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MCGRAM: Linking multi-scale CNN With a Graph-Based Recurrent Attention Model for Subject-Independent ERP Detection

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第12期69卷 5199-5203页

作者： Li, Zixing Yan, Chao Lan, Zhen Tang, Dengqing Xiang, Xiaojia Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Peoples R China

Electroencephalogram (EEG)-based event-related potential (ERP) detection is expected to play an important role in many applications. However, limited by the low signal-to-noise ratio and large inter-subject variability of EEG signals, it is still challenging to design a high-precision ERP detection method in subject-independent scenarios. In this brief, a novel graph-based multi-scale convolutional recurrent attention model (MCGRAM) is proposed to extract the underlying invariant features of EEG signals for subject-independent ERP detection. Specifically, considering the frequency factors of ERP signals, the multi-scale convolution module is designed for learning the frequency representations. Then, the graph convolution module is developed by constructing the links between each EEG node and designing trainable weights of the links to extract the discriminative spatial features. Besides, two-layer LSTM combined with a self-attention model is utilized to extract temporal representations of different EEG slices. Compared to the existing state-of-the-art methods, MCGRAM achieves the best performance in terms of AUC and F1-Score on a rapid serial visual presentation benchmark dataset. Lastly, the ablation studies demonstrate the superiority of each component in MCGRAM.

关键词： EEG subject-independent ERP detection multi-scale convolutional neural network graph convolution network deep learning

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Deep multi-scale convolutional transfer learning network: A novel method for intelligent fault diagnosis of rolling bearings under variable working conditions and domains

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NEUROCOMPUTING 2020年 407卷 24-38页

作者： Zhao, Bo Zhang, Xianmin Zhan, Zhenhui Pang, Shuiquan South China Univ Technol Sch Mech & Automot Engn Guangdong Key Lab Precis Equipment & Mfg Technol Guangzhou 510640 Peoples R China

Intelligent fault detection and diagnosis, as an important approach, play a crucial role in ensuring the stable, reliable and safe operation of rolling bearings, which is one of the most main components in the rotating machinery. However, the data distribution shift is inevitable in the practical scene due to changes in internal and external environments, it is still challenging to establish an effective fault di-agnosis model that can eliminate the same distribution assumption. In light of the above demands, a novel transfer learning framework based on deep multi-scale convolutional neural network (MSCNN) is presented in this paper. First, a novel multi-scale module is ingenious established based on dilated convolution, which is used as the key part to obtain differential features through different perceptual fields. Then, in order to further reduce the complexity of the proposed model, a global average pooling technol-ogy is adopted to replace the traditional fully-connected layer. Finally, the architecture and weights of the MSCNN pre-trained on source domain are transferred to the other different but similar tasks with proper fine-tuning instead of training a network from scratch. The proposed MSCNN is evaluated by different transfer scenarios constructed on two famous rolling bearing test-bed. Three case studies show that the proposed framework not only has excellent performance on the source domain, but also has superior transferability on variable working conditions and domains. (C) 2020 Published by Elsevier B.V.

关键词： Rolling bearing Fault diagnosis Transfer learning multi-scale convolutional neural network Global average pooling

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Series Arc Fault Detection under Vibration Condition Based on N-M-M-B

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SENSORS 2024年第3期24卷 959页

作者： Liu, Yanli Yang, Ganqing Wang, Huiyang Liaoning Tech Univ Dept Elect & Control Engn Huludao 125105 Peoples R China

Under the conditions of a mechanical fault in a motor, mechanical vibration of a specific frequency can be generated. The electrical contact points directly connected to the motor can vibrate at the same frequency. The electrical contact points with poor contact can easily produce a series arc fault under vibration conditions, which affects the reliability of the power supply. In order to detect the series arc fault under different vibration conditions, the arc fault generator is connected between the back end of the frequency converter and the motor. An arc fault experiment under different vibration conditions was carried out and the fault phase current and arc voltage signals were collected. In this paper, the noise-assisted multivariate empirical mode decomposition and the correlation coefficient between each intrinsic mode function are used to select the fault feature signals. Then, the reconstructed signal is input into the series arc fault model combining a multi-scale convolutional neural network and a bidirectional long short-term memory network for training. The research results show that the series arc fault detection method proposed in this paper can effectively detect the series arc fault and can preliminarily identify the type of motor fault causing the mechanical vibration of the motor;the model has good noise immunity and generalization.

