Deep Residual Shrinkage Networks for Fault Diagnosis

Deep Residual Learning for Image Recognition

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Squeeze-and-Excitation Networks

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Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks

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Fault-Tolerant Sun-Pointing Attitude Control Based on Physics-Guided Neural Networks

Wang, Z., Lun, X., Jiang, Y., & Luo, H. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3438256 · Cited by 5

Shallow water source depth discrimination based on a vertical linear array using deep learning

Li, Z., 龚丽佳, & Li, N. (2024) · Applied Ocean Research · doi:10.1016/j.apor.2024.104201 · Cited by 5

Multibeam water column image super-resolution by combining morphological and intensity features

Chen, J., Wu, Z., Wang, R., Wang, M., Liang, Q., Qi, C., & Fanlin, Y. (2024) · International Journal of Digital Earth · doi:10.1080/17538947.2024.2398760 · Cited by 5

EAFF-Net: Efficient attention feature fusion network for dual-modality pedestrian detection

Shen, Y., Xie, X., Wu, J., Chen, L., & Huang, F. C. (2025) · Infrared Physics & Technology · doi:10.1016/j.infrared.2024.105696 · Cited by 5

A deep learning-based adaptive denoising approach for fine identification of rock microcracks from noisy strain data

Zhao, S., Mu, D. S., & Tan, D. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110471 · Cited by 5

Uniform bullseye-guided contrastive learning with time-frequency strong augmentation for long-tailed fault diagnosis

Zhong, J., Mao, H., Mo, F., Li, X., & Tang, W. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.112639 · Cited by 5

Multilabel Recognition Method for Ship-Radiated Noise Signals Based on Multidomain Information Fusion With Deep Equilibrium Models

Duan, Y., Shen, X., Wang, H., & Yan, Y. (2025) · IEEE Journal of Oceanic Engineering · doi:10.1109/joe.2025.3545239 · Cited by 5

Digital twin-assisted fault diagnosis of induction motors with thermal analysis of inter-turn short circuit faults

Pengbo, Z., Chen, R., Liang, D., Yang, L., & Gao, L. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/addbfe · Cited by 5

Research on CNC Machine Tool Spindle Fault Diagnosis Method Based on DRSN–GCE Model

Li, X., Wang, J., Wang, J., Wang, J., Chen, J., & Yu, X. (2025) · Algorithms · doi:10.3390/a18060304 · Cited by 5

Modulation recognition of communication signals based on deep learning

He, J., Xu, C., Yin, C., & Li, P. (2021) · IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) · doi:10.1109/iciba52610.2021.9687989 · Cited by 5

Recognition of Rolling Bearing Life Status Based on FNER Performance Degradation Indicator and IDRSN

Dong, S., Pei, X., Tang, B., Kewei, T., Zhu, P. Y., Yang, L., & Xingxin, Z. (2021) · Journal of Mechanical Engineering · doi:10.3901/jme.2021.15.105 · Cited by 5

HQ2CL: A High-Quality Class Center Learning System for Deep Face Recognition

Lv, X., Chen, Y., Jin, H., & Liu, K. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3195638 · Cited by 5

Operation Mode Recognition of Airborne Radar Based on Multi-Feature Fusion RS-ConvNet

Zhang, Y., Zhang, C., Huo, W., Pei, J., Huang, Y., & Zhang, Y. (2022) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss46834.2022.9883094 · Cited by 5

Improved Deep Residual Shrinkage Network for Intelligent Interference Recognition with Unknown Interference

Wu, X., Zhou, Y., Wu, D., Xiao, H., Lu, Y., & Li, H. (2023) · Sensors · doi:10.3390/s23187909 · Cited by 5

Multiple frequency–spatial network for RGBT tracking in the presence of motion blur

Fan, S., Chen, X., He, C., Yu, L., Mao, Z., & Zheng, Y. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09024-8 · Cited by 5

An Efficient Method for Identifying Lower Limb Behavior Intentions Based on Surface Electromyography

Ling, L., Wang, Y., Ding, F., Li, J., Feng, B., Li, W., Wang, C., & Li, X. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.043383 · Cited by 5

Multi-Task Learning Based Channel Estimation for Hybrid-Field STAR-RIS Systems

Xiao, J. H., Wang, J., Liu, Y., Xie, W., Wang, J., & Liu, S. (2023) · Globecom · doi:10.1109/globecom54140.2023.10437505 · Cited by 5

