Search | Engineering

Subscribe Submit

Home Journals Focus Videos Achievement Fronts Contact Us 中文版

Resource Type

Journal Article 299

Conference Videos 6

Year

2023 12

2022 57

2021 49

2020 41

2019 27

2018 19

2017 23

2016 9

2015 11

2014 2

2013 3

2012 1

2011 4

2010 2

2009 3

2008 3

2007 5

2006 1

2005 2

2001 3

open ︾

Keywords

Machine learning 37

Deep learning 28

machine learning 21

deep learning 12

Artificial intelligence 11

Reinforcement learning 11

artificial neural network 4

Active learning 3

Attention 3

Big data 3

Data-driven 3

artificial intelligence 3

Adaptive dynamic programming 2

Autonomous learning 2

Bayesian belief network 2

Bayesian optimization 2

Classification 2

Computer vision 2

Feature learning 2

open ︾

Search scope:

排序： Display mode:

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

HTML PDF Collect

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

HTML PDF Collect

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

HTML PDF Collect

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

HTML PDF Collect

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

HTML PDF Collect

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

HTML PDF Collect

Coach-assisted multi-agent reinforcement learning framework for unexpected crashed agents Research Article

Jian ZHAO, Youpeng ZHAO, Weixun WANG, Mingyu YANG, Xunhan HU, Wengang ZHOU, Jianye HAO, Houqiang LI

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 7, Pages 1032-1042 doi: 10.1631/FITEE.2100594

Abstract: We have designed three coaching strategies (fixed crash rate, curriculum learning, and adaptive crashdemonstrate the efficacy of the adaptive strategy compared with the fixed crash rate strategy and curriculum learning

Keywords： Multi-agent system Reinforcement learning Unexpected crashed agents

HTML PDF Collect

Cooperative channel assignment for VANETs based on multiagent reinforcement learning Research Articles

Yun-peng Wang, Kun-xian Zheng, Da-xin Tian, Xu-ting Duan, Jian-shan Zhou,ypwang@buaa.edu.cn,zhengkunxian@buaa.edu.cn,dtian@buaa.edu.cn,duanxuting@buaa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7, Pages 1047-1058 doi: 10.1631/FITEE.1900308

Abstract: (DCA) plays a key role in extending vehicular ad-hoc network capacity and mitigating congestion. However, channel assignment under vehicular direct communication scenarios faces mutual influence of large-scale nodes, the lack of centralized coordination, unknown global state information, and other challenges. To solve this problem, a multiagent (RL) based cooperative DCA (RL-CDCA) mechanism is proposed. Specifically, each vehicular node can successfully learn the proper strategies of channel selection and backoff adaptation from the real-time channel state information (CSI) using two cooperative RL models. In addition, neural networks are constructed as nonlinear Q-function approximators, which facilitates the mapping of the continuously sensed input to the mixed policy output. Nodes are driven to locally share and incorporate their individual rewards such that they can optimize their policies in a distributed collaborative manner. Simulation results show that the proposed multiagent RL-CDCA can better reduce the one-hop packet delay by no less than 73.73%, improve the packet delivery ratio by no less than 12.66% on average in a highly dense situation, and improve the fairness of the global network resource allocation.

Keywords： Vehicular ad-hoc networks Reinforcement learning Dynamic channel assignment Multichannel

HTML PDF Collect

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

HTML PDF Collect

Behavioral control task supervisor with memory based on reinforcement learning for human–multi-robot Research Article

Jie HUANG, Zhibin MO, Zhenyi ZHANG, Yutao CHEN,yutao.chen@fzu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 8, Pages 1174-1188 doi: 10.1631/FITEE.2100280

Abstract: In this study, a novel (RLTS) with memory in a behavioral control framework is proposed for ; (HMRCSs). Existing HMRCSs suffer from high decision-making time cost and large task tracking errors caused by repeated human intervention, which restricts the autonomy of multi-robot systems (MRSs). Moreover, existing s in the (NSBC) framework need to formulate many priority-switching rules manually, which makes it difficult to realize an optimal behavioral priority adjustment strategy in the case of multiple robots and multiple tasks. The proposed RLTS with memory provides a detailed integration of the deep Q-network (DQN) and long short-term memory (LSTM) within the NSBC framework, to achieve an optimal behavioral priority adjustment strategy in the presence of task conflict and to reduce the frequency of human intervention. Specifically, the proposed RLTS with memory begins by memorizing human intervention history when the robot systems are not confident in emergencies, and then reloads the history information when encountering the same situation that has been tackled by humans previously. Simulation results demonstrate the effectiveness of the proposed RLTS. Finally, an experiment using a group of mobile robots subject to external noise and disturbances validates the effectiveness of the proposed RLTS with memory in uncertain real-world environments.

