Search Results (237)

Structural damage identification provides a theoretical foundation for the operational safety and preventive maintenance of in-service bridges. However, practical bridge health monitoring faces challenges in poor signal quality, difficulties in feature extraction, and insufficient damage classification accuracy. This study presents a bridge damage identification framework integrating time-varying filtering-based empirical mode decomposition (TVFEMD) with pre-trained convolutional neural networks (CNNs). The proposed method enhances the key frequency-domain features of signals and suppresses the interference of non-stationary noise on model training through adaptive denoising and time–frequency reconstruction. TVFEMD was demonstrated in numerical simulation experiments to have a better performance than the traditional EMD in terms of frequency separation and modal purity. Furthermore, the performances of three pre-trained CNN models were compared in damage classification tasks. The results indicate that ResNet-50 has the best optimal performance compared with the other networks, particularly exhibiting better adaptability and recognition accuracy when processing TVFEMD-denoised signals. In addition, the principal component analysis visualization results demonstrate that TVFEMD significantly improves the clustering and separability of feature data, providing clearer class boundaries and reducing feature overlap. Full article

Despite advances in Convolutional Neural Networks (CNNs) for intelligent fault diagnosis in CNC machine tools, bearing fault diagnosis in CNC feed systems remains challenging, particularly in multi-scale feature extraction and generalization across operating conditions. This study introduces an enhanced multi-scale feature network (MSFN) that addresses these limitations through three integrated modules designed to extract critical fault features from vibration signals. First, a Soft-Scale Denoising (S2D) module forms the backbone of the MSFN, capturing multi-scale fault features from input signals. Second, a Multi-Scale Adaptive Feature Enhancement (MS-AFE) module based on long-range weighting mechanisms is developed to enhance the extraction of periodic fault features. Third, a Dynamic Sequence–Channel Attention (DSCA) module is incorporated to improve feature representation across channel and sequence dimensions. Experimental results on two datasets demonstrate that the proposed MSFN achieves high diagnostic accuracy and exhibits robust generalization across diverse operating conditions. Moreover, ablation studies validate the effectiveness and contributions of each module. Full article

The rapid evolution of deep learning (DL) has fundamentally transformed the paradigm for detecting special components in agricultural products, addressing critical challenges in food safety, quality control, and precision agriculture. This comprehensive review systematically analyzes many seminal studies to evaluate cutting-edge DL applications across three core domains: contaminant surveillance (heavy metals, pesticides, and mycotoxins), nutritional component quantification (soluble solids, polyphenols, and pigments), and structural/biomarker assessment (disease symptoms, gel properties, and physiological traits). Emerging hybrid architectures—including attention-enhanced convolutional neural networks (CNNs) for lesion localization, wavelet-coupled autoencoders for spectral denoising, and multi-task learning frameworks for joint parameter prediction—demonstrate unprecedented accuracy in decoding complex agricultural matrices. Particularly noteworthy are sensor fusion strategies integrating hyperspectral imaging (HSI), Raman spectroscopy, and microwave detection with deep feature extraction, achieving industrial-grade performance ($RPD$ > 3.0) while reducing detection time by 30–100× versus conventional methods. Nevertheless, persistent barriers in the “black-box” nature of complex models, severe lack of standardized data and protocols, computational inefficiency, and poor field robustness hinder the reliable deployment and adoption of DL for detecting special components in agricultural products. This review provides an essential foundation and roadmap for future research to bridge the gap between laboratory DL models and their effective, trusted application in real-world agricultural settings. Full article

