Search Results (538)

Spectral analysis technology has emerged as a vital tool for quantifying complex samples because of its simplicity and high efficiency. However, spectral data has a high-dimensional characteristic and traditional variable selection methods struggle to balance computational efficiency and prediction accuracy. Hence, a discretized wild horse optimizer (WHO) algorithm was introduced in this study. Firstly, transfer functions were introduced to solve the discrete optimization in spectral variable selection. The optimal number of latent variables (LVs) in partial least squares (PLS), DWHO iterations and the population size were determined to establish the DWHO-PLS quantitative analysis model. Then, three spectral datasets of pork, marzipan and DOSY samples were used to assess the effectiveness of the method. Finally, the DWHO-PLS model was compared with full-spectrum PLS, uninformative variable elimination-PLS (UVE-PLS), Monte Carlo-UVE-PLS (MC-UVE-PLS), randomization test-PLS (RT-PLS), gray wolf optimizer-PLS (GWO-PLS) and whale optimization algorithm-PLS (WOA-PLS). Results show that the number of variables selected by DWHO-PLS was the smallest and the root mean squared error of prediction (RMSEP) had the lowest value compared with other methods for the three datasets. The research indicates that DWHO can effectively simplify the PLS model while enhancing its accuracy and stability. Full article

We study indoor visible light positioning (VLP) in a fully three-dimensional setting under thermal noise and signal-dependent shot noise. Existing shot-noise-aware least-squares (LS) VLP methods largely rely on a fixed receiver height, so their key simplifications break down when the vertical coordinate is unknown. We derive a 3D likelihood-inspired surrogate, lift the nonlinear geometry into a weighted LS form, and add a geometry-aware regularizer that restores approximate consistency among redundant lifted variables. To make the iterative solver reliable in sparse layouts, we introduce room-feasible projection, consistent pilot reconstruction, clipped frozen weights, and damped objective-accepted updates. Each inner step remains a closed-form 4 × 4 linear solve. In deterministic synthetic simulations of an 8 × 8 × 3 m room with layouts containing 4, 9, and 16 light-emitting diodes (LEDs), high- and low-power regimes, and 400 test samples per scenario, the stabilized solver removes the catastrophic sparse-layout failures observed for the tested LS-family baselines and retains a 14×–23× runtime advantage over the nonlinear baseline. The nonlinear baseline often attains lower mean error in sparse and moderate layouts; the contribution here is the resulting speed–tail-control trade-off within the fixed-orientation line-of-sight model. The resulting estimator is intended for synthetic 3D VLP simulations; hardware validation is outside the present study. Full article

Patchy ionospheric clutter in high frequency surface wave radar (HFSWR) exhibits significant non-uniformity and local space time coupling characteristics. However, the best channel method (BCM) algorithm based on the maximum output signal-to-clutter-plus-noise ratio (SCNR) criterion suffers from inaccurate estimation of the clutter-plus-noise covariance matrix under non-homogeneous conditions. In addition, the reduced-dimension clutter subspace does not lie on a single clutter ridge, leading to performance degradation of the space time adaptive processing (STAP) algorithm. To address these issues, this paper proposes a reduced-dimension STAP algorithm based on local space time coupling characteristics. First, a sparse representation method is employed to reconstruct the clutter covariance matrix to improve estimation accuracy. Then, sparse representation is performed in the region near the target to extract candidate clutter channels. Based on the maximum output SCNR criterion, a bilateral alternating channel removal strategy is used to iteratively eliminate clutter channels, while a stopping condition is set to progressively approach a single clutter ridge. Finally, considering the errors introduced by sparse representation and iterative processing, a least-squares method is applied to fit the clutter ridge, and reduced-dimension channels are constructed along the fitted ridge. Experimental results based on measured data demonstrate the effectiveness of the proposed algorithm. Full article