Keywords： Human– multi-robot coordination systems Null-space-based behavioral control Task supervisor Reinforcementlearning Knowledge base

HTML PDF Collect

Plant with Solvent-Based Carbon Capture under a CO₂ Allowance Market: A Solution with a ReinforcementLearning-Based Sarsa Temporal-Difference Algorithm

Ziang Li,Zhengtao Ding,Meihong Wang

Engineering 2017, Volume 3, Issue 2, Pages 257-265 doi: 10.1016/J.ENG.2017.02.014

Abstract:

In this paper, a reinforcement learning (RL)-based Sarsa temporal-difference (TD) algorithm is applied

Keywords： Post-combustion carbon capture Chemical absorption CO₂ allowance market Optimal decision-making Reinforcementlearning

HTML PDF Collect

MDLB: a metadata dynamic load balancing mechanism based on reinforcement learning Research Articles

Zhao-qi Wu, Jin Wei, Fan Zhang, Wei Guo, Guang-wei Xie,17034203@qq.com

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7, Pages 963-1118 doi: 10.1631/FITEE.1900121

Abstract: With the growing amount of information and data, s have been widely used in many applications, including the Google File System, Amazon S3, Hadoop Distributed File System, and Ceph, in which load balancing of plays an important role in improving the input/output performance of the entire system. Unbalanced load on the server leads to a serious bottleneck problem for system performance. However, most existing load balancing strategies, which are based on subtree segmentation or hashing, lack good dynamics and adaptability. In this study, we propose a (MDLB) mechanism based on (RL). We learn that the algorithm and our RL-based strategy consist of three modules, i.e., the policy selection network, load balancing network, and parameter update network. Experimental results show that the proposed MDLB algorithm can adjust the load dynamically according to the performance of the servers, and that it has good adaptability in the case of sudden change of data volume.

Keywords：面向对象的存储系统；元数据；动态负载均衡；强化学习；Q_learning

HTML PDF Collect

Punching of reinforced concrete slab without shear reinforcement: Standard models and new proposal

Luisa PANI, Flavio STOCHINO

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 5, Pages 1196-1214 doi: 10.1007/s11709-020-0662-z

Abstract: In this study an experimental database of 113 RC slabs without shear reinforcement under punching loadspunching shear strength assessment was conducted, which highlighted the importance of the flexural reinforcementstandards, a new proposed model for punching shear strength and rotation of RC slabs without shear reinforcementbased on a simplified load-rotation curve and new failure criteria that takes into account the flexural reinforcement

Keywords： punching shear strength reinforced concrete slabs reinforcement ratio

HTML PDF Collect

Spatial embedded reinforcement of 20-node block element for analysis PC bridges

LONG Peiheng, DU Xianting, CHEN Weizhen

Frontiers of Structural and Civil Engineering 2008, Volume 2, Issue 3, Pages 274-280 doi: 10.1007/s11709-008-0039-1

Abstract: The formula for the contribution of prestressed reinforcement on embedded reinforcement element is derivedReinforcement element model allows generating a finite element mesh without taking into consideration

Keywords： arithmetic analysis calculation prestressed reinforcement mechanical arbitrary

HTML PDF Collect

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. It has been widely used in various fields, such as end-to-end control, robotic control, recommendation systems, and natural language dialogue systems. In this survey, we systematically categorize the deep RL algorithms and applications, and provide a detailed review over existing deep RL algorithms by dividing them into model-based methods, model-free methods, and advanced RL methods. We thoroughly analyze the advances including exploration, inverse RL, and transfer RL. Finally, we outline the current representative applications, and analyze four open problems for future research.

HTML PDF Collect

Title Author Date Type Operation

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

Journal Article

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

Recent development on statistical methods for personalized medicine discovery

Yingqi Zhao, Donglin Zeng

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

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

Oguzhan Dogru, Kirubakaran Velswamy, Biao Huang

Journal Article

Coach-assisted multi-agent reinforcement learning framework for unexpected crashed agents

Jian ZHAO, Youpeng ZHAO, Weixun WANG, Mingyu YANG, Xunhan HU, Wengang ZHOU, Jianye HAO, Houqiang LI

Journal Article

Cooperative channel assignment for VANETs based on multiagent reinforcement learning

Yun-peng Wang, Kun-xian Zheng, Da-xin Tian, Xu-ting Duan, Jian-shan Zhou,ypwang@buaa.edu.cn,zhengkunxian@buaa.edu.cn,dtian@buaa.edu.cn,duanxuting@buaa.edu.cn

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

Behavioral control task supervisor with memory based on reinforcement learning for human–multi-robot

Jie HUANG, Zhibin MO, Zhenyi ZHANG, Yutao CHEN,yutao.chen@fzu.edu.cn

Journal Article

Plant with Solvent-Based Carbon Capture under a CO₂ Allowance Market: A Solution with a ReinforcementLearning-Based Sarsa Temporal-Difference Algorithm

Ziang Li,Zhengtao Ding,Meihong Wang

Journal Article

MDLB: a metadata dynamic load balancing mechanism based on reinforcement learning

Zhao-qi Wu, Jin Wei, Fan Zhang, Wei Guo, Guang-wei Xie,17034203@qq.com

Journal Article

Punching of reinforced concrete slab without shear reinforcement: Standard models and new proposal

Luisa PANI, Flavio STOCHINO

Journal Article

Spatial embedded reinforcement of 20-node block element for analysis PC bridges

LONG Peiheng, DU Xianting, CHEN Weizhen

Journal Article

Deep reinforcement learning: a survey

Journal Article

关注我们