Robust Features Research Articles

Advanced Bearing-Fault Diagnosis and Classification Using Mel-Scalograms and FOX-Optimized ANN

Accurate and reliable bearing-fault diagnosis is important for ensuring the efficiency and safety of industrial machinery. This paper presents a novel method for bearing-fault diagnosis using Mel-transformed scalograms obtained from vibrational signals (VS). The signals are windowed and pass through a Mel filter bank, converting them into a Mel spectrum. These scalograms are subsequently fed into an autoencoder comprising convolutional and pooling layers to extract robust features. The classification is performed using an artificial neural network (ANN) optimized with the FOX optimizer, which replaces traditional backpropagation. The FOX optimizer enhances synaptic weight adjustments, leading to superior classification accuracy, minimal loss, improved generalization, and increased interpretability. The proposed model was validated on a laboratory dataset obtained from a bearing testbed with multiple fault conditions. Experimental results demonstrate that the model achieves perfect precision, recall, F1-scores, and an AUC of 1.00 across all fault categories, significantly outperforming comparison models. The t-SNE plots illustrate clear separability between different fault classes, confirming the model’s robustness and reliability. This approach offers an efficient and highly accurate solution for real-time predictive maintenance in industrial applications.

Multimodal Fake News Detection with Contrastive Learning and Optimal Transport

IntroductionThe proliferation of social media platforms has facilitated the spread of fake news, posing significant risks to public perception and societal stability. Existing methods for multimodal fake news detection have made important progress in combining textual and visual information but still face challenges in effectively aligning and merging these different types of data. These challenges often result in incomplete or inaccurate feature representations, thereby limiting overall performance.MethodsTo address these limitations, we propose a novel framework named MCOT (Multimodal Fake News Detection with Contrastive Learning and Optimal Transport). MCOT integrates textual and visual information through three key components: cross-modal attention mechanism, contrastive learning, and optimal transport. Specifically, we first use cross-modal attention mechanism to enhance the interaction between text and image features. Then, we employ contrastive learning to align related embeddings while distinguishing unrelated pairs, and we apply optimal transport to refine the alignment of feature distributions across modalities.ResultsThis integrated approach results in more precise and robust feature representations, thus enhancing detection accuracy. Experimental results on two public datasets demonstrate that the proposed MCOT outperforms state-of-the-art methods.DiscussionOur future work will focus on improving its generalization and expanding its capabilities to additional modalities.

A hypergraph transformer method for brain disease diagnosis

ObjectiveTo address the high-order correlation modeling and fusion challenges between functional and structural brain networks.MethodThis paper proposes a hypergraph transformer method for modeling high-order correlations between functional and structural brain networks. By utilizing hypergraphs, we can effectively capture the high-order correlations within brain networks. The Transformer model provides robust feature extraction and integration capabilities that are capable of handling complex multimodal brain imaging.ResultsThe proposed method is evaluated on the ABIDE and ADNI datasets. It outperforms all the comparison methods, including traditional and graph-based methods, in diagnosing different types of brain diseases. The experimental results demonstrate its potential and application prospects in clinical practice.ConclusionThe proposed method provides new tools and insights for brain disease diagnosis, improving accuracy and aiding in understanding complex brain network relationships, thus laying a foundation for future brain science research.

Instant and Synergistic Antibacterial Coating of Silver Loaded With Silica Nanoparticles for Different Applications

ABSTRACTThe synthesis of antibacterial nanoparticles is one of the most promising strategies to get rid of the primary threat that pathogenic bacteria pose to public health. Silver nanoparticles (AgNPs) are a promising antibacterial agent with robust and broad antibacterial characteristics that have the potential to resolve this issue. A straightforward reduction–impregnation approach for creating an Ag‐loaded SiO2 nanocomposite has been presented in the present study. First, the well‐known Stöber method was employed to produce silica nanoparticles, which were subsequently examined utilizing an X‐ray diffraction (XRD), a field emission scanning electron microscope (FE‐SEM), and a high‐resolution transmission electron microscope (HR‐TEM). After that, a composite made of Ag@SiO2 was produced and examined. AgNPs, which are loaded with silica and exhibit instantaneous and synergistic antibacterial activity against Gram‐positive “Bacillus subtilis” (B. subtilis) bacteria, were demonstrated. The coating layer that was produced also showed strong adherence to the steel substrate, a high inhibitory effect against the (B. subtilis), and versatility in its application across various sectors.

