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Intelligent Fault Diagnosis Method for Shearer Rocker Gear Based on Swin Transformer and multiscale convolution Parallel Integration

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Sun, Xiaochun Ding, Hua Li, Ning Dong, Xiaoxin Sun, Jiacheng Zheng, Guangyu Taiyuan Univ Technol Coll Mech & Vehicle Engn Shanxi Key Lab Fully Mechanized Coal Min Equipment Taiyuan 030024 Peoples R China

In the harsh underground environment, the transmission gear in the rocker arm of the shearer is susceptible to impact load, frequently leading to failures that result in economic losses or casualties. Therefore, real-time and accurate gear fault diagnosis is crucial. Existing diagnostic models struggle with insufficient feature extraction and low diagnostic accuracy when addressing nonlinear, nonsmooth signals with strong noise in this environment. Therefore, an innovative parallel integration model of Swin Transformer and multiscale convolution is proposed to extract both global and local features under different fault modes and noise conditions. A spatial interaction block (SIB) is introduced into the Swin Transformer branch to enhance global information acquisition. In multiscale convolutional neural network (CNN) branches, depthwise separable convolutions with different expansion coefficients are utilized for multiscale extraction of local features to reduce computational complexity. Considering the differences between global and local features, a feature interaction module (FIM) is added between the two branches to realize the interaction between local and global features, considerably improving the model diagnosis performance. Furthermore, to meet the high real-time diagnosis requirements, a cloud-edge collaboration (CEC) framework is designed to realize the timely alarm of gear faults through the efficient collaboration between edge nodes and the industrial cloud platform. Using the vibration data collected from the rocker arm loading test bed of Taizhong Coal Machinery and the gear data provided by Southeast University for validation, the results show that the diagnostic precision of the proposed method can reach up to 99.55%. Even when the signal-to-noise ratio (SNR) is -2 db, the accuracy is still 93.67%, which is superior to other advanced comparison models, and significantly reduces the completion time of fault diagnosis tasks, and has strong anti-noise

关键词： Feature extraction Transformers Gears convolution Data mining Real-time systems Accuracy convolutional neural networks Collaboration Cloud-edge collaboration (CEC) fault diagnosis multiscale convolution shearer rocker arm gear Swin Transformer Swin Transformer

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FLANet: A multiscale temporal convolution and spatial-spectral attention network for EEG artifact removal with adversarial training

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JOURNAL OF NEURAL ENGINEERING 2025年第1期22卷

作者： Wang, Junkongshuai Luo, Yangjie Wang, Haoran Wang, Lu Zhang, Lihua Gan, Zhongxue Kang, Xiaoyang Fudan Univ Fudan Peoples R China Ji Hua Lab Foshan Peoples R China Fudan Univ Yiwu Res Inst Yiwu City Peoples R China Zhejiang Lab Res Ctr Intelligent Sensing Hangzhou Peoples R China

Objective. Denoising artifacts, such as noise from muscle or cardiac activity, is a crucial and ubiquitous concern in neurophysiological signal processing, particularly for enhancing the signal-to-noise ratio in electroencephalograph (EEG) analysis. Novel methods based on deep learning demonstrate a notably prominent effect compared to traditional denoising approaches. However, those still suffer from certain limitations. Some methods often neglect the multi-domain characteristics of the artifact signal. Even among those that do consider these, there are deficiencies in terms of efficiency, effectiveness and computation cost. Approach. In this study, we propose a multiscale temporal convolution and spatial-spectral attention network with adversarial training for automatically filtering artifacts, named filter artifacts network (FLANet). The multiscale convolution module can extract sufficient temporal information and the spatial-spectral attention network can extract not only non-local similarity but also spectral dependencies. To make data denoising more efficient and accurate, we adopt adversarial training with novel loss functions to generate outputs that are closer to pure signals. Main results. The results show that the method proposed in this paper achieves great performance in artifact removal and valid information preservation on EEG signals contaminated by different types of artifacts. This approach enables a more optimal trade-off between denoising efficacy and computational overhead. Significance. The proposed artifact removal framework facilitates the implementation of an efficient denoising method, contributing to the advancement of neural analysis and neural engineering, and can be expected to be applied to clinical research and to realize novel human-computer interaction systems.

