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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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Deep reinforcement learning-based critical element identification and demolition planning of frame structures

Frontiers of Structural and Civil Engineering   Pages 1397-1414 doi: 10.1007/s11709-022-0860-y

Abstract: quantitative indices considering the severity of the ultimate collapse scenario are proposed using reinforcementlearning and graph embedding.index-based methods, it is demonstrated that the computational cost is considerably reduced because the reinforcementlearning model is trained offline.Besides, it is proved that the Q values produced by the reinforcement learning agent can make

Keywords： progressive collapse     alternate load path     demolition planning     reinforcement learning     graph embedding    

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Toward Human-in-the-loop AI: Enhancing Deep Reinforcement Learning Via Real-time Human Guidance for Autonomous Article

Jingda Wu, Zhiyu Huang, Zhongxu Hu, Chen Lv

Engineering 2023, Volume 21, Issue 2,   Pages 75-91 doi: 10.1016/j.eng.2022.05.017

Abstract:

Due to its limited intelligence and abilities, machine learning is currentlytraining loop of artificial intelligence (AI), leveraging human intelligence to further advance machine learningIn this study, a real-time human-guidance-based (Hug)-deep reinforcement learning (DRL) method is developedvalidated by human-in-the-loop experiments with 40 subjects and compared with other state-of-the-art learning

Keywords： Human-in-the-loop AI     Deep reinforcement learning     Human guidance     Autonomous driving    

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Automated synthesis of steady-state continuous processes using reinforcement learning

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 288-302 doi: 10.1007/s11705-021-2055-9

Abstract: The present work demonstrates how reinforcement learning can be used for automated flowsheet synthesis

Keywords： automated process synthesis     flowsheet synthesis     artificial intelligence     machine learning     reinforcementlearning    

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

Frontiers of Structural and Civil Engineering   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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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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Recent development on statistical methods for personalized medicine discovery

Yingqi Zhao, Donglin Zeng

Frontiers of Medicine 2013, Volume 7, Issue 1,   Pages 102-110 doi: 10.1007/s11684-013-0245-7

Abstract:

It is well documented that patients can show significant heterogeneous responses to treatments so the best treatment strategies may require adaptation over individuals and time. Recently, a number of new statistical methods have been developed to tackle the important problem of estimating personalized treatment rules using single-stage or multiple-stage clinical data. In this paper, we provide an overview of these methods and list a number of challenges.

Keywords： dynamic treatment regimes     personalized medicine     reinforcement learning     Q-learning    

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Multi-agent deep reinforcement learning for end–edge orchestrated resource allocation in industrial wireless 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: First, we present the system model of IWNs, wherein each MTD is regarded as a self-learning agent.To break the time correlation of training data while accelerating the learning process of MADRL-RA, we

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

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

Frontiers of Structural and Civil Engineering   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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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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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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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: The classification accuracy of the popular machine learning methods has been evaluated in comparisonwith the proposed deep learning model.The average classification accuracy obtained using the proposed deep learning model was 9.55% higherthan the best machine learning algorithm considered in this paper.

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

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Deep reinforcement learning: a survey

Hao-nan Wang, Ning Liu, Yi-yun Zhang, Da-wei Feng, Feng Huang, Dong-sheng Li, Yi-ming Zhang,wanghaonan14@nudt.edu.cn,liuning17a@nudt.edu.cn,zhangyiyun213@163.com,fengdawei@nudt.edu.cn,huangfeng@nudt.edu.cn,dsli@nudt.edu.cn,zhangyiming@nudt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 12,   Pages 1671-1814 doi: 10.1631/FITEE.1900533

Abstract: Deep (RL) has become one of the most popular topics in artificial intelligence research.In this survey, we systematically categorize the deep RL algorithms and applications, and provide a detailedreview over existing deep RL algorithms by dividing them into model-based methods, model-free methods
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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

Deep reinforcement learning-based critical element identification and demolition planning of frame structures

Journal Article

Toward Human-in-the-loop AI: Enhancing Deep Reinforcement Learning Via Real-time Human Guidance for Autonomous

Jingda Wu, Zhiyu Huang, Zhongxu Hu, Chen Lv

Journal Article

Automated synthesis of steady-state continuous processes using reinforcement 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

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

Journal Article

Digital image correlation-based structural state detection through deep learning

Journal Article

Recent development on statistical methods for personalized medicine discovery

Yingqi Zhao, Donglin Zeng

Journal Article

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

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

Journal Article

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

Journal Article

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

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

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

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

Journal Article

Deep reinforcement learning: a survey

Hao-nan Wang, Ning Liu, Yi-yun Zhang, Da-wei Feng, Feng Huang, Dong-sheng Li, Yi-ming Zhang,wanghaonan14@nudt.edu.cn,liuning17a@nudt.edu.cn,zhangyiyun213@163.com,fengdawei@nudt.edu.cn,huangfeng@nudt.edu.cn,dsli@nudt.edu.cn,zhangyiming@nudt.edu.cn

Journal Article