Search Results (160)

Underwater multi-sensor passive tracking faces two critical challenges: the strong nonlinearity of Doppler–bearing measurements and underwater acoustic propagation delays. To address these issues, this paper proposes a distributed fusion filtering method based on extended measurement space modeling and delay compensation. First, an extended measurement space comprising range, Doppler frequency, bearing, and bearing rate is constructed to transform the nonlinear measurements into a linear framework. Within this space, linear prediction equations for constant velocity (CV) motion are derived to facilitate linearized local filtering. Furthermore, a closed-form linear solution for propagation delay is established within the constructed state space. To resolve the incompatibility of multi-node estimates caused by local coordinate frame discrepancies, a distributed architecture based on the Unscented Transform (UT) is designed. In this architecture, local states are transformed into a unified Cartesian coordinate system for temporal compensation and fast Covariance Intersection (FCI) fusion, followed by an inverse mapping back to the local space. Simulation results demonstrate that, compared with traditional nonlinear methods based on mixed coordinate systems, the proposed method significantly reduces nonlinear approximation errors, thereby enhancing tracking accuracy and robustness. Full article

This paper introduces a novel metric-driven evaluation framework for Remaining Useful Life (RUL) prognosis in rotating machinery, featuring robust uncertainty quantification. Accurate RUL prediction is vital for optimizing maintenance and preventing failures, but existing methods often struggle with complex nonlinear degradation or lack reliable uncertainty estimates. Our proposed framework integrates a probabilistic Deep State Space Model (DSSM) with a variational inference approach to model complex, non-linear degradation trends and inherent aleatoric uncertainty. A key innovation is the use of the Slime Mold Algorithm (SMA) for efficient hyperparameter optimization, ensuring maximum accuracy. Furthermore, an online adaptation mechanism, governed by a heuristic reinforcement learning agent, allows the model to continuously update its knowledge and adapt to concept drift in real-time. Experimental validation on the IMS bearing dataset demonstrates superior RUL prediction accuracy, evidenced by the lowest Root Mean Square Error (RMSE) of 8.1829 cycles, and a PICP of 0.59416. This dual capability makes the framework highly suitable for real-world predictive maintenance, enhancing safety and reliability. Full article

To overcome the limitations of conventional control strategies in simultaneously suppressing external sway disturbances and internal parameter variations—induced by strong eddy current effects in marine canned magnetic bearings (MBs)—this paper introduces an improved model reference adaptive control (MRAC) method. First, electromagnetic force and dynamic models of the marine canned MBs are developed, taking into account eddy current effects and oscillatory motion. On this basis, a state observer is designed to estimate the system’s unknown dynamics. A predictive error term is formulated to capture the combined influence of model uncertainties and external disturbances. An adaptive law is then applied to compensate for these unknown dynamics and external disturbances. Moreover, the stability of the marine canned MBs system under the proposed improved MRAC scheme is rigorously analyzed using Lyapunov stability theory. Simulation results confirm the effectiveness of the algorithm, showing that, compared with conventional PID control, the improved MRAC approach reduces rotor vibration by more than 53%, significantly strengthening the disturbance rejection performance of marine canned MBs. Full article

A photoelectric tracking system is a typical bearing-only target tracking system that faces significant challenges arising from measurement origin uncertainty due to clutter and the discrepancy between continuous-time target dynamics and discrete-time optical sampling, as well as the inherent nonlinearity of bearing-only tracking. This paper addresses these issues by proposing a novel distributed probabilistic data association feedback particle filter (DPDA-FPF) framework. To resolve the tracking ambiguity at the local level, we extend the feedback particle filter to a continuous-discrete setting integrated with probabilistic data association. Subsequently, the local state estimates and covariances from spatially separated tracking systems are transmitted to a fusion center and integrated using an optimal linear covariance-weighted fusion rule to improve global observability and mitigate biases of individual systems. Numerical simulations in a 3D scenario with moderate clutter density demonstrate that while individual sensor tracks suffer from fluctuations, the proposed fused estimate achieves substantially lower root mean square errors in both position and velocity. The results validate the efficiency of the proposed architecture as a robust solution for photoelectric tracking applications. Full article

