys, 0.55 years in girls) and MAE (0.45 years in boys, 0.43 years in girls). However, no significant differences between the AI systems were observed within the age range representing 90% of the clinically relevant cohort. Estimation of the CA was substantially less accurate than G&P-based BA assessment across all systems. All programs showed systematic overestimation of CA, particularly in adolescent girls. Conclusions: Compared to commercial AI systems, Deeplasia demonstrated excellent external validity for automated G&P-based BA assessment. However, the findings again highlight the intrinsic limitations of G&P-based models for precise CA estimation in contemporary pediatric populations. Full article

Reliable navigation in GPS-denied environments remains a critical challenge for autonomous vehicles (AVs), particularly in complex indoor and urban settings. GPS-based localization systems often fail under these conditions, highlighting the need for resilient multimodal solutions. In this article, we present a radar-assisted tracking system that integrates LiDAR and inertial measurements within a sensor-fusion architecture to achieve robust navigation. The principal methodological contribution is a unified tracking and prediction framework that combines Bayesian state estimation with learning-based temporal prediction, enabling accurate tracking while continuously forecasting the slave robot’s short-term future state from mapping observations generated by the master robot, with a typical end-to-end perception-to-action latency of 20–60 ms. The communication and prediction forecasting module operates with an update interval below 35 ms, enabling real-time cooperative robotic operation. Sensor data are fused through a pipeline incorporating Gaussian Mixture Models (GMMs) for post-processing, which helps mitigate the limitations associated with individual sensors during edge processing. Moreover, Kalman filtering is employed to mitigate sensor noise and drift, thereby improving state estimation accuracy through trajectory smoothing. The fused spatiotemporal information is subsequently exploited by a Convolutional Recurrent Neural Network (CRNN) coupled with a Nonlinear Autoregressive model with eXogenous Inputs (NARX) to model the robot’s motion dynamics and provide short-horizon state prediction. Through simulations and real-world indoor experiments conducted in GPS-denied environments, we validate the system’s ability to provide accurate and continuous pose estimation with low localization errors. Experimental results show that the proposed framework achieves root-mean-square errors of 0.12 m, 0.15 m, and 0.28 m along the X, Y, and Z axes, respectively, while maintaining sub-meter maximum position deviations throughout the evaluated trajectories. These results confirm that the proposed framework provides reliable localization and predictive state estimation for cooperative robotic navigation in indoor GPS-denied environments. Future work will investigate outdoor validation and extend the framework to additional data-driven decision-making models for future robotic services. Full article

The full operation of BDS-3 enables users to obtain high-performance positioning services, benefiting from the surge in the number of Global Navigation Satellite System (GNSS) observations with multi-constellation multi-frequency signals. This overabundance is beneficial to improve in-flight navigation accuracy for small unmanned aerial vehicles (UAVs). However, it brings about two-fold challenges for conventional airborne GNSS/SINS tightly coupled (TC) systems. On one hand, limited airborne computing resources suffer from the “curse of dimensionality” caused by extremely high-dimensional GNSS observations (i.e., GNSS pseudo-ranges, pseudo-range rates, and time-differenced carrier phases from multi-system and multi-frequency, such as GPS L1/L2 and BDS B1/B2/B3, totaling up to over 100 observables per epoch), leading to increased calculation burden and potential latency. On the other hand, possible outliers can degrade the obtained navigation accuracy. To enhance overall performance, this paper proposes a computationally efficient Kalman filtering framework for tight integration between airborne GNSS and SINS via a high-dimensional robust information filter. The strategy of kinematic and static information filtering is utilized to handle the matrix inversion complexity caused by high-rate and high-dimensional Kalman measurement updates, and the technique of robust adaptive factor is used to resist the adverse effects of GNSS outliers and modeling errors. Both land vehicular and UAV flight tests indicate that the proposed algorithm outperforms its traditional TC counterparts, demonstrating an over 90% improvement in overall computational efficiency without any loss in accuracy, compared with conventional batch or sequential tightly coupled Kalman filtering. Full article

