Search Results (768)

Multi-sensor data fusion is essential for accurate target tracking and trajectory reconstruction. However, common forms of systematic error in multi-sensor observations, including constant biases, linear drifts, and saturating exponential drifts, can degrade measurement consistency and trajectory estimation accuracy. Within the B-spline-constrained Error Model Best Estimate of Trajectory (EMBET) framework, B-spline coefficients and systematic-error parameters may produce similar observation responses, allowing part of the systematic-error response to be absorbed into the spline-coefficient correction and thereby weakening the identifiability of the systematic-error parameters. To avoid the weak-identifiability mechanism associated with the joint parametric estimation of trajectory and systematic-error terms, a residual-driven ResCompFormer method is proposed for systematic-error compensation and target trajectory reconstruction. First, a B-spline-constrained EMBET model is established to analyze the coupling between B-spline coefficients and systematic-error parameters. Systematic-error estimation is then removed from the joint EMBET parameter-estimation problem and reformulated as observation-domain error-sequence prediction, and ResCompFormer is employed to capture temporal dependencies and cross-channel correlations in multi-sensor residuals. The predicted errors are fed back to correct the observations, followed by iterative trajectory re-estimation. Simulation results confirm the systematic-error absorption mechanism and show that the proposed method outperforms the considered model-driven and data-driven methods in both systematic-error compensation and trajectory reconstruction, including iterative and stepwise EMBET variants. Additional experiments demonstrate the robustness of the proposed method to variations in systematic-error characteristics and sensor availability. Full article

To address the difficulty of simultaneously optimizing execution time, energy consumption, and motion smoothness for six-degrees-of-freedom (6-DOF) industrial robotic manipulators in continuous operations such as high-speed handling and assembly, this study proposes a multi-objective joint-space trajectory optimization method based on an improved Dung Beetle Optimizer (IDBO). First, to adapt DBO to constrained multi-objective trajectory optimization, an external archive, nondominated sorting, and a crowding distance mechanism were incorporated to construct and maintain the Pareto solution set. Second, Sobol low-discrepancy sequence initialization was used to improve the initial population distribution. Adaptive Lévy flight perturbation and an adaptive random perturbation mutation strategy for non-elite individuals were further combined to enhance global exploration and reduce the risk of premature convergence. Finally, seventh-degree B-spline curves were adopted to construct a continuous joint-space trajectory model. Based on this model, a multi-objective trajectory optimization model was established by considering total execution time, energy consumption, and jerk as the optimization objectives. Furthermore, simulation experiments were conducted using MATLAB R2024a, and the proposed algorithm was compared with multi-objective particle swarm optimization (MOPSO), an improved multi-objective differential evolution algorithm (GMODE), the nondominated sorting genetic algorithm II (NSGA-II), and the multi-objective Dung Beetle Optimizer (MODBO). The results showed that the proposed algorithm obtained a Pareto front with better convergence, wider coverage, and a more uniform distribution. Compared with MOPSO, GMODE, NSGA-II, and MODBO, the mean hypervolume (HV) obtained by IDBO was 13.24%, 8.43%, 2.29%, and 2.34% higher, respectively; the mean inverted generational distance (IGD) was 9.45%, 26.68%, 16.35%, and 11.05% lower, respectively; and the mean Spacing value was 55.09%, 64.81%, 58.95%, and 14.06% lower, respectively. The execution time, energy consumption index, and joint jerk of the selected compromise solution were 5.27 s, 2.48, and 9.79, respectively, which were 29.73%, 43.51%, and 18.14% lower than those of the unoptimized trajectory. Constraint verification showed that the peak joint velocities, accelerations, and jerks remained within their prescribed limits. These results indicate that the proposed method provides a feasible approach for multi-objective joint-space trajectory planning of industrial robotic manipulators. Full article

In microbiome data analysis, the interest is to infer the relationship among response, covariates and a large number of microbiomes. To model the larger number of microbiomes, we treat them as functional data, and use a semiparametric regression model for their relationship with the response, in which the effects of the covariates are specified as regression coefficients, and that of the microbiome is specified as a functional parameter, via the B-spline, and a logistic link for the outcome. We also infer the correlations among some selected microbiomes. Asymptotic study of model parameters is provided. Simulation study is conducted to evaluate the performance of the method, and the model is applied to analyze real operational taxonomic microbiome data. Full article

