Search Results (223)

To investigate the characteristics of pressure pulsation induced by vortex ropes in the draft tube of a centrifugal pump under runaway conditions, a closed double-layer hydraulic test bench was established in this study. Runaway characteristic experiments were conducted, and pressure pulsation signals were acquired at heads of 7.6 m, 9.6 m, and 11.9 m. The measured pressure data were analyzed in the time–frequency domain using Fast Fourier Transform (FFT) and Wavelet Transform (WT). The results show that both the runaway rotational speed and the reverse flow rate increase with increasing head. Under all three heads, the dominant frequency upstream of the elbow section of the draft tube is 0.53 times the rotational frequency, confirming that the vortex rope in the draft tube serves as the primary excitation source of the flow field. As the vortex rope is conveyed by the main flow through the elbow, it undergoes impingement and fragmentation, causing the dominant frequency downstream of the elbow to decrease to 0.1 times the rotational frequency. The dominant frequency induced by the vortex rope remains continuous over time, whereas the frequency arising from the coupling between the vortex rope and rotor–stator interaction exhibits pronounced time-varying oscillations. These oscillations intensify with increasing head, and their frequency oscillation range broadens from 4 to 6 times the rotational frequency at low head to 2–8 times at high head. These findings provide a theoretical foundation for the preventive and protective design of centrifugal pumps under runaway conditions. Full article

The need for Structural Health Monitoring is evident in order to ensure the safety of civil infrastructure. The goal of vibration monitoring is to derive the eigenfrequencies, mode shapes and damping of a structure. A change in the eigenfrequency over time can indicate deterioration or damage in a structure. The amplitude can be used to calculate the damping ratio. As the damping ratio is amplitude-dependent, it is important to correctly determine the amplitude values. This study focuses on the amplitude correctness of high-rate Global Navigation Satellite System (GNSS) receiver data. In an experiment with controlled oscillations with a shaker and a Laser Triangulation Sensor (LTS) as a reference, the vibration amplitudes derived by GNSS measurements were analyzed, using time-frequency techniques like Short Time Fourier Transform (STFT) and Wavelet Transform (WT). We demonstrate that vibrations in the millimeter range can be derived from the measurements of satellites orbiting 20,000 km above Earth. However, the amplitudes of the determined frequencies show systematic errors up to 60% when compared to independent reference measurements. We introduce a correction method to reduce this error by applying a frequency-dependent correction function. Full article

Severe foliage occlusion and dynamically changing lighting conditions in complex orchard environments pose significant challenges for visual perception systems in automated apple harvesting, including low detection accuracy, poor robustness, and insufficient real-time performance. To address these issues, this study proposes an improved lightweight detection network based on YOLOv11, named YOLO-WBL, along with a precise yield estimation algorithm based on 3D point clouds, termed CLV. The YOLO-WBL network is optimized in three aspects: (1) A C3K2_WT module integrating wavelet transform is introduced into the backbone network to enhance multi-scale feature extraction capability; (2) A weighted bidirectional feature pyramid network (BiFPN) is adopted in the neck network to improve the efficiency of multi-scale feature fusion; (3) A lightweight shared convolution separated batch normalization detection head (Detect-SCGN) is designed to significantly reduce the parameter count while maintaining accuracy. Based on this detection model, the CLV algorithm deeply integrates depth camera point cloud information through 3D coordinate mapping, irregular point cloud reconstruction, and convex hull volume calculation to achieve accurate estimation of individual fruit volume and total yield. Experimental results demonstrate that: (1) The YOLO-WBL model achieves a precision of 93.8%, recall of 79.3%, and mean average precision (mAP@0.5) of 87.2% on the apple test set; (2) The model size is only 3.72 MB, a reduction of 28.87% compared to the baseline model; (3) When deployed on an NVIDIA Jetson Xavier NX edge device, its inference speed reaches 8.7 FPS, meeting real-time requirements; (4) In scenarios with an occlusion rate below 40%, the mean absolute percentage error (MAPE) of yield estimation can be controlled within 8%. Experimental validation was conducted using apple images selected from the dataset under varying lighting intensities and fruit occlusion conditions. The results demonstrate that the CLV algorithm significantly outperforms traditional average-weight-based estimation methods. This study provides an efficient, accurate, and deployable visual solution for intelligent apple harvesting and yield estimation in complex orchard environments, offering practical reference value for advancing smart orchard production. Full article