Accurate identification of manipulator dynamic parameters is a fundamental prerequisite for high-precision robotic control and optimized operational performance. Conventional mainstream identification schemes primarily adopt ordinary least squares (OLS) and weighted least squares (WLS) algorithms to solve dynamic parameters, yet these methods fail to fully incorporate nonlinear friction models and cannot satisfy physical feasibility constraints (PFCs). To address such limitations, this paper proposes a hierarchical iterative optimization identification framework for robot dynamic parameters integrated with nonlinear friction modeling. First, nonlinear joint friction parameters are pre-identified to lay a solid foundation for physically feasible base inertial parameter estimation. Second, the inertial torque and friction torque of the manipulator are decoupled and modeled separately. The robot dynamic equation is linearized while preserving the inherent nonlinear characteristics of joint friction, upon which a closed-loop iterative identification architecture is constructed to achieve high-precision dynamic parameter solving. The proposed algorithm strictly enforces physical feasibility constraints and supports compatibility with multiple nonlinear friction models, which substantially improves identification accuracy and ensures the identified parameters align closely with the actual physical properties of the robotic system. On this basis, a physics-informed long short-term memory recurrent neural network (PI-LSTM) is introduced to further suppress residual identification errors. Multiple groups of verification experiments are conducted on a JAKA Mini Cobo six degrees of freedom (6-DOF) collaborative manipulator platform. Comparative analyses against state-of-the-art identification algorithms and friction modeling strategies validate the practicality and superior performance of the proposed method. Full article

Inverse kinematics (IK) is fundamental to robot motion planning. Classical analytical solvers require complete Denavit–Hartenberg (DH) parameters that are often proprietary or degraded by mechanical wear, and numerical solvers based on damped least squares (DLS) are sensitive to initialization, particularly near singularities. We propose XGNN, an explainable graph neural network positioned as a model-free, interpretable warm-start initializer for downstream numerical IK refinement rather than as a standalone replacement for analytical solvers. Each IK query is encoded as a 12-node graph in which six pose nodes and six joint nodes are connected through bipartite pose-to-joint attention edges and chain edges along the kinematic structure. GATv2 message passing aggregates information at each joint node; two ablation-validated design contributions (a learnable node-type embedding and an angle-aware composite loss) enable training to convergence. Evaluated on 300,000 trajectory-style samples generated from the ABB IRB 2400 kinematic model, XGNN achieves ${3.66}^{\circ }$ joint mean absolute error (MAE), comparable to a multilayer perceptron baseline (${3.09}^{\circ }$) and a bidirectional LSTM (${3.14}^{\circ }$) under identical training. The standalone joint accuracy of all learned models is too coarse for direct industrial use, but XGNN provides the strongest warm start for DLS refinement: the convergence rate improves from $98.4\%$ to $100\%$, mean iterations drop from 14.6 to 3.2, and wall-clock time per pose drops $5.0\times$ on the IRB 2400. The benefit transfers cross-platform to the Universal Robots UR5 collaborative manipulator (convergence rate $82.2\%$ to $100\%$, $10.0\times$ speedup) and survives DH parameter perturbation of up to $\pm 10\%$, simulating calibration drift or mechanical wear. The GATv2 attention coefficients additionally provide an interpretability signal at zero inference cost. XGNN therefore complements analytical and numerical IK methods as an interpretable, calibration-robust warm start when DH parameters are unavailable, proprietary, or degraded. Full article

Non-uniform sound velocity profiles (SVPs) cause sound-ray refraction and propagation-path bending. The straight-line mapping among propagation time, propagation distance, and target position is, therefore, disrupted, leading to systematic errors in constant-sound-speed localization. To improve the consistency between propagation correction and geometric localization, an iterative ray-path correction method based on propagation-time consistency is proposed. The method contains three coupled steps. First, a path-dependent local layered SVP model is constructed for each target-to-base-station path, rather than using a global or fixed sound-speed model. Second, the ray parameter is inverted under the constraint of measured time-of-arrival (TOA), so that the corrected ray path remains consistent with the observed propagation time. Third, the corrected slant range obtained by layered ray tracing is fed back into a known-depth weighted least squares (WLS) localization model, forming a closed-loop position update. The method is evaluated through long-baseline (LBL) simulations with multiple SVPs and propagation geometries and is validated using measured TOA data and an observation-derived SVP. The simulation results show that sub-meter accuracy can be achieved under the tested TOA-noise conditions. In measured-data validation, the planar localization error is reduced from 4.6866 m to 0.1923 m. No divergence is observed in the tested small SVP-perturbation cases. Full article

