Search Results (350)

Adaptive cruise control (ACC) tracks a time-gap reference that trades efficiency against spacing safety; in practice this gap is fixed offline, although the safest choice depends on the closing speed and on the reliability of the perception that supplies the lead-vehicle state. We present GL-TGS-v2, a guarded, interpretable supervisor that selects the time-gap reference of an adaptive model predictive controller (MPC) online from tracked lead-vehicle kinematics. The supervisor is a decision-tree distillation of a calibrated adaptive expert, wrapped by a perception-aware safety shield; its only authority is the time-gap reference, so it retrofits onto an existing controller without re-opening the inner loop. We evaluate it in closed loop on a frozen stack that couples a YOLO11 detector, a joint probabilistic data association (JPDA) tracker, and the adaptive MPC, with documented runtime and per-run provenance gates. In a hard lead-braking scenario (ten runs per method), GL-TGS-v2 matches the expert at a matched mean time gap and improves minimum relative distance over fuzzy and fixed-gap baselines by 3.3–5.2 m, with 95% confidence intervals excluding zero, lower target loss, and no collisions. Benign testing shows no regression, while shield ablation characterizes the guard as an auditable protective override. Full article

Pedestrian safety at urban intersections remains a major challenge in Intelligent Transportation Systems (ITSs). This study investigates whether crossing intention can be reliably inferred directly from temporal body-pose dynamics to drive real-time collision warnings on embedded edge platforms. Existing vision-based approaches that rely primarily on bounding-box proximity or scene-level spatial grids are often prone to false alarms in complex urban environments with motorcycles, stationary pedestrians, and background clutter. To overcome these limitations, we propose an end-to-end framework consisting of four sequential processing stages: (1) a perception layer integrating YOLOv8s, ByteTrack, a displacement filter, and rider suppression to generate reliable pedestrian trajectories; (2) a skeleton extraction layer utilizing YOLOv8s-pose to construct temporal sequences of 17 anatomical keypoints; (3) an ultra-lightweight Skeleton Graph Convolutional Network (SkeletonGCN, comprising 33.8 K parameters, <0.2 MB) that models body-joint kinematics and temporal motion dynamics; and (4) an image-space Time-to-Collision (TTC) risk-fusion module. While this fusion approach avoids explicit geometric camera calibration, it still relies on predefined scene-profile parameters and image-space motion assumptions. Furthermore, while the intention classifier is quantitatively evaluated, the risk-fusion module is procedurally defined, and its resulting four-level collision warnings are demonstrated operationally rather than validated against ground-truth hazard annotations. Evaluated on 49,948 valid sequences from the JAAD and PIE benchmark datasets under a strict video-level partitioning protocol, the unified SkeletonGCN achieves a macro-F1 score of 0.717 (with per-scene subset macro-F1 scores of 0.761 on JAAD/PIE urban and 0.895 on intersections), significantly outperforming baseline models. When deployed on an NVIDIA Jetson Orin NX edge device using TensorRT FP16, the full pipeline achieves an instrumented latency of 70.7 ms per frame (~14 fps) and a sustained wall-clock throughput of 7.4 fps on real-world urban dashcam video. System limitations include sensitivity to 2D printed human imagery and reduced prediction reliability under low-light nighttime conditions. Full article

Rock-salt mines deform through chamber closure and vertical translation of the surrounding rock mass, yet these responses are commonly evaluated separately. This study integrates underground levelling, convergence monitoring, and mine geometry to identify the depth at which vertical motion changes from subsidence to uplift in the Kłodawa Salt Mine, Poland. The database comprises levelling campaigns conducted between 1962 and 2021 and 337 convergence series containing 7671 observations. Annual vertical velocities were calculated between actual survey epochs. The zero-vertical-velocity zone (ZVVZ) was estimated between adjacent measured levels with opposite median velocities; bootstrap resampling and mutual-nearest benchmark pairing quantified sampling uncertainty and local consistency. No transition was bracketed within the monitored 450–630 m interval in 1987–1992. In 1992–1994, shallow levels continued to subside while deeper levels uplifted, yielding crossing depths of 570.4 m in Field 1, 514.4 m in Field 2, and 476.8 m in Field 3. Field 1 retained the reversal in the 1994–2021 average, with a crossing near 585.3 m, although that long interval cannot resolve intermediate changes. Independent, mostly later convergence records show a marked increase in closure below 690 m and increasing horizontal dominance with depth. The ZVVZ is therefore a spatially variable kinematic boundary within a mining-modified deformation field, not a strain-free horizon: roof descent and floor uplift may generate rapid closure while mean vertical translation remains near zero. The method provides a reproducible monitoring state for deep salt mines. Future work should re-adjust the historical levelling networks, acquire synchronous levelling and convergence data at 690–780 m, and test excavation, geology, and backfilling controls using a calibrated three-dimensional viscoplastic model. Full article

