Search Results (14,514)

Ultra-Wideband (UWB) positioning is a promising technique for underground mine localization, but its accuracy is strongly affected by complex tunnel topology, multipath propagation, and non-line-of-sight (NLOS) ranging errors. To address these challenges, this paper proposes a three-dimensional UWB positioning algorithm based on a Multi-head Attention Feature Fusion Graph Neural Network (MAFF-GNN). The localization problem is formulated as a graph-based node position regression task, where anchors and tags are represented as nodes and UWB ranging links are represented as edges. The proposed model integrates graph message passing, multi-head attention-based feature fusion, global skip connections, and geometry-constrained regularization to learn reliability-aware spatial representations from noisy ranging measurements. A physics-guided simulated underground mine environment is constructed by considering tunnel geometry, wall roughness, coal dust concentration, humidity attenuation, and controlled NLOS conditions. Three mine-like corridor topologies are generated, with 10,500 localization samples in total. Experimental results under controlled simulation conditions show that MAFF-GNN achieves an RMSE of 0.323 ± 0.093 m, an MAE of 0.274 ± 0.024 m, and a P90 error of 0.480 ± 0.038 m. Compared with weighted least squares and support vector regression, the proposed method reduces RMSE by 74.16% and 40.30%, respectively. Robustness tests under different simulated NLOS ratios further indicate that the proposed graph-attention framework maintains a gradual error-growth trend as NLOS severity increases. These results indicate the potential of attention-enhanced graph learning for UWB localization in a challenging mine-like environment. Full article

The rapid growth of Internet of Things (IoT) systems has introduced significant challenges related to privacy, trust, scalability, and attack resilience, particularly in resource-constrained and location-sensitive environments. Existing blockchain consensus mechanisms provide decentralised trust, but they often suffer from high communication overhead, weak physical-context awareness, and limited privacy protection when deployed in large-scale IoT networks. This paper proposes Elastic Proof-of-Location Byzantine Fault Tolerance (EPoLBFT), a privacy-preserving and location-aware blockchain consensus framework for IoT systems. The proposed design enables IoT nodes to prove regional eligibility without revealing exact coordinates while restricting consensus participation to trusted and geographically verified validators. EPoLBFT is evaluated using the Blockchain IoT Consensus Algorithm (BICA) simulator under normal, high-load, Byzantine, Sybil, location-spoofing, and denial-of-service scenarios. The evaluation considers latency, throughput, communication overhead, energy consumption, and attack resilience. The results show that EPoLBFT reduces communication overhead and improves consensus efficiency compared with conventional PBFT-based approaches while strengthening resilience against location-based and identity-based attacks. The study also discusses the privacy–latency trade-off introduced by zk-PoL, the assumptions related to trusted location anchors, and the limitations of simulation-based evaluation. Full article

The ride stability and driving comfort of vehicles are highly dependent on the performance of the damping system. The fatigue life prediction of damping components using rubber as the core damping material has become a research hotspot in the field of vehicle vibration isolation. Taking an automotive engine rubber isolator as the research carrier, this paper jointly carries out finite element simulation analysis and structural component fatigue life tests. A dual-parameter mapping framework is proposed, which integrates maximum strain energy density and cyclic energy dissipation instead of using a single damage indicator. This approach comprehensively accounts for the coupling effect of energy storage and energy dissipation coexisting under actual service conditions. Through uniaxial tensile tests on rubber specimens, combined with finite element simulations and physical model parameters, a quantitative mapping relationship between laboratory specimens and full-scale engine rubber isolators is established. Based on this mapping, the fatigue life curve of the isolator is derived from the specimen-based failure characteristics. Validation tests under two randomly selected operating conditions yield prediction errors of 7.5% and 6.9%, demonstrating that the proposed model can accurately achieve equivalent fatigue life transformation from small specimens to actual components. Unlike conventional direct extrapolation methods, this approach does not require complex multiaxial fatigue tests on the component itself; it only needs simple specimen fatigue data, significantly reducing development costs, while providing a reliable theoretical basis for material selection, fatigue performance optimization, and forward structural design of rubber isolators. Full article

