Search Results (379)

Here we present a high-resolution landslide susceptibility model for Guiyang County, China, developed to support sustainable disaster risk management. Our approach couples optimized positive and negative training samples with an ensemble of machine-learning algorithms to maximize predictive fidelity. We compiled a georeferenced inventory of 146 landslides by integrating historical records with systematic field validation. Sample optimization was central to our methodology: landslide presence points were refined via buffer-based dilution, and four classifiers—SVM, LDA, RF, and ET—were trained with identical covariate sets to ensure comparability. Three strategies for selecting pseudo-absences—buffering, low-slope filtering, and coupling with the IOE—were benchmarked. The Slope-IOE-O model, which synergizes low-gradient screening with entropy-weighted sampling, yielded the highest predictive capacity (AUC = 0.965). SHAP-based interpretability revealed that slope, monthly maximum rainfall, surface roughness, and elevation collectively dominate susceptibility, with pronounced non-linearities and interactions. Slope contribution peaks at 20–30°, monthly maximum rainfall exhibits a critical threshold near 225 mm, and the synergy between high roughness and road density amplifies landslide risk. Spatially, susceptibility follows a pronounced north–south gradient, with high-hazard corridors aligned along northern and southern mountain belts and the urban core of southern Guiyang County. By integrating rigorously curated training data with robust machine-learning workflows, this study provides a transferable framework for proactive landslide risk assessment, offering scientific support for sustainable land-use planning and resilient development in mountainous regions. Full article

Small unmanned aerial vehicles are now commonly equipped with integrated navigation systems to obtain high-precision navigation parameters. However, affected by the dual impacts of multipath effects and dynamic environmental changes, their state estimation process is vulnerable to interference from measurement outliers, which in turn leads to the degradation of navigation accuracy and poses a threat to flight safety. To address this issue, this research presents an adaptive minimum error entropy cubature Kalman filter. Firstly, the cubature Kalman filter is introduced to solve the problem of model nonlinear errors; secondly, the cubature Kalman filter based on minimum error entropy is derived to effectively curb the interference that measurement outliers impose on filtering results; finally, a kernel bandwidth adjustment factor is designed, and the kernel bandwidth is estimated adaptively to further improve navigation accuracy. Through numerical simulation experiments, the robustness of the proposed method with respect to measurement outliers is validated; further flight experiment results show that compared with existing related filters, this proposed filter can achieve more accurate navigation and positioning. Full article

This study presents a deep learning–assisted integrated navigation scheme implemented on an autonomous underwater vehicle carrying a Chinese domestically developed strapdown inertial navigation system, designed for operation in surface and littoral environments. The system integrates measurements from SINS, the global positioning system, and a Doppler velocity log, while integrating a Decoder-based covariance estimator into the error state-extended Kalman filter. This hybrid architecture adaptively models time-varying processes and measurement noise from raw sensor inputs, greatly improving robustness for surface navigation in dynamic marine environments. To improve learning efficiency, we design a compact and informative feature representation that can be adapted to navigation error dynamics. The novel structure captures temporal dependencies and the evolution of nonlinear error more effectively than typical sequence models, achieving faster convergence and superior accuracy compared to GRU and Transformer baselines. The experimental results based on real sea trial data show that our method significantly outperforms model-based and learning-based methods in terms of navigation solution accuracy and stability, and the adaptive estimation of noise covariance. Specifically, it achieves the lowest RMSE of 0.0274, reducing errors by 94.6–34.6%, compared to conventional ES-EKF-integrated navigation, Transformer, GRU, and a DCE variant. These findings underscore the practical significance of integrating domain-informed filtering methodologies with deep noise modeling frameworks to achieve robust and accurate AUV surface navigation. Full article

This study proposes a novel approach to improve the reliability of trading signals for gold market prediction by integrating technical analysis indicators, Moving Averages (MAs), MACD, and Ichimoku Cloud, with a Particle Swarm-Optimized Extreme Learning Machine (PSO-ELM). Traditional time-series models often fail to capture the complex, non-linear dynamics of financial markets, whereas technical indicators combined with machine learning enhance predictive accuracy. Using daily gold prices from January–October 2020, the PSO-ELM model demonstrated superior performance in filtering false signals, achieving high precision, recall, and overall accuracy. The results highlight the effectiveness of combining technical analysis with machine learning for robust signal validation, providing a practical framework for traders and investors. While focused on gold, this methodology can be extended to other financial assets and market conditions. The integration of machine learning and blockchain enhances both predictive reliability and operational trust, offering traders, investors, and institutions a robust framework for decision support in dynamic financial environments. Full article

