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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    

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

Shaojun ZHU; Makoto OHSAKI; Kazuki HAYASHI; Shaohan ZONG; Xiaonong GUO

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 11,   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    

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    

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    

Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning Research Article

Shihmin WANG, Binqi ZHAO, Zhengfeng ZHANG, Junping ZHANG, Jian PU

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11,   Pages 1541-1556 doi: 10.1631/FITEE.2300084

Abstract: As one of the most fundamental topics in (RL), is essential to the deployment of deep RL algorithms

Keywords: Reinforcement learning     Sample efficiency     Sampling process     Clustering methods     Autonomous driving    

Toward Trustworthy Decision-Making for Autonomous Vehicles: A Robust Reinforcement Learning Approach

Xiangkun He,Wenhui Huang,Chen Lv,

Engineering doi: 10.1016/j.eng.2023.10.005

Abstract: Therefore, we present a novel robust reinforcement learning approach with safety guarantees to attain

Keywords: Autonomous vehicle     Decision-making     Reinforcement learning     Adversarial attack     Safety guarantee    

Anthropomorphic Obstacle Avoidance Trajectory Planning for Adaptive Driving Scenarios Based on Inverse ReinforcementLearning Theory

Jian Wu,Yang Yan,Yulong Liu,Yahui Liu,

Engineering doi: 10.1016/j.eng.2023.07.018

Abstract: , a trajectory expectation feature-matching algorithm is proposed that uses maximum entropy inverse reinforcementlearning theory to learn the extracted expert-demonstrated trajectories and achieve automatic acquisition

Keywords: Obstacle avoidance trajectory planning     Inverse reinforcement theory     Anthropomorphic     Adaptive driving    

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    

Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Frontiers of Structural and Civil Engineering   Pages 994-1010 doi: 10.1007/s11709-023-0942-5

Abstract: Hence, a gated recurrent unit (GRU)-based deep learning framework is proposed herein to dynamically predicteffective decision support for moving trajectory control and serve as a foundation for the application of deeplearning in the automatic control of pipe jacking.

Keywords: dynamic prediction     moving trajectory     pipe jacking     GRU     deep learning    

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    

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    

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    

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    

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    

Incentive-Aware Blockchain-Assisted Intelligent Edge Caching and Computation Offloading for IoT Article

Qian Wang, Siguang Chen, Meng Wu

Engineering 2023, Volume 31, Issue 12,   Pages 127-138 doi: 10.1016/j.eng.2022.10.014

Abstract: Furthermore, a blockchain incentive and contribution co-aware federated deep reinforcement learning algorithm

Keywords: Computation offloading     Caching     Incentive     Blockchain     Federated deep reinforcement learning    

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

Shaojun ZHU; Makoto OHSAKI; Kazuki HAYASHI; Shaohan ZONG; Xiaonong GUO

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

Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning

Shihmin WANG, Binqi ZHAO, Zhengfeng ZHANG, Junping ZHANG, Jian PU

Journal Article

Toward Trustworthy Decision-Making for Autonomous Vehicles: A Robust Reinforcement Learning Approach

Xiangkun He,Wenhui Huang,Chen Lv,

Journal Article

Anthropomorphic Obstacle Avoidance Trajectory Planning for Adaptive Driving Scenarios Based on Inverse ReinforcementLearning Theory

Jian Wu,Yang Yan,Yulong Liu,Yahui Liu,

Journal Article

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

Journal Article

Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Journal Article

Digital image correlation-based structural state detection through deep 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

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

Incentive-Aware Blockchain-Assisted Intelligent Edge Caching and Computation Offloading for IoT

Qian Wang, Siguang Chen, Meng Wu

Journal Article