A Mixed-Feed Identification Method for Sizer Crusher Based on Squeeze-and-Excitation Residual Network

Bi, Y., Pan, Y., & Li, G. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3400519 · Cited by 5

Deep residual shrinkage network with multichannel VMD inputs for noise reduction of micro-thrust measurement

Liu, Z., Chen, X., Xu, J., & Zhao, L. (2024) · AIP Advances · doi:10.1063/5.0200682 · Cited by 5

WavFormer: An Interpretable Wavelet-Constrained Transformer for Industrial Acoustics Diagnosis

Ren, J., Hu, C., Shang, Z., Li, Y., Zhao, Z., & Yan, R. (2024) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc60896.2024.10560738 · Cited by 5

An efficient driving behavior prediction approach using physiological auxiliary and adaptive LSTM

Jun, G., Yi, J., & Murphey, Y. L. (2024) · Machine Vision and Applications · doi:10.1007/s00138-024-01600-9 · Cited by 5

A simplified adversarial architecture for cross-subject silent speech recognition using electromyography

Cui, Q., Zhang, X., Zhang, Y., Zheng, C., Xie, L., Ye, Y., Wu, E. Q., & Yin, E. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad7321 · Cited by 5

Stretchable Capacitive Tactile Sensor Array for Accurate Distributed Pressure Recognition

Yu, J., Yao, S., Jiang, X., & Yao, Z. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3470250 · Cited by 5

A novel cross domain diagnosis method based on physical feature weighting and deep residual shrinkage network

Ma, C., Nan, S., Zhang, K., XiangFeng, Z., Chen, J., & Xu, Y. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad9f87 · Cited by 5

End-to-End Methane Concentration Measurement Model With Deep Residual Shrinkage Network for Direct Absorption Spectroscopy

Jiang, Z., Wu, J., Yu, H., Lan, L., Pan, D., & Gui, W. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3529579 · Cited by 5

High-Resistance Grounding Fault Location in High-Voltage Direct Current Transmission Systems Based on Deep Residual Shrinkage Network

Huang, P., Huang, J., Huang, S., Yang, G., & Wu, Z. (2025) · Electronics · doi:10.3390/electronics14030628 · Cited by 5

Multiperspective Temporal Pooling Convolutional Neural Networks for Fault Diagnosis of Mechanical Transmission Systems

Xu, Y., Li, S., Feng, K., Sun, B., Yang, X., Kou, L., Zhao, Z., & Huang, G. Q. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3548060 · Cited by 5

Attention-Guided Residual Spatiotemporal Network with Label Regularization for Fault Diagnosis with Small Samples

Xu, Y., Zhang, L., Chen, L., Tan, T., Wang, X., & Xiao, H. (2025) · Sensors · doi:10.3390/s25154772 · Cited by 5

Capsule Network Based on Self-Attention Mechanism

Shang, Y., Xu, N., Jin, Z., & Yao, X. L. (2021) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp52459.2021.9613717 · Cited by 5

Fault Diagnosis Based on Deep Residual Shrinkage Network and Maximum Mean Discrepancy

Zhou, Y., Yuan, X., Cui, X., Song, Y. H., Sun, Z., & Fan, J. (2021) · China Automation Congress (CAC) · doi:10.1109/cac53003.2021.9728005 · Cited by 5

GAN-DRSN based Inter-turn Short Circuit Fault Diagnosis of PMSM

Li, M., Wang, M., Chen, L., & Wei, L. (2022) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma54519.2022.9856224 · Cited by 5

A Graph Convolutional Shrinkage Network-based Fault Diagnosis Method for Industrial Process

Xu, Y., Zou, X. L., Ke, W., Zhu, Q., He, Y., Zhang, M., & Zhang, Y. (2023) · IEEE Data Driven Control and Learning Systems Conference (DDCLS) · doi:10.1109/ddcls58216.2023.10165809 · Cited by 5

Deep Unrolling Shrinkage Network for Dynamic MR Imaging

Zhang, Y., Li, X., Li, W., & Hu, Y. H. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10223077 · Cited by 5

Fault Diagnosis of Highway Machinery Hydraulic System Based on LS-TF

Wang, M., Wan, Y., Liang, X., He, X., Chen, Y., & Zhang, S. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3325868 · Cited by 5

Pre-trained 1DCNN-BiLSTM Hybrid Network for Temperature Prediction of Wind Turbine Gearboxes