Nonlinear filtering algorithms have significant implications in the optimal estimation of navigation states and in improving the accuracy, reliability, and robustness of navigation systems. This manuscript surveys the developments of the nonlinear filtering algorithms (extended Kalman filtering (EKF), unscented Kalman filtering (UKF), Cubature Kalman filtering (CKF), particle filtering (PF), neural network filtering (NNF)) and adaptive/robust KF in integrated navigation systems. The principle, application, and existing problems of these nonlinear filtering algorithms are mainly studied, and the comparative analysis and prospect are carried out. Full article

Due to the highly nonlinear, dynamic, and slowly time-varying nature of lithium-ion batteries (LIBs) during operation, achieving accurate and real-time parameters online identification in first-order RC equivalent circuit models (ECMs) remains a significant challenge, including low accuracy and poor real-time performance. This paper establishes a fractional-order chaotic system for first-order RC-ECM based on a charge-controlled memristor. The system exhibits chaotic behavior when parameters are tuned. Then, based on the principle of the state observer, an identification observer is designed for each unknown parameter of the first-order RC-ECM, achieving online identification of these unknown parameters of the first-order RC-ECM of LIB. The proposed method addresses key limitations of traditional parameter identification techniques, which often rely on large sample datasets and are sensitive to variations in ambient temperature, road conditions, load states, and battery chemistry. Experimental validation was conducted under the HPPC, DST, and UDDS conditions. Using the actual terminal voltage of a single cell as a reference, the identified first-order RC-ECM parameters enabled accurate prediction of the online terminal voltage. Comparative results demonstrate that the proposed state observer achieves significantly higher accuracy than the forgetting factor recursive least squares (FFRLS) algorithm and Kalman filter (KF) algorithm, while offering superior real-time performance, robustness, and faster convergence. Full article

The study presents a robust methodology for simultaneous state and parameter estimation in nonlinear thermal systems, demonstrated on a counter-current heat exchanger model operating with nitrogen under industrial conditions. To address challenges of ill-conditioning and parameter correlation, local sensitivity analysis is combined with regularization through optimal parameter subset selection using orthogonalization and D-optimal experimental design. The Unscented Kalman Filter (UKF) is employed to jointly estimate the augmented state vector in real time, leveraging high-fidelity dynamic simulations generated in Unisim Design with the Peng–Robinson equation of state. The proposed framework achieves high estimation accuracy and numerical stability, even under limited sensor availability and measurement noise. Monte Carlo simulations confirm robustness to ±2.5% uncertainty in initial conditions, while residual autocorrelation analysis validates estimator optimality. The approach provides a practical solution for real-time monitoring and model-based control in industrial heat exchangers and offers a generalizable strategy for building identifiable, noise-resilient models of complex nonlinear systems. Full article

In this paper, we propose a novel method to reduce non-Gaussian errors in measurements using sampling-based distribution estimation. Although non-Gaussian errors are often treated as statistical deviations, they can frequently arise in practical unmanned aerial systems that depend on global navigation satellite systems (GNSS), where position measurements are degraded by multipath effects. However, nonlinear or robust filters have shown limited effectiveness in correcting such errors, particularly when they appear as persistent biases in measurements over time. In such cases, adaptive techniques have often demonstrated greater effectiveness. The proposed method estimates the distribution of observed measurements using a sampling-based approach and derives a reformed measurement from this distribution. By incorporating this reformed measurement into the filter update, the proposed approach achieves lower error levels than traditional adaptive filters. To validate the effectiveness of the method, Kalman filter simulations are conducted for drone GNSS/INS navigation. The results show that the proposed method outperforms conventional non-Gaussian filters in handling measurement bias caused by non-Gaussian errors. Furthermore, it achieves nearly twice the estimation accuracy compared to adaptive approaches. These findings confirm the robustness of the proposed technique in scenarios where measurement accuracy temporarily deteriorates before recovering. Full article