Defect-insensitive cylindrical surface lattice resonance array and its batch replication for enhanced immunoassay.

Surface lattice resonances (SLR) have been demonstrated to enhance the sensitivity and reduce the full width at half maximum (FWHM) of the plasmonic resonances. However, their widespread application in immunoassays has been hindered by limitations of high structural defect sensitivity and fabrication costs. Here, we design a novel three-layer cylindrical SLR array that exhibits high tolerance against structural defects, which would facilitate straightforward fabrication. By integrating metal evaporation and nanoimprint lithography, we demonstrate the replication of the SLR array with exceptional quality. Theoretical simulations indicate that the resonance dips of these arrays exhibit are not sensitive to various structural defects. The experimental results reveal that the FWHM of these arrays can be as low as 5.1 nm while maintaining robust resonance characteristics. Furthermore, we demonstrated the high spectral sensitivity of the SLR array, which enabled the detection of immunoglobulin G (IgG) at concentrations as low as 609 pg/mL. These findings emphasize the potential of the defect-insensitive SLR array as a highly scalable immunoassay platform with exceptional performance.

CONCEALING TEXT AND EXECUTABLE FILES IN JPG FORMAT FILES WITH A STEGANOGRAPHIC COMMAND-LINE TOOL IN THE OPERATING SYSTEM KALI LINUX

In this scientific paper concealing text and executable files in JPG format files with a steganographic command-line tool in the Kali Linux operating system is presented. Among the myriad of steganographic tools, Steghide has gained prominence for its robust features and ease of use. Primarily focused on embedding and extracting secret messages within image files, the software tool Steghide enables users to encode text data within JPEG and BMP image formats while maintaining data integrity and minimizing visual alterations. This paper provides an in-depth examination of Steghide’s functionality, workflow, and security considerations in the context of digital steganography. It is also discussed the algorithms underpinning Steghide’s operations, its strengths, limitations and common use cases.

The voice of depression: speech features as biomarkers for major depressive disorder.

Psychiatry faces a challenge due to the lack of objective biomarkers, as current assessments are based on subjective evaluations. Automated speech analysis shows promise in detecting symptom severity in depressed patients. This project aimed to identify discriminating speech features between patients with major depressive disorder (MDD) and healthy controls (HCs) by examining associations with symptom severity measures. Forty-four MDD patients from the Psychiatry Department, University Hospital Aachen, Germany and fifty-two HCs were recruited. Participants described positive and negative life events, which were recorded for analysis. The Beck Depression Inventory (BDI-II) and the Hamilton Rating Scale for Depression gauged depression severity. Transcribed audio recordings underwent feature extraction, including acoustics, speech rate, and content. Machine learning models including speech features and neuropsychological assessments, were used to differentiate between the MDD patients and HCs. Acoustic variables such as pitch and loudness differed significantly between the MDD patients and HCs (effect sizes 𝜼2 between 0.183 and 0.3, p < 0.001). Furthermore, variables pertaining to temporality, lexical richness, and speech sentiment displayed moderate to high effect sizes (𝜼2 between 0.062 and 0.143, p < 0.02). A support vector machine (SVM) model based on 10 acoustic features showed a high performance (AUC = 0.93) in differentiating between HCs and patients with MDD, comparable to an SVM based on the BDI-II (AUC = 0.99, p = 0.01). This study identified robust speech features associated with MDD. A machine learning model based on speech features yielded similar results to an established pen-and-paper depression assessment. In the future, these findings may shape voice-based biomarkers, enhancing clinical diagnosis and MDD monitoring.