Zhuang, K., Ma, C., Lam, H., Zou, L., & Hu, J. L. (2023) · Processes · doi:10.3390/pr11123324 · Cited by 5

Failure Diagnosis of Antenna Array Using Residual Convolutional Neural Network

Zheng, G., Zhang, Q., & Li, S. (2021) · 2021 International Applied Computational Electromagnetics Society (ACES-China) Symposium · doi:10.23919/aces-china52398.2021.9581935 · Cited by 5

Low-Quality 3D Face Recognition with Soft Thresholding

Xiao, S., Li, S., & Zhao, Q. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86608-2_46 · Cited by 5

VLP-BERT: BERT-Enhanced IMU and Visible Light Tightly Coupled Integration Positioning System

Wang, X., Cao, X., Yu, T., Zheng, Z., Zhang, Z., & Yu, Y. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3583331 · Cited by 4

Gold purity detection via pulsed eddy current testing with adaptive compensation residual shrinkage network

Shen, W., Li, J., Zhai, Z., Wang, X., Feng, W., & Lei, Z. (2025) · NDT & E International · doi:10.1016/j.ndteint.2025.103563 · Cited by 4

An unsupervised long-tailed fine-grained few-shot fault diagnosis method based on feature space adjustment strategy

Zhao, Z., Jiao, Y., Xu, Y., Zhang, X., Zhao, R., & Chen, Z. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111998 · Cited by 4

Semi-Supervised Radar Work Mode Recognition Based on Contrastive Learning

Sun, P., Du, M., Li, Z., Chen, X., & Shi, J. (2025) · Sensors · doi:10.3390/s25247440 · Cited by 4

Dual-stream spatiotemporal graph convolutional networks for EEG-based human emotion recognition

Ren, J., Han, F., & Xu, Y. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2025.104597 · Cited by 4

Pulse Harmonic Cluster Sensing Network and Its Application in Rolling Bearing Fault Diagnosis

Tang, L., Liu, Y., Peng, Y., Zhou, J., & Shao, H. (2026) · IEEE Transactions on Reliability · doi:10.1109/tr.2026.3693551 · Cited by 4

A Current Noise Cancellation Method Based on Fractional Linear Prediction for Bearing Fault Detection

Xu, K., & Song, X. (2023) · Sensors · doi:10.3390/s24010052 · Cited by 4

Fault diagnosis method for rolling bearings based on BICNN under complex operating conditions

Duan, X., Shi, J., Lei, C., & Zhao, Z. (2024) · Journal of the Brazilian Society of Mechanical Sciences and Engineering · doi:10.1007/s40430-024-05105-4 · Cited by 4

TCBGY net for enhanced wear particle detection in ferrography using self attention and multi scale fusion

He, L., Wei, H., & Sun, C. (2024) · Scientific Reports · doi:10.1038/s41598-024-82961-z · Cited by 5

Global Convolutional Network and Sample Weighted Loss for the Robust Gear Fault Diagnosis of Reciprocating Manipulator

Jiang, Y., Zhou, X., Wang, M., Chen, T., & Chen, L. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3529568 · Cited by 4

Vibration Analysis and Fault Diagnosis of Dynamical System Under Strong Noise Background Based on Transformer-CNN

Fang, Y., Zhang, Z., & Ma, X. (2024) · Global Reliability and Prognostics and Health Management Conference (PHM) · doi:10.1109/phm-beijing63284.2024.10874537 · Cited by 4

Noise-robust spatial-temporal dynamic graph domain adaptation network for fault diagnosis under varying working conditions

Lin, B., Zhu, G., Zhang, Q., Sun, G., & Huang, Q. (2025) · Engineering Research Express · doi:10.1088/2631-8695/adc3eb · Cited by 4

SAR Image Target Segmentation Guided by the Scattering Mechanism-Based Visual Foundation Model

Zhang, C., Chen, J., Huang, Z., Zeng, H., Huang, Z., Li, Y., Xu, H. F., Pu, X., & Sun, L. (2025) · Remote Sensing · doi:10.3390/rs17071209 · Cited by 4

Power Transformer Fault Diagnosis Method Based on SMOTE and Convolution Soft Threshold Network

Lv, X., Liu, F., Jiang, M., Zhang, F., & Jia, L. (2025) · IET Electric Power Applications · doi:10.1049/elp2.70044 · Cited by 4

A Lightweight Deep Optical Flow Network With Soft Threshold for Particle Image Velocimetry

