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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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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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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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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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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: have been considered in this study: those that perform feature extraction by using the convolutional neuralnetworks and those based on an explicit feature extraction procedure.accuracy 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 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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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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Novel interpretable mechanism of neural networks based on network decoupling method

Frontiers of Engineering Management 2021, Volume 8, Issue 4,   Pages 572-581 doi: 10.1007/s42524-021-0169-x

Abstract: The lack of interpretability of the neural network algorithm has become the bottleneck of its wide applicationdecoupled dimension reduction method of high-dimensional system and reveal the calculation mechanism of the neuralthat a simple linear mapping relationship exists between network structure and network behavior in the neuralnew interpretation mechanism provides not only the potential mathematical calculation principle of neuralor animal activities, which can further expand and enrich the interpretable mechanism of artificial neural

Keywords： neural networks     interpretability     dynamical behavior     network decouple    

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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), forThe model is comprised of self-organizing-map and the neural network parts.self-organizing-map part maps the input data into multiple two-dimensional planes and sends them to the neuralIn the neural network part, residual blocks enhance the convergence and accuracy, ensuring that the structurethe product yields and properties than the previously introduced self-organizing-map convolutional neural

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

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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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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 highMore specifically, a convolutional neural network (CNN) model called the image-based rainfall CNN (irCNN

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

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Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networks

J. Sargolzaei, A. Hedayati Moghaddam

Frontiers of Chemical Science and Engineering 2013, Volume 7, Issue 3,   Pages 357-365 doi: 10.1007/s11705-013-1336-3

Abstract: Several simulation systems including a back-propagation neural network (BPNN), a radial basis functionneural network (RBFNN) and an adaptive-network-based fuzzy inference system (ANFIS) were tested andThe performance of these networks was evaluated using the coefficient of determination ( ) and the mean

Keywords： oil recovery     artificial intelligence     extraction     neural networks     supercritical extraction    

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The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for

Tiago Miguel FERREIRA, João ESTÊVÃO, Rui MAIO, Romeu VICENTE

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 3,   Pages 609-622 doi: 10.1007/s11709-020-0623-6

Abstract: grades obtained resorting to a classic damage formulation and an innovative approach based on Artificial NeuralNetworks (ANNs).

Keywords： Artificial Neural Networks     seismic vulnerability     masonry buildings     damage estimation     vulnerability curves    

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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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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

Diffractive Deep Neural Networks at Visible Wavelengths

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

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

Survey on deep learning for pulmonary medical imaging

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

Journal Article

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

Journal Article

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

Journal Article

A Survey of Accelerator Architectures for Deep Neural Networks

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

Journal Article

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

Journal Article

Novel interpretable mechanism of neural networks based on network decoupling method

Journal Article

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

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

Estimating Rainfall Intensity Using an Image-Based Deep Learning Model

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

Journal Article

Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networks

J. Sargolzaei, A. Hedayati Moghaddam

Journal Article

The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for

Tiago Miguel FERREIRA, João ESTÊVÃO, Rui MAIO, Romeu VICENTE

Journal Article

Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models

Changde Du, Jinpeng Li, Lijie Huang, Huiguang He

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