关键词： series arc fault mechanical fault of the motor noise-assisted multivariate empirical mode decomposition multi-scale convolutional neural network bidirectional long short-term memory network

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A multiscale electricity theft detection model based on feature engineering

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BIG DATA RESEARCH 2024年 36卷

作者： Zhang, Wei Dai, Yu Univ Shanghai Sci & Technol Sch Mech Engn Shanghai 200093 Peoples R China

With the widespread adoption of smart meters and the growing availability of data mining and machine learning algorithms, there is a pressing demand for methods that are both accurate and explicable in identifying electricity theft patterns among end-users. To address this need, this study proposes a multi-scale anomaly detection model based on feature ***, tsfresh is utilized in feature engineering to extract electricity consumption features from the raw data, and XGBoost is employed to select features that are highly correlated with anomalous behavior, which have clear physical interpretations. multi-scale convolutional neural networks are then used to analyze and process the data at different temporal and frequency scales. Attention mechanisms are applied to assign weights to different feature channels, and all of the extracted information is fused for anomaly detection. The combination of feature engineering and multi-scale convolutional neural networks not only enhances the interpretability of the model but also improves its performance, as demonstrated by the experimental results, which show that the proposed method outperforms traditional anomaly detection approaches across multiple evaluation metrics.

关键词： Feature engineering Electricity theft detection Smart grid multi-scale convolutional neural network Attention model

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A hybrid deep learning model for robust aeroengine remaining useful life prediction

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SIGNAL IMAGE AND VIDEO PROCESSING 2025年第7期19卷 1-18页

作者： Wan, Anping Zhang, Hua Chen, Ting AL-Bukhaiti, Khalil Wang, Wenhui Hangzhou City Univ Dept Mech Engn Hangzhou 310015 Peoples R China Zhejiang Univ Dept Mech Engn Hangzhou 310058 Peoples R China Zhejiang Univ Technol Coll Mech Engn Hangzhou 310014 Peoples R China

Accurate prediction of Remaining Useful Life (RUL) for aeroengines is critical for ensuring flight safety and optimizing maintenance. However, existing deep learning models often struggle to effectively capture multi-scale degradation patterns and long-term dependencies, particularly under varying operating conditions. To address these challenges, we propose multi-scale Temporal-Gated Attention (MTGA), a novel hybrid deep learning framework designed to enhance RUL prediction accuracy and robustness. MTGA uniquely integrates a multi-scale Temporal convolutional network (MTCN) for extracting degradation features across different temporal resolutions, a Gated Recurrent Unit (GRU) to model long-term dependencies efficiently, and an Improved Self-Attention Mechanism (ISA) to dynamically refine feature importance while suppressing noise. Unlike conventional hybrid models that apply standard attention mechanisms, MTGA's ISA enhances interpretability by adaptively weighting input features based on their relevance to degradation progression. Extensive evaluations of the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset demonstrate that MTGA consistently outperforms state-of-the-art methods such as IMDSSN, PI-CNN, and ATCN. For instance, MTGA achieved RMSE reductions of 19.43% and 25.73% over IMDSSN on FD002 and FD004 datasets, respectively, and Score reductions of up to 50.17% compared to ATCN on FD004, particularly under complex operational conditions. The framework's ability to balance predictive accuracy and computational efficiency makes it well-suited for real-time predictive maintenance in aerospace applications. These findings highlight MTGA's potential to improve aviation maintenance and safety strategy decision-making.

关键词： Remaining useful life (RUL) prediction Aeroengine prognostics multi-scale convolutional neural network Gated recurrent unit (GRU) Attention mechanism Hybrid deep learning

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DeepPPF: A deep learning framework for predicting protein family

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NEUROCOMPUTING 2021年 428卷 19-29页

作者： Yusuf, Shehu Mohammed Zhang, Fuhao Zeng, Min Li, Min Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China