This study addresses a critical knowledge gap in quantifying strategic mineral resources within hyper-arid, tectonically complex terrains by establishing a recursive framework that reconciles deterministic resource estimation with the nonlinear dynamics of tectonically mediated metamorphic systems. Using Libya’s Wadi El Shati as a case study, legacy lithological misclassifications are rectified through the fusion of Sentinel-1 Synthetic Aperture Radar, Sentinel-2 multispectral imagery, and Digital Elevation Model analytics within a unified geospatial workflow. The methodology synergizes atmospherically corrected optical data, processed via supervised Maximum Likelihood Classification, with calibrated radar-derived structural lineaments. Classified marble-bearing zones within the Al Mahruqah Formation are integrated with DEM data and field-validated thickness measurements using Triangulated Irregular Network models to resolve surface–subsurface dependencies and compute volumes. The results demonstrate a 91% lithological classification accuracy, rectifying a 22% error in legacy maps. Structural analysis of 1213 lineaments confirms a dominant NE–SW extensional regime (σ₃) that facilitated fluid conduits. The quantified marble-bearing horizon spans ~334 km² with a volume of 6.0 km³ (±9%). Spatial analysis reveals a causal link between high-grade marble clusters, basaltic intrusions, and NE–SW fault systems, refining models of contact metamorphism in rift-related settings. Full article

Routine chemical assays, which are more readily available than direct mineralogical analyses, offer a rapid and cost-efficient approach of estimating mineral grades for geometallurgical modelling. This paper addresses the prediction of ore minerology from chemical assays for lithium-bearing pegmatites by implementing and comparing two element-to-mineral conversion (EMC) approaches: (1) mass balance techniques using two calculation variants and (2) machine learning methods (MLM). Both routines of the mass balance approach achieved satisfactory R² values exceeding 0.8, although calculation routine 1 was unable to automatically differentiate between the two lithium-bearing phases (spodumene and cookeite). Of the eight algorithms applied for the MLM approach, the top three performing models achieved R² values greater than 0.6 for both training and testing datasets, with slightly lower error evaluation metrics compared to the mass balance approach. Based on data accuracy requirements across the Mine Value Chain, the mass balance approach is suitable for the feasibility and operational stages, while the MLM approach meets the minimum data accuracy requirements of the scoping and pre-feasibility stages. However, it should be noted that the mass balance approach is limited to deposits with simple mineral assemblages while the MLM approach can handle deposits with greater elemental overlap among minerals. Full article

Camellia oleifera, a woody oil crop unique to China, plays a crucial role in alleviating the global pressure of edible oil supply and maintaining ecological security. However, it remains challenging to accurately forecast Camellia oleifera yield in complex landscapes using only remote sensing data. The aim of this study is to develop an interpretable deep learning model, namely Shapley Additive Explanations–guided Attention–long short-term memory (SALSTM), for estimating Camellia oleifera yield by integrating an improved spectral bloom index and environmental parameters. The study area is located in Hengyang City in Hunan Province. Sentinel-2 imagery, meteorological observation from 2019 to 2023, and topographic data were collected. First, an improved spectral bloom index (ISBI) was constructed as a proxy for flowering density, while average temperature, precipitation, accumulated temperature, and wind speed were selected to represent environmental regulation variables. Second, a SALSTM model was designed to capture temporal dynamics from multi-source inputs, in which the LSTM module extracts time-dependent information and an attention mechanism assigns time-step-wise weights. Feature-level importance derived from SHAP analysis was incorporated as a guiding prior to inform attention distribution across variable dimensions, thereby enhancing model transparency. Third, model performance was evaluated using root mean square error (RMSE) and coefficient of determination (R²). The result show that the constructed SALSTM model achieved strong predictive performance in predicting Camellia oleifera yield in Hengyang City (RMSE = 0.5738 t/ha, R² = 0.7943). Feature importance analysis results reveal that ISBI weight > 0.26, followed by average temperature and precipitation from flowering to fruit stages, these features are closely associated with C. oleifera yield. Spatially, high-yield zones were mainly concentrated in the central–southern hilly regions throughout 2019–2023, In contrast, low-yield zones were predominantly distributed in the northern and western mountainous areas. Temporally, yield hotspots exhibited a gradual increasing while low-yield zones showed mild fluctuations. This framework provides an effective and transferable approach for remote sensing-based yield estimation of flowering and fruit-bearing crops in complex landscapes. Full article

Due to the significant nonlinear characteristics of the magnetic bearing, it is difficult to establish an accurate mathematical model, and it is susceptible to external disturbances. Traditional control methods struggle to meet the control requirements. Active disturbance rejection control (ADRC) does not rely on accurate models and has outstanding anti-interference ability. In order to improve the anti-disturbance ability and control stability of the system, a cascaded linear–nonlinear active disturbance rejection control method (CL-NLADRC) based on the improved artificial jellyfish algorithm is proposed and applied to the magnetic levitation ball system. Firstly, the mathematical model of the magnetic levitation ball system is established, and based on this model, a cascaded linear–nonlinear extended state observer is constructed to estimate and compensate for the system state, thereby enhancing the dynamic response capability of the system. Subsequently, the tangent spiral motion and the lens reversal learning strategy are introduced to improve the artificial jellyfish algorithm to further improve the global optimization performance of the algorithm. Finally, the improved artificial jellyfish algorithm is used to optimize the CL-NLADRC controller parameters. The simulation and experimental results show that compared with the traditional LADRC and PID controllers, the proposed CL-NLADRC has a significant improvement in the steady-state error, response speed, and anti-disturbance performance of the system. Among them, the root mean square error decreased by 14% and 47%, respectively, which verified the effectiveness and stability of the method in the magnetic levitation ball system. Full article

