Search Results (706)

The control stability and accuracy of quad tiltrotor UAVs is improved when encountering external disturbances during automatic flight by an active disturbance rejection control (ADRC) parameter self-tuning control strategy based on a radial basis function (RBF) neural network. Firstly, a nonlinear flight dynamics model of the quad tiltrotor UAV is established based on the approach of component-based mechanistic modeling. Secondly, the effects of internal uncertainties and external disturbances on the model are eliminated, whilst the online adaptive parameter tuning problem for the nonlinear active disturbance rejection controller is addressed. The superior nonlinear function approximation capability of the RBF neural network is then utilized by taking both the control inputs computed by the controller and the system outputs of the quad tiltrotor model as neural network inputs to implement adaptive parameter adjustments for the Extended State Observer (ESO) component responsible for disturbance estimation and the Nonlinear State Error Feedback (NLSEF) control law of the active disturbance rejection controller. Finally, an adaptive attitude control system for the quad tiltrotor UAV is constructed, centered on the ADRC-RBF controller. Subsequently, the efficacy of the attitude control system is validated through simulation, encompassing a range of flight conditions. The simulation results demonstrate that the Integral of Absolute Error (IAE) of the pitch angle response controlled by the ADRC-RBF controller is reduced to 37.4° in comparison to the ADRC controller in the absence of external disturbance in the full-states mode state of the quad tiltrotor UAV, and the oscillation amplitude of the pitch angle response controlled by the ADRC-RBF controller is generally reduced by approximately 50% in comparison to the ADRC controller in the presence of external disturbance. In comparison with the conventional ADRC controller, the proposed ADRC-RBF controller demonstrates superior performance with regard to anti-disturbance capability, adaptability, and tracking accuracy. Full article

Accurate and spatially consistent rainfall estimation is essential for hydrological modeling and flood risk mitigation, especially in mountainous tropical regions with sparse observational networks and highly heterogeneous rainfall. This study presents a comparative analysis of six radar–gauge merging methods, including three statistical approaches—Quantile Adaptive Gaussian (QAG), Empirical Quantile Mapping (EQM), and radial basis function (RBF)—and three geostatistical approaches—external drift kriging (EDK), Bayesian Kriging (BAK), and Residual Kriging (REK). The evaluation was conducted over the Huong River Basin in Central Vietnam, a region characterized by steep terrain, monsoonal climate, and frequent hydrometeorological extremes. Two observational scenarios were established: Scenario S1 utilized 13 gauges for merging and 7 for independent validation, while Scenario S2 employed all 20 stations. Hourly radar and gauge data from peak rainy months were used for the evaluation. Each method was assessed using continuous metrics (RMSE, MAE, CC, NSE, and KGE), categorical metrics (POD and CSI), and spatial consistency indicators. Results indicate that all merging methods significantly improved the accuracy of rainfall estimates compared to raw radar data. Among them, RBF consistently achieved the highest accuracy, with the lowest RMSE (1.24 mm/h), highest NSE (0.954), and strongest spatial correlation (CC = 0.978) in Scenario S2. RBF also maintained high classification skills across all rainfall categories, including very heavy rain. EDK and BAK performed better with denser gauge input but required recalibration of variogram parameters. EQM and REK yielded moderate performance and had limitations near basin boundaries where gauge coverage was sparse. The results highlight trade-offs between method complexity, spatial accuracy, and robustness. While complex methods like EDK and BAK offer detailed spatial outputs, they require more calibration. Simpler methods are easier to apply across different conditions. RBF emerged as the most practical and transferable option, offering strong generalization, minimal calibration needs, and computational efficiency. These findings provide useful guidance for integrating radar and gauge data in flood-prone, data-scarce regions. Full article

In order to improve the poor prediction effect of current filling body strength design, a support vector machine (SVM) and Lib Toolbox were used to build an optimal match model or strength index of unclassified tailings filling body. Eight main factors were analyzed and screened as condition attributes, and backfill strength as a decision attribute. Next, we selected 72 groups of training samples and 6 groups of calibration samples. Our model adopts a radial basis function (RBF) as the kernel function and uses a grid search method to optimize parameters; it then tests the combination of optimal parameters by cross-validation. Results show that the mean error of regression prediction and verified predictions made by the SVM match model were 1.01%, which were more accurate than the BP neural network model’s predictions. On the premise that stope stability is ensured, the SVM match model may decrease cement consumption and the cost of backfill more effectively, and improve economic efficiency. Full article

