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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.First, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controllingSecond, a new structure of DRL is designed by combining deep deterministic policy gradient and long short-termACNN is also compared with other published machine learning (ML) and deep learning (DL) methods.

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

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

Frontiers of Structural and Civil Engineering   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 potential

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

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

Frontiers of Mechanical Engineering   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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Multiclass classification based on a deep convolutional

Ying CAI,Meng-long YANG,Jun LI

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 11,   Pages 930-939 doi: 10.1631/FITEE.1500125

Abstract: In this paper we propose a novel method to estimate head pose based on a deep convolutional neural networkThen two convolutional neural networks are set up to train the head pose classifier and then comparedBefore training the network, two reasonable strategies including shift and zoom are executed to prepare

Keywords： Head pose estimation     Deep convolutional neural network     Multiclass classification    

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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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A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 2, doi: 10.1007/s11783-023-1622-3

Abstract:

● A novel deep learning framework for short-term water demand forecasting

Keywords： Short-term water demand forecasting     Long-short term memory neural network     Convolutional Neural Network    

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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: this challenge, this paper presents a method for automating concrete damage classification using a deepconvolutional neural network.The convolutional neural network was designed after an experimental investigation of a wide number ofTo increase the network robustness compared to images in real-world situations, different image configurationsmodel, with the highest validation accuracy of approximately 94%, was selected as the most suitable network

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

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Slope stability analysis based on big data and convolutional neural network

Frontiers of Structural and Civil Engineering   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 neuralnetwork show that the CNN has significant superiority in slope stability evaluation.

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

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Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques

Frontiers of Structural and Civil Engineering   Pages 347-358 doi: 10.1007/s11709-022-0819-z

Abstract: These include support-vector machine model and various deep convolutional neural network models, namely

Keywords： support vector machine     deep convolutional neural network     microscope     digital image     curing period    

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

Frontiers of Structural and Civil Engineering   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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Development of deep neural network model to predict the compressive strength of FRCM confined columns

Frontiers of Structural and Civil Engineering   Pages 1213-1232 doi: 10.1007/s11709-022-0880-7

Abstract: confinement influence with Fabric-Reinforced Cementitious Matrix (FRCM). through both physical models and DeepNeural Network model (artificial neural network (ANN) with double and triple hidden layers).

Keywords： FRCM     deep neural networks     confinement effect     strength model     confined concrete    

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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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Online recognition of drainage type based on UV-vis spectra and derivative neural network algorithm

Frontiers of Environmental Science & Engineering 2021, Volume 15, Issue 6, doi: 10.1007/s11783-021-1430-6

Abstract:

• UV-vis absorption analyzer was applied in drainage type online recognition.

Keywords： Drainage online recognition     UV-vis spectra     Derivative spectrum     Convolutional neural network    

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Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples

Xin ZHANG, Tao HUANG, Bo WU, Youmin HU, Shuai HUANG, Quan ZHOU, Xi ZHANG

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 2,   Pages 340-352 doi: 10.1007/s11465-021-0629-3

Abstract: Deep learning has achieved much success in mechanical intelligent fault diagnosis in recent years.However, many deep learning methods cannot fully extract fault information to recognize mechanical healthTherefore, a multi-model ensemble deep learning method based on deep convolutional neural network (DCNNCompared with other classical deep learning methods, the proposed fault diagnosis method has considerable

Keywords： fault intelligent diagnosis     deep learning     deep convolutional neural network     high-dimensional samples    

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DAN: a deep association neural network approach for personalization recommendation Research Articles

Xu-na Wang, Qing-mei Tan,Xuna@nuaa.edu.cn,tanchina@nuaa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7,   Pages 963-980 doi: 10.1631/FITEE.1900236

Abstract: At present, s mostly use deep s to model some of the auxiliary information, and in the process of modelingHowever, these deep algorithms ignore the combined effects of different categories of data, which canAimed at this problem, in this paper we propose a feedforward deep method, called the deep associationEmpirical evidence shows that deep, joint s can provide better performance.

Keywords： Neural network     Deep learning     Deep association neural network (DAN)     Recommendation    

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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

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

Journal Article

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

Journal Article

Multiclass classification based on a deep convolutional

Ying CAI,Meng-long YANG,Jun LI

Journal Article

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

Journal Article

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

Journal Article

Automated classification of civil structure defects based on convolutional neural network

Pierclaudio SAVINO, Francesco TONDOLO

Journal Article

Slope stability analysis based on big data and convolutional neural network

Journal Article

Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques

Journal Article

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

Journal Article

Development of deep neural network model to predict the compressive strength of FRCM confined columns

Journal Article

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

Liang XUE, Jie LIU, Guilin WEN, Hongxin WANG

Journal Article

Online recognition of drainage type based on UV-vis spectra and derivative neural network algorithm

Journal Article

Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples

Xin ZHANG, Tao HUANG, Bo WU, Youmin HU, Shuai HUANG, Quan ZHOU, Xi ZHANG

Journal Article

DAN: a deep association neural network approach for personalization recommendation

Xu-na Wang, Qing-mei Tan,Xuna@nuaa.edu.cn,tanchina@nuaa.edu.cn

Journal Article