Temperature–time profiles obtained through thermistor-based monitoring provide a rich but noise-sensitive source of information for estimating kinetic and thermal parameters in exothermic batch reactions. Conventional workflows typically combine deterministic smoothing with numerical differentiation, an approach that can amplify measurement noise and fail to propagate preprocessing uncertainty into the resulting reaction-rate and parameter estimates. To address these limitations, this study presents an uncertainty-aware Bayesian machine-learning framework that integrates scalable random-Fourier-feature Gaussian-process (RFF–GP) smoothing, analytical differentiation, temperature-derived apparent conversion, Bayesian parameter inference, posterior validation, predictive calibration, model comparison, ablation, sensitivity analysis, probabilistic benchmarking, simulation of thermal nonideality, and endpoint diagnostics. The framework was applied to $379,631$ cleaned thermistor observations. The production RFF–GP achieved a validation root-mean-square error of $0.04805\mathrm{K}$, yielding a stable latent temperature trajectory and an uncertainty-aware estimate of $dT/dt$. On a smaller matched subset, exact Gaussian-process regression achieved the highest predictive accuracy and the best probabilistic scores, whereas the RFF–GP reduced central-processing-unit runtime by approximately $4.1$-fold and remained applicable to the larger production fit. A Monte Carlo dropout neural comparator produced larger prediction errors and substantially wider predictive intervals. Six apparent thermokinetic structures were evaluated using mean-field variational inference, after which the nth-order and autocatalytic structures were validated using the No-U-Turn Sampler (NUTS). Under mean-field variational inference, the apparent autocatalytic structure achieved the lowest point estimate of the widely applicable information criterion (WAIC), the lowest derivative-domain error, and the lowest full-profile temperature-reconstruction root-mean-square error of $0.2920\mathrm{K}$. Its posterior obtained using NUTS yielded $E_a=40.98\mathrm{kJ}{\mathrm{mol}}^{-1}$, $k_{\mathrm{ref}}=0.005815{\min }^{-1}$, $\Delta T_{\mathrm{ad}}=56.11\mathrm{K}$, $m=0.1694$, and $n=1.0784$. The sampling diagnostics indicated satisfactory convergence, large effective sample sizes, and no divergent transitions. Although the MFVI posterior means and NUTS posterior medians were similar, variational inference produced narrower uncertainty intervals for several correlated parameters. Moving-block bootstrap intervals did not establish a decisive separation in WAIC among the leading structures. Expanded sensitivity, ablation, imperfect-insulation simulation, and endpoint-holdout analyses further showed that the apparent parameter estimates were sensitive to optimization, thermal nonideality, sensor response, and Gaussian-process boundary behavior. The autocatalytic formulation should therefore be interpreted as the best-performing apparent structure among the candidates tested rather than as evidence of a unique chemical mechanism. Overall, the framework extracted physically plausible apparent thermokinetic information from noisy temperature-only measurements while explicitly quantifying uncertainty arising from prediction, parameter estimation, model form, computation, thermal nonideality, and boundary behavior. Full article

This study aimed to test the reliability of measurements obtained using different technologies for sprint time and force–velocity profiles during sprint running. Seventeen elite rugby union players completed three experimental sessions, separated by one week. During each session, players completed two 30 m sprints and measurements were performed simultaneously with a linear encoder, a 10 Hz GPS unit, timing gates and video analysis. Split time (5, 10, 15, 20, 25 and 30 m), maximal velocity (V_MAX, m·s⁻¹) and force–velocity variables (maximal power, P_MAX, W·kg⁻¹; theoretical maximal force, F₀, N·kg⁻¹; theoretical maximal velocity, V₀, m·s⁻¹; maximal ratio of force, RF_MAX, percentage) from the best sprint of each session were computed. Statistical significance was set at p < 0.05 for all analyses. Linear encoder and video analysis showed moderate-to-very-high reliability for sprint time (intraclass correlation coefficient (ICC) = 0.68 to 0.94; standard error of measurement (SEM) = 0.95 to 2.59%), while timing gates showed poor-to-high reliability (ICC = 0.23 to 0.85; SEM = 1.80 to 7.23%). Linear encoder showed very-high reliability for maximal velocity (ICC = 0.94) and force–velocity variables for P_MAX (ICC = 0.90), high reliability for V₀ (ICC = 0.88) as well as RF_MAX (ICC = 0.75), and moderate reliability for F₀ (ICC = 0.66). Linear encoder (ICC = 0.66 to 0.94) and video analysis (ICC = 0.50 to 0.94) were the most reliable methods to measure sprint times while linear encoder, due to a higher sampling frequency, was the most reliable to establish the force–velocity profile. Full article

