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Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 1,   Pages 80-98 doi: 10.1007/s11709-021-0682-3

Abstract: probabilistic framework for evaluating liquefaction-induced lateral displacement using the Bayesian beliefnetwork (BBN) approach based on an interpretive structural modeling technique.

Keywords： Bayesian belief network     seismically induced soil liquefaction     interpretive structural modeling     lateral    

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Online Monitoring of Welding Status Based on a DBN Model During Laser Welding Article

Yanxi Zhang, Deyong You, Xiangdong Gao, Seiji Katayama

Engineering 2019, Volume 5, Issue 4,   Pages 671-678 doi: 10.1016/j.eng.2019.01.016

Abstract: Based on these real-time quantized features of the welding process, a deep belief network (DBN) is establishedand robustness in monitoring welding status in comparison with a traditional back-propagation neural network

Keywords： Online monitoring     Multiple sensors     Wavelet packet decomposition     Deep belief network    

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Tandem hiddenMarkovmodels using deep belief networks for offline handwriting recognition Article

Partha Pratim ROY, Guoqiang ZHONG, Mohamed CHERIET

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 7,   Pages 978-988 doi: 10.1631/FITEE.1600996

Abstract: In this paper, we propose a deep architecture-based tandem approach for unconstrained offline handwritingIn the proposed model, deep belief networks are adopted to learn the compact representations of sequential

Keywords： Handwriting recognition     Hidden Markov models     Deep learning     Deep belief networks     Tandem approach    

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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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Finite-sensor fault-diagnosis simulation study of gas turbine engine using information entropy and deepbelief networks Article

De-long FENG,Ming-qing XIAO,Ying-xi LIU,Hai-fang SONG,Zhao YANG,Ze-wen HU

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 12,   Pages 1287-1304 doi: 10.1631/FITEE.1601365

Abstract: problem of engine fault diagnosis using finite sensory data under complicated circumstances, and propose deepbelief networks based on information entropy, IE-DBNs, for engine fault diagnosis.Second, the deep belief networks (DBNs) is analyzed and a logistic regression layer is added to the output

Keywords： Deep belief networks (DBNs)     Fault diagnosis     Information entropy     Engine    

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

Khuong LE-NGUYEN; Quyen Cao MINH; Afaq AHMAD; Lanh Si HO

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 10,   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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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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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 networkBefore 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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A deep feed-forward neural network for damage detection in functionally graded carbon nanotube-reinforced

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 6,   Pages 1453-1479 doi: 10.1007/s11709-021-0767-z

Abstract: This paper proposes a new Deep Feed-forward Neural Network (DFNN) approach for damage detection in functionallyA trial-and-error procedure is implemented to determine suitable parameters of the network such as the

Keywords： damage detection     deep feed-forward neural networks     functionally graded carbon nanotube-reinforced composite    

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Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 6, doi: 10.1007/s11783-023-1667-3

Abstract:

● Hybrid deep-learning model is proposed for water quality prediction

Keywords： Water quality prediction     Soft-sensor     Anaerobic process     Tree-structured Parzen Estimator    

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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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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 recent years, great attention has focused on the development of automated procedures for infrastructures control. Many efforts have aimed at greater speed and reliability compared to traditional methods of assessing structural conditions. The paper proposes a multi-level strategy, designed and implemented on the basis of periodic structural monitoring oriented to a cost- and time-efficient tunnel control plan. Such strategy leverages the high capacity of convolutional neural networks to identify and classify potential critical situations. In a supervised learning framework, Ground Penetrating Radar (GPR) profiles and the revealed structural phenomena have been used as input and output to train and test such networks. Image-based analysis and integrative investigations involving video-endoscopy, core drilling, jacking and pull-out testing have been exploited to define the structural conditions linked to GPR profiles and to create the database. The degree of detail and accuracy achieved in identifying a structural condition is high. As a result, this strategy appears of value to infrastructure managers who need to reduce the amount and invasiveness of testing, and thus also to reduce the time and costs associated with inspections made by highly specialized technicians.

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

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Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 2,   Pages 490-505 doi: 10.1007/s11709-020-0669-5

Abstract: potential of soil based on the cone penetration test field case history records using the Bayesian beliefnetwork (BBN) learning software Netica.

Keywords： seismic soil liquefaction     Bayesian belief network     cone penetration test     parameter learning     structural    

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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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Title Author Date Type Operation

Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD

Journal Article

Online Monitoring of Welding Status Based on a DBN Model During Laser Welding

Yanxi Zhang, Deyong You, Xiangdong Gao, Seiji Katayama

Journal Article

Tandem hiddenMarkovmodels using deep belief networks for offline handwriting recognition

Partha Pratim ROY, Guoqiang ZHONG, Mohamed CHERIET

Journal Article

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

Journal Article

Finite-sensor fault-diagnosis simulation study of gas turbine engine using information entropy and deepbelief networks

De-long FENG,Ming-qing XIAO,Ying-xi LIU,Hai-fang SONG,Zhao YANG,Ze-wen HU

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

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

Khuong LE-NGUYEN; Quyen Cao MINH; Afaq AHMAD; Lanh Si HO

Journal Article

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

Journal Article

Multiclass classification based on a deep convolutional

Ying CAI,Meng-long YANG,Jun LI

Journal Article

A deep feed-forward neural network for damage detection in functionally graded carbon nanotube-reinforced

Journal Article

Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured

Journal Article

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

Journal Article

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

Journal Article

Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU

Journal Article

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

Journal Article