Wang, Z., Chen, T., Xu, C., & Cai, S. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3577856 · Cited by 4

Open set recognition of radar specific emitter based on adversarial reciprocal point learning

Zhang, W., An, L., Yang, W., Zhao, Z., & Liu, F. (2025) · Signal Processing · doi:10.1016/j.sigpro.2025.110137 · Cited by 4

Wavelet kernel and convolution neural network based accurate detection of incipient stator and rotor faults of induction motor

Samanta, S., Bera, J., & Biswas, A. (2025) · Journal of Electrical Systems and Information Technology · doi:10.1186/s43067-025-00244-7 · Cited by 4

Adaptive hybrid attention mechanism deep residual threshold networks for bearing fault diagnosis under noisy environments

Wang, Y., Sun, K., Li, Y., & Liang, H. (2025) · Electronic Research Archive · doi:10.3934/era.2025237 · Cited by 4

Interpretable fault diagnosis of coal-mine conveyor-belt idler bearings using multi-source fusion diagrams and mobile residual soft threshold–MobileViT

Chen, W., Shen, J., Zhu, H., & Xu, P. B. (2025) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2025.103777 · Cited by 4

A Robust Transformer–Residual Hybrid Framework with Soft Thresholding for High-Performance Image Emotion Classification

Al-shamasneh, A. R. M., Karim, F. K., & Wang, Y. (2025) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-025-00974-6 · Cited by 4

Bearing fault diagnosis method based on contrastive learning and domain adaptation under variable working conditions

Pan, X., Shen, A., Chen, H., & Wu, K. (2026) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02946-w · Cited by 4

Cross-scenarios few-shot fault diagnosis for rolling bearings via inter-domain similarity-guided meta-learning with dual-attention multiscale feature denoising network

Huang, Q., Li, J., Wang, J., Meng, Z., & Zhang, J. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.113828 · Cited by 4

Adaptive Relation Network for Rotating Machinery Fault Detection in Zero Fault Samples and Fluctuating Operating Conditions

Chen, Z., Wu, J., Deng, Z., & Huang, H. (2026) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2026.3711691 · Cited by 4

Embedding Chinese Face Painting Into the StyleGAN Latent Space

Ma, P., & Xue, T. (2022) · International Conference on Advanced Cloud and Big Data · doi:10.1109/cbd54617.2021.00033 · Cited by 4

Combination with Continual Learning Update Scheme for Power System Transient Stability Assessment

Hu, B., Hao, Z., Chen, Z., & Zhang, J. (2022) · Sensors · doi:10.3390/s22228982 · Cited by 4

State of Charge Estimation of Lithium-Ion Battery Based on IDRSN and BiGRU

Zhang, J., Chen, J., He, L., Liu, D., Yang, K., & Liu, Q. (2023) · Journal of Electrochemical Energy Conversion and Storage · doi:10.1115/1.4063173 · Cited by 4

RMCW: An Improved Residual Network With Multi-Channel Weighting for Machinery Fault Diagnosis

Liu, Z., Hu, Y., Xu, K., & Miao, X. (2023) · IEEE Access · doi:10.1109/access.2023.3328906 · Cited by 4

Deep residual shrinkage networks with adaptively convex global parametric rectifier linear units for fault diagnosis

Zhang, Z., Zhang, C., Zhang, X., Gao, S., & Li, H. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/ad0939 · Cited by 4

Self-Supervised Health Index Curve Generation for Condition-Based Predictive Maintenance

Seitz, S., Arnold, M., Tetzlaff, R., & Holstein, P. (2023) · Electronics · doi:10.3390/electronics12244941 · Cited by 5

Deep Domain Generalization-Based Indoor Pedestrian Identification Using Footstep-Induced Vibrations

Xu, X., Deng, R., Zhao, G., Zhang, B. L., & Liu, C. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3351227 · Cited by 4

Convex Dual Theory Analysis of Two-Layer Convolutional Neural Networks With Soft-Thresholding

Xiong, C., Zhang, C., Lu, M., Yu, X., Cao, J., Chen, Z., Guo, D., & Qu, X. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3353795 · Cited by 4

A novel flow rate measurement method for fire hose based on vibration signal and neural network

Lu, D., & Su, L. (2024) · Flow Measurement and Instrumentation · doi:10.1016/j.flowmeasinst.2024.102600 · Cited by 4

Dual Attention-Based Global-Local Feature Extraction Network for Unsupervised Change Detection in PolSAR Images