Rapid and reliable analytical methods are required to support quality control and decision-making in lithium-bearing mineral processing. In this study, the application of Fourier Transform Infrared (FTIR) spectroscopy combined with Partial Least Squares (PLS) chemometric modeling is evaluated for the simultaneous prediction of lithium oxide (Li₂O) and spodumene contents in pegmatitic samples. Two independent PLS models were developed using FTIR spectra preprocessed with first derivative and/or Standard Normal Variate (SNV). Spectral regions were selected based on the vibrational response of Al–O, Si–O, and OH⁻ groups, which are indirectly influenced by lithium-bearing phases. The spectral datasets were divided into calibration and independent external test sets, and model performance was assessed using statistical metrics and Principal Component Analysis (PCA). The Li₂O model achieved an R² of 0.9934 and an RMSEP of 0.185 in external validation, with a mean absolute error below 0.15%. The spodumene model achieved an R² of 0.9961, an RMSEP of 1.79, and a mean absolute error of 2.80%. These results demonstrate that the FTIR-PLS approach enables efficient quantitative estimation of lithium-bearing minerals, with reduced analytical time, good predictive accuracy, and suitability for application in process control and mineralogical sorting environments. PCA confirmed the statistical representativeness of the test sets, with no evidence of spectral extrapolation. Full article

Low back pain (LBP) is a common cause of activity limitation in individuals that can result in socioeconomic costs up to $200 billion per year. Most cases of LBP lack a known underlying pathology. The L4/L5 motion segment is the most impaired lumbar segment, likely due to high load-bearing function. The ability to model L4/L5 compressive loading from surface electromyography (sEMG) data during dynamic activity may add to the understanding of LBP. Eight volunteers with no history of LBP participated in this study. Muscle activity of the erector spinae, rectus abdominus, and external obliques were recorded by a wireless EMG system (Trigno, Delsys, Natick, MA, USA) during a straight-leg stoop-to-stand task. L4/L5 compressive loading was estimated using a subject-specific sEMG model and validated by comparison with an AnyBody model and publicly available data from OrthoLoad. A specific trendline showed a significant decrease in percent error of estimated force for all muscles. Significantly lower impulse values were estimated by the AnyBody model than the sEMG subject-specific model (p = 0.007). Although our sEMG model was subject to high variability, loading values largely remained within those reported in the literature. Significant variation was found comparing the sEMG model with the AnyBody model, which may validate continued development and testing of personalized measurements of L4/L5 loading. Full article

Earthwork volume calculation is a fundamental process in civil engineering and construction, requiring high-precision terrain data to assess ground stability encompassing load-bearing capacity, susceptibility to settlement, and slope stability and to ensure accurate cost estimation. However, seasonal and environmental constraints pose significant challenges to surveying. This study employed unmanned aerial vehicle (UAV) photogrammetry and light detection and ranging (LiDAR) mapping to evaluate the accuracy of digital terrain model (DTM) generation and earthwork volume estimation in densely vegetated areas. For ground extraction, color-based indices (excess green minus red (ExGR), visible atmospherically resistant index (VARI), green-red vegetation index (GRVI)), a geometry-based algorithm (Lasground (new)) and their combinations were compared and analyzed. The results indicated that combining a color index with Lasground (new) outperformed the use of single techniques in both photogrammetric and LiDAR-based surveying. Specifically, the ExGR–Lasground (new) combination produced the most accurate DTM and achieved the highest precision in earthwork volume estimation. The LiDAR-based results exhibited an error of only 0.3% compared with the reference value, while the photogrammetric results also showed only a slight deviation, suggesting their potential as a practical alternative even under dense summer vegetation. Therefore, although prioritizing LiDAR in practice is advisable, this study demonstrates that UAV photogrammetry can serve as an efficient supplementary tool when cost or operational constraints are present. Full article

This paper proposes an adaptive gain twisting sliding-mode control (AGTSMC) strategy for trapezoidal variable-stiffness joints (TVSJs) to achieve accurate trajectory tracking under both matched and mismatched uncertainties. The TVSJ employs a compact trapezoidal leaf spring with grooved bearing followers (GBFs), enabling wide-range stiffness modulation through low-friction rolling contact. To address the strong nonlinearities and unmodeled dynamics introduced by stiffness variation, a Lyapunov-based adaptive twisting controller is developed, where the gains are automatically adjusted without conservative overestimation. A second-order sliding-mode differentiator is integrated to estimate velocity and disturbance terms in finite time using only position measurements, effectively reducing chattering. The proposed controller guarantees finite-time stability of the closed-loop system despite bounded uncertainties and measurement noise. Extensive simulations and hardware-in-the-loop experiments on a TVSJ platform validate the method. Compared with conventional sliding mode controller (CSMC), terminal sliding mode controller (TSMC), and fixed-gain twisting control (TC), the AGTSMC achieves faster convergence, lower steady-state error, and improved vibration suppression across low, high, and variable stiffness modes. Experimental results confirm that the proposed approach enhances tracking accuracy and energy efficiency while maintaining robustness under large stiffness variations. Full article