Rolling bearings serve as the most widely utilized general components in drive systems for rotating machinery, and they are susceptible to regular malfunctions. To address the performance degradation encountered by current convolutional neural network-based rolling-bearing-fault diagnosis methods due to significant noise interference and variable working conditions in industrial settings, we propose a rolling-bearing-fault diagnosis method based on dual multi-scale mechanism applicable to noisy-variable operating conditions. The suggested approach begins with the implementation of Variational Mode Decomposition (VMD) on the initial vibration signal. This is succeeded by a denoising process that utilizes the goodness-of-fit test based on the Anderson–Darling (AD) distance for enhanced accuracy. This approach targets the intrinsic mode functions (IMFs), which capture information across multiple scales, to obtain the most precise denoised signal possible. Subsequently, we introduce the Dynamic Weighted Multi-Scale Feature Convolutional Neural Network (DWMFCNN) model, which integrates two structures: multi-scale feature extraction and dynamic weighting of these features. Ultimately, the signal that has been denoised is utilized as input for the DWMFCNN model to recognize different kinds of rolling-bearing faults. Results from the experiments show that the suggested approach shows an improved denoising performance and a greater adaptability to changing working conditions. Full article

This paper proposes an Adam-optimized Deep Belief Networks (Adam-DBNs) denoising method for throat-attached piezoelectric signals. The method aims to process mechanical vibration signals captured through polyvinylidene fluoride (PVDF) sensors attached to the throat region, which are typically contaminated by environmental noise and physiological noise. First, the short-time Fourier transform (STFT) is utilized to convert the original signals into the time–frequency domain. Subsequently, the masked time–frequency representation is reconstructed into the time domain through a diagonal average-based inverse STFT. To address complex nonlinear noise structures, a Deep Belief Network is further adopted to extract features and reconstruct clean signals, where the Adam optimization algorithm ensures the efficient convergence and stability of the training process. Compared with traditional Convolutional Neural Networks (CNNs), Adam-DBNs significantly improve waveform similarity by 6.77% and reduce the local noise energy residue by 0.099696. These results demonstrate that the Adam-DBNs method exhibits substantial advantages in signal reconstruction fidelity and residual noise suppression, providing an efficient and robust solution for throat-attached piezoelectric sensor signal enhancement tasks. Full article

The vibration and noise of friction pairs are severe when cutting titanium alloy with cemented carbide tools, and the surface micro-texture can significantly reduce noise and friction. Therefore, it is very important to clarify the correlation mechanism between friction noise and friction force for processing quality control. Consequently, investigating the underlying mechanisms that link friction noise and friction is of considerable importance. This study focuses on the friction and wear acoustic signals generated by micro-textured cemented carbide–titanium alloy. A friction testing platform specifically designed for the micro-textured cemented carbide grinding of titanium alloy has been established. Acoustic sensors are employed to capture the acoustic signals, while ultra-depth-of-field microscopy and scanning electron microscopy are utilized for surface analysis. A novel approach utilizing the dung beetle algorithm (DBO) is proposed to optimize the parameters of variational mode decomposition (VMD), which is subsequently combined with wavelet packet threshold denoising (WPT) to enhance the quality of the original signal. Continuous wavelet transform (CWT) is applied for time–frequency analysis, facilitating a discussion on the underlying mechanisms of micro-texture. Additionally, features are extracted from the time domain, frequency domain, wavelet packet, and entropy. The Relief-F algorithm is employed to identify 19 significant features, leading to the development of a hybrid model that integrates Bayesian optimization (BO) and Transformer-LSTM for predicting friction. Experimental results indicate that the model achieves an R² value of 0.9835, a root mean square error (RMSE) of 0.2271, a mean absolute error (MAE) of 0.1880, and a mean bias error (MBE) of 0.1410 on the test dataset. The predictive performance and stability of this model are markedly superior to those of the BO-LSTM, LSTM–Attention, and CNN–LSTM–Attention models. This research presents a robust methodology for predicting friction in the context of friction and wear of cemented carbide–titanium alloys. Full article