This study aims to characterize the emissions of ammonia (NH₃) and methane (CH₄) from naturally ventilated dairy barns located in cold regions during the winter season, thereby providing a scientific basis for optimizing dairy barn environmental management. The target barn was selected at a commercial dairy farm in Ulanchab, Inner Mongolia, China. Environmental factors, including temperature, humidity, wind speed, and concentrations of NH₃, CH₄, and CO₂, were monitored both inside and outside the barn. The ventilation rate and emission rate were calculated using the CO₂ mass balance method. Additionally, NH₃ and CH₄ emission prediction models were developed using the adaptive neural fuzzy inference system (ANFIS). Correlation analyses were conducted to clarify the intrinsic links between environmental factors and NH₃ and CH₄ emissions, as well as the degree of influence of each factor on gas emissions. The ANFIS model with a Gaussian membership function (gaussmf) achieved the highest performance in predicting NH₃ emissions (R² = 0.9270), while the model with a trapezoidal membership function (trapmf) was most accurate for CH₄ emissions (R² = 0.8977). The improved ANFIS model outperformed common models, such as multilayer perceptron (MLP) and radial basis function (RBF). This study revealed the significant effects of environmental factors on NH₃ and CH₄ emissions from dairy barns in cold regions and provided reliable data support and intelligent prediction methods for realizing the precise control of gas emissions. Full article

Clean and safe air quality is essential for public health, yet particulate matter (PM) significantly degrades air quality and poses serious health risks. The Gulf Cooperation Council (GCC) countries are particularly vulnerable to frequent and intense dust storms due to their vast desert landscapes. This study presents the first health risk assessment of carcinogenic and non-carcinogenic risks associated with exposure to PM2.5 and PM10 bound heavy metals and polycyclic aromatic hydrocarbons (PAHs) based on air quality data collected during the years of 2016–2018 near Dubai International Airport and Abu Dhabi International Airport. The results reveal no significant carcinogenic risks for lead (Pb), cobalt (Co), nickel (Ni), and chromium (Cr). Additionally, AI-based regression analysis was applied to time-series dust monitoring data to enhance predictive capabilities in environmental monitoring systems. The estimated incremental lifetime cancer risk (ILCR) from PAH exposure exceeded the acceptable threshold (10⁻⁶) in several samples at both locations. The relationship between visibility and key environmental variables—PM1, PM2.5, PM10, total suspended particles (TSPs), wind speed, air pressure, and air temperature—was modeled using three machine learning algorithms: linear regression, support vector machine (SVM) with a radial basis function (RBF) kernel, and artificial neural networks (ANNs). Among these, SVM with an RBF kernel showed the highest accuracy in predicting visibility, effectively integrating meteorological data and particulate matter variables. These findings highlight the potential of machine learning models for environmental monitoring and the need for continued assessments of air quality and its health implications in the region. Full article

In this paper, a robust neural adaptive controller is proposed for the trajectory tracking control problem of unmanned surface vessels (USVs), considering model uncertainty, time-varying environmental disturbance, and actuator saturation. First, measurement errors in acceleration signals are eliminated through filtering techniques and a series of auxiliary variables, and after linearly parameterizing the USV dynamic model, a parameter adaptive update law is developed based on Lyapunov’s second method to estimate unknown dynamic parameters in the USV dynamics model. This parameter adaptive update law enables online identification of all USV dynamic parameters during trajectory tracking while ensuring convergence of the estimation errors. Second, a radial basis function neural network (RBF-NN) is employed to approximate unmodeled dynamics in the USV system, and on this basis, a robust damping term is designed based on neural damping technology to compensate for environmental disturbances and unmodeled dynamics. Subsequently, a trajectory tracking controller with parameter adaptation law and robust damping term is proposed using Lyapunov theory and adaptive control techniques. In addition, finite-time auxiliary variables are also added to the controller to handle the actuator saturation problem. Signal delay compensators are designed to compensate for input signal delays in the control system, thereby enhancing controller reliability. The proposed controller ensures robustness in trajectory tracking under model uncertainties and time-varying environmental disturbances. Finally, the convergence of each signal of the closed-loop system is proved based on Lyapunov theory. And the effectiveness of the control system is verified by numerical simulation experiments. Full article

