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MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 6, doi: 10.1007/s11783-023-1677-1

Abstract:

● MSWNet was proposed to classify municipal solid waste.

Keywords： Municipal solid waste sorting     Deep residual network     Transfer learning     Cyclic learning rate     Visualization    

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Prediction of bearing capacity of pile foundation using deep learning approaches

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 6,   Pages 870-886 doi: 10.1007/s11709-024-1085-z

Abstract: This research compares the Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Recurrent

Keywords： deep learning algorithms     high-strain dynamic pile test     bearing capacity of the pile    

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Deep learning based water leakage detection for shield tunnel lining

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 6,   Pages 887-898 doi: 10.1007/s11709-024-1071-5

Abstract: A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced

Keywords： water leakage detection     deep learning     deconvolutional-feature pyramid     spatial attention    

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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: CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learningFirst, 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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Forecasting measured responses of structures using temporal deep learning and dual attention

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 6,   Pages 832-850 doi: 10.1007/s11709-024-1092-0

Abstract: The key idea is to design a deep learning architecture to leverage the relationships, between external

Keywords： structural dynamic     time-varying excitation     deep learning     signal processing     response forecasting    

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Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 7,   Pages 994-1010 doi: 10.1007/s11709-023-0942-5

Abstract: Hence, a gated recurrent unit (GRU)-based deep learning framework is proposed herein to dynamically predicteffective decision support for moving trajectory control and serve as a foundation for the application of deeplearning in the automatic control of pipe jacking.

Keywords： dynamic prediction     moving trajectory     pipe jacking     GRU     deep learning    

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Digital image correlation-based structural state detection through deep learning

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 1,   Pages 45-56 doi: 10.1007/s11709-021-0777-x

Abstract: This paper presents a new approach for automatical classification of structural state through deep learningdesigned to fuse both the feature extraction and classification blocks into an intelligent and compact learning

Keywords： structural state detection     deep learning     digital image correlation     vibration signal     steel frame    

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Survey on deep learning for pulmonary medical imaging

Jiechao Ma, Yang Song, Xi Tian, Yiting Hua, Rongguo Zhang, Jianlin Wu

Frontiers of Medicine 2020, Volume 14, Issue 4,   Pages 450-469 doi: 10.1007/s11684-019-0726-4

Abstract: As a promising method in artificial intelligence, deep learning has been proven successful in severalWith medical imaging becoming an important part of disease screening and diagnosis, deep learning-basedDeep learning has been widely applied in medical imaging for improved image analysis.This paper reviews the major deep learning techniques in this time of rapid evolution and summarizesLastly, the application of deep learning techniques to the medical image and an analysis of their future

Keywords： deep learning     neural networks     pulmonary medical image     survey    

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Investigation of crack segmentation and fast evaluation of crack propagation, based on deep learning

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 4,   Pages 516-535 doi: 10.1007/s11709-024-1040-z

Abstract: To address this issue, we explore the potential of deep learning (DL) to increase the efficiency of crack

Keywords： deep learning     crack segmentation     crack propagation     encoder−decoder     recurrent neural network    

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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

Frontiers of Medicine 2022, Volume 16, Issue 3,   Pages 496-506 doi: 10.1007/s11684-021-0828-7

Abstract: In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture

Keywords： XGBoost     deep neural network     healthcare     risk prediction    

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Efficient Identification of water conveyance tunnels siltation based on ensemble deep learning

Xinbin WU; Junjie LI; Linlin WANG

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 5,   Pages 564-575 doi: 10.1007/s11709-022-0829-x

Abstract: This paper introduces the idea of ensemble deep learning.At the same time, the fully-connected network is applied as the meta-learner, and stacking ensemble learning

Keywords： water conveyance tunnels     siltation images     remotely operated vehicles     deep learning     ensemble learning    

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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 (DCNNseveral 1D DCNN models with different activation functions are trained through dimension reduction learningCompared 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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A novel deep learning framework with variational auto-encoder for indoor air quality prediction

Frontiers of Environmental Science & Engineering 2024, Volume 18, Issue 1, doi: 10.1007/s11783-024-1768-7

Abstract:

● PLS-VAER is proposed for modeling of PM2.5 concentration.

Keywords： Indoor air quality     PM2.5 concentration     Variational auto-encoder     Latent variable     Soft measurement modeling    

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Bridging finite element and deep learning: High-resolution stress distribution prediction in structural

Hamed BOLANDI; Xuyang LI; Talal SALEM; Vishnu Naresh BODDETI; Nizar LAJNEF

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 11,   Pages 1365-1377 doi: 10.1007/s11709-022-0882-5

Abstract: Instead, Deep Learning (DL) techniques can generate results significantly faster than conventional run-timeOur proposed method uses deep neural networks in the form of convolutional neural networks (CNN) to bypass

Keywords： Deep Learning     finite element analysis     stress contours     structural components    

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Deep learning in digital pathology image analysis: a survey

Shujian Deng, Xin Zhang, Wen Yan, Eric I-Chao Chang, Yubo Fan, Maode Lai, Yan Xu

Frontiers of Medicine 2020, Volume 14, Issue 4,   Pages 470-487 doi: 10.1007/s11684-020-0782-9

Abstract: deep learning (DL) has achieved state-of-the-art performance in many digital pathology analysis tasksterms of feature extraction, DL approaches are less labor intensive compared with conventional machine learning

Keywords： pathology     deep learning     segmentation     detection     classification    

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Title Author Date Type Operation

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

Journal Article

Prediction of bearing capacity of pile foundation using deep learning approaches

Journal Article

Deep learning based water leakage detection for shield tunnel lining

Journal Article

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

Journal Article

Forecasting measured responses of structures using temporal deep learning and dual attention

Journal Article

Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Journal Article

Digital image correlation-based structural state detection through deep learning

Journal Article

Survey on deep learning for pulmonary medical imaging

Jiechao Ma, Yang Song, Xi Tian, Yiting Hua, Rongguo Zhang, Jianlin Wu

Journal Article

Investigation of crack segmentation and fast evaluation of crack propagation, based on deep learning

Journal Article

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

Journal Article

Efficient Identification of water conveyance tunnels siltation based on ensemble deep learning

Xinbin WU; Junjie LI; Linlin WANG

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

A novel deep learning framework with variational auto-encoder for indoor air quality prediction

Journal Article

Bridging finite element and deep learning: High-resolution stress distribution prediction in structural

Hamed BOLANDI; Xuyang LI; Talal SALEM; Vishnu Naresh BODDETI; Nizar LAJNEF

Journal Article

Deep learning in digital pathology image analysis: a survey

Shujian Deng, Xin Zhang, Wen Yan, Eric I-Chao Chang, Yubo Fan, Maode Lai, Yan Xu

Journal Article