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user-centric multi-dimensional resource allocation for a wide-area coverage signaling cell based on DQN Research Article

Zhou TONG, Na LI, Huimin ZHANG, Quan ZHAO, Yun ZHAO, Junshuai SUN, Guangyi LIU,tongzhou@chinamobile.com,linawx@chinamobile.com,zhanghuiminyjy@chinamobile.com

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 1,   Pages 154-163 doi: 10.1631/FITEE.2200220

Abstract: The sixth-generation wireless communication system () network is faced with more stringent and diverse, the problem of high energy consumption in the fifth-generation wireless communication system (5G) networkensure the ultimate performance requirements of users, and its effect will affect the efficiency of networkThis paper constructs a user-centric dynamic allocation model of wireless resources, and proposes a deepQ-network based dynamic resource allocation algorithm.

Keywords： 6G     Wide-area coverage signaling cell     Multi-dimensional resource allocation     Deep Q-network (DQN)    

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A traffic-aware Q-network enhanced routing protocol based on GPSR for unmanned aerial vehicle ad-hoc Research Articles

Yi-ning Chen, Ni-qi Lyu, Guang-hua Song, Bo-wei Yang, Xiao-hong Jiang,ch19930611@zju.edu.cn,lvniqi@gmail.com,ghsong@zju.edu.cn,boweiy@zju.edu.cn,jiangxh@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 9,   Pages 1308-1320 doi: 10.1631/FITEE.1900401

Abstract: In dense traffic unmanned aerial vehicle (UAV) ad-hoc networks, traffic congestion can cause increased delay and packet loss, which limit the performance of the networks; therefore, a strategy is required to control the traffic. In this study, we propose TQNGPSR, a traffic-aware enhanced protocol based on greedy perimeter stateless routing (GPSR), for UAV ad-hoc networks. The protocol enforces a strategy using the congestion information of neighbors, and evaluates the quality of a wireless link by the algorithm, which is a algorithm. Based on the evaluation of each wireless link, the protocol makes routing decisions in multiple available choices to reduce delay and decrease packet loss. We simulate the performance of TQNGPSR and compare it with AODV, OLSR, GPSR, and QNGPSR. Simulation results show that TQNGPSR obtains higher packet delivery ratios and lower end-to-end delays than GPSR and QNGPSR. In high node density scenarios, it also outperforms AODV and OLSR in terms of the packet delivery ratio, end-to-end delay, and throughput.

Keywords： Traffic balancing     Reinforcement learning     Geographic routing     Q-network    

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A deep Q-learning network based active object detection model with a novel training algorithm for service Research Article

Shaopeng LIU, Guohui TIAN, Yongcheng CUI, Xuyang SHAO

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 11,   Pages 1673-1683 doi: 10.1631/FITEE.2200109

Abstract: Therefore, an AOD model based on a (DQN) with a novel training algorithm is proposed in this paper.The DQN model is designed to fit the Q-values of various actions, and includes state space, feature extractioncontrast to existing research, a novel training algorithm based on memory is designed for the proposed DQN

Keywords： Active object detection     Deep Q-learning network     Training method     Service robots    

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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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On-Line Supervisory Technology for Q-Factor in the Optical Supervisory Channel of Optical Transport Network

Tang Yong,Sun Xiaohan,Zhang Mingde,Ding Dong

Strategic Study of CAE 2001, Volume 3, Issue 12,   Pages 71-75

Abstract:

In this paper, a hierarchical model of the optical transport network (OTN) with an optical supervisoryBasing on the one-to-one correspondence of Q-factor with bit-error-ratio (BER) in data communicationsystems, an on-line supervisory scheme for Q-factor of signals in OSC is presented, and the supervisorymodule by a digital signal processor (DSP) approach is designed to implement on-line supervision for Q-factor

Keywords： optical transport network (OTN)     optical supervisory channel (OSC)     OAM     Q-factor supervision    

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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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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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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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Multiclass classification based on a deep convolutional

Ying CAI,Meng-long YANG,Jun LI

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 11,   Pages 930-939 doi: 10.1631/FITEE.1500125

