Search Results (457)

Condition monitoring and fault detection in induction motors (IMs) are priorities in the industrial environment to secure safe conditions for the processes and production. Convolutional Neural Networks (CNNs) are gaining interest in these tasks as they allow automatic extraction of features from the inputs, sometimes Time–Frequency Distributions (TFDs) obtained with various transforms, directly into large models for data classification. This work presents a proposal for the application of a widely used texture analysis tool in the medical field, the 2D Discrete Orthonormal Stockwell Transform (2D-DOST), to improve the accuracy of a lightweight CNN when using different TFDs and comparing the results to the use of the TFDs in RGB and grayscale. The results show that the use of the 2D-DOST improves the classification accuracy in a two to five percent range for all motor conditions under study, while having minimal variations to the training times when compared to RGB or grayscale images, opening the possibility for the use of image processing tools on TFDs to improve automatic feature extraction while using small CNN models. Full article

This study proposes a hybrid analytical framework for detecting chondromalacia using vibroacoustic (VAG) signals from patients with knee osteoarthritis (OA) and healthy controls (HCs). The methodology combines nonlinear signal decomposition, feature extraction, and deep learning classification. Raw VAG signals, recorded with a custom multi-sensor system during open (OKC) and closed (CKC) kinetic chain knee flexion–extension, underwent preprocessing (denoising, segmentation, normalization). Ensemble Empirical Mode Decomposition (EEMD) was used to isolate Intrinsic Mode Functions (IMFs), and Detrended Fluctuation Analysis (DFA) computed local (α₁) and global (α₂) scaling exponents as well as breakpoint location. Frequency–energy features of IMFs were statistically assessed and selected via Neighborhood Component Analysis (NCA) for support vector machine (SVM) classification. Additionally, reconstructed α₁/α₂-based signals and raw signals were converted into continuous wavelet transform (CWT) scalograms, classified with convolutional neural networks (CNNs) at two resolutions. The SVM approach achieved the best performance in CKC conditions (accuracy 0.87, AUC 0.91). CNN classification on CWT scalograms also demonstrated robust OA/HC discrimination with acceptable computational times at higher resolutions. Results suggest that combining multiscale decomposition, nonlinear fluctuation analysis, and deep learning enables accurate, non-invasive detection of cartilage degeneration, with potential for early knee pathology diagnosis. Full article

Stand density is one among a multitude of factors impacting the growth of trees and their responses to climatic variables, but its effect on wood quality at the scale of anatomical structure is hardly investigated. Therefore, we analyzed the radial growth and wood structure of Siberian spruce (Picea obovata Ledeb.) and Scots pine (Pinus sylvestris L.) in an experimental conifer plantation with a wide gradient of stand density in the Siberian southern taiga. The measured and indexed chronologies of the tree-ring width (TRW), number of tracheid cells per radial row in the ring produced in the cambial zone (N), cell radial diameter (D), and cell wall thickness (CWT) demonstrated the influence of the planting density. The TRW and N have a negative allometric dependence on the stand density (R² = 0.75–0.88), likely due to competition for resources. The consistent negative dependence of the D on the stand density (R² = 0.85–0.97) is log-linear and also seems to be related to tree size, while the CWT is not significantly dependent on the stand density. These findings can be used as insights in regulating cellular structure and procuring desired wood quality by silvicultural means. Both conifer species have similar climatic reactions. We observed significant suppression of TRW and D related to water deficit in May–July (both species), as well as frosty (more for pine) and low-snow (for spruce) conditions in winters, as shown by both dendroclimatic correlation and pointer year analysis. Temporal shifts in the climatic responses indicate later transition to latewood and growth cessation in sparse stands, especially in spruce. Better performance was observed in sparce and medium-density stands for both species. Full article