To address the instability in extracting key parameters such as time-of-flight (ToF) from ultrasonic echoes due to noise and multi-echo superposition, this paper proposes a robust parameter estimation method based on the secretary bird optimization algorithm (SBOA). The proposed approach adheres to the Gaussian convolution-based echo parameterization and cosine-similarity matching framework, while innovatively introducing SBOA to perform global optimization of model parameters. Consequently, the multi-echo ToF estimation is formulated as a nonlinear optimization problem aimed at maximizing waveform shape consistency. To evaluate the method’s performance, simulations are conducted under multi-echo superposition scenarios. Comparisons are made with representative baseline techniques, including wavelet transform (WT), empirical mode decomposition (EMD), and variational mode decomposition (VMD), using mean squared error (MSE), estimated signal-to-noise ratio (ESNR), and normalized cross-correlation (NCC) as performance metrics. Experimental results demonstrate that, in challenging low-SNR and echo-interference environments, the proposed method achieves overall superiority across all quantitative metrics and exhibits a stronger capability to preserve the main-lobe morphology and structural features of echoes. Validation on semi-synthetic signals further confirms its effectiveness, with practical applicability to be verified by measured datasets in future work. This work provides an effective and robust solution for ultrasonic signal processing in complex field conditions. Full article

Research on muscle fatigue during dynamic movement using surface electromyography (sEMG) constitutes a significant challenge within biomechanics. Despite a degree of standardization, measurements and their resultant findings continue to attract considerable debate, attributable to factors such as skin impedance, perspiration, and electrode displacement, as well as subjective fatigue perception. Further questions remain regarding signal normalization and the selection of appropriate analytical methodologies. Recent years have witnessed notable progress in dynamic fatigue research, highlighting the limitations of classical metrics (e.g., EMG Median Frequency) and introducing time–frequency methods, such as the wavelet transform (WT), which are better equipped to handle signal non-stationarity. Interest has also expanded to include non-linear metrics (e.g., entropy) and the analysis of multiple signals (EMG, accelerometers, fNIRS, EEG). The inherent complexity of conducting studies under conditions that approximate real-world sporting disciplines requires the consideration of the influence of various confounding factors. The judicious selection of relevant physical activities and the rigorous validation of the measurement apparatus are paramount for the accurate execution of the calculations. Current research is substantially predicated on artificial intelligence (AI) algorithms. The synergistic application of AI with wavelet transform, particularly in the decomposition and extraction of EMG signals, demonstrates efficacy in fatigue detection. Nevertheless, the full realization of these potential mandates requires further investigation into system generalization, the integration of data from multiple sensors, and the standardization of protocols, coupled with the establishment of publicly accessible datasets. This article delineates selected guidelines and challenges pertinent to the planning and execution of research on muscle fatigue in dynamic movement, focusing on activity selection, equipment validation, EMG signal analysis, and AI utilization. Full article

This work addresses the challenges of inadequate data acquisition and the limited availability of labeled samples for shearer rocker arm bearing faults by developing a data augmentation methodology that synergistically incorporates the Genetic Algorithm-optimized Wavelet Transform Symmetrical Dot Pattern (GA-WT-SDP) with a Wasserstein Conditional Generative Adversarial Network (WCGAN). In the initial step, the Genetic Algorithm (GA) is employed to refine the mapping parameters of the Wavelet Transform Symmetrical Dot Pattern (WT-SDP), facilitating the transformation of raw vibration signals into advanced and discriminative graphical representations. Thereafter, the Wasserstein distance in conjunction with a gradient penalty mechanism is introduced through the WCGAN, thereby ensuring higher-quality generated samples and improved stability during model training. Experimental results validate that the proposed approach yields accelerated convergence and superior performance in sample generation. The augmented data significantly bolsters the generalization ability and predictive accuracy of fault diagnosis models trained on small datasets, with notable gains achieved in deep architectures (CNNs, LSTMs). The research substantiates that this technique helps overcome overfitting, enhances feature representation capacity, and ensures consistently high identification accuracy even in complex working environments. Full article