Xu, D., Li, M., Wu, Y., Zhang, P., & Xin, X. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3406421 · Cited by 4

Insect Recognition Method With Strong Anti-Interference Capability for Next-Generation Consumer Imaging Technology

Yang, M., Cai, J., Yang, Z., Wang, X., Bashir, A. K., Dabel, M. M. A., Feng, H., & Fang, K. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3411567 · Cited by 4

Automatic diagnosis of pediatric high myopia via Attention-based Patch Residual Shrinkage network

Peng, H., Li, J., Cheng, W., Zhao, L., Guan, Y., Li, Z., Li, L., & Xu, X. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124704 · Cited by 4

DRLFormer: A Data Rebalancing Loss-Constrained Light Transformer for OSA Detection

Li, C., Shi, Z., You, Z., Li, N., Zhou, L., Zhang, Z., Ye, L., Zhang, Y., Ren, X., Hei, X., et al. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3440369 · Cited by 4

Adaptive Denoising With Efficient Channel Attention for Automatic Modulation Recognition

Zhu, H., Ma, Y., Zhang, X., & Hao, C. (2024) · International Conference on Communications · doi:10.1109/icc51166.2024.10622929 · Cited by 4

An Early Fault Detection Framework for Wind Turbines using Vibration Signals

Hoffmann, J. L. C., Lucena, M., de Araújo, G. M., & Fröhlich, A. A. (2024) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case59546.2024.10711324 · Cited by 4

Temporal Group Attention Network with Affective Complementary Learning for Gait Emotion Recognition

Zhu, Z., Chen, C. L. P., Liu, H., & Zhang, T. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822501 · Cited by 4

JotlasNet: Joint tensor low-rank and attention-based sparse unrolling network for accelerating dynamic MRI

Zhang, Y., Gui, H., Yang, N., & Hu, Y. (2025) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2025.110337 · Cited by 4

Speed–Load Insensitive Fault Diagnosis Method of Wind Turbine Gearbox Based on Adversarial Training

Zhou, W., Zhou, Q., & Zhang, J. (2025) · Electronics · doi:10.3390/electronics14040732 · Cited by 4

YOLO-GEA: infrared target detection algorithm based on multi-scale feature fusion

Da, M., Tao, Y., Jiang, L., Hu, J., & Zhang, Z. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/adbe96 · Cited by 4

A new parallel PQDs classification method based on an optimized NLM and an improved DRSN-TCN model

Ren, Z., Liu, D., Liu, Y., Zhang, S., Hu, H., & Jiang, A. (2025) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2025.110326 · Cited by 4

A non-negative garrote shrinkage network with adaptive Swish for rotating machinery fault diagnosis under noisy environment

Zhong, P., Liu, Z., Li, R., Liu, H., Yang, X., Dong, Z., & Tan, J. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111845 · Cited by 4

Fault diagnosis of hydropower generator based on LSTM- weight

Huang, J., Guo, B., Dian, S., Li, X., & Lai, W. (2022) · International Conference on Automation, Control and Robotics Engineering (CACRE) · doi:10.1109/cacre54574.2022.9834205 · Cited by 4

Weak celestial source fringes detection based on channel attention shrinkage networks and cluster-based anchor boxes generation algorithm

Yan, R., Ma, R., Liu, W., Yin, Z., Zhao, Z., Chen, S., Chang, S.-P., Zhu, H., Hu, D., & Yu, X. (2022) · Digital Signal Processing · doi:10.1016/j.dsp.2022.103663 · Cited by 4

Associated Fault Diagnosis of Power Supply Systems Based on Graph Matching: A Knowledge and Data Fusion Approach

Tao, L., Liu, H., Zhang, J., Su, X., Li, S., Hao, J., Lü, C., Suo, M., & Wang, C. (2022) · Mathematics · doi:10.3390/math10224306 · Cited by 4

THz Wave Defect Detection Technology Based on Attention Autoencoder and Semisupervised Ladder Network

Zhang, Z., Ding, D., & Wang, L. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3246040 · Cited by 4

ConvNeXt-CNN Based Intelligent Fault Diagnosis Method for Rotating Equipment

Zhang, W., Lin, T., Wang, H., & Song, L. (2022) · International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) · doi:10.1109/icsmd57530.2022.10058420 · Cited by 4

Highly Imbalanced Fault Diagnosis of Rolling Bearings Based on Variational Mode Gaussian Distortion and Deep Residual Shrinkage Networks