Abstract: In this paper we propose a novel method to estimate head pose based on a deep convolutional neural networkBefore training the network, two reasonable strategies including shift and zoom are executed to prepare

Keywords： Head pose estimation     Deep convolutional neural network     Multiclass classification    

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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 functionallyA trial-and-error procedure is implemented to determine suitable parameters of the network such as the

Keywords： damage detection     deep feed-forward neural networks     functionally graded carbon nanotube-reinforced composite    

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Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 6, doi: 10.1007/s11783-023-1667-3

Abstract:

● Hybrid deep-learning model is proposed for water quality prediction

Keywords： Water quality prediction     Soft-sensor     Anaerobic process     Tree-structured Parzen Estimator    

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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    

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Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 2,   Pages 214-223 doi: 10.1007/s11709-021-0800-2

Abstract: In recent years, great attention has focused on the development of automated procedures for infrastructures control. Many efforts have aimed at greater speed and reliability compared to traditional methods of assessing structural conditions. The paper proposes a multi-level strategy, designed and implemented on the basis of periodic structural monitoring oriented to a cost- and time-efficient tunnel control plan. Such strategy leverages the high capacity of convolutional neural networks to identify and classify potential critical situations. In a supervised learning framework, Ground Penetrating Radar (GPR) profiles and the revealed structural phenomena have been used as input and output to train and test such networks. Image-based analysis and integrative investigations involving video-endoscopy, core drilling, jacking and pull-out testing have been exploited to define the structural conditions linked to GPR profiles and to create the database. The degree of detail and accuracy achieved in identifying a structural condition is high. As a result, this strategy appears of value to infrastructure managers who need to reduce the amount and invasiveness of testing, and thus also to reduce the time and costs associated with inspections made by highly specialized technicians.

Keywords： concrete structure     GPR     damage classification     convolutional neural network     transfer learning    

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Development of a new method for RMR and Q classification method to optimize support system in tunneling

Asghar RAHMATI,Lohrasb FARAMARZI,Manouchehr SANEI

Frontiers of Structural and Civil Engineering 2014, Volume 8, Issue 4,   Pages 448-455 doi: 10.1007/s11709-014-0262-x

Abstract: Based on these advantages of method, in this study, according to data of five deep and long tunnels

Keywords： JH classification     Q and RMR classification     new method    

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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    

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Title Author Date Type Operation

user-centric multi-dimensional resource allocation for a wide-area coverage signaling cell based on DQN

Zhou TONG, Na LI, Huimin ZHANG, Quan ZHAO, Yun ZHAO, Junshuai SUN, Guangyi LIU,tongzhou@chinamobile.com,linawx@chinamobile.com,zhanghuiminyjy@chinamobile.com

Journal Article

A traffic-aware Q-network enhanced routing protocol based on GPSR for unmanned aerial vehicle ad-hoc

Yi-ning Chen, Ni-qi Lyu, Guang-hua Song, Bo-wei Yang, Xiao-hong Jiang,ch19930611@zju.edu.cn,lvniqi@gmail.com,ghsong@zju.edu.cn,boweiy@zju.edu.cn,jiangxh@zju.edu.cn

Journal Article

A deep Q-learning network based active object detection model with a novel training algorithm for service

Shaopeng LIU, Guohui TIAN, Yongcheng CUI, Xuyang SHAO

Journal Article

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

Journal Article

On-Line Supervisory Technology for Q-Factor in the Optical Supervisory Channel of Optical Transport Network

Tang Yong,Sun Xiaohan,Zhang Mingde,Ding Dong

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

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

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

Journal Article

Multiclass classification based on a deep convolutional

Ying CAI,Meng-long YANG,Jun LI

Journal Article

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

Journal Article

Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured

Journal Article

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

Journal Article

Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

Journal Article

Development of a new method for RMR and Q classification method to optimize support system in tunneling

Asghar RAHMATI,Lohrasb FARAMARZI,Manouchehr SANEI

Journal Article

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

Journal Article