Bridging the real-to-sim gap in low-cost autonomous mobile robotics requires careful cross-domain alignment of kinematic geometry, actuator behavior, and sensor characteristics. This paper presents a systematic Real-to-Sim parameter calibration and multi-stage experimental validation framework for a low-cost differential-drive unmanned ground vehicle (Jackson UGV) operating within NVIDIA Isaac Sim. The calibration process distinguishes initial product/design references, directly measured physical geometry, empirically adjusted ROS 2 runtime parameters, and simulation-specific PhysX parameters. By tuning virtual wheel geometry, inertial sensor profiles, and PhysX joint-drive damping, the proposed framework enables controlled comparison between physical execution and digital-twin behavior. Benchmark evaluations across three experimental stages—square waypoint-tracking trajectories, continuous figure-eight maneuvers, and dynamic obstacle avoidance in a mapped maze course—quantify rotational repeatability, temporal alignment, estimator consistency, and cross-domain trajectory deviation. The square and figure-eight trials reveal a proprioceptive “estimator optimism gap” in which onboard EKF estimates remain internally repeatable while underestimating terminal displacement relative to external floor measurements or simulator-provided reference poses. In Stage 3, 2D LiDAR-based localization and Nav2/DWB local planning reduce dependence on purely proprioceptive dead reckoning, achieving $100\%$ goal completion without observed collision events across both physical and virtual deployments. The results support the calibrated digital twin as a controlled simulation baseline for studying cross-domain navigation behavior and for future sim-to-real evaluation of autonomous mobile robot navigation algorithms. Full article

The performance and accuracy of tipping-bucket rain gauges (TBRGs) have been widely examined. However, whilst for proper operation TBRGs must be carefully levelled, to date there appears to be no published analysis of the errors that can arise in tilted gauges. Although tilt could exist in any direction with respect to the rotation axis of the buckets, the greatest effect occurs for tilt orthogonal to the rotation axis. Here, potential errors associated with tilt in this direction are analysed experimentally for the first time. Tests on a TBRG were made with the gauge carefully levelled, when inclined at inclinations of 1°, 2°, 3°, 4°, and 5°, and at several pumped flow rates equivalent to rainfall rates in the range 9.6–114.6 mm h⁻¹. Results confirm that in a tilted TBRG, the buckets tip with unequal volumes of water. This results in the gauge requiring a progressively larger mean depth of rainfall to trigger tips as tilt increases, because the bucket tilted up requires more water to tip than the TBRG calibration would suggest, whilst the bucket tilted down requires less. Consequently, a tilted TBRG reports too few tips, and so under-reports the rainfall depth. The increase in the mean tipped volume is ~1% for a tilt of 1° and can exceed 8% for a tilt of 5°. The kinematic error associated with the TBRG mechanism is also shown to persist in tilted TBRGs, such that the potential aggregate error from both sources in a field installation may seriously degrade the quality of rainfall data. Full article