Background/Objectives: Accurate absorbed dose estimation is essential for optimising targeted radionuclide therapy (TRT) in metastatic castration-resistant prostate cancer, where kidney toxicity is dose-limiting. [¹⁷⁷Lu]Lu-rhPSMA-10.1 is a novel PSMA-targeted radioligand with favourable tumour-to-kidney uptake ratios; however, inter-patient pharmacokinetic variability can lead to differences in organ and tumour absorbed doses under fixed-activity administration. Personalised dosimetry offers a means to address this variability. This work aims to create mouse PBPK model-based digital twins for [¹⁷⁷Lu]Lu-rhPSMA-10.1 to test the model’s resistance to noise and evaluate its impact on accuracy and absorbed dose calculations. Methods: Five CB-17 SCID mice bearing LNCaP tumour xenografts received 2.6–3.1 MBq [¹⁷⁷Lu]Lu-rhPSMA-10.1 intravenously. Biodistribution was assessed 24 h post-injection by organ weighing and gamma counting. The PBPK model, implemented in MATLAB SimBiology (R2023a), was fitted to individual biodistribution data using mouse-specific physiological parameters. Digital twins—combining the model with fitted parameters—were used to generate time–activity curves (TACs) for kidneys, tumours, and the whole body. Gaussian noise ($\sigma$ = 0–0.35) was added to TACs to simulate measurement error. The model was refitted, and absorbed doses from time-integrated activities (TIAs) were compared to digital twin references. Results: The digital twin approach reproduced experimental data with physiologically plausible parameters. Absorbed dose estimates remained consistent and robust, deviating by <2.3% in kidneys and <1.0% in tumours. Conclusions: PBPK-based digital twins enable reliable, individualised dosimetry, even under substantial measurement uncertainty. Full article

Amid the global energy transition, the rapid growth of wind turbine deployment has highlighted the need for accurate fatigue load prediction to support structural design and ensure operational reliability. This study proposes a neural network-based method for estimating fatigue loads at critical locations of large wind turbines. Wind speed, turbulence intensity, and yaw angle were used as input features, while the damage equivalent loads at the blade root, tower base, and yaw bearing served as prediction targets. A dataset comprising 2139 operating conditions was constructed, and two predictive models—an artificial neural network (ANN) and a Bayesian neural network (BNN)—were developed and evaluated using standard error metrics. The results show that the BNN consistently achieves lower prediction errors and higher goodness-of-fit values than the ANN across all outputs, demonstrating improved accuracy and stability. The BNN model attained excellent predictive performance, with an overall coefficient of determination (R²) of 0.9998, a root mean square error (RMSE) of 0.012, and a mean absolute percentage error (MAPE) of only 0.1877%. These findings indicate that probabilistic neural networks hold strong potential for enhancing fatigue load prediction and can provide valuable support for wind turbine structural assessment, design optimization, and active yaw control strategies. Full article

This paper investigates bearing-based formation control of multiple unmanned aerial vehicles (UAVs) flying in low-altitude wind fields. In such environments, time-varying wind disturbances can distort the formation geometry, enlarge bearing errors, and even induce potential collisions among neighboring UAVs, yet they also contain components that can be beneficial for the formation motion. Conventional disturbance compensation methods treat wind as a purely harmful factor and aim to reject it completely, which may sacrifice responsiveness and energy efficiency. To address this issue, we propose a pure bearing-based formation control framework with Conditional Disturbance Utilization (CDU). First, a real-time disturbance observer is designed to estimate the wind-induced disturbances in both translational and rotational channels. Then, based on the estimated disturbances and the bearing-dependent potential function, CDU indicators are constructed to judge whether the current disturbance component is beneficial or detrimental with respect to the formation control objective. These indicators are embedded into the bearing-based formation controller so that favorable wind components are exploited to accelerate formation convergence, whereas adverse components are compensated. Using an angle-rigid formation topology and a Lyapunov-based analysis, we prove that the proposed CDU-based controller guarantees global asymptotic stability of the desired formation. Simulation results on triangular and hexagonal formations under complex wind disturbances show that the proposed method achieves faster convergence and improved responsiveness compared with traditional disturbance observer-based control, while preserving formation stability and safety. Full article