Smartphone-based pedestrian dead reckoning (PDR) provides an infrastructure-free solution for two-dimensional (2D) planar localization in GPS-denied environments, but its open-loop nature makes it sensitive to accumulated step-length and heading errors. These errors grow when pedestrian actions and phone carrying modes change, because conventional methods use a fixed step-length model with a constant Weinberg coefficient. This paper proposes a motion-state-aware PDR method with two key designs. First, a joint motion state defined by action type and carrying mode is recognized from smartphone sensor data using a random-forest classifier. Second, the Weinberg coefficient is modeled through two adaptive variants: a state-wise linear model as the main lightweight adaptation mechanism, and a Transformer-enhanced extension that uses historical step-feature sequences to provide additional temporal smoothing for the per-step coefficient K. Both variants keep the predicted coefficient inside the Weinberg equation to preserve the physical structure of step-length estimation, with offline training minimizing the distance error over each calibrated segment. Heading is estimated by fusing gyroscope increments and magnetometer observations to improve continuity under magnetic disturbance. Experiments on routes with frequent motion-state transitions, including a representative indoor corridor with magnetic disturbance and turns, compare a fixed-parameter baseline and two established adaptive step-length baselines against the proposed variants using coefficient-modeling diagnostics and trajectory-level metrics. More challenging deployments such as underground or multi-floor environments are left for future work. Full article

This study investigates adhesively bonded composite patch repair to enhance the load-carrying capacity of damaged metallic structures, introducing a novel FE-augmented machine learning (ML) framework that addresses the limited availability of experimental data in structural repair applications. To evaluate this approach, aluminum and steel specimens with central fatigue cracks were repaired using glass-fiber/epoxy and carbon-fiber/epoxy composite patches and tested under quasi-static loading at room ($70 \mathrm{F} °$), high ($145 \mathrm{F} °$), and low (−$60 \mathrm{F} °$) temperatures. Finite element (FE) models were then developed in ABAQUS© to predict the failure loads of the patched specimens under varying temperature conditions, showing excellent agreement with the experimental data. The high accuracy of the FE predictions enabled their use as additional training data, effectively augmenting the limited experimental dataset and allowing the development of more robust regression models. Ten machine learning (ML) regression models, including linear regression (LR), polynomial regression (PR), support vector regression (SVR), random forest (RF), gradient boosting (GB), XGBoost (XGB), LightGBM (LGBM), Gaussian process (GP) regression, artificial neural networks (ANNs), and Kolmogorov–Arnold networks (KANs), were trained to predict the failure load of both unpatched and patched specimens as a function of material type, temperature, specimen thickness, crack length, and, for patched specimens, patch type and thickness. The datasets combined a limited set of physical results (75 patched samples: 63 experimental and 12 finite-element; 72 unpatched samples: 27 experimental and 45 theoretical) with Gaussian-mixture-model synthetic samples used only to augment the training data up to 300 samples per case. Under a configuration-grouped, leakage-free nested cross-validation (entire configurations held out for testing, hyperparameters tuned on inner folds only), the best models predicted the failure load of unseen configurations with mean absolute percentage errors of 2.78% (Gradient Boosting, patched, $R^2=0.87$) and 3.33% (Gaussian Process, unpatched, $R^2=0.98$). A paired ablation showed that Gaussian-mixture-model augmentation did not improve accuracy and, for several models, actually reduced it; the final models therefore rely on the real multi-source (experimental, FE, and theoretical) data, with the synthetic pipeline reported as a validated but non-beneficial component for these datasets. Overall, this study provides a novel, data-efficient framework combining experimental testing, FE simulation, and validated regression modeling to predict the performance of adhesively bonded composite patch repairs under varying thermal and mechanical conditions. Full article