关键词： electroencephalogram (EEG) artifact removal multiscale convolution spatial-spectral attention adversarial training

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Inter-patient ECG arrhythmia heartbeat classification network based on multiscale convolution and FCBA

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 90卷

作者： Zhou, Fei-yan Sun, Yu-hao Wang, Ya-wen Guangxi Normal Univ Minist Educ Key Lab Educ Blockchain & Intelligent Technol Guilin 541004 Peoples R China Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China

Cardiac arrhythmias that can lead to sudden cardiac death are common. Electrocardiograms (ECGs) offer valuable information about cardiac status and play a crucial role in evaluating patients with arrhythmia in clinical diagnosis. The development of machine learning technologies has made the computer-aided diagnosis of cardiac arrhythmias possible, which can improve the efficiency and quality of medical treatment. In this study, a new method for the automatic classification of heartbeats is developed. This paper presents a novel approach to detect arrhythmias in the inter-patient paradigm using a convolutional neural network (CNN) that integrates multiscale convolutional blocks, frequency convolutional block attention (FCBA) modules, and RR interval features. The proposed method also includes oversampling for the heartbeats of the minority class and the addition of random noise to increase training samples and alleviate the problem of data imbalance. We used the MIT-BIH-AR arrhythmia database , which is a globally recognized ECG database, to evaluate the classification performance of the proposed model in this paper. Based on the experiments, the sensitivities of the normal beats (N), supraventricular ectopic beats (SVEB), and ventricular ectopic beats (VEB) were 96.9%, 89.3%, and 93.3%, respectively. In the inter-patient heartbeat classification paradigm, the overall accuracy of the proposed method is 95.60% in classifying heartbeats. The study shows that the proposed scheme outperforms other published schemes in terms of classification results.

关键词： RR interval Deep learning ECG classification multiscale convolution Frequency convolution block attention

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multiscale Residual convolution Neural Network and Sector Descriptor-Based Road Detection Method

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IEEE ACCESS 2019年 7卷 173377-173392页

作者： Dai, Jiguang Du, Yang Zhu, Tingting Wang, Yang Gao, Lin Liaoning Tech Univ Sch Geomat Fuxin 12300 Peoples R China Natl Adm Surveying Mapping & Geoinformat Key Lab Natl Geog State Monitoring Wuhan 430079 Peoples R China

Road detection is a focus of research in the field of remote sensing image analysis. This task is normally difficult due to the complexity of the data, which are heterogeneous in appearance with large intra-class and lower inter-class variations that frequently lead to large numbers of gaps and errors in road extraction. In this paper, a novel road detection method is proposed that combines a multiscale deep residual convolution neural network (MDRCNN) with postprocessing. The MDRCNN is used to obtain road areas more accurately and quickly. multiscale convolution, which provides greater accuracy, is used to acquire the hierarchical features of different dimensions. The residual connections and global average pooling are introduced to improve the efficiency of the network in the process of backpropagation and forward propagation, respectively. In the postprocessing stage, the centerline of the road can be obtained based on the road area. Geometric constraints and mathematical morphological refinement as well as leaf-to-leaf connection are used to obtain the road line. Rectangular buffer analysis and a sector descriptor tracking connection are subsequently used to improve the integrity and accuracy of the road. We experimented on two datasets with different resolutions and different scenes. Compared with other neural network methods, our method is better at connecting road fractures and eliminating errors.

关键词： Geometric constraints global average pooling mathematical morphological refinement multiscale convolution multiscale deep residual convolution neural network remote sensing residual connections road detection sector descriptor

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Multilevel Assessment of Exercise Fatigue Utilizing Multiple Attention and convolution Network (MACNet) Based on Surface Electromyography

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2025年 33卷 243-254页

作者： Zhang, Guofu Yang, Banghua Zan, Peng Zhang, Dingguo Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Univ Bath Dept Elect & Elect Engn Bath BA2 7AY England

Background: Assessment of exercise fatigue is crucial for enhancing work capacity and minimizing the risk of injury. Surface electromyography (sEMG) has been used to quantitatively assess exercise fatigue as a new technology in recent years. However, the currently available research primarily distinguishes between fatigue and non-fatigue states, offering limited and less robust findings in multilevel evaluations. Methods: This study proposes a multiple attention and convolution network (MACNet) for a three-level assessment of muscle fatigue based on sEMG. Under the designed 50% maximum voluntary contraction experimental paradigm, sEMG signals and rate of perceived exertion scale are collected from 48 subjects. MACNet is developed to assess sEMG fatigue, incorporating improved temporal attention based on sliding window, multiscale convolution, and channel-spatial attention. Finally, GradCAM visualization is used to verify the developed model's interpretation, exploring the effects of sEMG channels and time-domain characteristics on exercise fatigue. Results: The average classification F1-Score and accuracy of MACNet are 83.95% and 84.11% for subject-wise and 82.83% and 82.43% for cross-subject, respectively. The GradCAM visualization highlights the greater contribution of the flexor digitorum superficialis and flexor digitorum profundus in evaluating high fatigue, along with the varied impact of time-domain features on exercise fatigue assessment. Conclusion: MACNet achieves the highest average classification accuracy and F1-Score, significantly higher than other state-of-the-art methods like SVM, RF, MFFNet, TSCNN, LMDANet, Conformer and MSFEnet, enhancing the extraction of exercise fatigue insights from sEMG channels and time-domain features.