Background: We aimed to map the scientific production on sports nutrition applied to soccer. Methods: A scientometric analysis was performed using articles published between 2004 and 2024, retrieved from Web of Science, PubMed, and Scopus. The search yielded 2636 documents, and 526 original articles were included after removing reviews, meta-analyses, duplicates, and studies outside the scope. Data were analyzed using Bibliometrix version 5.0.1; Massimo Aria & Corrado Cuccurullo; Naples; Italy. and VOSviewer version 1.6.20; Centre for Science and Technology Studies (CWTS), Leiden University; Leiden; The Netherlands software. Results: There was a 1.450% increase in publications over the period, with a peak in 2024. Nutrients was the leading publication source, while Morton J. and Maughan R. were the most productive authors. Liverpool John Moores University stood out as a collaboration hub. The United Kingdom 371 took the lead in both publication volume and citations. Early research trends focused on hydration and dietary optimization, whereas recent studies emphasized low energy availability, polyphenols, anthropometry, and recovery strategies. The conceptual structure focused on terms such as sports, nutrition, energy intake, food intake, performance, soccer, and training load. Peripheral terms included fluid balance and sweat rate. The co-occurrence analysis revealed underexplored topics such as oxidative stress, lipid peroxidation, beta-alanine supplementation, and antioxidant markers. Conclusions: Advancing these research areas is essential to consolidating nutritional strategies with direct effects on performance and health in soccer players. Full article

In recent years, the powerful feature extraction capabilities of deep learning have attracted widespread attention in the field of bearing fault diagnosis. To address the limitations of single-modal and single-channel feature extraction methods, which often result in incomplete information representation and difficulty in obtaining high-quality fault features, this paper proposes a dual-channel parallel multimodal feature fusion model for bearing fault diagnosis. In this method, the one-dimensional vibration signals are first transformed into two-dimensional time-frequency representations using continuous wavelet transform (CWT). Subsequently, both the one-dimensional vibration signals and the two-dimensional time-frequency representations are fed simultaneously into the dual-branch parallel model. Within this architecture, the first branch employs a combination of a one-dimensional convolutional neural network (1DCNN) and a bidirectional gated recurrent unit (BiGRU) to extract temporal features from the one-dimensional vibration signals. The second branch utilizes a dilated convolutional to capture spatial time–frequency information from the CWT-derived two-dimensional time–frequency representations. The features extracted by both branches were are input into the feature fusion layer. Furthermore, to leverage fault features more comprehensively, a channel attention mechanism is embedded after the feature fusion layer. This enables the network to focus more effectively on salient features across channels while suppressing interference from redundant features, thereby enhancing the performance and accuracy of the dual-branch network. Finally, the fused fault features are passed to a softmax classifier for fault classification. Experimental results demonstrate that the proposed method achieved an average accuracy of 99.50% on the Case Western Reserve University (CWRU) bearing dataset and 97.33% on the Southeast University (SEU) bearing dataset. These results confirm that the suggested model effectively improves fault diagnosis accuracy and exhibits strong generalization capability. Full article

In the contemporary energy landscape, the increasing demand for electricity and the inherent uncertainties associated with the integration of renewable resources have rendered the accurate and reliable forecasting of short- and long-term demand imperative. Energy demand forecasting, fundamentally a time series problem, can be inherently complex, nonlinear, and multi-scale. Therefore, interest in artificial intelligence–based methods that provide high performance for short- and long-term forecasting, rather than traditional methods, has increased in order to solve these problems. In this study, a hybrid artificial intelligence model based on LSTM, GRU, and Random Forest, utilizing a distinct mechanism to address these types of problems, is proposed. The Multi-Scale Sliding Window (MSSW) approach was utilized for the model’s input data to capture the dynamics of the time series at different scales. The optimization of windows was conducted using the Continuous Wavelet Transform (CWT) method to determine the optimal window sizes within the MSSW structure in a data-driven manner. Experimental studies on Panama’s real energy demand data from 2015 to 2020 show that the CWT-aided MSSW-hybrid model forecasts better with lower error rates (0.007 MAE, 0.009 RMSE, 1.051% MAPE) than single models and manually determined window sizes. The results of the study demonstrate the importance of hybrid structures and window optimization in energy demand forecasting. Full article