Machine learning pipelines for protein functional family prediction are urgently needed especially now that only 1% of raw protein sequences have been manually annotated. Although existing machine learning algorithms have achieved a decent performance in modeling and predicting the functional families of protein sequences, they still have two drawbacks. First, biological dependencies among nucleotides are not rich enough to describe motifs for these methods. Also, existing algorithms are not accurate enough to predict the functional families of newly discovered proteins. To address the above limitations simultaneously, we propose a novel deep learning framework for predicting protein family, DeepPPF, which employs the word2vec technique in capturing distributional dependencies among nucleotides and discovers rich features from diverse motif lengths to characterize proteins. The novelty of the DeepPPF is in utilizing distributional dependencies among nucleotides. Experimental results on G protein-coupled receptor hierarchical datasets show the effectiveness of DeepPPF in achieving the state-of-the-art performance in items of Mathew's correlation coefficients (MCC) of 97.62%, 88.45% and, 83.09% for family, subfamily and, sub-subfamily hierarchical levels, respectively. Also, DeepPPF outperformed existing methods in terms of prediction accuracy and Mathew's correlation coefficients on the cluster of orthologous groups (COG) and phage of orthologous groups (POG) datasets. Furthermore, we analyzed the ability of DeepPPF framework to discover rich motifs for functional classes with the least sets of protein sequences. The experimental results show that rich motif discovery is key to improving the modeling performance of protein families through deep learning techniques. Finally, we investigated the effect of transferring a low-level functional domain level to a high-level functional domain and results show that the target domain prediction can be improved with transfer le

关键词： multi-scale convolutional neural network Protein functional family Protein sequence Deep learning

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MAG-Res2Net: a novel deep learning network for human activity recognition

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PHYSIOLOGICAL MEASUREMENT 2023年第11期44卷 115007-115007页

作者： Liu, Hanyu Zhao, Boyang Dai, Chubo Sun, Boxin Li, Ang Wang, Zhiqiong Northeastern Univ Coll Med & Biol Informat Engn Shenyang 110819 Peoples R China

Objective. Human activity recognition (HAR) has become increasingly important in healthcare, sports, and fitness domains due to its wide range of applications. However, existing deep learning based HAR methods often overlook the challenges posed by the diversity of human activities and data quality, which can make feature extraction difficult. To address these issues, we propose a new neural network model called MAG-Res2Net, which incorporates the Borderline-SMOTE data upsampling algorithm, a loss function combination algorithm based on metric learning, and the Lion optimization algorithm. Approach. We evaluated the proposed method on two commonly utilized public datasets, UCI-HAR and WISDM, and leveraged the CSL-SHARE multimodal human activity recognition dataset for comparison with state-of-the-art models. Main results. On the UCI-HAR dataset, our model achieved accuracy, F1-macro, and F1-weighted scores of 94.44%, 94.38%, and 94.26%, respectively. On the WISDM dataset, the corresponding scores were 98.32%, 97.26%, and 98.42%, respectively. Significance. The proposed MAG-Res2Net model demonstrates robust multimodal performance, with each module successfully enhancing model capabilities. Additionally, our model surpasses current human activity recognition neural networks on both evaluation metrics and training efficiency. Source code of this work is available at: https://***/LHY1007/MAG-Res2Net.

关键词： human activity recognition borderline-SMOTE multi-scale convolutional neural network attention mechanism metric learning

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Micro-Motion Classification of Flying Bird and Rotor Drones via Data Augmentation and Modified multi-scale CNN

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REMOTE SENSING 2022年第5期14卷 1107-1107页

作者： Chen, Xiaolong Zhang, Hai Song, Jie Guan, Jian Li, Jiefang He, Ziwen Naval Aviat Univ Marine Target Detect Res Grp Yantai 264001 Peoples R China East China Normal Univ Sch Commun & Elect Engn Shanghai 200050 Peoples R China

Aiming at the difficult problem of the classification between flying bird and rotary-wing drone by radar, a micro-motion feature classification method is proposed in this paper. Using K-band frequency modulated continuous wave (FMCW) radar, data acquisition of five types of rotor drones (SJRC S70 W, DJI Mavic Air 2, DJI Inspire 2, hexacopter, and single-propeller fixed-wing drone) and flying birds is carried out under indoor and outdoor scenes. Then, the feature extraction and parameterization of the corresponding micro-Doppler (m-D) signal are performed using time-frequency (T-F) analysis. In order to increase the number of effective datasets and enhance m-D features, the data augmentation method is designed by setting the amplitude scope displayed in T-F graph and adopting feature fusion of the range-time (modulation periods) graph and T-F graph. A multi-scale convolutional neural network (CNN) is employed and modified, which can extract both the global and local information of the target's m-D features and reduce the parameter calculation burden. Validation with the measured dataset of different targets using FMCW radar shows that the average correct classification accuracy of drones and flying birds for short and long range experiments of the proposed algorithm is 9.4% and 4.6% higher than the Alexnet- and VGG16-based CNN methods, respectively.

