Search Results (150)

Gravitational-wave (GW) detection has become a significant area of research following the first successful observation by the Laser Interferometer Gravitational-Wave Observatory (LIGO). The detection of signals emerging from binary black hole (BBH) mergers have challenges due to the presence of non-Gaussian and non-stationary noise in observational data. Using traditional matched filtering techniques to detect BBH merging are computationally expensive and may not generalize well to unexpected GW events. As a result, deep learning-based methods have emerged as powerful alternatives for robust GW signal detection. In this study, we propose a novel Transformer-based architecture that introduces energy-aware modulation into the attention mechanism through dual-energy attention masks. In the proposed framework, Q-transform and discrete wavelet transform (DWT) are employed to extract time–frequency energy representations from gravitational-wave signals which are fused into energy masks that dynamically guide the Transformer encoder. In parallel, the raw one-dimensional signal is used directly as input and segmented into temporal patches, which enables the model to leverage both learned representations and physically grounded priors. This proposed architecture allows the model to focus on energy-rich and informative regions of the signal in order to enhance the robustness of the model under realistic noise conditions. Experimental results on BBH datasets embedded in real LIGO noise show that EDAT-BBH outperforms CNN-based and standard Transformer-based approaches, achieving an accuracy of 0.9953, a recall of 0.9950, an F1-score of 0.9953, and an AUC of 0.9999. These findings demonstrate the effectiveness of energy-modulated attention in improving both the interpretability and performance of deep learning models for gravitational-wave signal classification. Full article

Pipeline leak detection through acoustic signal measurement faces critical challenges, including insufficient utilization of time-frequency domain features, poor adaptability to noisy environments, and inadequate exploitation of frequency-domain prior knowledge in existing deep learning approaches. This paper proposes a Multi-Scale Frequency-Aware Transformer (MSFAT) architecture that integrates measurement-based acoustic signal analysis with artificial intelligence techniques. The MSFAT framework consists of four core components: a frequency-aware embedding layer that achieves joint representation learning of time-frequency dual-domain features through parallel temporal convolution and frequency transformation, a multi-head frequency attention mechanism that dynamically adjusts attention weights based on spectral distribution using frequency features as modulation signals, an adaptive noise filtering module that integrates noise detection, signal enhancement, and adaptive fusion functions through end-to-end joint optimization, and a multi-scale feature aggregation mechanism that extracts discriminative global representations through complementary pooling strategies. The proposed method addresses the fundamental limitations of traditional measurement-based detection systems by incorporating domain-specific prior knowledge into neural network architecture design. Experimental validation demonstrates that MSFAT achieves 97.2% accuracy and an F1-score, representing improvements of 10.5% and 10.9%, respectively, compared to standard Transformer approaches. The model maintains robust detection performance across signal-to-noise ratio conditions ranging from 5 to 30 dB, demonstrating superior adaptability to complex industrial measurement environments. Ablation studies confirm the effectiveness of each innovative module, with frequency-aware mechanisms contributing most significantly to the enhanced measurement precision and reliability in pipeline leak detection applications. Full article

Accurate diagnosis of myocardial infarction (MI) holds significant clinical importance for public health systems. Deep learning-based ECG, classification and localization methods can automatically extract features, thereby overcoming the dependence on manual feature extraction in traditional methods. However, these methods still face challenges such as insufficient utilization of dynamic information in cardiac cycles, inadequate ability to capture both global and local features, and data imbalance. To address these issues, this paper proposes a ResNet-Transformer cascaded network (RTCN) to process time frequency features of ECG signals generated by the S-transform. First, the S-transform is applied to adaptively extract global time frequency features from the time frequency domain of ECG signals. Its scalable Gaussian window and high phase resolution can effectively capture the dynamic changes in cardiac cycles that traditional methods often fail to extract. Then, we develop an architecture that combines the Transformer attention mechanism with ResNet to extract multi-scale local features and global temporal dependencies collaboratively. This compensates for the existing deep learning models’ insufficient ability to capture both global and local features simultaneously. To address the data imbalance problem, the Denoising Diffusion Probabilistic Model (DDPM) is applied to synthesize high-quality ECG samples for minority classes, increasing the inter-patient accuracy from 61.66% to 68.39%. Gradient-weighted Class Activation Mapping (Grad-CAM) visualization confirms that the model’s attention areas are highly consistent with pathological features, verifying its clinical interpretability. Full article