Smart grids with high renewable penetration are increasingly evaluated through long-horizon production simulation, but conventional decomposed simulation mainly reports energy balance and unit feasibility, while power-quality stress remains weakly quantified in the storage correction layer. This paper presents a power-quality-proxy-guided state-replay framework for renewable-rich smart grids. Instead of claiming feeder-level electromagnetic simulation, the method defines planning-level proxy indicators that can be exported by production-simulation software: a voltage-deviation proxy obtained from net-power sensitivity, a net-load ramp proxy, an inverter/charger harmonic-risk proxy, and a renewable-curtailment exposure proxy. These normalized indicators are combined into a composite score $S_{PQ}$, which is then used to distinguish two storage values: charge retention during renewable-surplus voltage-rise intervals and discharge support during voltage-dip, ramp-stress, or inverter-stress intervals. A base decomposed production-simulation schedule is first obtained. The proposed layer then constructs storage accounting cycles independent of monthly and rolling-window boundaries, attaches the proxy ledger to each interval, backtracks terminal residual storage energy to low-value charging actions, and reallocates physically feasible discharge to high-$S_{PQ}$ intervals. The corrected storage path is projected onto power and energy limits and replayed before storage and conventional-unit states are inherited by the next monthly solve; cycles outside the replay validity envelope are escalated to full redispatch rather than counted as successful corrections. An eight-interval case reports explicit $S_{PQ}$ values and shows that a trajectory ending 45 MWh above the 30 MWh reference can be corrected by trimming 35 MWh of low-proxy-value charging and adding 10 MWh of discharge in two high-score intervals. A 96-interval experiment further shows that the full method reduces explicitly discarded residual energy from 214 MWh to 31 MWh, provides 128 MWh of proxy-guided support, and lowers weighted PQ-proxy exposure by 46.3%. The framework links smart-grid data analysis, renewable integration, and power-quality improvement within a traceable production-simulation workflow. Full article

Ultrasonic anemometers are widely used for wind measurement owing to their fast response, high temporal resolution, and long operational lifetime with minimal recalibration requirements. However, most configurations suffer from transducer shadowing, where cylindrical probes disrupt local airflow and introduce systematic errors in time-of-flight (TOF) measurements. While prior computational fluid dynamics (CFD)-based investigations have characterized this effect, their analyses remain confined to low wind speeds, and no existing study has explicitly proposed a method to mitigate shadowing-induced bias. This paper presents a coupled CFD and acoustic propagation framework to analyze wake-induced velocity deficits and their effects on TOF measurements for a three-transducer ultrasonic anemometer, with simulations spanning 5 to 75 m/s over the full 360° range. The results show that shadowing distortions are strongly direction-dependent, peaked within approximately ±5° angular sectors, with a near-constant velocity deficit of approximately 40% along affected paths. A shadowing-aware acoustic path selection method is then proposed that selectively excludes corrupted acoustic paths, reducing the average velocity root-mean-square error (RMSE) to 0.349 m/s and the directional RMSE to 1.14°, representing improvements of more than an order of magnitude over shadow-unaware methods. These findings provide a physically grounded, simulation-based framework for shadowing-aware wind measurement using ultrasonic anemometers. Full article

Estimating the shape of space objects helps infer key characteristics such as object type, mass, and potential operational status, thereby providing critical decision-making support for space threat assessment. This paper proposes a Physics-Informed Transformer deep learning model for space object shape classification based on photometric time series data. The model achieves a classification accuracy of 89.22% on a hybrid dataset combining simulated and measured data, outperforming eight classical models with an average accuracy improvement of 3.61%. Ablation experiments demonstrate that the introduction of physical gating yields an average accuracy improvement of 1.43%. Using evaluation metrics including the confusion matrix, PR curve, ROC curve, and confidence distribution histogram, we demonstrate that the model possesses high accuracy, strong robustness, and good interpretability. Full article