关键词： radar target classification micro-motion flying bird rotor drone frequency modulated continuous wave radar data augmentation multi-scale convolutional neural network

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multi-Task Learning Model Based on multi-scale CNN and LSTM for Sentiment Classification

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IEEE ACCESS 2020年 8卷 77060-77072页

作者： Jin, Ning Wu, Jiaxian Ma, Xiang Yan, Ke Mo, Yuchang China Jiliang Univ Coll Informat Engn Key Lab Elect Wave Informat Technol & Metrol Zhej Hangzhou 310018 Peoples R China Huaqiao Univ Fujian Prov Univ Key Lab Computat Sci Sch Math Sci Quanzhou 362021 Peoples R China

Sentiment classification is an interesting and crucial research topic in the field of natural language processing (NLP). Data-driven methods, including machine learning and deep learning techniques, provide one direct and effective solution to solve the sentiment classification problem. However, the classification performance declines when the input includes review comments for multiple tasks. The most appropriate way of constructing a sentiment classification model under multi-tasking circumstances remains questionable in the related field. In this study, aiming at the multi-tasking sentiment classification problem, we propose a multi-task learning model based on a multi-scale convolutional neural network (CNN) and long short term memory (LSTM) for multi-task multi-scale sentiment classification (MTL-MSCNN-LSTM). The model comprehensively utilizes and properly handles global features and local features of different scales of text to model and represent sentences. The multi-task learning framework improves the encoder quality, simultaneously improving the results of emotion classification. Six different types of commodity review datasets were employed in the experiment. Using accuracy and F1-score as the metrics to evaluate the performance of the proposed model, comparing with methods such as single-task learning and LSTM encoder, the proposed MTL-MSCNN-LSTM model outperforms most of the existing methods.

关键词： Task analysis Feature extraction Machine learning Sentiment analysis convolutional neural networks Licenses Sentiment classification multi-task learning model long short term memory multi-scale convolutional neural network

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Coronary angiography image segmentation based on PSPNet

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021年 200卷 105897-105897页

作者： Zhu, Xiliang Cheng, Zhaoyun Wang, Sheng Chen, Xianjie Lu, Guoqing Henan Cardiovasc Hosp Dept Cardiovasc Surg Henan Prov Peoples Hosp Fuwai Cent China Cardiovasc Hosp Zhengzhou Peoples R China Zhengzhou Univ Zhengzhou Peoples R China

Purpose: Coronary artery disease (CAD) is known to have high prevalence, high disability and mortality. The incidence and mortality of cardiovascular disease are also gradually increasing worldwide. Therefore, our paper proposes to use a more efficient image processing method to extract accurate vascular structures from vascular images by combining computer vision and deep learning. Method: Our proposed segmentation of coronary angiography images based on PSPNet network was compared with FCN, and analyzed and discussed the experimental results using three evaluation indicators of precision, recall and Fl-score. Aiming at the complex and changeable structure of coronary angiography images and over-fitting or parameter structure destruction, we implemented the parallel multi-scale convolutional neural network model using PSPNet, using small sample transfer learning that limits parameter learning method. Results: The accuracy of our technique proposed in this paper is 0.957. The accuracy of PSPNet is 26.75% higher than the traditional algorithm and 4.59% higher than U-Net. The average segmentation accuracy of the PSPNet model using transfer learning on the test set increased from 0.926 to 0.936, the sensitivity increased from 0.846 to 0.865, and the specificity increased from 0.921 to 0.949. The segmentation effect in this paper is closest to the segmentation result of the human expert, and is smoother than that of U-Net segmentation. Conclusion: The PSPNet network reduces manual interaction in diagnosis, reduces dependence on medical personnel, improves the efficiency of disease diagnosis, and provides auxiliary strategies for subsequent medical diagnosis systems based on cardiac coronary angiography. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Coronary angiography images blood vessel segmentation deep learning multi-scale convolutional neural network transfer learning

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