Bearings, as commonly used elements in mechanical apparatus, are essential in transmission systems. Fault diagnosis is of significant importance for the normal and safe functioning of mechanical systems. Conventional fault diagnosis methods depend on one or more vibration sensors, and their diagnostic results are often unsatisfactory under strong noise interference. To tackle this problem, this research develops a bearing fault diagnosis technique utilizing a multi-channel, multi-scale spatiotemporal convolutional cross-attention fusion network. At first, continuous wavelet transform (CWT) is applied to convert the raw 1D acoustic and vibration signals of the dataset into 2D time–frequency images. These acoustic and vibration time–frequency images are then simultaneously fed into two parallel structures. After rough feature extraction using ResNet, deep feature extraction is performed using the Multi-Scale Temporal Convolutional Module (MTCM) and the Multi-Feature Extraction Block (MFE). Next, these traits are input into a dual cross-attention mechanism module (DCA), where fusion is achieved using attention interaction. The experimental findings validate the efficacy of the proposed method using tests and comparisons on two bearing datasets. The testing findings validate that the suggested method outperforms the existing advanced multi-sensor fusion diagnostic methods. Compared with other existing multi-sensor fusion diagnostic methods, the proposed method was proven to outperform the five existing methods (1DCNN-VAF, MFAN-VAF, 2MNET, MRSDF, and FAC-CNN). Full article

Fault diagnosis in Computer Numerical Control (CNC) machines remains challenging due to severe class imbalance, scarcity of fault data, and distribution shifts across machines. This paper introduces Undersampling-based Few-shot Learning (UnderFSL), a simple yet effective framework that integrates strategic undersampling using Condensed Nearest Neighbor (U-CNN) with a Relation Network few-shot classifier. The proposed method first transforms raw 1D vibration signals into 2D Continuous Wavelet Transform (CWT) scalograms to capture time–frequency structure and then reduces the majority (normal) class using U-CNN, yielding a compact set of boundary-informative prototypes while alleviating imbalance. Finally, a Relation Network is trained in an episodic FSL regime on the balanced set to support cross-machine generalization. On the Bosch CNC machining benchmark under leave-one-machine-out validation, UnderFSL attains a macro F1-Score of 0.96, an accuracy of 0.96, a recall of 0.92, and a precision of 1.00, surpassing traditional and standard deep baselines. The results suggest that boundary-preserving undersampling combined with metric learning provides a robust and scalable path for industrial fault diagnosis when fault data are extremely limited. Full article

Quadrotor UAV-based aeromagnetic survey for mineral exploration has become a crucial solution in modern airborne geophysics due to its prominent advantages of cost-effectiveness and high efficiency. During the detection process, the magnetic anomaly interference generated by the quadrotor UAV itself reduces the signal-to-noise ratio (SNR) of the target signal, and some noise overlaps with the target signal in both time and frequency domains. Traditional methods exhibit poor compensation capability for such noise. To address these issues, this paper proposes an aeromagnetic compensation method based on a time–frequency joint representation neural network. This method combines continuous wavelet transform (CWT) and bidirectional long short-term memory (Bi-LSTM) to establish a prediction model. It uses wavelet transform to extract the frequency variation characteristics of the UAV’s magnetic interference, and it inputs these frequency characteristics along with the original time-domain data into the Bi-LSTM network to predict the UAV’s noise. Bi-LSTM can effectively extract the temporal logical connections in time-series signals, thereby improving the accuracy of the compensation model and ensuring high robustness. In this study, magnetic interference data from quadrotor UAV compensation flights were collected for experiments to evaluate the performance of the proposed method. Experimental results show that the neural network fused with time–frequency features, when applied to UAV aeromagnetic compensation, significantly enhances the accuracy and robustness of the compensation method. To verify the method’s effectiveness in removing UAV-generated noise during actual exploration, aeromagnetic survey data from a specific area were compensated using this method. Full article