Assembly-oriented geometric models of aero-engine casings require the spatial distribution of deviations at mating interfaces. Conventional scalar descriptors, including flatness, axial runout, and radial runout, cannot retain this information. This study proposes a measurement-driven integrated modelling method based on measured point clouds. After boundary completion, gross-error removal, and Gaussian filtering, the interface morphology is reconstructed as a tensor-product cubic B-spline surface in unit-weight non-uniform rational B-spline (NURBS) form. The reconstructed surface is then integrated with the nominal computer-aided design (CAD) model. Validation was performed using two cuboidal specimens and three representative casing flange surfaces. The relative differences between the reconstructed and measured flatness values of the cuboidal specimens were $-8.58\%$ and $-1.04\%$. At the withheld verification points of the casing flange surfaces, the mean absolute reconstruction errors were 0.0022 mm, 0.0017 mm, and 0.0049 mm. These results show that measured interface morphology can be transferred into a CAD-compatible component model while retaining its spatial characteristics. The present study provides a geometric basis for subsequent assembly analysis. Full article

Safe navigation in coastal waters remains a persistent challenge for conventional grid-based path planning methods, which prioritize shortest-distance optimization while neglecting spatial risk distribution and kinematic trackability. This study proposes a local path planning framework that integrates an integral image-based risk field with kinematic constraints for small vessel navigation in high-resolution coastal environments. The proposed method evaluates local obstacle density through an area-based spatial risk model and employs an integral image structure to reduce risk computation complexity from O(W²) to O(1). A 16-direction node expansion strategy incorporating kinematic filtering and cubic B-spline smoothing was applied to improve maneuvering feasibility and trajectory continuity. Simulation results across two topologically distinct coastal environments, Mokpo–Sinan and Myeongnyang Strait, demonstrated that the proposed framework increased the minimum clearance distance from 20 m to 238.32 m and the average clearance distance from 884.2 m to 1038.3 m relative to the conventional A* algorithm, and consistently outperformed a static buffer-based baseline. The 16-direction search reduced the maximum course change angle by up to 39% and the average course change angle by up to 55% relative to the 8-direction configuration across both environments while maintaining practical computational efficiency. Kinematic feasibility was further verified through curvature-based analysis of the final smoothed trajectories, confirming that the minimum turning radius consistently exceeded the vessel’s theoretical minimum turning radius across all tested configurations. The results demonstrate that the proposed framework can generate risk-aware and kinematically feasible navigation routes using coastline-based occupancy information alone, suggesting potential applicability to coastal autonomous navigation in environments with limited bathymetric data. Full article

This paper addresses the problem that autonomous bicycles struggle to achieve precise obstacle avoidance and robust dynamic balance at the same time in rugged terrain and narrow constrained spaces. A hybrid hierarchical control architecture that integrates Rapidly-exploring Random Trees (RRT*) with a dual-layer Proximal Policy Optimization (PPO) scheme is proposed, referred to hereafter as the RRT*-2LPPO architecture. In the global planning layer, RRT* combined with cubic B-spline smoothing generates $C^2$-continuous reference trajectories. In the local control layer, decision-making and execution are hierarchically coupled through a dual-layer cooperation scheme. The upper-layer PPO network incorporates LiDAR data and uses the vehicle body pitch angle to enhance rugged terrain perception for heading planning, while the lower-layer PPO network coordinates the momentum wheel and the steering mechanism to maintain vehicle stability. Validation across various challenging scenarios demonstrates that the proposed framework achieves excellent robustness and consistently attains the highest navigation success rates, significantly outperforming traditional control methods and non-hierarchical architectures. Full article

Parkinson’s disease (PD) diagnosis and severity assessment increasingly rely on gait analysis. To capture complex gait dynamics from limited sensor data, we propose a novel multi-branch framework integrating a one-dimensional convolutional neural network (1D-CNN), a bidirectional gated recurrent unit (Bi-GRU), and Kolmogorov–Arnold networks (KANs) (CNN-GRU-KAN) utilizing 18-channel vertical ground reaction force (VGRF) signals. Each module serves a distinct clinical purpose: the 1D-CNN branch with squeeze-and-excitation (SE) attention extracts localized spatial plantar pressure patterns, while the Bi-GRU branch with temporal attention captures long-range rhythm abnormalities. Crucially, the KAN serves as the classification head. By utilizing learnable B-spline functions instead of traditional fixed activations, KAN adaptively models the highly non-linear boundaries between healthy controls and varying PD severities, effectively mitigating overfitting. Under rigorous subject-independent cross-validation, our model achieves 98.43% accuracy for binary PD detection and 93.46% for five-class UPDRS severity grading. These results highlight the framework’s strong potential for low-cost, unobtrusive clinical tracking and home monitoring. Full article

