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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: Most of the current AOD methods are based on reinforcement learning with low training efficiency andThe DQN model is designed to fit the Q-values of various actions, and includes state space, feature extraction

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

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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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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.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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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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Minimax Q-learning design for H∞ control of linear discrete-time systems Research Articles

Xinxing LI, Lele XI, Wenzhong ZHA, Zhihong PENG,lixinxing_1006@163.com,xilele.bit@gmail.com,zhawenzhong@126.com,peng@bit.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 3,   Pages 438-451 doi: 10.1631/FITEE.2000446

Abstract: Different from existing -learning methods, a novel gradient-based policy improvement scheme is proposedconverges to the saddle solution under initially admissible control policies and an appropriate positive learning

Keywords： H∞ control     Zero-sum dynamic game     Reinforcement learning     Adaptive dynamic programming     Minimax Q-learning    

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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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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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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 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 a supervised learning framework, Ground Penetrating Radar (GPR) profiles and the revealed structural

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

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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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Adversarial Attacks and Defenses in Deep Learning Feature Article

Kui Ren, Tianhang Zheng, Zhan Qin, Xue Liu

Engineering 2020, Volume 6, Issue 3,   Pages 346-360 doi: 10.1016/j.eng.2019.12.012

Abstract:

With the rapid developments of artificial intelligence (AI) and deep learning (DL) techniques, itadversarial attack and defense techniques have attracted increasing attention from both machine
learning

Keywords： Machine learning     Deep neural network Adversarial example     Adversarial attack     Adversarial defense    

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Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques

Amit SHIULY; Debabrata DUTTA; Achintya MONDAL

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 3,   Pages 347-358 doi: 10.1007/s11709-022-0819-z

Abstract: In the present study, a new image-based machine learning method is used to predict concrete compressiveThese include support-vector machine model and various deep convolutional neural network models, namelyThe images and corresponding compressive strength were then used to train machine learning models toOverall, the present findings validated the use of machine learning models as an efficient means of estimating

Keywords： support vector machine     deep convolutional neural network     microscope     digital image     curing period    

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Deep learning model for estimating the mechanical properties of concrete containing silica fume exposed

Harun TANYILDIZI, Abdulkadir ŞENGÜR, Yaman AKBULUT, Murat ŞAHİN

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 6,   Pages 1316-1330 doi: 10.1007/s11709-020-0646-z

Abstract: In this study, the deep learning models for estimating the mechanical properties of concrete containingTwo well-known deep learning approaches, i.e., stacked autoencoders and long short-term memory (LSTM)The forecasting experiments were carried out using MATLAB deep learning and neural network tools, respectivelyThis study found that the LSTM network achieved better results than the stacked autoencoders.In addition, this study found that deep learning, which has a very good prediction ability with little

Keywords： concrete     high temperature     strength properties     deep learning     stacked auto-encoders     LSTM network    

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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 predictproject in Shanghai and comparisons with other conventional models (i.e., long short-term memory (LSTM) networkand recurrent neural network (RNN)) are conducted.effective 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    

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Machine vision-based automatic fruit quality detection and grading

Frontiers of Agricultural Science and Engineering doi: 10.15302/J-FASE-2023532

Abstract:

● A machine vision-based prototype system was developed for fruit grading.

Keywords： Computer and machine vision     convolution neural network     deep learning     defective fruit detection     fruit    

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Title Author Date Type Operation

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

Recent development on statistical methods for personalized medicine discovery

Yingqi Zhao, Donglin Zeng

Journal Article

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

Journal Article

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

Journal Article

Minimax Q-learning design for H∞ control of linear discrete-time systems

Xinxing LI, Lele XI, Wenzhong ZHA, Zhihong PENG,lixinxing_1006@163.com,xilele.bit@gmail.com,zhawenzhong@126.com,peng@bit.edu.cn

Journal Article

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

Journal Article

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

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

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

Adversarial Attacks and Defenses in Deep Learning

Kui Ren, Tianhang Zheng, Zhan Qin, Xue Liu

Journal Article

Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques

Amit SHIULY; Debabrata DUTTA; Achintya MONDAL

Journal Article

Deep learning model for estimating the mechanical properties of concrete containing silica fume exposed

Harun TANYILDIZI, Abdulkadir ŞENGÜR, Yaman AKBULUT, Murat ŞAHİN

Journal Article

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

Journal Article

Machine vision-based automatic fruit quality detection and grading

Journal Article