Rock–ice avalanches and repeated freeze–thaw cycles pose coupled challenges to baffle-type mitigation structures in high-altitude cold regions. This study used controlled small-scale pull-out tests to quantify changes in baffle–soil friction over 0–30 freeze–thaw cycles and then calibrated a discrete element method (DEM) model to the terminal 30-cycle condition to evaluate baffle geometry, particle size, interparticle cohesion, and pull-out velocity. Moisture redistribution approached equilibrium after approximately 7–10 cycles, whereas the friction response stabilized only after approximately 16 cycles, indicating that hydraulic stabilization preceded mechanical and interfacial stabilization. The friction coefficient decreased from 0.83 before cycling to 0.48 after 30 cycles, corresponding to an attenuation of 42.17%, and the friction force decreased from 130 to 75 N. The decay showed three stages: limited change over 0–3 cycles, accelerated degradation over 3–16 cycles, and a near-plateau thereafter. The DEM results indicate that lateral prop-root projections can increase pull-out resistance by enlarging the mobilized soil volume and enhancing mechanical interlocking; the response also depends nonlinearly on particle size and cohesion. The proposed baffle is therefore presented as a preliminary structural concept rather than a field-ready design. Because the experiments were not performed under complete geometric, kinematic, or dynamic similitude and the DEM calibration represents only one post-freeze–thaw state, the numerical values should be interpreted as laboratory-scale comparative results. Full article

Smart-city energy platforms—peer-to-peer markets and tokenized renewable energy certificates—settle against metered generation, yet never check the physical plausibility of those claims. For wind, attainable energy is not measured but derived from anemometry through a vertical extrapolation whose exponent—the wind-shear coefficient—is a discretionary modeling choice. Using a five-height, 52,192-record campaign from Phangan Island, Thailand—reproduced here as a statistically anchored reconstruction, the raw archive not being redistributable—we show that the conventional $1/7$ rule understates attainable energy by $29.8\%$, and that an adversary asserting the exponent could inflate a resource claim by $87.4\%$. WindChain sits beneath the smart-city transactive layer rather than beside it: it does not mint certificates from wind data but bounds what a revenue meter may claim. It enforces boundary-layer, kinematic, and thermodynamic admissibility as a consensus predicate and commits wind statistics to a hierarchical Merkle–Weibull accumulator whose 104-byte root lets any verifier recompute the Weibull parameters, power density, and shear exponent in constant time. We prove that an epoch-level admissibility gate bounds over-issuance, and that attestation windows must be thirty-six times longer than independence assumes. On the reconstruction, WindChain detects six of eight manipulation classes—four of them with recall $\ge 0.99$—at a $1.75\%$ false-positive rate; we also report a camouflage regime defeating every per-record test, and a sustained bias at or below $2.6\%$ that the epoch detector does not see. Consensus performance is modeled, not deployed. WindChain narrows the trust boundary rather than removing it: the guarantee is conditional on an independently certified site reference and on physical calibration of the mast and is best read as an auditable plausibility layer beneath settlement rather than as a trustless one. Full article

Granular flows generate intense acoustic emissions, yet the quantitative relationship between particle size and sound frequency remains poorly characterized for natural, irregular materials. This study investigates the inverse relationship between mean particle size and the spectral centroid of acoustic emissions under controlled laboratory conditions. We conducted vertical-drop and inclined flume experiments using steel spheres as a control, alongside dacite and pumice sieved into narrow size fractions. Acoustic signals were acquired at a 96 kHz sampling rate and analyzed using the short-time Fourier transform (STFT) to compute spectral centroids. Vertical-drop experiments confirmed a robust inverse size–frequency trend across all materials, with spectral centroids decreasing systematically as particle diameter increased. Flume experiments preserved the general inverse relationship but exhibited greater variability and a systematic shift toward lower frequencies, indicating that boundary interactions and flow kinematics modulate the acoustic signature beyond pure particle size effects. The present work establishes a controlled physical basis for acoustics-based granulometric characterization of granular avalanches, as the first stage of a factor-isolation research program in which the particle size contribution is quantified under dry, single-phase conditions. Operational field deployment lies beyond the scope of this study; the results provide the controlled calibration baselines required before extensions to multiphase, field-scale flows can be meaningfully pursued and highlight the necessity of regime-specific calibration for confined flows. Full article