Landslides remain a critical geohazard in mountainous regions, where intensified extreme rainfall and rapid land-use changes exacerbate slope instability, challenging the reliability of traditional single-sensor susceptibility assessments. To overcome the limitations of data heterogeneity and noise, this study presents a decision-level fusion strategy integrating Permanent Scatterer InSAR (PS-InSAR) deformation dynamics with multi-source optical remote sensing indicators via a Wavelet Transform (WT) enhanced Multi-source Additive Model Based on Bayesian Analysis (MAMBA). San Martín del Castañar (Spain), a region characterized by rugged terrain and active deformation, served as the study area. We utilized Sentinel-1A C-band datasets (January 2020–February 2025) as the primary source for continuous monitoring, complemented by L-band ALOS-2 observations to ensure coherence in vegetated zones, yielding 24,102 high-quality persistent scatterers. The WT-based multi-scale enhancement improved the signal-to-noise ratio by 23.5% and increased deformation anomaly detection by 18.7% across 24,102 validated persistent scatterers. Bayesian fusion within MAMBA produced high-resolution susceptibility maps, indicating that very-high and high susceptibility zones occupy 24.0% of the study area while capturing 84.5% of the inventoried landslides. Quantitative validation against 1247 landslide events (2020–2025) achieved an AUC of 0.912, an overall accuracy of 87.3%, and a recall of 84.5%, outperforming Random Forest, Logistic Regression, and Frequency Ratio models by 6.8%, 10.8%, and 14.3%, respectively (p < 0.001). Statistical analysis further demonstrates a strong geo-ecological coupling, with landslide susceptibility significantly correlated with ecological vulnerability (r = 0.72, p < 0.01), while SHapley Additive exPlanations identify land-use type, rainfall, and slope as the dominant controlling factors. Full article

Accurate global irradiance (GI) forecasting is essential for improving photovoltaic (PV) energy management, stabilizing renewable power systems, and enabling intelligent control in solar-powered applications, including electric vehicles and smart grids. The highly stochastic and non-stationary nature of solar radiation, influenced by rapid atmospheric fluctuations and seasonal variability, makes short-term GI prediction a challenging task. To overcome these limitations, this work introduces a new hybrid forecasting architecture referred to as WTX–CBO, which integrates a Wavelet Transform (WT)-based decomposition module, an encoder–decoder Transformer model, and an XGBoost regressor, optimized using the Chaotic Billiards Optimizer (CBO) combined with the Adam optimization algorithm. In the proposed architecture, WT decomposes solar irradiance data into multi-scale components, capturing both high-frequency transients and long-term seasonal patterns. The Transformer module effectively models complex temporal and spatio-temporal dependencies, while XGBoost enhances nonlinear learning capability and mitigates overfitting. The CBO ensures efficient hyperparameter tuning and accelerated convergence, outperforming traditional meta-heuristics such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). Comprehensive experiments conducted on real-world GI datasets from diverse climatic conditions demonstrate the outperformance of the proposed model. The WTX–CBO ensemble consistently outperformed benchmark models, including LSTM, SVR, standalone Transformer, and XGBoost, achieving improved accuracy, stability, and generalization capability. The proposed WTX–CBO framework is designed as a high-accuracy decision-support forecasting tool that provides short-term global irradiance predictions to enable intelligent energy management, predictive charging, and adaptive control strategies in solar-powered applications, including solar electric vehicles (SEVs), rather than performing end-to-end vehicle or photovoltaic power simulations. Overall, the proposed hybrid framework provides a robust and scalable solution for short-term global irradiance forecasting, supporting reliable PV integration, smart charging control, and sustainable energy management in next-generation solar systems. Full article

The peak height of the ionospheric F2 layer (hmF2) is a critical parameter in ionospheric physics and high-frequency radio wave propagation research. This study presents a backpropagation neural network (BPNN) enhanced by wavelet transform (WT) decomposition for one-hour-ahead hmF2 forecasting. The WT method decomposes and reconstructs the hmF2 time series, preserving its primary structural characteristics. Subsequently, the BPNN provides high-accuracy predictions. The model is trained and evaluated using 2014 hmF2 measurements from four observation stations in China. Utilizing only hmF2 data, the model produces accurate one-hour-ahead forecasts. The predicted values closely align with observed diurnal variations and exhibit lower fluctuations than those of the IRI and standalone BPNN models. On the test set, the proposed model achieves an average RMSE of 17.16 km, which is 10.10 km and 8.39 km lower than the IRI and BPNN models, respectively. The average RRMSE is 5.72%, representing reductions of 2.88% and 2.64% compared to the IRI and BPNN models, respectively. These findings indicate that the hybrid model is well-suited for the Chinese region and substantially enhances short-term hmF2 forecast accuracy. Full article