Developing digital twins for legacy hydropower units is difficult when turbine documentation, calibrated performance data, and integrated measurements are incomplete. This study presents a Computational Fluid Dynamics (CFD)-assisted reconstruction workflow for a Kaplan turbine at the Wały Śląskie Hydropower Plant and evaluates its use as a physics-informed foundation for a digital twin. The flow passage was reconstructed from archival documentation, direct measurements, and optical 3D scanning of the runner. A steady-state Reynolds-averaged Navier–Stokes model was then prepared in OpenFOAM v2506 for selected head levels, guide-vane openings, and runner-blade angles. The simulations determined hydraulic performance, flow-field structures, and combinatory characteristics of the double-regulated turbine. The computed hydraulic efficiency reached approximately 85% in the nominal-head range, and the highest-efficiency region formed a broad plateau rather than a sharp optimum. CFD-derived and measurement-derived combinatory trends were consistent, although absolute values remain limited by relative field measurements and uncalibrated Winter–Kennedy flow estimation, a differential-pressure-based method. The CFD results were reduced to compact response surfaces and integrated with reconstructed geometry into an advisory digital twin for operating-point assessment, visualization, documentation, and training. This study establishes a robust workflow for this specific Kaplan turbine case where reverse engineering, integrated with CFD analysis, generates high-fidelity surrogate models for hydropower digital twins, effectively addressing the challenge of incomplete legacy documentation. Full article

In this study, a high-order Galerkin–Numerov approach is presented to solve the transient vibration problem of anisotropic Kirchhoff plates supported by a uniform Vlasov foundation. A discretization of the governing fourth-order plate equation is derived based on a mixed boundary value problem and a hybrid Hermite–sine Galerkin formulation, which maintains the C1-continuity properties of classical plate theory. The resulting reduced-order modal system is integrated in time with the Numerov scheme, which is fourth-order accurate, and has a small numerical dispersion and good phase-preserving properties for oscillatory dynamics. The proposed methodology is evaluated using stability and convergence tests and parametric investigations. The fourth-order temporal convergence and rapid spectral-like spatial convergence of the numerical results are validated, and the long-time accuracy and robustness of the formulation is confirmed by the negligible phase error and bounded energy drift. The results from the parametric study indicate that the thickness of the plates and the stiffness of the Winkler foundation are the two most important mechanisms for vibration suppression, while the orthotropic coupling and the Vlasov shear interaction have substantial effects on the modal redistribution and transient deformation properties. The proposed method is compared with the conventional lower-order integration schemes, and it is observed that the method gives better phase fidelity and computational efficiency, and it is possible to predict the vibration amplitude and vibration timing accurately. In addition to the numerical benefits, the framework also provided physical insights on the coupled effect of anisotropy, foundation interaction and boundary restraint. The suggested model is directly applicable for composite floor systems, aerospace panels, foundation supported slabs, biomechanical plate analogs, etc., and smart vibration control platforms. This work thus lays the groundwork for future studies of nonlinear behavior, adaptive foundations and digital twin simulation of structural systems and presents a strong and scalable computational tool for the study of plate–foundation dynamics. Full article

Arctic shipping should not be understood as a sustainable transport outcome that will emerge automatically as sea ice declines. Rather, its long-term viability depends on whether stable operations can be achieved under persistent environmental and political uncertainties. Existing studies have examined Arctic shipping risks, governance arrangements, and route feasibility, but have paid limited attention to how cargo owners, shipping companies, and governments interact strategically under composite risk constraints. To address this gap, this paper develops a three-party evolutionary game model involving cargo owners, shipping companies, and the government under weather risk and political risk, and uses numerical simulation to examine system evolution, policy thresholds, and external shock responses. The results show that the system tends to converge toward a high-coordination equilibrium under low-risk conditions, whereas medium-risk conditions may lead to bistability and a low-coordination trap when government support remains below a critical threshold. Composite political shocks further amplify system vulnerability and weaken sustainable route operation. These findings suggest that the key challenge of Arctic shipping lies not in physical route accessibility alone, but in whether risk governance, market participation, and institutional support can jointly stabilize expectations and promote sustainable Arctic shipping operations. Full article