In planar compliant mechanisms, single-input dual-output (SIDO) displacement amplifiers driven by piezoelectric actuators are frequently used in precision positioning, micro/nano manipulation, and biomedical microdevices. Accurate experimental verification of these mechanisms remains challenging because traditional contact sensors can add excess stiffness and impact the structure’s normal behavior, and single-axis interferometers cannot measure multiple points simultaneously. In contrast, 2D vision-based measurement offers a non-contact alternative capable of capturing full planar motion and synchronized displacement tracking within a single image frame. This paper reviews the literature on camera calibration, homography-based planar reconstruction, sub-pixel edge extraction, vision-based characterization of compliant mechanisms, and benchmarking of vision systems against laser interferometers and coordinate measuring machines (CMMs). In the review, the SIDO-CDAM developed by Ozarkar et al. based on the Instantaneous Center Building Block (IC-BB) approach has been chosen as the target characterization system. The reviewed studies confirm that the major technical components needed for a high-precision 2D vision framework have been independently validated. However, there seems to be a lack of an integrated framework designed for synchronized dual-output SIDO-CDAM characterization. To overcome this gap, a seven-layer 2D vision-based characterization framework is proposed for scalable inspection of prototype-scale and MEMS-scale compliant mechanisms. Full article

Industrial robots, particularly six-axis serial manipulators, have been widely deployed in manufacturing workflows including assembly, welding, material handling, inspection and precision machining. As the demand for higher end-effector positioning accuracy and trajectory tracking performance grows, end-position errors induced during manipulator operation—stemming from geometric deviations, joint friction, load fluctuations, current surges, as well as variations in velocity and acceleration—have emerged as a critical bottleneck limiting high-precision applications. Conventional error compensation approaches mostly rely on geometric calibration, empirical formulas or fixed regression algorithms, which struggle to adequately characterize error trends featuring strong temporal dependencies, nonlinearity and multi-factor coupling. To address the aforementioned limitations, this paper takes the UR5 industrial manipulator as the research object. Leveraging the NIST-released dataset for manipulator positional accuracy degradation monitoring, this study develops and implements a physics-aware Transformer-based compensation framework that integrates a physics-consistent constraint loss and a nonlinear exponential error amplification strategy with a standard Transformer encoder for end-effector positional accuracy degradation. Multiple variables including target joint position, velocity, acceleration, torque, motor current and control current are selected to construct time-window input vectors, which are used to train the Transformer regression model to capture the correlation between historical motion states and real-time end-effector positional accuracy degradation. Experimental results demonstrate that the proposed Transformer model can fully capture temporal contextual correlations and multi-feature fusion information embedded within manipulator kinematic data, delivering superior error compensation performance for the six-dimensional end-effector pose error prediction task. The self-attention-based time-series modeling framework is well-suited to the nonlinear, coupled and time-varying characteristics of manipulator operational errors. This work provides valuable references for accuracy enhancement of industrial robots and the design of intelligent error compensation schemes. This work provides valuable references for accuracy enhancement of industrial robots and the design of intelligent error compensation schemes, with the proposed physics-aware strategies being model-agnostic and potentially extensible to other regression architectures. Full article

The present study evaluates three static recovery term (SRT) formulations incorporated into the Ahmadzadeh–Varvani (A-V) kinematic hardening framework for predicting ratcheting under tensile peak hold loading at room temperature. Linear, power-law, and nonlinear SRTs were assessed using experimental data for austenitic stainless steels SUS304 and SS304. The constitutive parameters were first calibrated using monotonic, strain-controlled, hysteresis loop, and no-hold ratcheting data. The SRT coefficients were then calibrated using peak hold experiments involving hold durations of 60 s for SUS304 and 10 s for SS304. All three formulations reproduced the increased hysteresis loop translation and ratcheting strain caused by the tensile holds more accurately than the baseline model without static recovery, which underpredicted ratcheting strain by an absolute strain difference up to 1.5%. The three SRTs produced comparable overall ratcheting predictions after calibration; however, they generated different coefficient evolutions, recovery histories, and intermediate cycle responses. The nonlinear formulation provided improved agreement for portions of the SUS304 loop evolution, while the linear model offered the simplest implementation. The results demonstrate that static recovery is essential for modelling dwell-assisted ratcheting and that model selection should consider internal variable evolution in addition to final accumulated strain. Full article