The separation of respiratory and cardiac sounds is a significant challenge in biomedical signal processing due to their overlapping frequency and time characteristics. Traditional methods struggle with accurate extraction in noisy or diverse clinical environments. This study explores the application of machine learning, particularly convolutional neural networks (CNNs), to overcome these obstacles. Advanced machine learning models, denoising algorithms, and domain adaptation strategies address challenges such as frequency overlap, external noise, and limited labeled datasets. This study presents a robust methodology for detecting heart and lung diseases from audio signals using advanced preprocessing, feature extraction, and deep learning models. The approach integrates adaptive filtering and bandpass filtering as denoising techniques and variational autoencoders (VAEs) for feature extraction. The extracted features are input into a CNN, which classifies audio signals into different heart and lung conditions. The results highlight the potential of this combined approach for early and accurate disease detection, contributing to the development of reliable diagnostic tools for healthcare. Full article

Pulmonary dynamic contrast-enhanced (DCE) MRI is clinically useful for assessing pulmonary perfusion, but its signal-to-noise ratio (SNR) is limited. A self-supervised learning network-based plug-and-play (PnP) denoising model was developed to improve the image quality of pulmonary perfusion MRI. A dataset of patients with suspected pulmonary diseases was used. Asymmetric pixel-shuffle downsampling blind-spot network (AP-BSN) training inputs were two-dimensional background-subtracted perfusion images without clean ground truth. The AP-BSN is incorporated into a PnP model (PnP-BSN) for balancing noise control and image fidelity. Model performance was evaluated by SNR, sharpness, and overall image quality from two radiologists. The fractal dimension and k-means segmentation of the pulmonary perfusion images were calculated for comparing denoising performance. The model was trained on 29 patients and tested on 8 patients. The performance of PnP-BSN was compared to denoising convolutional neural network (DnCNN) and a Gaussian filter. PnP-BSN showed the highest reader scores in terms of SNR, sharpness, and overall image quality as scored by two radiologists. The expert scoring results for DnCNN, Gaussian, and PnP-BSN were 2.25 ± 0.65, 2.44 ± 0.73, and 3.56 ± 0.73 for SNR; 2.62 ± 0.52, 2.62 ± 0.52, and 3.38 ± 0.64 for sharpness; and 2.16 ± 0.33, 2.34 ± 0.42, and 3.53 ± 0.51 for overall image quality (p < 0.05 for all). PnP-BSN outperformed DnCNN and a Gaussian filter for denoising pulmonary perfusion MRI, which led to improved quantitative fractal analysis. Full article

The accurate and early detection of respiratory diseases is vital for effective diagnosis and treatment. This study presents a new approach for classifying lung sounds using a double denoising method combined with a 1D Convolutional Neural Network (CNN). The preprocessing uses Fast Fourier Transform to clean up sounds and High-Pass Filtering to improve the quality of breathing sounds by eliminating noise and low-frequency interruptions. The Short-Time Fourier Transform (STFT) extracts features that capture localised frequency variations, crucial for distinguishing normal and abnormal respiratory sounds. These features are input into the 1D CNN, which classifies diseases such as bronchiectasis, pneumonia, asthma, COPD, healthy, and URTI. The dual denoising method enhances signal clarity and classification performance. The model achieved 96% validation accuracy, highlighting its reliability in detecting respiratory conditions. The results emphasise the effectiveness of combining signal augmentation with deep learning for automated respiratory sound analysis, with future research focusing on dataset expansion and model refinement for clinical use. Full article

