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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 controllingACNN 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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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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Stochastic pedestrian avoidance for autonomous vehicles using hybrid reinforcement learning Research Article

Huiqian LI, Jin HUANG, Zhong CAO, Diange YANG, Zhihua ZHONG,lihq20@mails.tsinghua.edu.cn,huangjin@tsinghua.edu.cn,caoc15@mails.tsinghua.edu.cn,ydg@tsinghua.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 1,   Pages 131-140 doi: 10.1631/FITEE.2200128

Abstract: Classical avoidance strategies cannot handle uncertainty, and learning-based methods lack performanceThe method integrates the rule-based strategy and reinforcement learning strategy.

Keywords： Pedestrian     Hybrid reinforcement learning     Autonomous vehicles     Decision-making    

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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    

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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    

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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    

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Layout optimization of steel reinforcement in concrete structure using a truss-continuum model

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 5,   Pages 669-685 doi: 10.1007/s11709-023-0963-0

Abstract: Owing to advancement in advanced manufacturing technology, the reinforcement design of concrete structuresevolutionary structural optimization (BESO), a new approach is developed in this study to optimize the reinforcementThis approach combines a minimum compliance objective function with a hybrid truss-continuum model.To demonstrate the effectiveness of the proposed procedures, reinforcement layout optimizations of a

Keywords： bi-directional evolutionary structural optimization     steel-reinforced concrete     concrete stress     reinforcementmethod     hybrid model    

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Fatigue shear performance of concrete beams reinforced with hybrid (glass-fiber-reinforced polymer+ steel

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 3,   Pages 576-594 doi: 10.1007/s11709-021-0728-6

Abstract: Therefore, beams with hybrid longitudinal bars and hybrid stirrups were designed, and fatigue shear tests

Keywords： fatigue     shear     hybrid stirrups     hybrid reinforcement     fiber-reinforced polymer    

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Innovative hybrid reinforcement constituting conventional longitudinal steel and FRP stirrups for improved

Mostafa FAKHARIFAR,Ahmad DALVAND,Mohammad K. SHARBATDAR,Genda CHEN,Lesley SNEED

Frontiers of Structural and Civil Engineering 2016, Volume 10, Issue 1,   Pages 44-62 doi: 10.1007/s11709-015-0295-9

Abstract: The current research has proposed a novel economical hybrid reinforcement scheme for the next generationThe hybrid reinforcement consists of conventional longitudinal steel reinforcement and FRP stirrups.The key feature of the proposed hybrid reinforcement is the enhanced strength and ductility owing toThe proposed hybrid reinforcement, when compared with conventional steel stirrups, is found to have higherDesign methods, structural behavior, and applicability of the proposed hybrid reinforcement are discussed

Keywords： FRP     ductility     confinement     seismic     shear    

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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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Predicting shear strength of slender beams without reinforcement using hybrid gradient boosting trees

Thuy-Anh NGUYEN; Hai-Bang LY; Van Quan TRAN

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 10,   Pages 1267-1286 doi: 10.1007/s11709-022-0842-0

Abstract: The primary purpose of this work is to develop machine learning models capable of reliably predictingbeam's geometrical components and material used to achieve the desired shear strength of SB without reinforcement

Keywords： slender beam     shear strength     gradient boosting     optimization algorithms    

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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.than the best machine learning algorithm considered in this paper.Moreover, the robustness of the hybrid model has been studied by considering the white Gaussian and coherentHence, the proposed hybrid model provides an efficient way of fusing different sources of process data

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

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Hybrid flexural components: Testing pre-stressed steel and GFRP bars together as reinforcement for flexural

Mohammed FARUQI, Oved I. MATA, Francisco AGUINIGA

Frontiers of Structural and Civil Engineering 2018, Volume 12, Issue 3,   Pages 352-360 doi: 10.1007/s11709-017-0453-3

Abstract: However, the flexure behavior of a hybrid system reinforced by the combination of pre-stressed steelThe theoretical slabs were either reinforced with pre-stressed steel or GFRP rebars, or a hybrid systemIt was found that the hybrid system produces better results.

Keywords： Partial pre-stressing     composite structures     GFRP bars    

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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     Wavelet multi-resolution analysis     Data-driven models    

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Actor–Critic Reinforcement Learning and Application in Developing Computer-Vision-Based Interface Tracking Article

Oguzhan Dogru, Kirubakaran Velswamy, Biao Huang

Engineering 2021, Volume 7, Issue 9,   Pages 1248-1261 doi: 10.1016/j.eng.2021.04.027

Abstract: A reinforcement learning (RL) agent successfully tracks an interface between two liquids, which is oftenUnlike supervised learning (SL) methods that rely on a huge number of parameters, this approach requires

Keywords： Interface tracking     Object tracking     Occlusion     Reinforcement learning     Uniform manifold approximation    

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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

Automated synthesis of steady-state continuous processes using reinforcement learning

Journal Article

Stochastic pedestrian avoidance for autonomous vehicles using hybrid reinforcement learning

Huiqian LI, Jin HUANG, Zhong CAO, Diange YANG, Zhihua ZHONG,lihq20@mails.tsinghua.edu.cn,huangjin@tsinghua.edu.cn,caoc15@mails.tsinghua.edu.cn,ydg@tsinghua.edu.cn

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 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

Layout optimization of steel reinforcement in concrete structure using a truss-continuum model

Journal Article

Fatigue shear performance of concrete beams reinforced with hybrid (glass-fiber-reinforced polymer+ steel

Journal Article

Innovative hybrid reinforcement constituting conventional longitudinal steel and FRP stirrups for improved

Mostafa FAKHARIFAR,Ahmad DALVAND,Mohammad K. SHARBATDAR,Genda CHEN,Lesley SNEED

Journal Article

Recent development on statistical methods for personalized medicine discovery

Yingqi Zhao, Donglin Zeng

Journal Article

Predicting shear strength of slender beams without reinforcement using hybrid gradient boosting trees

Thuy-Anh NGUYEN; Hai-Bang LY; Van Quan TRAN

Journal Article

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

Journal Article

Hybrid flexural components: Testing pre-stressed steel and GFRP bars together as reinforcement for flexural

Mohammed FARUQI, Oved I. MATA, Francisco AGUINIGA

Journal Article

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

Journal Article

Actor–Critic Reinforcement Learning and Application in Developing Computer-Vision-Based Interface Tracking

Oguzhan Dogru, Kirubakaran Velswamy, Biao Huang

Journal Article