Target tracking in typical radar applications faces critical challenges in complex environments, including nonlinear dynamics, non-Gaussian noise, and sensor outliers. Current robustness-enhanced approaches remain constrained by empirical kernel tuning and computational trade-offs, failing to achieve balanced noise suppression and real-time efficiency. To address these limitations, this paper proposes the variational Bayesian-based maximum correntropy criterion cubature Kalman filter (VBMCC-CKF), which integrates variational Bayesian inference with CKF to establish a fully adaptive robust filtering framework for nonlinear systems. The core innovation lies in constructing a joint estimation framework of state and kernel size, where the kernel size is modeled as an inverse-gamma distributed random variable. Leveraging the conjugate properties of Gaussian-inverse gamma distributions, the method synchronously optimizes the state posterior distribution and kernel size parameters via variational Bayesian inference, eliminating reliance on manual empirical adjustments inherent to conventional correntropy-based filters. Simulation confirms the robust performance of the VBMCC-CKF framework in both single and multi-target tracking under non-Gaussian noise conditions. For the single-target case, it achieves a reduction in trajectory average root mean square error (Avg-RMSE) by at least 14.33% compared to benchmark methods while maintaining real-time computational efficiency. Integrated with multi-Bernoulli filtering, the method achieves a 40% lower optimal subpattern assignment (OSPA) distance even under 10-fold covariance mutations, accompanied by superior hit rates (HRs) and minimal trajectory position RMSEs in cluttered environments. These results substantiate its precision and adaptability for dynamic tracking scenarios. Full article

Hammerstein adaptive filters (HAFs) are widely used for nonlinear system identification due to their structural simplicity and modeling effectiveness. However, their performance can degrade significantly in the presence of impulsive disturbance or other more complex non-Gaussian noise, which are common in real-world scenarios. To address this limitation, this paper proposes a robust HAF algorithm based on the kernel mean p-power error (KMPE) criterion. By extending the p-power loss into the kernel space, KMPE preserves its symmetry while providing enhanced robustness against non-Gaussian noise in adaptive filter design. In addition, random Fourier features are employed to flexibly and efficiently model the nonlinear component of the system. A theoretical analysis of steady-state excess mean square error is presented, and our simulation results validate the superior robustness and accuracy of the proposed method over the classical HAF and its robust variants. Full article

Synthetic Aperture Radar (SAR) is one of the few instruments capable of providing high-resolution global two-dimensional (2D) measurements of ocean waves. Since 2014 and then 2016, the Sentinel-1A/B satellites, whenever operating in a specific wave mode (WV), have been providing ocean swell spectrum data as Level-2 (L2) OCeaN products (OCN), derived through a quasi-linear inversion process. This WV acquires small SAR images of 20 × 20 km footprints alternating between two sub-beams, WV1 and WV2, with incidence angles of approximately 23° and 36°, respectively, to capture ocean surface dynamics. The SAR imaging process is influenced by various modulations, including hydrodynamic, tilt, and velocity bunching. While hydrodynamic and tilt modulations can be approximated as linear processes, velocity bunching introduces significant distortion due to the satellite’s relative motion with respect to the ocean surface and leads to constructive but also destructive effects on the wave imaging process. Due to the associated azimuth cut-off, the quasi-linear inversion primarily detects ocean swells with, on average, wavelengths longer than 200 m in the SAR azimuth direction, limiting the resolution of smaller-scale wave features in azimuth but reaching 10 m resolution along range. The 2D spectral partitioning technique used in the Sentinel-1 WV OCN product separates different swell systems, known as partitions, based on their frequency, directional, and spectral characteristics. The accuracy of these partitions can be affected by several factors, including non-linear effects, large-scale surface features, and the relative direction of the swell peak to the satellite’s flight path. To address these challenges, this study proposes a novel quality control framework using a machine learning (ML) approach to develop a quality flag (QF) parameter associated with each swell partition provided in the OCN products. By pairing collocated data from Sentinel-1 (S1) and WaveWatch III (WW3) partitions, the QF parameter assigns each SAR-derived swell partition one of five quality levels: “very good,” “good,” “medium,” “low,” or “poor”. This ML-based method enhances the accuracy of wave partitions, especially in cases where non-linear effects or large-scale oceanic features distort the data. The proposed algorithm provides a robust tool for filtering out problematic partitions, improving the overall quality of ocean wave measurements obtained from SAR. Moreover, the variability in the accuracy of swell partitions, depending on the swell direction relative to the satellite’s flight heading, is effectively addressed, enabling more reliable data for oceanographic studies. This work contributes to a better understanding of ocean swell dynamics derived from SAR observations and supports the numerical swell modeling community by aiding in the refinement of models and their integration into operational systems, thereby advancing both theoretical and practical aspects of ocean wave forecasting. Full article