Concealed shallow karst along long-distance pipeline corridors can trigger subsidence, uneven settlement, and leakage, creating environmental and infrastructure hazards. In the Huyuanxi area (Fuyang District, Hangzhou, Zhejiang, China), strong electromagnetic interference and limited site access motivated an integrated electrical resistivity tomography (ERT) plus ambient-noise microtremor (SPAC) workflow for shallow-karst screening. Three ERT lines (900 m each) were deployed along the pipeline axis and at ±15 m offsets with 10 m spacing using a WDJD-4 system (100 V constant-voltage; Wenner array, 30 layers), followed by resistivity inversion; Res2Dinv v3.65 was adopted as the inversion software. The L2 norm was selected for the objective function, and the error model was set to the default error floor plus 5%. The regularization parameter was set as λ = 0.01, and adaptive gridding was used for the mesh with a minimum cell size of 0.5 m × 0.5 m. The number of iterations was set to 15, with a final root mean square (RMS) misfit of 3.2%. The depth of investigation (DOI) was calculated via the built-in algorithm of the software, yielding a maximum value of 30 m. Low-resistivity anomalies were used to focus eight perpendicular microtremor profiles (3 m spacing) acquired with SmartSolo IGU-16HR 1C and 10 geophones (5 Hz; 1 ms sampling interval) in a nested SPAC array (0.5/1/2 m radii); processing removed segments with SNR < 3 and inverted 2-D Vs structure by damped least-squares. Resistivity sections show 50–8600 Ω·m near surface, including a <100 Ω·m fracture-zone anomaly (28–30 m wide; 8–18 m depth) and a cavity-zone anomaly (55–70 m wide; 10–20 m depth). Joint interpretation places karst development mainly at 10–25 m depth near the bedrock–cover interface (~16 m). At HYXK3, microtremor versus shear-wave logging yielded a void-layer bottom depth of 21.28 m versus 20.12 m (5.76% error) and Vs of 457 versus 446 m/s (2.40% error). Example profiles show microtremor-derived depths (18.3/14.5/18.7 m) consistent with ERT (16.8/15.1/19.5 m; 4.1–8.1% errors). Drilling verification accuracy was approximately 81.7%, precision approximately 90%, recall approximately 74.0%, and the F1-score 81.1% supporting practical corridor risk screening under complex field constraints. Full article