Power IoT acts as the final frontier of the modern grid, where the reliability of energy supply hinges on precise monitoring. However, current systems often suffer from delayed responses, poor feature separation, and a computational wall when dealing with high-frequency data on the edge. We move past the traditional reactive detection mindset and propose STF-MKD, a framework built on spatio-temporal-frequency graph decoupling and Mamba-WKAN knowledge distillation. Our goal is to shift the operational focus from responding to failures to forecasting them. The first part of the system is the STF-Extractor. It uses dynamic graph attention to map the connections between nodes and a masking game to pull structural features out of the background noise. Following this, we address the wild nonlinear nature of equipment failure with the Mamba-WKAN backbone. By embedding Mexican Hat wavelets and B-splines into the Mamba architecture, the model maintains efficiency while splitting the work: splines track the daily cycles and wavelets lock onto sudden transients. To prevent the model from smoothing away rare anomaly signals, we introduce the TGAR (Teacher-Guided Anomaly-focused Reconstruction) distillation scheme. This one-teacher-two-students setup uses a teacher model with a global view to guide the student predictor. In doing so, the system triggers early warnings based on faint structural shifts before a fault fully develops. Tests on six major datasets, including ETTh/m and WADI, show that STF-MKD outperforms mainstream methods. Full article

Designing time-optimal trajectories for solar sail spacecraft is highly difficult due to the strong nonlinearity of the solar radiation pressure model, the attitude–orbit coupling, and the prevalence of local minima, which often lead to the failure of gradient-based solvers lacking adequate initial guesses. To address this issue, the present study proposes a two-stage hybrid optimization framework: a coarse-search stage uses B-spline-parameterized metaheuristics to identify a dynamically feasible trajectory, which then serves as a physics-informed warm start for direct-collocation-based gradient refinement, strictly satisfying the full nonlinear dynamics and terminal constraints. The methodology is validated through three increasingly difficult space missions: a rendezvous between Earth and Mars (Case A), a near-Earth asteroid rendezvous considering non-ideal optical reflection (Case B), and a Solar Polar Orbiter mission necessitating an 82.75° inclination adjustment with a thermal safety constraint (Case C). Statistical evaluations reveal that no single metaheuristic dominates universally; each algorithm’s suitability is contingent on the problem’s constraint structure. The hybrid framework further shows a consistent advantage over stand-alone direct collocation: by redirecting the gradient solver toward favorable convergence basins, it locates transfer solutions that remain inaccessible from a cold start. Full article

Urban traffic systems are increasingly vulnerable to non-recurrent congestion and abnormal traffic fluctuations, posing significant challenges to intelligent traffic management and resilient transportation operations. Existing traffic anomaly detection methods often struggle to simultaneously characterize heterogeneous anomaly patterns under dynamically evolving traffic states, while severe class imbalance and limited data plausibility further constrain detection reliability. To address these challenges, this study proposes a traffic-prior-guided state-aware framework for robust urban traffic anomaly detection. A Multi-Scale Natural Neighborhood (MS-NaN) module transforms one-dimensional traffic flow sequences into a nine-dimensional representation integrating sequence dynamics, multiscale statistical deviations, and spatiotemporal phase characteristics, thereby embedding traffic state priors into the detection process. Building upon these representations, the Dual-Branch Context-Gated Network (DB-CGNet) separately captures instantaneous traffic disruptions and trend-evolving congestion patterns. An adaptive context-aware gated fusion mechanism then combines the branch features to enhance robustness under complex and non-stationary traffic conditions. To improve evaluation realism, high-fidelity baseline traffic data are generated through B-spline smoothing and first-order autoregressive residual modeling, and anomaly patterns are constructed under Highway Capacity Manual (HCM)-constrained capacity reduction mechanisms. Experiments conducted on a 91-day urban expressway dataset demonstrate that the proposed method achieves the best overall performance among eight benchmark models under a 72 min observation window, attaining an F1-score of 0.7757 and an area under the receiver operating characteristic curve (AUC) of 0.9112. Ablation studies further reveal the critical role of traffic prior features in detecting short-duration evolving anomalies. The proposed framework provides a robust and interpretable solution for intelligent urban traffic monitoring, anomaly warning, and resilient traffic operation management. Full article