Complete suspension culture of human induced pluripotent stem cells supplemented with suppressors of spontaneous differentiation

Human induced pluripotent stem cells (hiPSCs) are promising resources for producing various types of tissues in regenerative medicine; however, the improvement in a scalable culture system that can precisely control the cellular status of hiPSCs is needed. Utilizing suspension culture without microcarriers or special materials allows for massive production, automation, cost-effectiveness, and safety assurance in industrialized regenerative medicine. Here, we found that hiPSCs cultured in suspension conditions with continuous agitation without microcarriers or extracellular matrix components were more prone to spontaneous differentiation than those cultured in conventional adherent conditions. Adding PKCβ and Wnt signaling pathway inhibitors in the suspension conditions suppressed the spontaneous differentiation of hiPSCs into ectoderm and mesendoderm, respectively. In these conditions, we successfully completed the culture processes of hiPSCs, including the generation of hiPSCs from peripheral blood mononuclear cells with the expansion of bulk population and single-cell sorted clones, long-term culture with robust self-renewal characteristics, single-cell cloning, direct cryopreservation from suspension culture and their successful recovery, and efficient mass production of a clinical-grade hiPSC line. Our results demonstrate that precise control of the cellular status in suspension culture conditions paves the way for their stable and automated clinical application.

Complete suspension culture of human induced pluripotent stem cells supplemented with suppressors of spontaneous differentiation.

Human induced pluripotent stem cells (hiPSCs) are promising resources for producing various types of tissues in regenerative medicine; however, the improvement in a scalable culture system that can precisely control the cellular status of hiPSCs is needed. Utilizing suspension culture without microcarriers or special materials allows for massive production, automation, cost-effectiveness, and safety assurance in industrialized regenerative medicine. Here, we found that hiPSCs cultured in suspension conditions with continuous agitation without microcarriers or extracellular matrix components were more prone to spontaneous differentiation than those cultured in conventional adherent conditions. Adding PKCβ and Wnt signaling pathway inhibitors in the suspension conditions suppressed the spontaneous differentiation of hiPSCs into ectoderm and mesendoderm, respectively. In these conditions, we successfully completed the culture processes of hiPSCs, including the generation of hiPSCs from peripheral blood mononuclear cells with the expansion of bulk population and single-cell sorted clones, long-term culture with robust self-renewal characteristics, single-cell cloning, direct cryopreservation from suspension culture and their successful recovery, and efficient mass production of a clinical-grade hiPSC line. Our results demonstrate that precise control of the cellular status in suspension culture conditions paves the way for their stable and automated clinical application.

Trans-retinal predictive signals of visual features are precise, saccade-specific and operate over a wide range of spatial frequencies.

Saccadic eye movements successively project the saccade target on two retinal locations: a peripheral one before the saccade, and the fovea after the saccade. Typically, performance in discriminating stimulus features changes between these two projections is very poor. However, a short (~ 200 ms) blanking of the target upon saccade onset drastically improves performance, demonstrating that a precise signal of the peripheral projection is retained during the saccade. While little is known about the nature of that transsaccadic signal, previous reports conjectured that it relies on information processed by the magnocellular system. Across two experiments, we investigated the feature blanking effect for a wide range of spatial frequencies (0.5 to 8 cycles per degree of visual angle, dva), stimulus sizes (1 to 4 dva) and eccentricities (6 to 10 dva). In each trial, participants executed a saccade to a high-contrast grating presented either left or right of fixation. During the saccade, the grating changed orientation (clockwise or counter-clockwise) either instantaneously or after a 200 ms blank, and participants reported the change's direction. We contrasted this saccade condition with a trans-retinal fixation condition mimicking the peripheral-then-foveal sequence of the target stimulus occurring across a saccade. Remarkably, blanking improved performance reliably for each spatial frequency, stimulus size, and eccentricity, but only in the saccade condition. Performance with blanking in saccade trials systematically exceeded performance in the fixation condition. Our results demonstrate a robust feature blanking effect across saccades, suggesting that transsaccadic processes involve low-level visual features beyond those processed in the magnocellular system.