Objective: This study aims to explore the differences in chemical composition among Tibetan medicinal Rhododendron species and their potential correlation with anti-complement activity, with the goal of identifying promising medicinal resources. In Tibetan medicinal practice, the two groups of large-leaved Rhododendron (Tibetan: Dama) and small-leaved Rhododendron (Tibetan: Tali) are often used interchangeably despite unclear chemical and taxonomic bases. By comparing chemical profiles and evaluating anti-complement effects, this investigation seeks to provide preliminary scientific evidence for clarifying medicinal origins and facilitating the targeted development of high-quality resources. Methods: Ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) was employed to analyze seven Rhododendron samples. Separation was achieved on a Waters CORTECS UPLC C18 column (2.1 × 100 mm, 1.6 μm) using a gradient mobile phase system consisting of acetonitrile and 0.1% formic acid in water, at a flow rate of 0.3 mL/min and a column temperature of 30 °C. Data were acquired in both positive and negative electrospray ionization (ESI) modes. Compound identification was performed using Peakview 1.2 software by comparison with databases and literature. Grey relational analysis and partial least squares (PLS) regression, combined with 5000 bootstrap resampling iterations, were applied to establish spectrum–effect relationships and to screen for characteristic peaks potentially associated with anti-complement activity. Results: A total of 52 compounds were tentatively identified, including flavonoids (e.g., hyperin, isoquercitrin, taxifolin-3-O-arabinoside), terpenoids (e.g., grayanotoxin I/III), and chromanes (e.g., anthopogochromane series). The CH₅₀ values of the ethanol extracts ranged from 179.29 to 579.47 μg/mL, with Rhododendron principis showing the strongest activity (179.29 ± 11.86 μg/mL), followed by Rhododendron vellereum (198.61 ± 7.93 μg/mL). Spectrum–effect analysis revealed that four unidentified peaks (F5315, F5822, F5368, F5991) exhibited negative regression coefficients and VIP means close to or above 0.8, suggesting a possible positive correlation with anti-complement activity. Among these, F5315 (VIP = 0.909), F5822 (VIP = 0.877), and F5368 (VIP = 0.834) showed relatively higher values and were considered preliminary candidate peaks warranting further investigation. Conclusions: This study tentatively identifies 52 compounds from the ethanol extracts of seven Tibetan medicinal Rhododendron species and reports their anti-complement activities. The findings reveal chemical distinctions between the large-leaved (Dama) and small-leaved (Tali) groups, offering a potential chemical basis for species differentiation and quality evaluation. Furthermore, four unknown peaks were preliminarily screened through spectrum–effect analysis as potential anti-complement candidates, which may serve as a foundation for future activity-guided isolation and quality marker studies. Full article

Accurate prediction of overpressured formations in deepwater is important for drilling safety and reservoir evaluation. However, conventional two-step inversion workflows are affected by cumulative errors and parameter crosstalk, which limits their ability to characterize the sharp pressure-transition interfaces at the top of overpressured zones. In this study, we propose a direct prestack nonlinear inversion method for the formation pressure coefficient ($\lambda$), a dimensionless and drilling-relevant indicator of overpressure intensity. Unlike previous exact-Zoeppritz direct inversions that target effective stress or elastic moduli, here a single formation pressure coefficient drives the pressure-sensitive rock-physics chain—linking pore pressure, effective stress, and pore-space stiffness to the seismic response—thereby reducing the number of free inversion variables. This single-parameter mapping is then coupled with the exact Zoeppritz equation to build a nonlinear prestack forward operator, helping to reduce the parameter coupling and error propagation associated with conventional multiparameter inversion workflows. To describe the typical blocky structural features of overpressured strata, a nonconvex Lp-norm (0 < p < 1) regularization is introduced as a structural prior, and a decoupled optimization strategy combining the alternating direction method of multipliers (ADMM) and iteratively reweighted least squares (IRLS) is developed for a stable solution. In a single pseudo-well synthetic test, the proposed method achieved a higher correlation coefficient and lower root mean square error (RMSE) than the indirect workflow, indicating improved agreement with the reference formation-pressure-coefficient profile. Application to field seismic data from the Yinggehai Basin, South China Sea, shows that the method produces clearer pressure-transition boundaries and pressure-coefficient profiles more consistent with the available well constraints. These results suggest that, under the tested conditions, the proposed method can provide useful geophysical support for pressure prediction and the characterization of deepwater overpressured reservoirs. Full article

Bipolar junction transistor (BJT)-based 2D temperature-sensor arrays are factory-calibrated to ±0.1 °C, but post-deployment thermal and mechanical stresses drift their per-sensor gain–offset parameters by an order of magnitude, and in-lab recalibration is impractical. We present RASC (Region-Aware Self-Calibration), a five-stage algorithm that decomposes the global ill-posed problem into local cluster-level problems, runs robust alternating estimation (trimmed-mean field reconstruction + Huber iteratively reweighted least squares (IRLS)) inside each cluster, and reconciles overlapping estimates by linear consensus on the cluster-overlap graph with provable exponential convergence. On 7632 frames from a deployed 16 × 16 array exhibiting ≈5× factory-spec non-uniformity, RASC cuts the locally non-smooth fixed-pattern residual by 71 ± 5% (10-fold cross-validation (CV)), reducing this residual to a level comparable to the ±0.1 °C factory specification (as assessed by local-smoothness residual metrics, not independent absolute-temperature validation) while perturbing the calibrated field by only 0.041 °C RMSE; reduction concentrates at the edges (78% vs. 55% interior). In simulations on 8 × 8 to 32 × 32 arrays, RASC matches an oracle centralised extended Kalman filter (EKF) within 0.10 °C with ≈4× lower bandwidth. The real-data evaluation is a single-deployment proof of concept on one array and one host PCB; broader, longitudinal validation remains future work. Full article