This study presents a radiation-aware path planning framework for mobile robots operating in hazardous environments containing radiation sources, shielding structures, and moving obstacles. The proposed method integrates A*-based global planning, chaotic particle swarm optimization (CPSO)-based route refinement, B-Spline trajectory smoothing, and exposure-dependent speed adaptation within a unified dynamic planning architecture. The framework represents the radiation field as a physically parameterized dose-rate map in mSv/h by combining inverse-square source decay with line-of-sight material attenuation through shielding materials. Route generation is therefore evaluated in terms of cumulative absorbed dose, path length, mission time, trajectory roughness, computational cost, success rate, and dynamic obstacle interaction. The proposed method is compared with Pure A*, Informed RRT*, A*-PSO, A*-CPSO, and a risk-aware A*+DWA baseline under identical seed sets and computational budgets. In static scenarios, the proposed method reduced cumulative absorbed dose by approximately 33.3%, 47.2%, and 21.4% compared with Pure A* under low-, medium-, and high-risk conditions, respectively. In dynamic scenarios, the corresponding dose reductions were approximately 32.5%, 41.4%, and 40.1%. Additional ablation, sensitivity, statistical significance, and latency analyses were conducted to isolate the contribution of each component and evaluate computational feasibility. The results show that the proposed framework provides a balanced trade-off between absorbed dose reduction, trajectory feasibility, mission time, and online replanning performance under the tested simulation conditions. Full article

We outline an intermediate step toward a semirelativistic BSR–RMT interface by using inner-region structure information generated with the B-spline R-matrix (BSR) method as the input to the Seaton/Badnell STGF/STGBF outer-region asymptotic codes used in R-matrix photoionization calculations. The long-term goal is to exploit the compact, nonorthogonal, term-dependent target descriptions available in BSR for time-dependent R-matrix calculations with the R matrix with time dependence (RMT) method, especially for processes sensitive to semirelativistic and spin-orbit effects. To probe these effects, we consider the ground-state photoionization of Ne⁸⁺, Ar¹⁶⁺, Fe²⁴⁺, and Kr³⁴⁺, focusing on resonance structures and the singlet-triplet separation of the predominantly ${2s2p}^3{P}^o$ and ${2s2p}^1{P}^o$ autoionizing states and their spin-orbit mixing. For Fe²⁴⁺, we also analyze higher resonances and the region between the ionic thresholds, with R-matrix I (RM-I) calculations using Badnell’s version for comparison. The BSR results agree well overall with the available Iron Project data and with the NIST separations between the predominantly ${2s2p}^3{P}^o$ and ${2s2p}^1{P}^o$ levels. Since semirelativistic RMT currently uses RM-I input; the successful interfacing of BSR inner-region data with STGF/STGBF codes that likewise use RM-I input represents a direct precursor to semirelativistic BSR–RMT capability. Full article

Clustered proportion responses often exhibit bounded support, skewness, heterogeneous dispersion, and within-cluster dependence. We propose a correlated mean–precision random-effects beta regression model that jointly represents cluster-level heterogeneity in the conditional mean and conditional precision. Its main innovation is to treat the cross-submodel random-effect correlation as a scientific estimand. The same frequentist Beta mixed model estimates and tests this correlation while allowing nonlinear adjustment and cluster-level interpretation. Penalized B-splines allow nonlinear effects in both submodels, and estimation is performed by maximizing a Laplace-approximated penalized marginal likelihood. The fitted model provides likelihood-based inference for the mean–stability association and empirical Bayes estimates for cluster ranking and quadrant classification. Among M1–M5, M5 gives the lowest average errors for conditional-mean and conditional-precision recovery and the best average AIC, cluster-level BIC, and full-data NLPD across the Monte Carlo settings considered. It is also the only model compared here that estimates and tests the latent association while retaining paired cluster effects for interpretation. All M5 fits succeeded in the enlarged 30-replication stress suite, and the five aggregate mean M5–M4 criteria favored M5 across 299 successful pairs; quadrature checks indicate where safeguards are needed under weak information. In both CDC PLACES and the representative World Bank panel of 128 eligible countries, M5 has the largest marginal likelihood, the smallest AIC and cluster-level BIC, a significant M4–M5 likelihood-ratio test, and the lowest application-specific point-prediction errors. For the World Bank data, $\widehat{\rho }=-0.500$ (profile 95% interval $-0.621$ to $-0.295$), the likelihood-ratio statistic is $17.370$ ($p=3.08\times {10}^{-5}$), and M5 has the lowest rolling-origin MSPE, RMSE, and MAE. On the combined evidence from fit, prediction, and correlation inference, M5 is the best overall model evaluated in both applications. Full article

