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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.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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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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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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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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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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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 hybrid spatial-temporal deep learning prediction model of industrial methanol-to-olefins process

Frontiers of Chemical Science and Engineering 2024, Volume 18, Issue 4, doi: 10.1007/s11705-024-2403-7

Abstract: In this paper, we propose a novel hybrid spatial-temporal deep learning prediction model to address theseSubsequently, convolutional neural network integrated with the self-attention mechanism are utilizedMeanwhile, a multi-graph convolutional network is leveraged to model the spatial interactions.Afterward, the extracted temporal and spatial features are fused as input into a fully connected neural network

Keywords： methanol-to-olefins     process variables prediction     spatial-temporal     self-attention mechanism     graph convolutional network    

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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: In a supervised learning framework, Ground Penetrating Radar (GPR) profiles and the revealed structural

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

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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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Adversarial Attacks and Defenses in Deep Learning Feature Article

Kui Ren, Tianhang Zheng, Zhan Qin, Xue Liu

Engineering 2020, Volume 6, Issue 3,   Pages 346-360 doi: 10.1016/j.eng.2019.12.012

Abstract:

With the rapid developments of artificial intelligence (AI) and deep learning (DL) techniques, itadversarial attack and defense techniques have attracted increasing attention from both machine
learning

Keywords： Machine learning     Deep neural network Adversarial example     Adversarial attack     Adversarial defense    

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

Amit SHIULY; Debabrata DUTTA; Achintya MONDAL

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 3,   Pages 347-358 doi: 10.1007/s11709-022-0819-z

Abstract: In the present study, a new image-based machine learning method is used to predict concrete compressiveThese include support-vector machine model and various deep convolutional neural network models, namelyThe images and corresponding compressive strength were then used to train machine learning models toOverall, the present findings validated the use of machine learning models as an efficient means of estimating

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

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Deep learning model for estimating the mechanical properties of concrete containing silica fume exposed

Harun TANYILDIZI, Abdulkadir ŞENGÜR, Yaman AKBULUT, Murat ŞAHİN

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 6,   Pages 1316-1330 doi: 10.1007/s11709-020-0646-z

Abstract: In this study, the deep learning models for estimating the mechanical properties of concrete containingTwo well-known deep learning approaches, i.e., stacked autoencoders and long short-term memory (LSTM)The forecasting experiments were carried out using MATLAB deep learning and neural network tools, respectivelyThis study found that the LSTM network achieved better results than the stacked autoencoders.In addition, this study found that deep learning, which has a very good prediction ability with little

Keywords： concrete     high temperature     strength properties     deep learning     stacked auto-encoders     LSTM network    

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Machine vision-based automatic fruit quality detection and grading

Frontiers of Agricultural Science and Engineering doi: 10.15302/J-FASE-2023532

Abstract:

● A machine vision-based prototype system was developed for fruit grading.

Keywords： Computer and machine vision     convolution neural network     deep learning     defective fruit detection     fruit    

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Combining graph neural network with deep reinforcement learning for resource allocation in computing Research Article

Xueying HAN, Mingxi XIE, Ke YU, Xiaohong HUANG, Zongpeng DU, Huijuan YAO,hanxueying@bupt.edu.cn,yuke@bupt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2024, Volume 25, Issue 5,   Pages 701-712 doi: 10.1631/FITEE.2300009

Abstract: by the explosive growth of ultra-low-latency and real-time applications with specific computing and networkThe primary CFN challenge is to leverage network resources and computing resources.Although recent advances in deep reinforcement learning (DRL) have brought significant improvement innetwork optimization, these methods still suffer from topology changes and fail to generalize for thoseThis paper proposes a (GNN) based DRL framework to accommodate network traffic and computing resources

Keywords： Computing force network     Routing optimization     Deep learning     Graph neural network     Resource allocation    

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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), Recurrentmodel is better than LSTM because the BiLSTM model, which increases the amount of information for the networkconsiderable multicollinearity level has been determined for the model based on the recurrent neural networkIn this study, the recurrent neural network model has the least performance and accuracy in predicting

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

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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

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

Journal Article

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

Journal Article

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

Journal Article

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

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 hybrid spatial-temporal deep learning prediction model of industrial methanol-to-olefins process

Journal Article

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

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

Adversarial Attacks and Defenses in Deep Learning

Kui Ren, Tianhang Zheng, Zhan Qin, Xue Liu

Journal Article

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

Amit SHIULY; Debabrata DUTTA; Achintya MONDAL

Journal Article

Deep learning model for estimating the mechanical properties of concrete containing silica fume exposed

Harun TANYILDIZI, Abdulkadir ŞENGÜR, Yaman AKBULUT, Murat ŞAHİN

Journal Article

Machine vision-based automatic fruit quality detection and grading

Journal Article

Combining graph neural network with deep reinforcement learning for resource allocation in computing

Xueying HAN, Mingxi XIE, Ke YU, Xiaohong HUANG, Zongpeng DU, Huijuan YAO,hanxueying@bupt.edu.cn,yuke@bupt.edu.cn

Journal Article

Prediction of bearing capacity of pile foundation using deep learning approaches

Journal Article