MSMGE-CNN: A multi-scale multi-graph embedding convolutional neural network for motor related EEG decoding

Abstract Deep learning (DL) technique has been widely used for decoding motor related electroencephalography (EEG) signals, which has considerably driven the development of motor related brain-computer interfaces (BCIs). However, traditional convolutional neural networks (CNNs) cannot fully represent spatial topology information and dynamic temporal characteristics of multi-channel EEG signals, resulting in limited decoding accuracy. To address such challenges, a novel multi-scale multi-graph embedding convolutional neural network (MSMGE-CNN) is proposed in this study. The proposed MSMGE-CNN contains two crucial components: multi-scale time convolution and multi-graph embedding. Specifically, we design a multi-branch CNN architecture with mixed-scale time convolutions based on EEGNet to sufficiently extract robust time domain features. Afterward, we embed multi-graph information obtained based on physical distance proximity and functional connectivity of multi-channel EEG signals into the time-domain features to capture rich spatial topological dependencies via multi-graph convolution operation. We extensively evaluated the proposed method on three benchmark EEG datasets commonly used for motor imagery/execution (MI/ME) classification and obtained accuracies of 79.59 % (BCICIV-2a Dataset), 69.77 % (OpenBMI Dataset) and 96.34 % (High Gamma Dataset), respectively. These results powerfully demonstrate that MSMGE-CNN outperforms several state-of-the-art algorithms. In addition, we further conducted a series of ablation experiments to validate the rationality of our network architecture. Overall, the proposed MSMGE-CNN method dramatically improves the accuracy and robustness of MI/ME-EEG decoding, which can effectively enhance the performance of motor related BCI system.&amp;#xD;

CNSC-65. ELECTROPHYSIOLOGICAL AND SPATIAL TRANSCRIPTOMIC PROFILING OF GLIOMA-INFILTRATED CORTEX REVEAL LAYER SPECIFIC ALTERATIONS

Abstract Specialized neocortical circuits selectively organize neuronal signals and encode features of cognitive processing via local and long-range connections. Since glioma-infiltrated cortex is excitable and can participate in cognitive processing, the underlying laminar structure and functionality may still be preserved despite infiltration. Moreover, since glioma cells remodel existing neural circuits and microenvironmental factors drive invasion and proliferation, neuron-glioma interactions may demonstrate layer specificity. As glioma-infiltrated cortex remains poorly understood, we sought to characterize these regions using electrophysiological, structural, and genomic approaches. We assessed the power spectra of normal-appearing and glioma-infiltrated cortex using magnetoencephalography and subdural high-density electrode arrays. Immunohistochemistry of formalin-fixed paraffin-embedded (FFPE) samples of infiltrated cortex enabled protein-level neuronal and glioma identification to determine laminar preservation and spatial invasion patterns. Spatial transcriptomics of FFPE tissues and single-nucleus RNA-sequencing were used to identify cell populations, genomic alterations, and cell-cell communication within and across samples at testing and validation sites across the US and Europe. Increased delta range (1-4 Hz) power and decreased power in the beta range (12-20 Hz), activity thought to originate from deep layers, were identified as robust features of infiltrated cortex which were maintained across glioma subtypes (n = 139 patients) and preserved in a validation cohort (n = 8 patients). Immunohistochemistry revealed greater tumor burden within deep laminae regardless of glioma subtype (n = 20 samples). Tissue proteomics and spatial genomics analyses performed using glioma-infiltrated cortical samples (n = 10 samples; 29,011 spots) confirmed routine preservation of laminar organization, greater tumor burden in deep layers, as well as layer specific differences in glioma-related expression programs. Cell-cell communication analyses demonstrated increased interactions in glioma-infiltrated cortex across layers. These findings suggest that laminar structure may be preserved in glioma-infiltrated cortex while remodeling of neural circuits alters spatiotemporal activity in a predictable manner and support a deep to superficial invasion pattern. Thus, investigating glioma infiltration using layer-centric approaches may facilitate novel insights into neuron-glioma interactions.