The rapid expansion of Edge and Industrial Internet of Things (IIoT) systems has intensified the risk and complexity of cyberattacks. Detecting advanced intrusions in these heterogeneous and high-dimensional environments remains challenging. As the IIoT becomes integral to critical infrastructure, ensuring security is crucial to prevent disruptions and data breaches. Traditional IDS approaches often fall short against evolving threats, highlighting the need for intelligent and adaptive solutions. While deep learning (DL) offers strong capabilities for pattern recognition, single-model architectures often lack robustness. Thus, hybrid and optimized DL models are increasingly necessary to improve detection performance and address data imbalance and noise. In this study, we propose an optimized hybrid DL framework that combines a transformer, generative adversarial network (GAN), and autoencoder (AE) components, referred to as Transformer–GAN–AE, for robust intrusion detection in Edge and IIoT environments. To enhance the training and convergence of the GAN component, we integrate an improved chimp optimization algorithm (IChOA) for hyperparameter tuning and feature refinement. The proposed method is evaluated using three recent and comprehensive benchmark datasets, WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT, widely recognized as standard testbeds for IIoT intrusion detection research. Extensive experiments are conducted to assess the model’s performance compared to several state-of-the-art techniques, including standard GAN, convolutional neural network (CNN), deep belief network (DBN), time-series transformer (TST), bidirectional encoder representations from transformers (BERT), and extreme gradient boosting (XGBoost). Evaluation metrics include accuracy, recall, AUC, and run time. Results demonstrate that the proposed Transformer–GAN–AE framework outperforms all baseline methods, achieving a best accuracy of 98.92%, along with superior recall and AUC values. The integration of IChOA enhances GAN stability and accelerates training by optimizing hyperparameters. Together with the transformer for temporal feature extraction and the AE for denoising, the hybrid architecture effectively addresses complex, imbalanced intrusion data. The proposed optimized Transformer–GAN–AE model demonstrates high accuracy and robustness, offering a scalable solution for real-world Edge and IIoT intrusion detection. Full article

In high-resolution microscopic imaging, using shorter-wavelength ultraviolet (UV) lasers as illumination sources is a common approach. However, the high spatial coherence of such lasers, combined with the surface roughness of the sample, often introduces disturbances in the received optical field, resulting in strong speckle noise. This paper presents a novel speckle noise suppression method specifically designed for coherent laser-based microscopic imaging. The proposed approach integrates statistical physical modeling and image gradient discrepancy into the training of a Cycle Generative Adversarial Network (CycleGAN), capturing the perturbation mechanism of speckle noise in the optical field. By incorporating these physical constraints, the method effectively enhances the model’s ability to suppress speckle noise without requiring annotated clean data. Experimental results under high-resolution laser microscopy settings demonstrate that the introduced constraints successfully guide network training and significantly outperform traditional filtering methods and unsupervised CNNs in both denoising performance and training efficiency. While this work focuses on microscopic imaging, the underlying framework offers potential extensibility to other laser-based imaging modalities with coherent noise characteristics. Full article

Thickness inspection of thermal barrier coatings is crucial to safeguard the reliability of high-temperature components of aero-engines, but traditional destructive inspection methods are difficult to meet the demand for rapid assessment in the field. In this study, a new non-destructive testing method integrating terahertz time-domain spectroscopy and machine learning algorithms is proposed to systematically study the thickness inspection of 8YSZ coatings prepared by two processes, namely atmospheric plasma spraying (APS) and electron beam physical vapor deposition (EB-PVD). By optimizing the preparation process parameters, 620 sets of specimens with thicknesses of 100–400 μm are prepared, and three types of characteristic parameters, namely, time delay Δt, frequency shift Δf, and energy decay η, are extracted by combining wavelet threshold denoising and time-frequency joint analysis. A CNN-RF cascade model is constructed to realize coating process classification, and an attention-LSTM and SVR weighted fusion model is developed for thickness regression prediction. The results show that the multimodal feature fusion reduces the root-mean-square error of thickness prediction to 8.9 μm, which further improves the accuracy over the single feature model. The classification accuracy reaches 96.8%, of which the feature importance of time delay Δt accounts for 62%. The hierarchical modeling strategy reduces the detection mean absolute error from 6.2 μm to 4.1 μm. the method provides a high-precision solution for intelligent quality assessment of thermal barrier coatings, which is of great significance in promoting the progress of intelligent manufacturing technology for high-end equipment. Full article