Cough is a key symptom reflecting respiratory health, with its frequency and pattern providing valuable insights into disease progression and clinical management. Objective and reliable cough detection systems are therefore of broad significance for healthcare and remote monitoring. However, existing algorithms often struggle to jointly model spatial and temporal information, limiting their robustness in real-world applications. To address this issue, we propose a cough recognition framework based on frequency-modulated continuous-wave (FMCW) radar, integrating a deep convolutional neural network (CNN) with a Self-Attention mechanism. The CNN extracts spatial features from range-Doppler maps, while Self-Attention captures temporal dependencies, and effective data augmentation strategies enhance generalization by simulating position variations and masking local dependencies. To rigorously evaluate practicality, we collected a large-scale radar dataset covering diverse positions, orientations, and activities. Experimental results demonstrate that, under subject-independent five-fold cross-validation, the proposed model achieved a mean F1-score of $0.974\pm 0.016$ and an accuracy of $99.05\pm 0.55$ %, further supported by high precision of $98.77\pm 1.05$ %, recall of $96.07\pm 2.16$ %, and specificity of $99.73\pm 0.23$ %. These results confirm that our method is not only robust in realistic scenarios but also provides a practical pathway toward continuous, non-invasive, and privacy-preserving respiratory health monitoring in both clinical and telehealth applications. Full article

Accurate reactive power optimization in wind farms (WFs) is critical for optimizing operations and ensuring grid stability, yet it faces challenges from noisy, nonlinear, and dynamic Supervisory Control and Data Acquisition (SCADA) data. This study proposes an innovative framework, WBS-BiGRU, integrating three novel components to address these issues. Firstly, the Wavelet-DBSCAN (WDBSCAN) method combines wavelet transform’s time–frequency analysis with density-based spatial clustering of applications with noise (DBSCAN)’s density-based clustering to effectively remove noise and outliers from complex WF datasets, leveraging multi-scale features for enhanced adaptability to non-stationary signals. Subsequently, a Boomerang Evolutionary Optimization (BAEO) with the Seasonal Decomposition Improved Process (SDIP) synergistically decomposes time series into trend, seasonal, and residual components, generating diverse candidate solutions to optimize data inputs. Finally, a Bidirectional Gated Recurrent Unit (BiGRU) network enhanced with an attention mechanism captures long-term dependencies in temporal data and dynamically focuses on key features, improving reactive power forecasting precision. The WBS-BiGRU framework significantly enhances forecasting accuracy and robustness, offering a reliable solution for WF operation optimization and equipment health management. 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

Electrocardiograms (ECGs) are critical for cardiovascular disease diagnosis, but their accuracy is often compromised by electrode motion (EM) artifacts—large, nonstationary distortions caused by patient movement and electrode-skin interface shifts. These artifacts overlap in frequency with genuine cardiac signals, rendering traditional filtering methods ineffective and increasing the risk of false alarms and misdiagnosis, particularly in wearable and ambulatory ECG applications. To address this, we propose the Feature-Shuffle Multi-Head Attention Autoencoder (FMHA-AE), a novel architecture integrating multi-head self-attention (MHSA) and a feature-shuffle mechanism to enhance ECG denoising. MHSA captures long-range temporal and spatial dependencies, while feature shuffling improves representation robustness and generalization. Experimental results show that FMHA-AE achieves an average signal-to-noise ratio (SNR) improvement of 25.34 dB and a percentage root mean square difference (PRD) of 10.29%, outperforming conventional wavelet-based and deep learning baselines. These results confirm the model’s ability to retain critical ECG morphology while effectively removing noise. FMHA-AE demonstrates strong potential for real-time ECG monitoring in mobile and clinical environments. This work contributes an efficient deep learning approach for noise-robust ECG analysis, supporting accurate cardiovascular assessment under motion-prone conditions. Full article

Frequency-Modulated Continuous-Wave (FMCW) Laser Detection and Ranging (LiDAR) systems are widely used due to their high accuracy and resolution. Nevertheless, conventional distance extraction methods often lack robustness in noisy and complex environments. To address this limitation, we propose a deep learning-based signal extraction framework that integrates a Dual Convolutional Neural Network (DCNN) with a Transformer model. The DCNN extracts multi-scale spatial features through multi-layer and pointwise convolutions, while the Transformer employs a self-attention mechanism to capture global temporal dependencies of the beat-frequency signals. The proposed DCNN–Transformer network is evaluated through beat-frequency signal inversion experiments across distances ranging from 3 m to 40 m. The experimental results show that the method achieves a mean absolute error (MAE) of 4.1 mm and a root-mean-square error (RMSE) of 3.08 mm. These results demonstrate that the proposed approach provides stable and accurate predictions, with strong generalization ability and robustness for FMCW LiDAR systems. Full article