NIMG-42. ANALYSIS OF TUMOR RELAPSE PROBABILITY AND OVERALL SURVIVAL PREDICTION FOLLOWING TEMOZOLOMIDE CHEMORADIATION USING FET PET INCLUDING RADIOMICS IN PATIENTS WITH GLIOBLASTOMA

Abstract BACKGROUND Early after surgery and completion of first-line concomitant chemoradiation with temozolomide, the evaluation of tumor relapse probability and overall survival prediction is of considerable interest for the management of patients with glioblastoma METHODS Sixty-three newly diagnosed patients with glioblastoma (age range, 19-82) who received static and dynamic PET imaging using the radiolabeled amino acid O-(2-[18F]fluoroethyl)-L-tyrosine (FET) after surgery or biopsy and completion of concomitant chemoradiation with temozolomide were evaluated. Static FET PET parameters such as maximum and mean tumor-to-brain ratios (TBRmax, TBRmean) and metabolic tumor volumes (MTV), and the dynamic FET PET parameter time-to-peak (TTP) were obtained. Additionally, FET PET radiomics features (n=1,303) were extracted for each patient and a test-retest analysis was performed to identify robust features prior to feature selection. The prognostic value of FET PET parameters and radiomics features was evaluated using receiver-operating-characteristic (ROC) analyses with regard to a significantly prolonged progression-free and overall survival (PFS, OS). Subsequently, univariate and multivariate survival estimates were performed to assess the prognostic value of these parameters to predict a significantly longer PFS and OS RESULTS ROC analyses revealed that the parameter TBRmax was a powerful parameter to predict both a significantly longer PFS (20.3 vs. 8.9 months; P=0.002; threshold, 2.75) and OS (34.9 vs. 18.7 months; P=0.005; threshold, 2.75). MTV had a similar prognostic value for both PFS (16.7 vs. 7.2 months; P=0.002; threshold, 34.0 mL) and OS (30.6 vs. 14.8 months; P=0.002; threshold, 32.6 mL). After feature selection, 3 of 15 selected radiomics features predicted a significantly longer PFS and OS in univariate analyses. TBRmax, MTV, and one of 15 radiomics features remained significant in multivariate survival analysis (all P≤0.03) CONCLUSION Our results suggest that FET PET and radiomics parameters were highly prognostic in this group of patients at an early stage of first-line therapy.

A Quad‐Port MIMO Antenna With Improved Isolation Developed by Colored Resin Fiber Material for Wideband Applications

ABSTRACTThe article explores a quad‐port compact (56 × 56 × 1.5 mm3) multiple‐input multiple‐output (MIMO) antenna developed on a colored resin fiber material encompassing enhanced isolation for wideband applications. The design antenna configuration incorporates four ground stubs positioned among antenna pairs on the upper section of the substrate and eight rectangular‐shaped ground slots to enhance antenna isolation. The antenna functions over a broad spectral range from 4.21 to 10.95 GHz, providing coverage for WLAN (5.15–5.35 GHz/5.725–5.825 GHz), C band uplink (5.925–6.425 GHz), downlink defense (7.2–7.7 GHz), and ITU band (8–8.5 GHz), with 88.91% fractional bandwidth. The antenna design ensures exceptional isolation, exceeding 21 dB among orthogonal antennas and 25 dB between oppositely placed antennas across the entire functioning bandwidth. Across the working band, the antenna showcases favorable diversity characteristics, including a lower envelope correlation coefficient (ECC &lt; 0.085), higher diversity gain (DG &gt; 9.95 dB), minimal channel capacity loss (&lt; 0.39 bits/s/Hz), and negligible magnitude variation in mean effective gain among the antenna ports (&lt; 0.1 dB). The acquired total active reflection (TARC) curve exhibits the antenna's robust resonance features over the application band and commendable time domain properties in the active spectral region. The findings of the simulation and measurements revealed a close alignment.