Accurate global mapping of lunar iron oxide (FeO) abundance is essential for understanding the Moon’s geological evolution and for supporting future in situ resource utilization (ISRU). While hyperspectral data from the Moon Mineralogy Mapper (M3) provide a unique combination of high spectral dimensionality, hectometre-scale spatial resolution, and near-global coverage, existing FeO retrieval approaches struggle to fully exploit the high dimensionality, nonlinear spectral variability, and planetary-scale volume of the Global Mode dataset. To address these limitations, we present an integrated machine learning pipeline for estimating lunar FeO abundance from M3 hyperspectral observations. Unlike traditional methods based on raw reflectance or empirical spectral indices, the proposed framework combines Discrete Wavelet Transform (DWT), deep autoencoder-based feature compression, and ensemble regression to achieve robust and scalable FeO prediction. M3 spectra (83 bands, 475–3000 nm) are transformed using a Daubechies-4 (db4) DWT to extract 42 representative coefficients per pixel, capturing the dominant spectral information while filtering high-frequency noise. These features are further compressed into a six-dimensional latent space via a deep autoencoder and used as input to a Random Forest regressor, which outperforms kernel-based and linear Support Vector Regression (SVR) as well as Lasso regression in predictive accuracy and stability. The proposed model achieves an average prediction error of 1.204 wt.% FeO and demonstrates consistent performance across diverse lunar geological units. Applied to 806 orbital tracks (approximately $3.5\times {10}^9$ pixels), covering more than 95% of the lunar surface, the pipeline produces a global FeO abundance map at 150 m per pixel resolution. These results demonstrate the potential of integrating multiscale wavelet representations with nonlinear feature learning to enable large-scale, geochemically constrained planetary mineral mapping. Full article

In response to the safety threats posed by unauthorized unmanned aerial vehicles (UAVs), the importance of anti-UAV systems is becoming increasingly apparent. In tasks involving UAV detection, small UAVs are particularly difficult to detect due to their low resolution. Therefore, this study proposed YOLO-CoOp, a lightweight multi-module collaborative optimization framework for detecting small UAVs. First, a high-resolution feature pyramid network (HRFPN) was proposed to retain more spatial information of small UAVs. Second, a C3k2-WT module integrated with wavelet transform convolution was proposed to enhance feature extraction capability and expand the model’s receptive field. Then, a spatial-channel synergistic attention (SCSA) mechanism was introduced to integrate spatial and channel information and enhance feature fusion. Finally, the DyATF method replaced the upsampling with Dysample and the confidence loss with adaptive threshold focal loss (ATFL), aiming to restore UAV details and balance positive–negative sample weights. The ablation experiments show that YOLO-CoOp achieves 94.3% precision, 93.1% recall, 96.2% mAP⁵⁰, and 57.6% mAP⁵⁰⁻⁹⁵ on the UAV-SOD dataset, with improvements of 3.6%, 10%, 5.9%, and 5% over the baseline model, respectively. The comparison experiments demonstrate that YOLO-CoOp has fewer parameters while maintaining superior detection performance. Cross-dataset validation experiments also demonstrate that YOLO-CoOp exhibits significant performance improvements in small object detection tasks. Full article

Frequency-modulated continuous-wave (FMCW) light detection and ranging (LiDAR) is a high-precision ranging and imaging system that has been widely used in various areas, such as self-driving vehicles and industrial inspection. However, during detection, the system is susceptible to noise interference. This interference results in a decrease in the signal-to-noise ratio (SNR) of mixed signals, which affects the ranging accuracy. In this study, a MATLAB r2021b simulation is used to generate LiDAR transmitted and echo signals, and Gaussian noise is introduced. After mixing, empirical mode decomposition (EMD) and wavelet transform (WT) are used to denoise mixed signals, and the denoising effects of different wavelet basis functions under different SNRs are analysed. Furthermore, an experimental FMCW LiDAR system is set up to collect practical target echo signals, and the simulation results are validated through experiments under various illumination conditions. The results also show that the noise in FMCW LiDAR signals is dominated by Gaussian noise and that the influence of environmental noise is minimal. The combined EMD-WT denoising algorithm and its wavelet basis optimisation strategy proposed in this study can be directly applied to practical scenarios with strict requirements for FMCW LiDAR signal quality, such as autonomous driving, aircraft navigation, and precision industrial measurement, providing theoretical basis and experimental support for wavelet basis selection and denoising strategies in different noise environments. Full article

