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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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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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Diffractive Deep Neural Networks at Visible Wavelengths Article

Hang Chen, Jianan Feng, Minwei Jiang, Yiqun Wang, Jie Lin, Jiubin Tan, Peng Jin

Engineering 2021, Volume 7, Issue 10,   Pages 1485-1493 doi: 10.1016/j.eng.2020.07.032

Abstract:

Optical deep learning based on diffractive optical elements offers unique advantages for parallelOne landmark method is the diffractive deep neural network (D2NN) based on three-dimensional printing

Keywords： Optical computation     Optical neural networks     Deep learning     Optical machine learning     Diffractive deepneural networks    

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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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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 belief

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 established

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

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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 Deep

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

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A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of

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

Abstract: been considered in this study: those that perform feature extraction by using the convolutional neural networksaccuracy of the popular machine learning methods has been evaluated in comparison with the proposed deepThe average classification accuracy obtained using the proposed deep learning model was 9.55% higher

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

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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 functionally

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

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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: This work introduces a deep-learning network, i.e., multi-input self-organizing-map ResNet (MISR), for

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

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Estimating Rainfall Intensity Using an Image-Based Deep Learning Model Article

Hang Yin, Feifei Zheng, Huan-Feng Duan, Dragan Savic, Zoran Kapelan

Engineering 2023, Volume 21, Issue 2,   Pages 162-174 doi: 10.1016/j.eng.2021.11.021

Abstract: This paper proposes an image-based deep learning model to estimate urban rainfall intensity with high

Keywords： Urban flooding     Rainfall images     Deep learning model     Convolutional neural networks (CNNs)     Rainfall    

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Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models Review

Changde Du, Jinpeng Li, Lijie Huang, Huiguang He

Engineering 2019, Volume 5, Issue 5,   Pages 948-953 doi: 10.1016/j.eng.2019.03.010

Abstract: Next, we show that there are correspondences between deep neural networks and human visual streams interms of the architecture and computational rules Furthermore, deep generative models (e.g., variationalautoencoders (VAEs) and generative adversarial networks (GANs)) have produced promising results in studies

Keywords： Brain encoding and decoding     Functional magnetic resonance imaging     Deep neural networks     Deep generative    

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A Survey of Accelerator Architectures for Deep Neural Networks Review

Yiran Chen, uan Xie, Linghao Song, Fan Chen, Tianqi Tang

Engineering 2020, Volume 6, Issue 3,   Pages 264-274 doi: 10.1016/j.eng.2020.01.007

Abstract: In this article, we focus on summarizing the recent advances in accelerator designs for deep neural networks

Keywords： Deep neural network     Domain-specific architecture     Accelerator    

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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 applicationsThus, network acceleration has become a hot topic within the deep learning community.As for hardware implementation of deep neural networks, a batch of accelerators based on a field-programmable

Keywords： Deep neural networks     Acceleration     Compression     Hardware accelerator    

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Multi-agent deep reinforcement learning for end–edge orchestrated resource allocation in industrial wirelessnetworks Research Article

Xiaoyu LIU, Chi XU, Haibin YU, Peng ZENG,liuxiaoyu1@sia.cn,xuchi@sia.cn,yhb@sia.cn,zp@sia.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 1,   Pages 47-60 doi: 10.1631/FITEE.2100331

Abstract: Edge artificial intelligence will empower the ever simple (IWNs) supporting complex and dynamic tasks by collaboratively exploiting the computation and communication resources of both machine-type devices (MTDs) and edge servers. In this paper, we propose a based resource allocation (MADRL-RA) algorithm for IWNs to support computation-intensive and -sensitive applications. First, we present the system model of IWNs, wherein each MTD is regarded as a self-learning agent. Then, we apply the Markov decision process to formulate a minimum system overhead problem with joint optimization of and . Next, we employ MADRL to defeat the explosive state space and learn an effective resource allocation policy with respect to computing decision, computation capacity, and transmission power. To break the time correlation of training data while accelerating the learning process of MADRL-RA, we design a weighted experience replay to store and sample experiences categorically. Furthermore, we propose a step-by-step -greedy method to balance exploitation and exploration. Finally, we verify the effectiveness of MADRL-RA by comparing it with some benchmark algorithms in many experiments, showing that MADRL-RA converges quickly and learns an effective resource allocation policy achieving the minimum system overhead.

Keywords： Multi-agent deep reinforcement learning     End–edge orchestrated     Industrial wireless networks     Delay    

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Title Author Date Type Operation

Tandem hiddenMarkovmodels using deep belief networks for offline handwriting recognition

Partha Pratim ROY, Guoqiang ZHONG, Mohamed CHERIET

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

Diffractive Deep Neural Networks at Visible Wavelengths

Hang Chen, Jianan Feng, Minwei Jiang, Yiqun Wang, Jie Lin, Jiubin Tan, Peng Jin

Journal Article

Survey on deep learning for pulmonary medical imaging

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

Journal Article

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

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

A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of

Journal Article

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

Journal Article

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

Journal Article

Estimating Rainfall Intensity Using an Image-Based Deep Learning Model

Hang Yin, Feifei Zheng, Huan-Feng Duan, Dragan Savic, Zoran Kapelan

Journal Article

Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models

Changde Du, Jinpeng Li, Lijie Huang, Huiguang He

Journal Article

A Survey of Accelerator Architectures for Deep Neural Networks

Yiran Chen, uan Xie, Linghao Song, Fan Chen, Tianqi Tang

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

Multi-agent deep reinforcement learning for end–edge orchestrated resource allocation in industrial wirelessnetworks

Xiaoyu LIU, Chi XU, Haibin YU, Peng ZENG,liuxiaoyu1@sia.cn,xuchi@sia.cn,yhb@sia.cn,zp@sia.cn

Journal Article