Mf-net: multi-feature fusion network based on two-stream extraction and multi-scale enhancement for face forgery detection

Due to the increasing sophistication of face forgery techniques, the images generated are becoming more and more realistic and difficult for human eyes to distinguish. These face forgery techniques can cause problems such as fraud and social engineering attacks in facial recognition and identity verification areas. Therefore, researchers have worked on face forgery detection studies and have made significant progress. Current face forgery detection algorithms achieve high detection accuracy within-dataset. However, it is difficult to achieve satisfactory generalization performance in cross-dataset scenarios. In order to improve the cross-dataset detection performance of the model, this paper proposes a multi-feature fusion network based on two-stream extraction and multi-scale enhancement. First, we design a two-stream feature extraction module to obtain richer feature information. Secondly, the multi-scale feature enhancement module is proposed to focus the model more on information related to the current sub-region from different scales. Finally, the forgery detection module calculates the overlap between the features of the input image and real images during the training phase to determine the forgery regions. The method encourages the model to mine forgery features and learns generic and robust features not limited to a particular feature. Thus, the model achieves high detection accuracy and performance. We achieve the AUC of 99.70% and 90.71% on FaceForensics++ and WildDeepfake datasets. The generalization experiments on Celeb-DF-v2 and WildDeepfake datasets achieve the AUC of 80.16% and 65.15%. Comparison experiments with multiple methods on other benchmark datasets confirm the superior generalization performance of our proposed method while ensuring model detection accuracy. Our code can be found at: https://github.com/1241128239/MFNet.

Adaptive Depth Estimation via SoftHebbLayer in a Hybrid ResNet-UNet Architecture

Abstract. Depth estimation is a key task in computer vision, critical for applications such as autonomous navigation and augmented reality. This paper introduces a novel hybrid neural network that combines ResNet and UNet architectures with a SoftHebbLayer, inspired by Hebbian learning principles, to improve depth estimation from RGB images. The ResNet backbone extracts robust hierarchical features, while the UNet decoder reconstructs fine-grained depth maps. The SoftHebbLayer dynamically adjusts feature connections based on co-activation, enhancing the models adaptability to diverse environments. This approach addresses common challenges in depth estimation, including poor generalization and computational inefficiency. We evaluated the model on the DIODE dataset, achieving strong results in Mean Squared Error (MSE), which is 0.0800 and Root Mean Squared Error (RMSE), which is 0.2805, demonstrating improved accuracy in both indoor and outdoor scenes. While the model excels in precision, further refinement is needed to reduce computational overhead and improve performance in challenging environments. This research paves the way for more efficient, adaptable depth estimation models, with potential applications in mobile robotics and real-time edge computing systems.

IShapEditing: Intelligent Shape Editing with Diffusion Models

AbstractRecent advancements in generative models have enabled image editing very effective with impressive results. By extending this progress to 3D geometry models, we introduce iShapEditing, a novel framework for 3D shape editing which is applicable to both generated and real shapes. Users manipulate shapes by dragging handle points to corresponding targets, offering an intuitive and intelligent editing interface. Leveraging the Triplane Diffusion model and robust intermediate feature correspondence, our framework utilizes classifier guidance to adjust noise representations during sampling process, ensuring alignment with user expectations while preserving plausibility. For real shapes, we employ shape predictions at each time step alongside a DDPM‐based inversion algorithm to derive their latent codes, facilitating seamless editing. iShapEditing provides effective and intelligent control over shapes without the need for additional model training or fine‐tuning. Experimental examples demonstrate the effectiveness and superiority of our method in terms of editing accuracy and plausibility.