During operation, dynamic cables endure coupled thermo-mechanical loads (mechanical: tension/bending; thermal: power transmission) that degrade stiffness, amplifying extreme responses and impairing configuration optimization. To address this, this study pioneers a multi-objective optimization framework integrating stiffness characteristics from mechanical/thermo-mechanical analyses, with objectives to minimize dynamic extreme tension and curvature under constraints of global configuration variables and safety thresholds. The framework employs a Radial Basis Function (RBF) surrogate model coupled with NSGA-II algorithm, yielding validated Pareto solutions (≤6.15% max error vs. simulations). Results demonstrate universal reduction in extreme responses across optimized configurations, with the thermo-mechanically optimized solution achieving 20.24% fatigue life enhancement. This work establishes the first methodology quantifying thermo-mechanical coupling effects on offshore cable safety and fatigue performance. This configuration design scheme exhibits better safety during actual service conditions. Full article

Long-term sea level variations in the northern South China Sea (SCS) are known to significantly impact coastal ecosystems and socio-economic activities. To improve sea level prediction accuracy, four models—harmonic analysis and three artificial neural networks (ANNs), namely genetic algorithm-optimized back propagation (GA-BP), radial basis function (RBF), and long short-term memory (LSTM)—are developed and compared using 52 years of observational data (1960–2004). Key evaluation metrics are presented to demonstrate the models’ effectiveness: for harmonic analysis, the root mean square error (RMSE) is reported as 14.73, the mean absolute error (MAE) is 12.61, the mean bias error (MBE) is 0.0, and the coefficient of determination (R²) is 0.84; for GA-BP, the RMSE is measured as 29.1371, the MAE is 24.9411, the MBE is 5.6809, and the R² is 0.4003; for the RBF neural network, the RMSE is calculated as 27.1433, the MAE is 22.7533, the MBE is 2.1322, and the R² is 0.4690; for LSTM, the RMSE is determined as 23.7929, the MAE is 19.7899, the MBE is 1.3700, and the R² is 0.5872. The key findings include the following: (1) A significant sea level rise trend at 1.4 mm/year is observed in the northern SCS. (2) Harmonic analysis is shown to outperform all ANN models in both accuracy and robustness, with sea level variations effectively characterized by four principal and six secondary tidal constituents. (3) Despite their complexity, ANN models (including LSTM) are found to fail in surpassing the predictive capability of the traditional harmonic method. These results highlight the continued effectiveness of harmonic analysis for long-term sea level forecasting, offering critical insights for coastal hazard mitigation and sustainable development planning. Full article

Atmospheric methane (CH₄) concentrations have increased to 2.5 times their pre-industrial levels, with a marked acceleration in recent decades. CH₄ is responsible for approximately 30% of the global temperature rise since the Industrial Revolution. This growing concentration contributes to environmental degradation, including ocean acidification, accelerated climate change, and a rise in natural disasters. The column-averaged dry-air mole fraction of methane (XCH₄) is a crucial indicator for assessing atmospheric CH₄ levels. In this study, the Sentinel-5P TROPOMI instrument was employed to monitor, map, and estimate CH₄ concentrations on both regional and global scales. However, TROPOMI data exhibits limitations such as spatial gaps and relatively coarse resolution, particularly at regional scales or over small areas. To mitigate these limitations, a novel Convolutional Neural Network Autoencoder (CNN-AE) model was developed. Validation was performed using the Total Carbon Column Observing Network (TCCON), providing a benchmark for evaluating the accuracy of various interpolation and prediction models. The CNN-AE model demonstrated the highest accuracy in regional-scale analysis, achieving a Mean Absolute Error (MAE) of 28.48 ppb and a Root Mean Square Error (RMSE) of 30.07 ppb. This was followed by the Random Forest (RF) regressor (MAE: 29.07 ppb; RMSE: 36.89 ppb), GridData Nearest Neighbor Interpolator (NNI) (MAE: 30.06 ppb; RMSE: 32.14 ppb), and the Radial Basis Function (RBF) Interpolator (MAE: 80.23 ppb; RMSE: 90.54 ppb). On a global scale, the CNN-AE again outperformed other methods, yielding the lowest MAE and RMSE (19.78 and 24.7 ppb, respectively), followed by RF (21.46 and 27.23 ppb), GridData NNI (25.3 and 32.62 ppb), and RBF (43.08 and 54.93 ppb). Full article