In UAV-based remote sensing, accurate and efficient image mosaicing is crucial for achieving real-time monitoring. Traditional cloud-centric processing paradigms, however, face core scientific challenges such as high latency, bandwidth bottlenecks, and limited autonomy, making them inadequate for dynamic, real-time scenarios. To address these issues, this paper proposes an edge-computing-enabled UAV image mosaicing system. The system consists of a UAV remote sensing platform and an edge computing terminal, with the core being our novel B-SIFT-ILS algorithm. The algorithm first uses geographic coordinates for unified registration, constructs a Gaussian scale space for multi-resolution representation, and then precisely locates extrema in the Difference of Gaussian (DoG) space using a 3D quadratic function. A BANSAC algorithm is subsequently employed to refine feature points and extract stable SIFT features, and finally, Iterative Least Squares (ILS) are used to achieve seamless mosaicing. Experimental results demonstrate that, compared with classical RANSAC, the proposed method achieves superior feature sampling accuracy (rotation: 0.879, translation: 0.877) and lower latency. The ILS-based smoothing stage effectively eliminates noise and ghosting without introducing gradient reversal, performing comparably to deep learning methods while significantly outperforming direct averaging and Gaussian approaches. On the NVIDIA Jetson Orin NX edge terminal, a single processing instance requires only 1124 ms, highlighting its strong potential for real-time, low-latency, and autonomous mosaicing tasks. Future research will focus on extending the approach to non-planar terrains and implementing adaptive parameter tuning for the BANSAC algorithm. Full article

To address the issues of low efficiency and large errors in traditional dimensional measurement strategies for fuel nozzles, this paper proposes an improved region-constrained Random Sample Consensus (RANSAC) circle fitting method for high-precision measurement of the inner hole cone angle. Three-dimensional point clouds are extracted using a shape-from-focus method. The point cloud slices are then projected onto a two-dimensional plane, and the slice edges are extracted. Based on the edge shape distribution, the candidate point selection strategy of RANSAC is optimized: the initial circle is divided into eight sector regions, and three points are randomly selected from three distinct regions to fit candidate circles. After multiple iterations, the optimal fitting circle is obtained. A comparative analysis is conducted among the least squares method, standard RANSAC, and the proposed algorithm, with three quantitative metrics—residual standard deviation (σ), root mean square error (RMSE), and inlier ratio (ε)—introduced to evaluate the fitting quality. Experimental results show that the proposed region-constrained RC-RANSAC method achieves the best performance among the three, yielding σ = 2.826 px, RMSE = 2.826 px, and ε = 95.2%, and attains a cone angle deviation of only 1.0°, which closely agrees with Keyence ultra-depth measurements (error 0.8°). This method provides a new approach for accurate and robust cone angle measurement of fuel nozzle inner holes. Full article

For the identification problem of Hammerstein models, this paper proposes a decoupled identification algorithm that combines the correlation analysis with the optimal gradient-based iterative algorithm. The proposed method firstly decouples the linear and the nonlinear subsystems of the Hammerstein model with the help of the correlation analysis. Then, the least squares algorithm is used to estimate the linear parameters. For the nonlinear subsystem, which is represented by a fuzzy neural network, a clustering algorithm is employed to initialize the parameters of the membership functions, so that the fuzzy rules can better reflect the distribution characteristics of the observation data. In the weight identification stage, the optimal gradient theory is introduced into the gradient-based iterative algorithm. Based on the solved optimal step size, the method achieves adaptive parameter updates, which significantly improves convergence speed while maintaining algorithm stability. Finally, the proposed algorithm is applied to two simulations, and the results demonstrate its effectiveness for both linear and nonlinear parameters in Hammerstein models. Full article