In OFDM wireless communication systems, low-resolution channel characteristics and noise interference pose significant challenges to accurate channel estimation. To solve these problems, we propose a super-resolution denoising residual network (SDRNet), which combines the advantages of the super-resolution convolutional neural network (SRCNN) and the denoising convolutional neural network (DnCNN) to construct a pilot-based OFDM signal model, train SDRNet using OFDM pilot data containing Gaussian noise, and optimize its feature enhancement ability in frequency-selective fading channels. To further explore the role of channel estimation in communication security, we propose a frequency-domain adversarial attack method based on SDRNet output. This method first converts the time-domain signal to the frequency domain by using the Fourier transform and then applies Gaussian noise and selective masking. By integrating the channel gradient information, the adversarial perturbation we generated significantly improves the attack success rate compared with the non-channel awareness method. The experimental results show that SDRNet is superior to traditional algorithms (such as the least square method, minimum mean square error estimation, etc.) in both mean square error and bit error rate. Furthermore, the adversarial samples optimized through channel awareness frequency-domain masking exhibit stronger attack performance, confirming that accurate channel estimation can not only enhance communication reliability but also provide key guidance for adversarial perturbation. The experimental results show that under the same noise conditions, the MSE of SDRNet is significantly lower than that of LS and MMSE. The bit error rate is lower than 0.01 when the signal-to-noise ratio is 10 dB, which is significantly better than the traditional algorithm. The attack success rate of the proposed adversarial attack method reached 79.9%, which was 16.3% higher than that of the non-channel aware method, verifying the key role of accurate channel estimation in enhancing the effectiveness of the attack. Full article

High-precision Direction-of-Arrival (DOA) estimation leveraging multi-sensor array architectures represents a frontier research domain in advanced array signal processing systems. Compared to traditional model-driven estimation methods like MUSIC and ESPRIT, data-driven approaches offer advantages such as higher estimation accuracy and simpler structures. Convolutional neural networks (CNNs) currently dominate deep learning approaches for DOA estimation. However, traditional CNNs suffer from limitations in capturing global features of covariance matrices due to their restricted local receptive fields, alongside challenges such as noise sensitivity and poor interpretability. To address these issues, we propose a novel Discrete Fourier Transform (DFT)-based deep learning framework for DOA estimation called DFNeT, leveraging the advantages of Fourier transform-enhanced networks in global modeling, computational efficiency, and noise robustness. Specifically, our approach introduces a DFT-based deep feature fusion network to denoise covariance matrices by integrating spatial and frequency-domain information. Subsequently, a series of DFT modules are designed to extract discriminative frequency-domain features, enabling accurate and robust DOA estimation. This method effectively mitigates noise interference while enhancing the interpretability of feature extraction through explicit frequency-domain operations. The simulation results demonstrate the effectiveness of the proposed method. Full article

Seismic signal processing often relies on general convolutional neural network (CNN)-based models, which typically focus on features in the time domain while neglecting frequency characteristics. Moreover, down-sampling operations in these models tend to cause the loss of critical high-frequency details. To this end, we propose a feedback information distillation network (FID-N) in the non-subsampled contourlet transform (NSCT) domain to remarkably suppress seismic noise. The method aims to enhance denoising performance by preserving the fine-grained details and frequency characteristics of seismic data. The FID-N mainly consists of a two-path information distillation block used in a recurrent manner to form a feedback mechanism, carrying an output to correct previous states, which fully exploits competitive features from seismic signals and effectively realizes the signal restoration step by step across time. Additionally, the NSCT has an excellent high-frequency response and powerful curve and surface description capabilities. We suggest converting the noise suppression problem into NSCT coefficient prediction, which maintains more detailed high-frequency information and promotes the FID-N to further suppress noise. Extensive experiments on both synthetic and real seismic datasets demonstrated that our method significantly outperformed the SOTA methods, particularly in scenarios with low signal-to-noise ratios and in recovering high-frequency components. Full article