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A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 2, doi: 10.1007/s11465-022-0673-7

Abstract: Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis.

Keywords： deep reinforcement learning     hyper parameter optimization     convolutional neural network     fault diagnosis    

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Automated classification of civil structure defects based on convolutional neural network

Pierclaudio SAVINO, Francesco TONDOLO

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 2,   Pages 305-317 doi: 10.1007/s11709-021-0725-9

Abstract: challenge, this paper presents a method for automating concrete damage classification using a deep convolutionalneural network.The convolutional neural network was designed after an experimental investigation of a wide number ofpretrained networks, applying the transfer-learning technique.

Keywords： concrete structure     infrastructures     visual inspection     convolutional neural network     artificial intelligence    

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Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 2,   Pages 214-223 doi: 10.1007/s11709-021-0800-2

Abstract: Such strategy leverages the high capacity of convolutional neural networks to identify and classify potentialprofiles and the revealed structural phenomena have been used as input and output to train and test such networks

Keywords： concrete structure     GPR     damage classification     convolutional neural network     transfer learning    

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Fault diagnosis of axial piston pumps with multi-sensor data and convolutional neural network

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 3, doi: 10.1007/s11465-022-0692-4

Abstract: pressure signals under different pump health conditions are fused into RGB images and then recognized by a convolutionalneural network.

Keywords： axial piston pump     fault diagnosis     convolutional neural network     multi-sensor data fusion    

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Automated identification of steel weld defects, a convolutional neural network improved machine learning

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 2,   Pages 294-308 doi: 10.1007/s11709-024-1045-7

Abstract: Classic and convolutional neural network-enhanced algorithms were used to classify, the extracted featuresThe convolutional neural network-enhanced support vector machine (SVM) outperformed six other algorithms

Keywords： steel weld     machine learning     convolutional neural network     weld defect detection     classification task    

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A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of thin-walled structural components

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 3, doi: 10.1007/s11465-022-0688-0

Abstract: classification schemes have been considered in this study: those that perform feature extraction by using the convolutionalneural networks and those based on an explicit feature extraction procedure.

Keywords： precision milling     dimensional accuracy     cutting force     convolutional neural networks     coherent noise    

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Multiple input self-organizing-map ResNet model for optimization of petroleum refinery conversion units

Frontiers of Chemical Science and Engineering 2023, Volume 17, Issue 6,   Pages 759-771 doi: 10.1007/s11705-022-2269-5

Abstract: The model is comprised of self-organizing-map and the neural network parts.self-organizing-map part maps the input data into multiple two-dimensional planes and sends them to the neuralIn the neural network part, residual blocks enhance the convergence and accuracy, ensuring that the structuremore accurately the product yields and properties than the previously introduced self-organizing-map convolutionalneural network model, thus leading to more accurate optimization of the hydrocracker operation.

Keywords： hydrocracking     convolutional neural networks     self-organizing map     deep learning     data-driven optimization    

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A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3 Regular Papers

Lu-di WANG, Wei ZHOU, Ying XING, Na LIU, Mahmood MOVAHEDIPOUR, Xiao-guang ZHOU

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 3,   Pages 405-413 doi: 10.1631/FITEE.1700413

Abstract: In this study, we present a novel method based on convolutional neural networks (CNNs) for the synthesis

Keywords： Convolutional neural networks (CNNs)     Electrocardiogram (ECG) synthesis     E-health    

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Detecting large-scale underwater cracks based on remote operated vehicle and graph convolutional neural

Wenxuan CAO; Junjie LI

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 11,   Pages 1378-1396 doi: 10.1007/s11709-022-0855-8

Abstract: The graph convolutional neural network (GCN) was used to segment the stitched image.The GCN’s m-IOU is 24.02% higher than Fully convolutional networks (FCN), proving that GCN has

Keywords： underwater cracks     remote operated vehicle     image stitching     image segmentation     graph convolutionalneural network    

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Efficient, high-resolution topology optimization method based on convolutional neural networks

Liang XUE, Jie LIU, Guilin WEN, Hongxin WANG

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 1,   Pages 80-96 doi: 10.1007/s11465-020-0614-2

Abstract: efficient, high-resolution topology optimization method is developed based on the super-resolution convolutionalneural network (SRCNN) technique in the framework of SIMP.