In marine seismic surveys, the indistinguishability of subsurface boundaries caused by the superimposition of the acoustic signals reflected from it, particularly at specific frequency ranges characterized by strong spectral interference, reduces the resolution of the seismic record. We processed sub-bottom profiler data, acquired using a Bubble Pulser (nominal central frequency: ~400 Hz; effective bandwidth extending to ~1 kHz), (i) by extracting continuous wavelet transform (CWT) coefficients at the dominant energy scale to form the envelope and (ii) by applying Hilbert-based instantaneous frequency analysis to characterize medium-dependent spectral shifts. Envelope accuracy was benchmarked against four conventional filters using the sum of squared error (SSE) relative to a cubic-spline reference. CWT yielded the lowest SSE, outperforming low-pass 1 kHz and band-pass 400–1000 Hz; band-pass 400–650 Hz and low-pass 650 Hz were the least effective. Instantaneous-frequency trends differentiated rock, sand, and mud layers. Thus, compared to fixed-band filters, the scale-adaptive CWT envelope replicates raw energy more faithfully, while frequency attributes improve sediment classification. Low-pass filtering at 1000 Hz provides a more accurate representation of energy distribution than does bandpass filtering, particularly in the 400–650 Hz range. The integrated workflow—a robust, parameter-light alternative for high-resolution stratigraphic interpretation—enhances offshore engineering safety. Full article

Cutaneous melanoma is an aggressive form of skin cancer and a leading cause of cancer-related mortality. In this sense, Raman Spectroscopy (RS) could represent a fast and effective method for melanoma-related diagnosis. We therefore introduced a new method based on RS to distinguish Compound Naevi (CN) from Primary Cutaneous Melanoma (PCM) from ex vivo solid biopsies. To this aim, integrating Confocal Raman Micro-Spectroscopy (CRM) with four Machine Learning (ML) algorithms: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM), and Random Forest Classifier (RFC). We focused our attention on the comparison between traditional pre-processing operations with Continuous Wavelet Transform (CWT). In particular, CWT led to the maximum classification accuracy, which was ∼89.0%, which highlighted the method as promising in view of future implementations in devices for everyday use. Full article

Rolling bearing vibration signals are often severely affected by strong external noise, which can obscure fault-related features and hinder accurate diagnosis. To address this challenge, this paper proposes an enhanced Deep Residual Shrinkage Network with Dynamic Convolution and Selective Kernel Attention (DDRSN-SKA). First, one-dimensional vibration signals are converted into two-dimensional time frequency images using the Continuous Wavelet Transform (CWT), providing richer input representations. Then, a dynamic convolution module is introduced to adaptively adjust kernel weights based on the input, enabling the network to better extract salient features. To improve feature discrimination, an Selective Kernel Attention (SKAttention) module is incorporated into the intermediate layers of the network. By applying a multi-receptive field channel attention mechanism, the network can emphasize critical information and suppress irrelevant features. The final classification layer determines the fault types. Experiments conducted on both the Case Western Reserve University (CWRU) dataset and a laboratory-collected bearing dataset demonstrate that DDRSN-SKA achieves diagnostic accuracies of 98.44% and 94.44% under −8 dB Gaussian and Laplace noise, respectively. These results confirm the model’s strong noise robustness and its suitability for fault diagnosis in noisy industrial environments. Full article