The epoxy-bonded joint between carbon-fiber-reinforced bismaleimide (CF-BMI) and quartz-fiber-reinforced bismaleimide (QF-BMI) composites can meet the structure–function integration requirements of next-generation aviation equipment, and the structural design of their bonding zones directly affects their service performance. Hence, in this study, the carbon-fiber-reinforced bismaleimide composite ZT7H/5429, the woven quartz-fiber-reinforced bismaleimide composite QW280/5429, and epoxy adhesive film J-116 were used as research materials to investigate the influence of the bonding area size on the mechanical properties, and this study proposes a novel design methodology combining radial basis function (RBF) neuron machine learning with the NSGA-II algorithm to enhance the mechanical properties of the bonded components. First, a finite element simulation model considering 3D hashin criteria and cohesion was established, and its accuracy was verified with experiments. Second, the RBF neuron model was trained using the finite element tensile strength and shear strength data from various adhesive layer parameter combinations. Then, the multi-objective parameter optimization of the surrogate model was accomplished through the NSGA-II algorithm. The research results demonstrate a high consistency between the finite element simulation results and experimental outcomes for the epoxy-bonded CF/QF-BMI composite joint. The stress distribution of the adhesive layers is similar under the different structural parameters of adhesive films, though the varying structural dimensions of the adhesive layers lead to distinct failure modes. The trained RBF neuron model controls the prediction error within 2.21%, accurately reflecting the service performance under various adhesive layer parameters. The optimized epoxy-bonded CF/QF-BMI composite joint exhibits 16.1% and 11.2% increases in the tensile strength and shear strength, respectively. Full article

This paper proposes a computational procedure to resolve the temporal fractional financial option pricing partial differential equation (PDE) using a localized meshless approach via the multiquadric radial basis function (RBF). Given that financial market information is best characterized within a martingale framework, the resulting option pricing model follows a modified Black–Sholes (BS) equation, requiring efficient numerical techniques for practical implementation. The key innovation in this study is the derivation of analytical weights for approximating first and second derivatives, ensuring improved numerical stability and accuracy. The construction of these weights is grounded in the second integration of a variant of the multiquadric RBF, which enhances smoothness and convergence properties. The performance of the presented solver is analyzed through computational tests, where the analytical weights exhibit superior accuracy and stability in comparison to conventional numerical weights. The results confirm that the new approach reduces absolute errors, demonstrating its effectiveness for financial option pricing problems. Full article

This study addresses the critical challenge of insufficient classification accuracy for different defect signals in rail magnetic flux leakage (MFL) detection by proposing an enhanced intelligent classification framework based on particle swarm optimized radial basis function neural network (PSO-RBF). Three key innovations drive this research: (1) A dynamic PSO algorithm incorporating adaptive learning factors and nonlinear inertia weight for precise RBF parameter optimization; (2) A hierarchical feature processing strategy combining mutual information selection with correlation-based dimensionality reduction; (3) Adaptive model architecture adjustment for small-sample scenarios. Experimental validation shows breakthrough performance: 87.5% accuracy on artificial defects (17.5% absolute improvement over conventional RBF), with macro-F1 = 0.817 and MCC = 0.733. For real-world limited samples (100 sets), adaptive optimization achieved 80% accuracy while boosting minority class (“spalling”) F1-score by 0.25 with 50% false alarm reduction. The optimized PSO-RBF demonstrates superior capability in extracting MFL signal patterns, particularly for discriminating abrasions, spalling, indentations, and shelling defects, setting a new benchmark for industrial rail inspection. Full article

This study proposes an RBF-PSO hybrid framework for efficient inversion analysis of hydration heat parameters in mass concrete temperature fields, addressing the computational inefficiency and accuracy limitations of traditional methods. By integrating a Radial Basis Function (RBF) surrogate model with Particle Swarm Optimization (PSO), the method reduces reliance on costly finite element simulations while maintaining global search capabilities. Three objective functions—integral-type (F₁), feature-driven (F₂), and hybrid (F₃)—were systematically compared using experimental data from a C40 concrete specimen under controlled curing. The hybrid F₃, incorporating Dynamic Time Warping (DTW) for elastic time alignment and feature penalties for engineering-critical metrics, achieved superior performance with a 74% reduction in the prediction error (mean MAE = 1.0 °C) and <2% parameter identification errors, resolving the phase mismatches inherent in F₂ and avoiding F₁’s prohibitive computational costs (498 FEM calls). Comparative benchmarking against non-surrogate optimizers (PSO, CMA-ES) confirmed a 2.8–4.6× acceleration while maintaining accuracy. Sensitivity analysis identified the ultimate adiabatic temperature rise as the dominant parameter (78% variance contribution), followed by synergistic interactions between hydration rate parameters, and indirect coupling effects of boundary correction coefficients. These findings guided a phased optimization strategy, as follows: prioritizing high-precision calibration of dominant parameters while relaxing constraints on low-sensitivity variables, thereby balancing accuracy and computational efficiency. The framework establishes a closed-loop “monitoring-simulation-optimization” system, enabling real-time temperature prediction and dynamic curing strategy adjustments for heat stress mitigation. Robustness analysis under simulated sensor noise (σ ≤ 2.0 °C) validated operational reliability in field conditions. Validated through multi-sensor field data, this work advances computational intelligence applications in thermomechanical systems, offering a robust paradigm for parameter inversion in large-scale concrete structures and multi-physics coupling problems. Full article