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

Laboratory friction measurements are central to material screening for High-Friction Surface Treatments (HFST), yet metrological aspects including a proposed metrological traceability framework or uncertainty, and reproducibility are consistently underreported. This study applies a metrology-aligned framework to British Pendulum Tester (BPT) measurements performed in three states: dry, wet (water), and water–soap to assess operational adhesion and hysteresis components, document traceability to the International System of Units (SI), and report GUM-style uncertainty with covariance for the adhesion difference. Measurements were obtained for calcined bauxite (CB) and rhyolite (Rhy) in HFST and Coarse gradations across seven polishing protocols (baseline; LAA-1000/2000; MDA-105/180; PSV-10 h/20 h), using n = 3 replicates per Treatment × Material × State. Replicate-based Type A uncertainties were combined with instrument/system Type B components geometry, slider hardness, temperature, soap film consistency, and calibration to yield combined u_c and expanded uncertainty U_(k=2). The Wet–Soap cross treatment physical correlation ellipses demonstrate strong positive correlations (r ≈ 0.88–0.98; p < 0.001) between wet and soap states, supporting the interpretation that wet friction is governed primarily by hysteresis, with adhesion acting as a small offset under BPT kinematics. The slider hardness and temperature typically control the wet state uncertainty budget; including measured Wet–Soap covariance reduces adhesion U_(k=2) by up to ~6.5%, consistent with GUM’s law of uncertainty propagation. Together, these measurement science practices enhance road safety by making laboratory friction data traceable, comparable, and decision-ready. Full article

Short-horizon collision–risk warning can use instantaneous assessment, forecast-then-assess, or direct prediction from encounter histories. We evaluate the third route with the Anticipatory TRAffic-context Collision–risk Transformer (ATRACT), which estimates the maximum near-future fuzzy Collision Risk Index (CRI) directly from own-ship kinematic and collision-geometry histories without trajectory rollout. Experiments use ten days of Automatic Identification System (AIS) data from the Danish straits, comprising 1.63 million decision windows. Using a vessel-day-track partition and five random initializations, ATRACT attains an area under the receiver operating characteristic curve (ROC-AUC) of $0.814\pm 0.003$, compared with $0.808\pm 0.006$ for the evaluated forecast-then-CRI baseline (VCRF) and 0.729 for instantaneous CRI. Thresholds fixed at a nominal 5% false-alarm rate (FAR) on disjoint calibration tracks yield test FARs of 4.98% and 5.38% for ATRACT and VCRF. Although seed-0 track-bootstrap intervals include zero at every evaluated offset, ATRACT shows 4.4–10.8 percentage points higher mean pre-onset detection across 0.5–4 min over five random initializations; VCRF detects more individual high-risk windows at this operating point. Four direct-risk encoders achieve a narrow ROC-AUC range of 0.813–0.819. These results support direct temporal interaction modeling as a viable route while limiting conclusions to the evaluated forecast-first implementations. Full article

Porous Ti-6Al-4V lattice implants combine high specific strength, osseointegrative porosity, and compatibility with additive manufacturing, but conventional stiffness tuning through porosity, pore size, or unit-cell topology compromises biological pore requirements. This study presents a computational design framework in which structural kurtosis, the normalized interlayer offset between neighboring layers of a periodic cubic lattice, regulates elastic response at fixed global porosity. Closed-form expressions for the effective modulus are derived from first principles: the aligned configuration from the axial load-bearing area fraction, and the interlayer-shifted configuration from Euler–Bernoulli beam theory for guided-end connecting members. The derivations reproduce the Gibson–Ashby exponents n = 1 and n = 2, replacing the previously asserted power law, and a calibrated one-parameter interpolation bridges intermediate offsets. At 65% porosity, the effective modulus falls from 16.5 GPa in the aligned lattice to 2.64 GPa in the shifted lattice. A local-yield analysis based on peak bending curvature gives recoverable elastic strains of 1.37% at 89% porosity and 0.68% at 65%; the compliance-based values of 20.5% and 5.12% are kinematic upper bounds that neglect plastic hinging. A prefactor-free benchmark shows that obtaining the same 6.25-fold reduction by increased porosity alone would require 85.9–94.4% porosity and 0.17–0.28 mm struts, outside the osseointegration window and the resolution of selective laser melting. A GAN-CAD-FEA workflow reproduced the analytical moduli to within 7% across six design cases. All results are analytical and numerical; no specimens were fabricated or tested, and experimental validation remains required. Full article