This study addresses the safety and efficiency challenges of manual inspection in complex open-pit mining environments, where terrain steepness, limited coverage, and personnel exposure to hazards render conventional methods inadequate. We propose an integrated UAV inspection framework that combines 3D environmental modeling with a hierarchical optimization paradigm. The framework operates in three sequential stages. First, a high-fidelity 3D terrain model is constructed from point cloud data via skeletal feature extraction, which reduces computational complexity while preserving topographic structure. Second, an upper-layer Traveling Salesman Problem (TSP) solver determines the optimal inspection sequence across mandatory points (loading sites, dump sites, and crushing stations). Third, a lower-layer Chaotic Adaptive Population-based Grey Wolf Optimizer (CAP-GWO) refines the 3D path between consecutive TSP-ordered points, augmented by B-spline smoothing to ensure kinematic feasibility. Key inputs include: (i) raw LiDAR point cloud data of the mining site, (ii) facility coordinates and operational constraints (safety margins, maximum pitch angle, minimum turn radius), and (iii) UAV kinematic parameters. Outputs comprise a smooth, collision-free 3D trajectory with verified constraint satisfaction. Comparative experiments against eight metaheuristic algorithms (PSO, GA, ACO, BA, COA, GWO, SRA, SFOA) demonstrate that the proposed method reduces total path length by 15–20% on synthetic benchmark scenarios while maintaining zero constraint violations. Statistical validation via the Sign Test confirms the significance of these improvements (p < 0.05) across repeated independent trials. The framework is further validated on measured airborne LiDAR data of the Bingham Canyon open-pit copper mine (Utah, USA; USGS 3D Elevation Program), one of the largest operating open-pit mines in the world: on this real terrain, CAP-GWO achieves the best performance among the GWO-family algorithms, with a statistically significant 12.5% improvement over SRA (Wilcoxon p < 0.001) and 24% lower variance than the standard GWO, and all 210 experimental runs produce collision-free trajectories. Notably, the proposed hierarchical optimization framework achieves structural symmetry between the upper-layer sequencing task and the lower-layer path refinement task. This symmetric decomposition significantly reduces computational complexity while preserving solution quality, aligning with the principles of symmetry in engineering optimization. The framework offers a practical solution for autonomous, adaptive inspection scheduling in dynamic mining environments. Full article

This study investigates the structural transformation of regional livestock production systems toward an integrated Circular Bioeconomy (CBE) framework. Using an integrated Bayesian Tree–Spline approach, spatially disaggregated regional data were analyzed to evaluate the environmental, technological, and logistical determinants of sustainable livestock production. The empirical model incorporated key indicators, including infrastructure readiness, market accessibility, manure intensity, biogas conversion rates, methane emissions, alternative feed utilization, and transport-related emissions. The probabilistic classification results revealed substantial spatial heterogeneity among the analysed regions, enabling the identification of areas with high circular bioeconomy implementation potential, transitional regions, and areas requiring further technological and infrastructural development. Shannon entropy measures were applied to quantify classification uncertainty and identify transitional zones, while penalized B-splines captured non-linear saturation effects associated with alternative feed utilization and the integration of bioenergy technologies. The findings provide a data-driven basis for supporting the implementation of circular bioeconomy principles, enhancing resource-use efficiency, reducing greenhouse gas emissions, and facilitating the transition toward climate-neutral livestock production systems. Compared with the deterministic benchmark model, the proposed Bayesian Tree–Spline framework additionally quantifies classification uncertainty, captures nonlinear threshold and saturation effects, and provides probabilistic regional typologies that support more robust and evidence-based policy recommendations. Full article