关键词： Fatigue Muscles Feature extraction convolution Force Electrodes Support vector machines Time-domain analysis Skin Real-time systems sEMG exercise fatigue multilevel assessment attention mechanism multiscale convolution

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MDSCN: multiscale depthwise separable convolutional network for underwater graphics restoration

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VISUAL COMPUTER 2025年第3期41卷 1999-2010页

作者： Li, Shiyu Liu, Zehao Gao, Meijing Bai, Yang Yin, Haozheng Yanshan Univ Coll Informat Sci & Engn Key Lab Special Fiber & Fiber Sensor Hebei Prov Qinhuangdao 066004 Hebei Peoples R China Beijing Inst Technol Coll Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol Tangshan Res Inst Tangshan 063000 Peoples R China

Underwater imaging techniques have been a focus of research for computer vision. Underwater imaging frequently encounters challenges for poor image quality and slow restoration speed, thereby hindering human underwater exploration endeavors. To enhance the quality and improve the real-time performance of underwater image restoration, the paper proposes a lightweight underwater color image restoration network based on multiscale depthwise separable convolution. First, the algorithm tackles the problems of difficult convergence and slow training by improving the AdamW optimizer. Then, we propose a multiscale depthwise separable convolution module with RGB channel, which allows efficient extraction of image features based on the underwater light propagation properties. The MDSCN can effectively improve the processing speed and recovery effect of underwater images. Through experimentation and analysis, our algorithm outperforms traditional image processing methods and recent deep learning approaches in terms of visual effects and objective evaluation metrics. Furthermore, our algorithm also has a better performs than existing deep learning methods in processing speed, which demonstrates excellent generalizability and practicality. The research in the article is highly informative for the field of underwater computer vision. The dataset, training weights files and codes are publicly available https://***/raining-li/underwater-image-processing/tree/master.

关键词： Computer graphics Underwater image restoration Optimizer multiscale convolution Depth separable convolution

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DmADs-Net: dense multiscale attention and depth-supervised network for medical image segmentation

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2025年第1期16卷 523-548页

作者： Fu, Zhaojin Li, Jinjiang Chen, Zheng Ren, Lu Shandong Technol & Business Univ Sch Comp Sci & Technol Yantai 264005 Peoples R China

Deep learning has made important contributions to the development of medical image segmentation. convolutional neural networks, as a crucial branch, have attracted strong attention from researchers. Through the tireless efforts of numerous researchers, convolutional neural networks have yielded numerous outstanding algorithms for processing medical images. The ideas and architectures of these algorithms have also provided important inspiration for the development of later *** extensive experimentation, we have found that currently mainstream deep learning algorithms are not always able to achieve ideal results when processing complex datasets and different types of datasets. These networks still have room for improvement in lesion localization and feature extraction. Therefore, we have created the dense multiscale attention and depth-supervised network (DmADs-Net).We use ResNet for feature extraction at different depths and create a Multi-scale convolutional Feature Attention Block to improve the network's attention to weak feature information. The Local Feature Attention Block is created to enable enhanced local feature attention for high-level semantic information. In addition, in the feature fusion phase, a Feature Refinement and Fusion Block is created to enhance the fusion of different semantic *** validated the performance of the network using five datasets of varying sizes and types. Results from comparative experiments show that DmADs-Net outperformed mainstream networks. Ablation experiments further demonstrated the effectiveness of the created modules and the rationality of the network architecture.

关键词： Medical image segmentation Attention mechanism Deep supervision multiscale convolution

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Lithology Identification of Imbalanced Well Log Data Based on Diffusion Model and multiscale CNN

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MATHEMATICAL GEOSCIENCES 2025年 1-22页

作者： Zhao, Fengda Zhao, Zhuoyi Lv, Hongjin Zhang, Pengwei Li, Xianshan Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Hebei Peoples R China Xinjiang Univ Sci & Technol Sch Informat Sci & Engn Korla 841000 Xinjiang Peoples R China Yanshan Univ Key Lab Software Engn Hebei Prov Qinhuangdao 066004 Hebei Peoples R China