The article presents Modified Scaled Adaptive Daqrouq Wavelet (MSADW) as an autonomous wavelet framework to overcome the analysis obstacles of traditional wavelets (Morlet and Daubechies) for signals with non-stationary characteristics. MSADW adjusts its waveform shape and frequency in real time based on the specific characteristics of the signal, allowing it to outperform conventional wavelet methods. The system reaches adaptability through three core methods featuring gradient-dependent scale adjustments for fast transient detection and smooth regions, and instantaneous frequency monitoring achieved by a combination of STFT and Hilbert transforms and an iterative error reduction process using gradient descent and genetic algorithms. Continuous Wavelet Transform (CWT) combined with Discrete Wavelet Transform (DWT) extracts features from ECG and speech signals. Throughout this process, MSADW maintains great time precision to detect transients as well as maintain sensitivity for the audio’s base stability. Testing MSADW in practical use reveals its superior performance because it detects R-peaks accurately within 0.01 s through zero-crossing methods, which combine P/T-wave detection with effective ECG signal segmentation and noise-free reconstructed speech ($\mathit{MSE:} 1.17\times {10}^{-31}$). The localized parameterization framework of MSADW, enabled by feedback refinement, fulfills missing aspects in biomedical signal evaluation and creates space for low-cost real-time evaluation methods for medical devices and arrhythmia and ischemic detection platforms. The theoretical backbone for MSADW establishes itself because this work shows how wavelet analysis can transition toward managing non-stationary and noise-prone domains. Full article

Drowsiness constitutes a significant risk factor in diverse occupational settings, including healthcare, industry, construction, and transportation, contributing to accidents, injuries, and fatalities. Electroencephalography (EEG) signals, offering direct measurements of brain activity, have emerged as a promising modality for drowsiness detection. However, the inherent non-stationary nature of EEG signals, coupled with substantial inter-subject variability, presents considerable challenges for reliable drowsiness detection. To address these challenges, this paper proposes a hybrid approach combining convolutional neural networks (CNNs), which excel at feature extraction, and support vector machines (SVMs) for drowsiness detection. The framework consists of two modules: a CNN for feature extraction from EEG scalograms generated by the Continuous Wavelet Transform (CWT), and an SVM for classification. The proposed approach is compared with 1D CNNs (using raw EEG signals) and transfer learning models such as VGG16 and ResNet50 to identify the most effective method for minimizing inter-subject variability and improving detection accuracy. Experimental evaluations, conducted on the publicly available DROZY EEG dataset, show that the CNN-SVM model, utilizing 2D scalograms, achieves an accuracy of 98.33%, outperforming both 1D CNNs and transfer learning models. These findings highlight the effectiveness of the hybrid CNN-SVM approach for robust and accurate drowsiness detection using EEG, offering significant potential for enhancing safety in high-risk work environments. Full article

This paper proposes a rolling bearing fault diagnosis method based on HFMD and a dual-branch parallel network, aiming to address the issue of diagnostic accuracy being compromised by the disparity in data quality across different source domains due to sparse feature separation in rolling bearing acoustic signals. Traditional methods face challenges in feature extraction, sensitivity to noise, and difficulties in handling coupled multi-fault conditions in rolling bearing fault diagnosis. To overcome these challenges, this study first employs the HawkFish Optimization Algorithm to optimize Feature Mode Decomposition (HFMD) parameters, thereby improving modal decomposition accuracy. The optimal modal components are selected based on the minimum Residual Energy Index (REI) criterion, with their time-domain graphs and Continuous Wavelet Transform (CWT) time-frequency diagrams extracted as network inputs. Then, a dual-branch parallel network model is constructed, where the multi-scale residual structure (Res2Net) incorporating the Efficient Channel Attention (ECA) mechanism serves as the temporal branch to extract key features and suppress noise interference, while the Swin Transformer integrating multi-stage cross-scale attention (MSCSA) acts as the time-frequency branch to break through local perception bottlenecks and enhance classification performance under limited resources. Finally, the time-domain graphs and time-frequency graphs are, respectively, input into Res2Net and Swin Transformer, and the features from both branches are fused through a fully connected layer to obtain comprehensive fault diagnosis results. The research results demonstrate that the proposed method achieves 100% accuracy in open-source datasets. In the experimental data, the diagnostic accuracy of this study demonstrates significant advantages over other diagnostic models, achieving an accuracy rate of 98.5%. Under few-shot conditions, this study maintains an accuracy rate no lower than 95%, with only a 2.34% variation in accuracy. HFMD and the dual-branch parallel network exhibit remarkable stability and superiority in the field of rolling bearing fault diagnosis. Full article