Tool micro-defect quantification is often degraded by optical sampling limits, boundary aliasing, and non-Gaussian industrial noise. To improve segmentation fidelity and physical measurement reliability, this study proposes a physically guided reconstruction and metrology framework for milling tool defects. The framework first uses an improved ResNet18-BiFPN segmentation network supervised by a distance transform-based boundary-aware loss, which encourages mask boundaries to fit high-curvature crack and chipping regions. A tensor-guided elliptical adaptive structuring element is then introduced to restore local topology while preserving tangential connectivity and normal boundary fidelity. Finally, a stable edge region search and curvature-adaptive gray/Zernike moment fusion strategy are used for noise-robust sub-pixel localization and physical area quantification. Image-based evaluation and controlled simulation-based stress tests show that the proposed method achieves an mIoU of 92.5%, an F1-score of 97.1%, and an HD95 of 2.15 pixels. Under strong synthetic noise, the average distance error remains below 0.28 pixels. For micro-defect area measurement, the mean relative error is reduced to approximately 1.7%. These results indicate that the proposed framework can support more reliable defect metrology for tool condition monitoring under complex industrial imaging conditions. Full article

The hydroforming performance of trailing arms is governed by the coupled effects of feed parameters, pressure schedules and frictional characteristics. Improper parameter matching readily induces typical forming defects such as wrinkling, cracking and uneven wall thickness. To address this issue, a multi-objective optimization method for hydroforming is proposed in this study. Taking the maximum wall thickness, minimum wall thickness and die-to-workpiece gap of the tubular blank as optimization objectives, and the internal pressure and right-side axial feed velocity as design variables, an integrated numerical simulation framework combining the Archive-based Micro Genetic Algorithm (AMGA) and LS-DYNA is established to analyze the hydroforming process. By adaptively adjusting the key control points of internal pressure and axial feed loading curves, the developed method expands the solution space and realizes the automatic optimization of loading paths. The results reveal that the maximum wall thinning rate of the tubular component drops from 20.4% to 14.8%. Meanwhile, the wall thickness uniformity is improved and forming defects are effectively suppressed while the thickening rate remains stable. Furthermore, a complete round of optimization calculation involving thousands of finite element solutions can yield a complete set of Pareto non-dominated solutions. In this paper, the AMGA multi-objective optimization algorithm is adopted to acquire the optimal loading paths, and physical prototype experiments are carried out relying on self-developed 2000 T hydroforming equipment. Comparisons between measured and simulated wall thickness values of the tubular component show that the maximum relative error is controlled within 7.46%, which verifies the reliable engineering applicability of the proposed optimization scheme and provides new insight into the process optimization for forming similar structural components. Full article

Thin-layer extra-heavy oil reservoirs are commonly characterized by small oil-bearing thickness, high crude oil viscosity, and severe steam override. To address these problems, a multi-component thermal composite flooding method with branched horizontal wells was proposed. The method combines steam, viscosity reducer, and N₂ injection. Three-dimensional physical experiments and laboratory-scale numerical simulations were conducted. The temperature field expansion, production performance, thermal sweep range, and reservoir utilization degree were compared under three development methods: steam flooding with branched horizontal wells, multi-component thermal composite flooding with horizontal wells, and multi-component thermal composite flooding with branched horizontal wells. The results show that multi-component thermal composite flooding with branched horizontal wells can effectively suppress steam override. It can also enlarge the steam chamber, improve the uniformity of reservoir heating, and enhance the production of remaining oil. The peak oil production rate reached 18.6 mL/min, and the final oil recovery factor was 56.19%. These values were 10.48 and 27.21 percentage points higher than those of multi-component thermal composite flooding with horizontal wells and steam flooding with branched horizontal wells, respectively. The numerical simulation results show that the effective heated zone ratio reached 51.14%, while the unswept zone ratio decreased to 20.30%. The enhancement mechanism is attributed to the synergistic effect of well structure, viscosity reducer, N₂, and steam, which improves thermal sweep efficiency and reservoir utilization. The results provide a reference for the efficient development of thin-layer extra-heavy oil reservoirs. Full article