Keywords： topology optimization     convolutional neural network     high resolution     density-based    

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Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 814-828 doi: 10.1007/s11465-021-0650-6

Abstract: Deep neural networks have provided unprecedented opportunities to condition monitoring from a new perspectiveTo address this issue, this paper explores a decision-tree-structured neural network, that is, the deepconvolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings.The proposed model effectively integrates the advantages of convolutional neural network (CNN) and decision

Keywords： bearing     cross-severity fault diagnosis     hierarchical fault diagnosis     convolutional neural network    

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Slope stability analysis based on big data and convolutional neural network

Yangpan FU; Mansheng LIN; You ZHANG; Gongfa CHEN; Yongjian LIU

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 7,   Pages 882-895 doi: 10.1007/s11709-022-0859-4

Abstract: In this case, the convolutional neural network (CNN) provides a better alternative.sample database for slope stability analysis reaches more than 99%, and the comparisons with the BP neural

Keywords： slope stability     limit equilibrium method     convolutional neural network     database for slopes     big data    

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Novel interpretable mechanism of neural networks based on network decoupling method

Frontiers of Engineering Management 2021, Volume 8, Issue 4,   Pages 572-581 doi: 10.1007/s42524-021-0169-x

Abstract: The lack of interpretability of the neural network algorithm has become the bottleneck of its wide applicationdecoupled dimension reduction method of high-dimensional system and reveal the calculation mechanism of the neuralthat a simple linear mapping relationship exists between network structure and network behavior in the neuralnew interpretation mechanism provides not only the potential mathematical calculation principle of neuralor animal activities, which can further expand and enrich the interpretable mechanism of artificial neural

Keywords： neural networks     interpretability     dynamical behavior     network decouple    

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Crack identification in concrete, using digital image correlation and neural network

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 4,   Pages 536-550 doi: 10.1007/s11709-024-1013-2

Abstract: Digital image correlation (DIC) technology can provide a large amount of experimental data, and neuralTherefore, NN, including convolutional neural networks (CNN) and back propagation neural networks (BP

Keywords： digital image correlation     convolutional neural network     back propagation neural neural network     damage    

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Recent advances in efficient computation of deep convolutional neural networks Review

Jian CHENG, Pei-song WANG, Gang LI, Qing-hao HU, Han-qing LU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1,   Pages 64-77 doi: 10.1631/FITEE.1700789

Abstract: Deep neural networks have evolved remarkably over the past few years and they are currently the fundamentalAt the same time, the computational complexity and resource consumption of these networks continue toThis poses a significant challenge to the deployment of such networks, especially in real-time applicationsAs for hardware implementation of deep neural networks, a batch of accelerators based on a field-programmablethe following topics: network pruning, low-rank approximation, network quantization, teacher–student networks

Keywords： Deep neural networks     Acceleration     Compression     Hardware accelerator    

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Title Author Date Type Operation

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Journal Article

Automated classification of civil structure defects based on convolutional neural network

Pierclaudio SAVINO, Francesco TONDOLO

Journal Article

Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

Journal Article

Fault diagnosis of axial piston pumps with multi-sensor data and convolutional neural network

Journal Article

Automated identification of steel weld defects, a convolutional neural network improved machine learning

Journal Article

A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of thin-walled structural components

Journal Article

Multiple input self-organizing-map ResNet model for optimization of petroleum refinery conversion units

Journal Article

A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3

Lu-di WANG, Wei ZHOU, Ying XING, Na LIU, Mahmood MOVAHEDIPOUR, Xiao-guang ZHOU

Journal Article

Detecting large-scale underwater cracks based on remote operated vehicle and graph convolutional neural

Wenxuan CAO; Junjie LI

Journal Article

Efficient, high-resolution topology optimization method based on convolutional neural networks

Liang XUE, Jie LIU, Guilin WEN, Hongxin WANG

Journal Article

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical

Journal Article

Slope stability analysis based on big data and convolutional neural network

Yangpan FU; Mansheng LIN; You ZHANG; Gongfa CHEN; Yongjian LIU

Journal Article

Novel interpretable mechanism of neural networks based on network decoupling method

Journal Article

Crack identification in concrete, using digital image correlation and neural network

Journal Article

Recent advances in efficient computation of deep convolutional neural networks

Jian CHENG, Pei-song WANG, Gang LI, Qing-hao HU, Han-qing LU

Journal Article