Zhang, Z., Zhang, C., & Li, H. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3308256 · Cited by 4

An Approach to Data Modeling via Temporal and Spatial Alignment

Zhang, D., Sun, K., & Zhang, S. (2023) · Processes · doi:10.3390/pr12010062 · Cited by 4

A Star-based Method for the Precise Flux Calibration of the Chinese Space Station Telescope Slitless Spectroscopic Survey

Yang, L., Yuan, H., Duan, F., Zhang, R., Huang, B., Xiao, K., Xu, S., & Zhang, J. (2024) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ad2207 · Cited by 4

Evaluation of bridge structure damage based on deep learning identification model

Yan, X., Zhai, L., & Feng, Z. (2024) · Journal of Computational Methods in Sciences and Engineering · doi:10.3233/jcm-247331 · Cited by 4

Deep Learning for Three Types of Keratitis Classification based on Confocal Microscopy Images

Zhang, X., Ding, G., Gao, C., Li, C., Hu, B., Zhang, C., & Wang, Q. (2020) · doi:10.1145/3432291.3432310 · Cited by 4

Diagnosis for IGBT Open-circuit Faults in Photovoltaic Inverters: A Compressed Sensing and CNN based Method

Wang, X., Yang, B., Liu, Q., Tu, J., & Chen, C. (2021) · IEEE International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin45523.2021.9557384 · Cited by 4

A New Neural Network Based on CNN for EMIS Identification

Xiao, Y., Zhu, F., Zhuang, S., & Yang, Y. (2022) · Journal of Electronic Testing · doi:10.1007/s10836-022-05985-1 · Cited by 4

CrodenseNet: An efficient parallel cross DenseNet for COVID-19 infection detection

Yang, J., Zhang, L., & Tang, X. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103775 · Cited by 4

Ground-state properties via machine learning quantum constraints

Zheng, P.-L., Du, S.-J., & Zhang, Y. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.l032043 · Cited by 4

Assessment of Power System Stability During Transients Using Deep Residual Shrinkage Network and CBAM Integration

Wang, Q., Nie, Z., & Liu, H. (2024) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126624502463 · Cited by 4

Multi-Scale Deep Residual Shrinkage Network for Atrial Fibrillation Recognition

Shi, D., Wu, Z., Zhang, L., Hu, B., & Meng, K. (2022) · International Journal of Computational Intelligence and Applications · doi:10.1142/s1469026822500158 · Cited by 4

Bearing fault diagnosis based on data missing and feature shift suppression strategy

Zhao, Y., & Jun, X. (2024) · Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering · doi:10.1177/09596518241237080 · Cited by 3

Research on broken wire identification method based on PoolConv for prestressed concrete cylinder pipe

Wang, Y., Sun, F. H., Gao, R., Ma, B., & Li, H. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03290-2 · Cited by 3

AGMFormer: An Explainable Intelligent Diagnosis Framework Based on Adaptive Gaussian Mixture Constrained Feature Aggregation Network

Lv, S., Huang, Q., Cui, T., Zhang, Y., & Han, Y. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3552870 · Cited by 3

Deep Residual Shrinkage Networks with Self-Adaptive Slope Thresholding for Fault Diagnosis

Zhang, Z., Li, H., & Chen, L. (2021) · International Conference on Condition Monitoring of Machinery in Non-Stationary Operations (CMMNO) · doi:10.1109/cmmno53328.2021.9467549 · Cited by 10

A novel residual global context shrinkage network based fault diagnosis method for rotating machinery under noisy conditions

Tong, J., Tang, S., Zheng, J., Zhao, H., & Wu, Y. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad3b29 · Cited by 2

Rolling Bearing Fault Diagnosis Using Improved Deep Residual Shrinkage Networks

Zhang, Z., Li, H., Chen, L., & Han, P. (2021) · Shock and Vibration · doi:10.1155/2021/9942249 · Cited by 24

Fault Diagnosis of Rotating Machinery Based on One-Dimensional Deep Residual Shrinkage Network with a Wide Convolution Layer

Yang, J., Gao, T., Jiang, S., Li, S., & Tang, Q. (2020) · Shock and Vibration · doi:10.1155/2020/8880960 · Cited by 21

Deep Residual Networks With Adaptively Parametric Rectifier Linear Units for Fault Diagnosis

Zhao, M., Zhong, S., Fu, X., Tang, B., Dong, S., & PECHT, M. G. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2020.2972458 · Cited by 208