Wind power forecasting remains a major challenge for renewable energy integration, as conventional models often perform poorly when confronted with complex atmospheric dynamics. This study addresses the problem by developing a Spectral-Attentive Spatio-Temporal Graph Convolutional Network (SA-STGCN) designed to capture the intricate temporal and spatial dependencies of wind systems. The approach first applies wavelet transform decomposition to separate volatile wind signals into distinct frequency components, enabling more interpretable representation of rapidly changing conditions. A dynamic temporal attention mechanism is then employed to adaptively identify historical patterns that are most relevant for prediction, moving beyond the fixed temporal windows used in many existing methods. In addition, spectral graph convolution is conducted in the frequency domain to capture farm-wide spatial correlations, thereby modeling long-range atmospheric interactions that conventional localized methods overlook. Although this design increases computational complexity, it proves critical for representing wind variability. Evaluation on real-world datasets demonstrates that SA-STGCN achieves substantial accuracy improvements, with a mean absolute error of 1.52 and a root mean square error of 2.31. These results suggest that embracing more expressive architectures can yield reliable forecasting performance, supporting the stable integration of wind power into modern energy systems. Full article

Schlieren imaging is a powerful optical sensing technique that captures flow-induced refractive index gradients, offering valuable visual data for analyzing complex fluid dynamics. However, the large volume and structural complexity of the data generated by this sensor pose significant challenges for extracting key physical insights and performing efficient reconstruction and temporal prediction. In this study, we propose a Spatial-Frequency-Scale variational autoencoder (SFS-VAE), a deep learning framework designed for the unsupervised feature decomposition of Schlieren sensor data. To address the limitations of traditional $\beta$-variational autoencoder ($\beta$-VAE) in capturing complex flow regions, the Progressive Frequency-enhanced Spatial Multi-scale Module (PFSM) is designed, which enhances the structures of different frequency bands through Fourier transform and multi-scale convolution; the Feature-Spatial Enhancement Module (FSEM) employs a gradient-driven spatial attention mechanism to extract key regional features. Experiments on flat plate film-cooled jet schlieren data show that SFS-VAE can effectively preserve the information of the mainstream region and more accurately capture the high-gradient features of the jet region, reducing the Root Mean Square Error (RMSE) by approximately 16.9% and increasing the Peak Signal-to-Noise Ratio (PSNR) by approximately 1.6 dB. Furthermore, when integrated with a Transformer for temporal prediction, the model exhibits significantly improved stability and accuracy in forecasting flow field evolution. Overall, the model’s physical interpretability and generalization ability make it a powerful new tool for advanced flow diagnostics through the robust analysis of Schlieren sensor data. Full article

To address the challenge that MOSFET temperature in motor controllers is influenced by multiple factors, exhibits strong temporal dependence, and involves complex feature interactions, this study proposes a temperature prediction model that integrates Temporal Convolutional Networks (TCNs) and the Informer architecture in parallel, enhanced with a cross-attention mechanism. The model leverages TCNs to capture local temporal patterns, while the Informer extracts long-range dependencies, and cross-attention strengthens feature interactions across channels to improve predictive accuracy. A dataset was constructed based on measured MOSFET temperatures under various operating conditions, with input features including voltage, load current, switching frequency, and multiple ambient temperatures. Experimental evaluation shows that the proposed method achieves a mean absolute error of 0.2521 °C, a root mean square error of 0.3641 °C, and an R² of 0.9638 on the test set, outperforming benchmark models such as Times-Net, Informer, and LSTM. These results demonstrate the effectiveness of the proposed approach in reducing prediction errors and enhancing generalization, providing a reliable tool for real-time thermal monitoring of motor controllers. Full article

Previous studies on Sound Event Localization and Detection (SELD) have primarily focused on CNN- and Transformer-based designs. While CNNs possess local receptive fields, making it difficult to capture global dependencies over long sequences, Transformers excel at modeling long-range dependencies but have limited sensitivity to local time–frequency features. Recently, the VMamba architecture, built upon the Visual State Space (VSS) model, has shown great promise in handling long sequences, yet it remains limited in modeling local spatial details. To address this issue, we propose a novel state space model with an attention-enhanced feature fusion mechanism, termed FFMamba, which balances both local spatial modeling and long-range dependency capture. At a fine-grained level, we design two key modules: the Multi-Scale Fusion Visual State Space (MSFVSS) module and the Wavelet Transform-Enhanced Downsampling (WTED) module. Specifically, the MSFVSS module integrates a Multi-Scale Fusion (MSF) component into the VSS framework, enhancing its ability to capture both long-range temporal dependencies and detailed local spatial information. Meanwhile, the WTED module employs a dual-branch design to fuse spatial and frequency domain features, improving the richness of feature representations. Comparative experiments were conducted on the DCASE2021 Task 3 and DCASE2022 Task 3 datasets. The results demonstrate that the proposed FFMamba model outperforms recent approaches in capturing long-range temporal dependencies and effectively integrating multi-scale audio features. In addition, ablation studies confirmed the effectiveness of the MSFVSS and WTED modules. Full article