Accurate transmission-line parameters are essential for reliable, efficient, and sustainable smart grid operation, especially under increasing renewable-energy integration and data-driven grid management. However, line aging, temperature variations, and measurement outliers may cause significant deviations between actual and nominal grid models, thereby degrading the state estimation, power-flow analysis, and operational security assessment. To address these challenges, this paper proposes a robust transmission-line parameter estimation method based on a variable-projection framework. The proposed framework decomposes the original high-dimensional, strongly coupled, and non-convex joint estimation problem into two subproblems associated with line-parameter identification and operating-state calibration. An iteratively reweighted least-squares algorithm based on the Huber M-estimator is introduced to dynamically adjust measurement weights and suppress the influence of outliers. The preconditioned conjugate-gradient method is further employed to avoid the explicit inversion of large-scale normal matrices. Simulations on the IEEE 118-bus system demonstrate that the proposed method achieves a higher parameter-estimation accuracy and stronger robustness than conventional weighted least-squares and joint state-parameter estimation methods. In the base case, the proposed method reduces the RMSRE of line reactance to 0.0794%, compared with 0.1558% for WLS and 0.1126% for JSE. Under the representative 5% gross-error case, the proposed method maintains lower RMSREs of 0.9772%, 0.0875%, and 5.8536% for $R_l$, $X_l$, and $B_{sh}$, respectively. Further sensitivity tests under contamination ratios from 1% to 20%, outlier magnitude factors from 1.5 to 5.0, and different outlier-location patterns confirm that the proposed method maintains a more stable estimation accuracy than WLS, conventional JSE, and Huber-JSE without VPM under diverse bad-data conditions. In downstream operational evaluations, it reduces the branch active-power flow RMSE from 1.6842 MW to 0.7215 MW, voltage-magnitude RMSE from 0.00482 p.u. to 0.00216 p.u., and active-power-loss error from 2.4368% to 0.9327% compared with WLS. These quantitative results indicate that the proposed approach can improve the grid model accuracy under imperfect measurements, thereby supporting reliable and sustainable smart-grid operation. Full article

This paper proposes a voltage-sensitivity estimation and droop-allocation framework for virtual power plant (VPP)-based frequency regulation in partially observable distribution feeders. In practical distribution systems, active-power adjustments by distributed energy resources (DERs) for frequency regulation may cause voltage excursions, while full real-time feeder information is often unavailable. To address this issue, an anchor-least-squares (Anchor-LS)-aided sensitivity-estimation method is developed using only point-of-common-coupling (PCC) voltage measurements and feeder-network information. Unlike state-estimation-based, data-driven, or optimization-heavy approaches that typically require wider measurement coverage, large training datasets, or repeated centralized computation, the proposed framework is designed for fast VPP-based frequency regulation under partial observability using only limited PCC measurements and feeder information. The proposed method reconstructs an approximate operating point and derives an operating-point-sensitive PCC voltage-magnitude-sensitivity matrix based on a coupled Z-bus formulation. Based on the estimated sensitivity, a voltage-constrained asymmetric droop-allocation framework is developed for under-frequency and over-frequency events, together with a practical iterative droop-adjustment method that mitigates PCC voltage violations without relying on a full optimization-based dispatch model. The proposed framework is validated through two case studies. In Monte Carlo simulations on the IEEE 33-bus feeder, the proposed sensitivity model reduced the mean RMSE by about 117 times compared with the common-path resistance method and by about 30 times compared with the conventional Z-bus method. In simulations on a practical 115-bus Korean distribution feeder, the proposed method achieved acceptable droop capacities comparable to those of a centralized LP baseline while reducing the mean computation time by about 3.2 times for both under-frequency and over-frequency events. These results confirm the practical usefulness of the proposed framework for fast VPP-based frequency regulation in real distribution networks under partial observability. Full article