Lithology identification using well log data is a crucial part of geophysical reservoir characterization. However, lithologies have different proportions in geological formations, which means that an imbalance exists in the well log datasets. When confronted with this issue, the performance of intelligent lithology identification models will be constrained. To address the issue, this paper proposes a lithology identification framework based on a denoising diffusion probabilistic model (DDPM) and a multiscale convolutional neural network (MCNN) for imbalanced well log data. The DDPM is designed to generate minority category well log data to mitigate the impact of dataset imbalance. These generated samples are then evaluated by a discriminator and filter module to select high-quality samples, which are subsequently integrated into the original dataset for balancing. The MCNN then identifies these balanced data using multiscale convolution and attention mechanisms. Experiments are conducted on two datasets from the Hugoton and Panoma fields in Kansas, USA, and the Daqing fields in China. The DDPM-MCNN achieves recognition accuracy of 84.99% and 85.58%, respectively, offering a novel approach for lithology identification on imbalanced well log datasets.

关键词： Lithology identification Denoising diffusion probability model Class imbalance multiscale convolution

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Comprehensive feature integrated capsule network for Machinery fault diagnosis

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 260卷

作者： Xing, Huangkun Jiang, Xingxing Song, Qiuyu Wang, Qian Liu, Jie Zhu, Zhongkui Soochow Univ Sch Rail Transportat Suzhou 215131 Peoples R China Soochow Univ Intelligent Urban Rail Engn Res Ctr Jiangsu Prov Suzhou 215131 Peoples R China Suzhou City Univ Sch Opt & Elect Informat Suzhou 215104 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China

Deep transfer learning is widely used for intelligent fault diagnosis due to its advantage of transferring knowledge already learnt. However, existing models still suffer from following issues: the failure to fully consider the effective information implied by vibration signals causes the unrepresentative feature extraction;the excessive focus on transferable features results in a reduction of feature discriminability;the selection of optimal diagnosis model totally depends on the iterative number or minimum loss. Our study proposes a comprehensive feature integrated capsule network (CFICN) to tackle these issues. Specifically, a multiscale jointing 1D-2D convolution is first presented to excavate the close dependency among different local features, in which the advantages of both 1D and 2D convolutions in feature extraction are fully utilized to obtain representative features. Then, a spectral penalization strategy is built to acquire comprehensive features by conducting an optimal balance between transferability and discriminability of representative features. Furthermore, comprehensive features are integrated into capsule network for the effective information fusion. Finally, a strategy for screening an optimal diagnosis model is constructed by defining a cumulative confidence index. Fourteen kinds of transfer tasks in two cases validate that our CFICN has an obvious advantage over five advanced methods for machinery fault diagnosis.

关键词： Capsule network Intelligent fault diagnosis multiscale convolution Spectral Penalization Transfer learning

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Dynamic Hierarchical convolutional Attention Network for Recognizing Motor Imagery Intention

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IEEE TRANSACTIONS ON CYBERNETICS 2025年第5期55卷 2202-2212页

作者： Lu, Bin Wang, Fuwang Chen, Junxiang Wen, Guilin Hua, Changchun Fu, Rongrong Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Peoples R China Northeast Elect Power Univ Sch Mech Engn Jilin 132012 Peoples R China Univ Pittsburgh Dept Biomed Informat Pittsburgh PA 15206 USA Yanshan Univ Sch Mech Engn Qinhuangdao 066004 Peoples R China

The neural activity patterns of localized brain regions are crucial for recognizing brain intentions. However, existing electroencephalogram (EEG) decoding models, especially those based on deep learning, predominantly focus on global spatial features, neglecting valuable local information, potentially leading to suboptimal performance. Therefore, this study proposed a dynamic hierarchical convolutional attention network (DH-CAN) that comprehensively learned discriminative information from both global and local spatial domains, as well as from time-frequency domains in EEG signals. Specifically, a multiscale convolutional block was designed to dynamically capture time-frequency information. The channels of EEG signals were mapped to different brain regions based on motor imagery neural activity patterns. The spatial features, both global and local, were then hierarchically extracted to fully exploit the discriminative information. Furthermore, regional connectivity was established using a graph attention network, incorporating it into the local spatial features. Particularly, this study shared network parameters between symmetrical brain regions to better capture asymmetrical motor imagery patterns. Finally, the learned multilevel features were integrated through a high-level fusion layer. Extensive experimental results on two datasets demonstrated that the proposed model performed excellently across multiple evaluation metrics, exceeding existing benchmark methods. These findings suggested that the proposed model offered a novel perspective for EEG decoding research.

关键词： convolution Electroencephalography Feature extraction Motors Kernel Decoding Time-frequency analysis Data mining Brain modeling Image recognition Electroencephalogram (EEG) graph attention network hierarchical feature extraction motor imagery multiscale convolution

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