The Denoising Wavelet Transform Convolutional Long Short-Term Memory Network (DWTConvLSTM) is a novel ionospheric total electron content (TEC) spatiotemporal prediction model proposed in 2025 that can simultaneously consider high-frequency and low-frequency features while suppressing noise. However, it also has flaws as it only considers unidirectional temporal features in spatiotemporal prediction. To address this issue, this paper adopts a bidirectional structure and designs a bidirectional DWTConvLSTM model that can simultaneously extract bidirectional spatiotemporal features from TEC maps. Furthermore, we integrate a lightweight attention mechanism called Convolutional Additive Self-Attention (CASA) to enhance important features and attenuate unimportant ones. The final model was named CASA-BiDWTConvLSTM. We validated the effectiveness of each improvement through ablation experiments. Then, a comprehensive comparison was performed on the 11-year Global Ionospheric Maps (GIMs) dataset, involving the proposed CASA-BiDWTConvLSTM model and several other state-of-the-art models such as C1PG, ConvGRU, ConvLSTM, and PredRNN. In this experiment, the dataset was partitioned into 7 years for training, 2 years for validation, and the final 2 years for testing. The experimental results indicate that the RMSE of CASA-BiDWTConvLSTM is lower than those of C1PG, ConvGRU, ConvLSTM, and PredRNN. Specifically, the decreases in RMSE during high solar activity years are 24.84%, 16.57%, 13.50%, and 10.29%, respectively, while the decreases during low solar activity years are 26.11%, 16.83%, 11.68%, and 7.04%, respectively. In addition, this article also verified the effectiveness of CASA-BiDWTConvLSTM from spatial and temporal perspectives, as well as on four geomagnetic storms. Full article

With the development of the Industrial Internet of Things (IIoT) technology, fault diagnosis has emerged as a critical component of its operational reliability, and machine learning algorithms play a crucial role in fault diagnosis. To achieve better fault diagnosis results, it is necessary to have a sufficient number of fault samples participating in the training of the model. In actual industrial scenarios, it is often difficult to obtain fault samples, and there may even be situations where no fault samples exist. For scenarios without fault samples, accurately identifying the unknown faults of equipment is an issue that requires focused attention. This paper presents a method for the normal-sample-based mechanical equipment unknown fault detection. By leveraging the characteristics of the autoencoder network (AE) in deep learning for feature extraction and sample reconstruction, normal samples are used to train the AE network. Whether the input sample is abnormal is determined via the reconstruction error and a threshold value, achieving the goal of anomaly detection without relying on fault samples. In terms of input data, the frequency domain features of normal samples are used to train the AE network, which improves the training stability of the AE network model, reduces the network parameters, and saves the occupied memory space at the same time. Moreover, this paper further improves the network based on the traditional AE network by incorporating a convolutional neural network (CNN) and a long short-term memory network (LSTM). This enhances the ability of the AE network to extract the spatial and temporal features of the input data, further improving the network’s ability to extract and recognize abnormal features. In the simulation part, through public datasets collected in factories, the advantages and practicality of this method compared with other algorithms in the detection of unknown faults are fully verified. Full article

Anthracnose on Illicium verum Hook.f (I. verum) significantly affects the yield and quality of I. verum, and timely detection methods are urgently needed for early control. However, early warning is difficult due to two major challenges, including the sparse availability of optical remote sensing observations due to frequent cloud and rain interference, and the weak spectral responses caused by infestation during early stages. In this article, a framework for early warning of anthracnose on I. verum that combines high-frequency environmental (meteorological and topographical) data and Sentinel-2 remote sensing time-series data, along with a Time-Aware Long Short-Term Memory (T-LSTM) network incorporating an attentional mechanism (At-T-LSTM) was proposed. First, all available environmental and remote sensing data during the study period were analyzed to characterize the early anthracnose outbreaks, and sensitive features were selected as the algorithm input. On this basis, to address the issue of unequal temporal lengths between environmental and remote sensing time series, the At-T-LSTM model incorporates a time-aware mechanism to capture intra-feature temporal dependencies, while a Self-Attention layer is used to quantify inter-feature interaction weights, enabling effective multi-source features time-series fusion. The results show that the proposed framework achieves a spatial accuracy (F1-score) of 0.86 and a temporal accuracy of 83% in early-stage detection, demonstrating high reliability. By integrating remote sensing features with environmental drivers, this approach enables multi-feature collaborative modeling for the risk assessment and monitoring of I. verum anthracnose. It effectively mitigates the impact of sparse observations and significantly improves the accuracy of early warnings. Full article