Photoplethysmography (PPG) is a non-invasive optical technique that quantifies blood volume pulsations and enables assessment of skin microvascular dynamics during vascular challenges. Its complex waveform can be decomposed by wavelet transform (WT) into physiological frequency bands reflecting cardiac, respiratory, myogenic, neurogenic, endothelial NO-dependent and endothelial NO-independent activity. Spectral activity may be expressed as either absolute power or relative contribution, which can capture different aspects of microvascular regulation. This study compared both metrics of PPG signals during post-occlusive reactive hyperemia (PORH). PPG was recorded bilaterally in twelve healthy adults (21.6 ± 1.9 years) during a 10-min baseline, 5-min occlusion, and 10-min recovery. Spectral power and percent contribution were calculated for each band and phase using nonparametric statistics (p < 0.05). Occlusion markedly suppressed higher-frequency oscillations (cardiac, respiratory, myogenic, neurogenic) while enhancing endothelial activity. Hyperemia produced a rebound of respiratory and endothelial oscillations above baseline. In the contralateral limb, occlusion induced a milder perfusion decrease, consistent with sympathetic vasoconstriction. Correlations between absolute and relative metrics were strong for cardiac, respiratory, and myogenic components (ρ > 0.80, p < 0.01), but weak or absent for endothelial bands. Absolute power primarily reflected perfusion magnitude, whereas relative contribution represented spectral redistribution among regulatory mechanisms. These metrics are complementary yet non-equivalent, underscoring the need for methodological consistency in the physiological interpretation of wavelet-derived PPG analyses. Full article

The wavelet transform (WT) is an integral transform primarily used for processing and analyzing nonstationary signals due to its multiresolution property. Multiresolution analysis is one method that finds applications in many fields because of the characteristics of the transform. Over the years, WT has become standard and is integrated into many coding protocols and applications without special mention. Decades of research in the field of wavelets have revealed several stages of development. In the initial stage, the focus was on wavelet families, with scientists deriving new families for emerging applications. The second stage addressed implementation issues, emphasizing more efficient implementation techniques. The next stage involved artificial neural networks (ANNs) that perform WT. This paper reviews the development of WT with examples from maritime applications. We also provide an overview of cutting-edge trends in wavelets and propose the aforementioned stages as a new taxonomy of WT development. Full article

Efficient power grid operations and effective business strategies require accurate prediction of power outages. However, predicting outages is a difficult task due to the large amount of heterogeneous, random, intermittent, and non-linear power grid data characterised by highly complex variable relationships. Attempting to simultaneously quantify these characteristics using a conventional single (linear or nonlinear) model may lead to inaccurate and costly results. To address this, we propose a hybrid RVM-WT-AdaBoostRT-RF framework using power grid data from the Electricity Supply Commission (Eskom) of South Africa. To achieve model interpretability, the least absolute shrinkage and selection operator (LASSO) is first applied to remedy the adverse effects of multicollinearity through regularisation and variable selection. Secondly, a random forest (RF) is used to select the top 10 most influential variables for each season for further analysis. A relevance vector machine (RVM) captures complex nonlinear relationships separately for each season, while the wavelet transform (WT) decomposes residuals generated from RVM into different frequency subseries (with reduced noise). These subseries are predicted with minimal bias using AdaBoost with regression and threshold (AdaBoostRT). Finally, we stack RVM, AdaBoostRT, RF, and residual individual predictions using RF as a meta-model to produce the final forecast with minimal error accumulation and efficiency. The comparative study, based on point forecast metrics, the Diebold-Mariano test, and prediction interval widths, shows that the proposed model outperforms vector autoregressive (VAR), RF, AdaBoostRT, RVM, and Naïve models. The study results can be utilised for optimising resource allocation, effective power grid management, and customer alerts. Full article