In this study, we introduce an advanced framework for state estimation in electro-hydraulic systems, utilizing a structurally adapted Kalman filter. The proposed model was designed to enhance estimation accuracy and robustness under dynamic load variations and evolving measurement conditions. A notable feature of the approach is the algebraic resolution of one system state during each iteration, enabling the seamless inclusion of variables that are otherwise difficult to measure, without disrupting the model’s linear formulation. In addition, the dynamics of the load torque are empirically characterized through a regression-based model derived from experimental observations. The framework integrates adaptive mechanisms for updating the model and measurement error covariance matrices, facilitating the real-time accommodation of system nonlinearities and environmental changes. Experimental results are presented in different operating modes, reflecting characteristic dynamic movements. They show that the method reduced the root mean square error (RMSE) when estimating angular velocity between five and more than six times, depending on the mode. When evaluating the load torque, even in modes with a sharply changing load, the RMSE value remains stable below 0.05 Nm, which indicates the absence of systematic drift and high stability of the estimates. This confirms the stable operation of the algorithm in dynamic conditions and its applicability in real systems. Full article

Speaker recognition has become a critical component of human–robot interaction (HRI), enabling personalized services based on user identity, as the demand for home service robots increases. In contrast to conventional speech recognition tasks, recognition in home service robot environments is affected by varying speaker–robot distances and background noises, which can significantly reduce accuracy. Traditional approaches rely on hand-crafted features, which may lose essential speaker-specific information during extraction like mel-frequency cepstral coefficients (MFCCs). To address this, we propose a novel speaker recognition technique for intelligent robots that combines SincNet-based raw waveform processing with an adaptive neuro-fuzzy inference system (ANFIS). SincNet extracts relevant frequency features by learning low- and high-cutoff frequencies in its convolutional filters, reducing parameter complexity while retaining discriminative power. To improve interpretability and handle non-linearity, ANFIS is used as the classifier, leveraging fuzzy rules generated by fuzzy c-means (FCM) clustering. The model is evaluated on a custom dataset collected in a realistic home environment with background noise, including TV sounds and mechanical noise from robot motion. Our results show that the proposed model outperforms existing CNN, CNN-ANFIS, and SincNet models in terms of accuracy. This approach offers robust performance and enhanced model transparency, making it well-suited for intelligent home robot systems. Full article

This paper proposes a nonlinear control strategy for a microgrid, comprising a PV generator, wind turbine, battery, solid oxide fuel cell (SOFC), electrolyzer, and a three-phase four-leg voltage source inverter (VSI) with an LC filter. The microgrid is designed to supply unbalanced AC loads while maintaining high power quality. To address chattering and enhance control precision, a super-twisting algorithm (STA) is integrated, outperforming traditional PI, IP, and classical SMC methods. The four-leg VSI enables independent control of each phase using a dual-loop strategy (inner voltage, outer current loop). Stability is ensured through Lyapunov-based analysis. Scalar PWM is used for inverter switching. The battery, SOFC, and electrolyzer are controlled using integral backstepping, while the SOFC and electrolyzer also use Lyapunov-based voltage control. A hybrid integral backstepping–STA strategy enhances PV performance; the wind turbine is managed via integral backstepping for power tracking. The system achieves voltage and current THD below 0.40%. An energy management algorithm maintains power balance under variable generation and load conditions. Simulation results confirm the control scheme’s robustness, stability, and dynamic performance. Full article

This paper investigates the joint state and fault estimation problem for a class of nonlinear systems subject to both measurement censoring (MC) and random missing measurements (MMs). Recognizing that state estimation for nonlinear systems in complex environments is frequently compromised by MMs, MC phenomena, and actuator faults, a novel joint estimation framework that integrates improved Tobit Kalman filtering and federated fusion is proposed, enabling simultaneous robust estimation of system states and fault signals. Among them, the Tobit measurement model is introduced to characterize the phenomenon of MC, a set of Bernoulli random variables is used to describe the MM phenomenon and common actuator faults (abrupt and ramp faults) are considered. In the fusion estimation stage, each sensor transmits observations to the local estimator for preliminary estimation, then sends the local estimated values to the fusion center for generating fusion estimates. The local filtering error covariance is ensured and the upper bound is minimized by reasonably determining the filter gain, while the fusion center performs fusion estimation based on the federated fusion criterion. In addition, this paper proves the boundedness of the filtering error of the designed estimator under certain conditions. Finally, the effectiveness of the estimation framework is demonstrated through two engineering experiments. Full article