We present a statistical study of low-surface-brightness (LSB) tidal structures in two large samples of edge-on disk galaxies. Our primary sample comprises 5606 galaxies from the Edge-on Galaxies In SDSS (EGIS) catalog, analyzed using imaging from the DESI Legacy Imaging Surveys, supplemented by Hyper Suprime-Cam Subaru Strategic Program (HSCSSP) data and deep Apache Point Observatory (APO) follow-up observations for selected objects. To assess the robustness of our results, we also examine an independent sample of 14,237 galaxies from the Edge-on Galaxies in the Pan-STARRS survey (EGIPS) catalog. All images were processed using a homogeneous procedure optimized for the detection of faint diffuse emission. Tidal structures were identified through visual inspection and classified into established morphological categories, with careful treatment of imaging artifacts and galactic cirrus contamination. We detected tidal features in 324 EGIS galaxies and 690 EGIPS galaxies, corresponding to incidence rates of 5.8% and 4.8%, respectively. Restricting the analysis to completeness-limited subsamples yields consistent fractions of 6.4% and 6.2%. At a typical DESI r-band surface-brightness depth of 28.6 mag arcsec⁻², these values are consistent with previous observational studies but lower than predictions from many cosmological simulations. Recent high-resolution simulations, however, produce incidence rates much closer to those measured here, suggesting that numerical resolution, realistic modeling of observational and instrumental effects, and galaxy formation physics are all critical for accurately predicting the abundance of LSB tidal structures. Full article

Advanced Persistent Threats (APTs) pose a serious threat to Internet of Things (IoT) systems because of their stealthiness, persistence, and ability to adapt to defensive responses. Communication-Based Train Control (CBTC) systems, as IoT-enabled railway signaling infrastructures, have evolved from relatively closed operational environments into interconnected cyber-physical networks, exposing train control systems to coupled cyber intrusion and operational-safety risks. To address this challenge, this paper proposes an alert-driven active defense framework for CBTC systems that integrates Bayesian belief updating, hypergame-based cognitive-bias modeling, and Hierarchical Reinforcement Learning (HRL). The framework converts intrusion detection system (IDS) alerts, network traffic observations, and cyber-physical observations into belief-state, transition, and reward inputs. The Bayesian model estimates attacker type and attack stage, the hypergame model represents deception-induced asymmetric cognition between attackers and defenders, and the HRL decouples strategic defense posture selection from tactical defense execution. The scenario-driven simulations in a CBTC APT defense setting show that the proposed model strategy achieves an 87.1% defense success rate against APT attacks while consuming 62.7% of the normalized defense resources, outperforming DQN, PG, and PPO under the same test conditions. These results suggest that explicitly coupling cyber observations, CBTC operational constraints, and hierarchical deception-aware policies can improve cost-aware active defense for railway signaling infrastructures. Full article

Physical access to robotic manipulators remains constrained by cost, safety requirements, and limited laboratory availability, creating barriers to both research and education. This paper presents a computational framework that combines stochastic error modeling with Digital Twin technology to characterize positioning uncertainty in a six-degree-of-freedom manipulator without requiring physical hardware. Four independent noise sources—joint encoder noise, thermal drift, elastic link deformation, and geometric parameter tolerances—are modeled as stochastic processes and propagated through the manipulator kinematics using Monte Carlo simulation with N = 10,000 trials across 50 workspace configurations. The results reveal that elastic deformation dominates the combined positioning error by a factor of 45.94 over encoder noise, contributing 99.97% of the total root-mean-square (RMS) uncertainty. A probabilistic workspace map constructed from 3000 sampled configurations quantifies accuracy and manipulability across the reachable space, exposing a counterintuitive trade-off: configurations with higher manipulability indices tend to exhibit larger positioning errors due to gravitational loading on extended links. Two control algorithms—a reverse process-based control law (RPBCL) and sliding mode control (SMC)—are evaluated under stochastic conditions over 200 trials. SMC achieves a mean steady-state error of 0.0029 mm, representing a 48.2% reduction compared to RPBCL (0.0056 mm), with the difference confirmed statistically significant by a two-sample t-test (t = 5.066, p = 0.000002). All results are visualized through a Unity3D Digital Twin interface that renders probabilistic workspace maps, three-dimensional error ellipsoids, and a real-time sliding surface monitor. The proposed framework provides a foundation for safe, hardware-free evaluation of manipulator control strategies in engineering education and research. Full article