Multi-task deep learning for quantifying methane emissions from 2-D plume imagery with Low Signal-to-Noise Ratio

ABSTRACT Methane is the second most significant greenhouse gas after carbon dioxide. As global warming intensifies, the quantification of methane point sources is becoming increasingly crucial. However, retrieving high-quality methane signals from remote sensing data remains challenging due to various factors, including surface reflectance, atmospheric interference, sensor noise, wind speed, and sensor sensitivity. In real-world scenarios, methane remote sensing quantification often encounters unfavourable conditions that lead to low signal-to-noise ratio (SNR) signals, resulting in reduced quantification accuracy. To address these challenges, we introduce a novel multi-task learning-based approach. Specifically, we incorporate a denoising auxiliary task into the quantification network by introducing an additional denoising branch that recovers clean plume column concentration maps. The network is trained with supervision using both denoising and quantification losses, which enables it to acquire robust feature representations to noise and benefits the quantification of low SNR images. During the inference phase, the denoising branch is removed, resulting in an efficient and robust single quantification network with reduced inferring time, supporting onboard computation. We construct a low SNR database based on the AVIRIS-NG sensor and evaluate the generalization ability of our method. DQNet achieves the highest RMSE, MAPE, and R of 16.478 kg/h, 15.296%, and 95.167% respectively on the synthetic dataset. Across the entire range of SNR, DQNet exhibits a greater relative advantage as SNR decreases. However, our approach is not limited to AVIRIS-NG and can be easily extended to various multispectral and hyperspectral satellites.

Enhanced Tuberculosis Detection in Chest X-Rays Using Optimal Feature Selection with Hybrid Binary Particle Swarm Optimization (HBPSO) and a Dual Classifier Framework Combining LSTM and Spiking Neural Networks

Tuberculosis (TB) remains a significant global health challenge, particularly in developing countries, where rapid and accurate diagnosis is crucial for effective treatment. Recent advancements in machine learning and image processing have shown promise in enhancing the detection of TB from chest X-ray images. This research introduces a novel approach that integrates a hybrid feature selection method with a dual classifier framework to improve the accuracy of tuberculosis detection. Feature extraction is conducted using Multi-Scale Local Binary Patterns (MSLBP), Speeded-Up Robust Features (SURF) with Bag of Words (BoW), and Cartoon Texture Features, combining them into a comprehensive feature vector. The Hybrid Binary Particle Swarm Optimization (HBPSO) is employed for optimal feature selection, reducing redundancy and improving classification efficiency. Classification is performed using a dual framework involving a Long Short-Term Memory (LSTM) network and a Spiking Neural Network (SNN), which work in tandem to provide robust predictions. Majority voting is utilized to finalize predictions, ensuring high accuracy and adaptability across varying TB presentations. Extensive evaluation on publicly available datasets shows that this hybrid approach significantly outperforms existing methods, achieving a highest accuracy of 99.73%, along with superior precision, recall, and F1 scores.

A Transfer Learning Model for Rolling Bearing Fault Diagnosis Based on Texture Loss Strategy and Nuclear Norm Regularization

Abstract In recent years, transfer learning (TL) approaches have seen extensive application in diagnosing bearing faults due to their exceptional performance. However, mechanical noise, equipment aging, and wear lead to notable disparities and differences in the multi-level feature distributions across the source and target domain signals. The issue is addressed by proposing a transfer learning model based on a texture loss strategy and nuclear norm regularization method. First, a Feature-Enhanced Network (MSRnet) is designed, which significantly improves the ability to capture local details and long-range dependencies by combining a multi-scale feature extraction module with a dilated residual module. Next, a texture loss strategy is proposed to align multi-scale features across domains by minimizing the Gram matrix of signal features. Finally, a nuclear norm regularization method is proposed to perform low-rank approximation on the signal matrix, facilitating the extraction of more robust feature data and mitigating the risk of overfitting. The experimental results demonstrate that the proposed method achieved an average accuracy of 98.58% on the University of Ottawa bearing fault dataset and 98.11% on the Jiangnan University (JNU) bearing dataset, surpassing eight other algorithms in bearing fault diagnosis.