YouthMappers chapters, utilizing OpenStreetMap (OSM), play a pivotal role in tackling climate challenges through education and activism. This study investigates the influence of a booster grant project on enhancing Climate Activism and Education efforts through YouthMappers chapters in Sri Lanka. Through a geometric approach, the research integrates measurable survey data from OSM platform data from 223 YouthMappers chapter respondents at four (04) universities in Sri Lanka to evaluate five critical factors/dimensions: Capacity Building and Funding Support (CBFS), Climate Activism and Education (CAE), Community Engagement and Collaboration (CEC), Technical Skills and Resources (TSR), and Sustainability and Policy Integration (SPI). The Friedman test confirmed statistically significant differences across all factors’ variables (p < 0.001), highlighting strengths in technical competence and educational integration, with gaps identified in community engagement and sustainability. A Radial Basis Function (RBF) model revealed moderate predictive accuracy, excelling in variables like CAE and TSR but indicating higher error rates in SPI and CEC. Practical outcomes include flood risk maps, curriculum-integrated teaching schemes, and localized mapping workshops. These results underscore the booster grant’s role in enabling impactful, youth-led geospatial initiatives. However, challenges such as internet access, training gaps, and language barriers remain. This study recommends expanding student and community participation, refining training strategies, and integrating OSM into university curricula. These scalable interventions offer valuable insights for replication in other vulnerable regions, enhancing climate resilience through community-driven, data-informed youth engagement. Full article

This paper introduces a robust neural adaptive MIMO control strategy to improve the stability and adaptability of bipedal locomotion amid uncertainties and external disturbances. The control combines nonlinear dynamic inversion, finite-time convergence, and radial basis function (RBF) neural networks for fast, accurate trajectory tracking. The main novelty of the presented control strategy lies in unifying instantaneous feedback, real-time learning, and dynamic adaptation within a multivariable feedback framework, delivering superior robustness, precision, and real-time performance under extreme conditions. The control scheme is implemented on a 5-DOF underactuated $\mathrm{R}\mathrm{A}\mathrm{B}\mathrm{B}\mathrm{I}\mathrm{T}$ robot using a $\mathrm{d}\mathrm{S}\mathrm{P}\mathrm{A}\mathrm{C}\mathrm{E}\mathrm{D}\mathrm{S}1103$ platform with a sampling rate of $∆t=1.5 \mathrm{m}\mathrm{s} (667 \mathrm{H}\mathrm{z})$. The experimental results show excellent performance with the following: The robot achieved stable cyclic gaits while keeping the tracking error within $e=\pm 0.04 \mathrm{r}\mathrm{a}\mathrm{d}$ under nominal conditions. Under severe uncertainties of trunk mass variations ${∆m}_{\mathrm{t}\mathrm{r}\mathrm{u}\mathrm{n}\mathrm{k}}=+100\%$, limb inertia changes ${∆I}_{\mathrm{l}\mathrm{i}\mathrm{m}\mathrm{b}}=\pm 30\%$, and actuator torque saturation at $\tau =\pm 150 \mathrm{N}\mathrm{m}$, the robot maintains stable limit cycles with smooth control. The performance of the proposed controller is compared with classical nonlinear decoupling, non-adaptive finite-time, neural-fuzzy learning, and deep learning controls. The results demonstrate that the proposed method outperforms the four benchmark strategies, achieving the lowest errors and fastest convergence with the following: $\mathrm{I}\mathrm{A}\mathrm{E}=1.36$, $\mathrm{I}\mathrm{T}\mathrm{A}\mathrm{E}=2.43$, $\mathrm{I}\mathrm{S}\mathrm{E}=0.68$, $t_{ss}=1.24 \mathrm{s}$, and $M_p=2.21\%$. These results demonstrate evidence of high stability, rapid convergence, and robustness to disturbances and foot-slip. Full article