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Multi-color space threshold segmentation and self-learning k-NN algorithm for surge test EUT status

Jian HUANG,Gui-xiong LIU

Frontiers of Mechanical Engineering 2016, Volume 11, Issue 3,   Pages 311-315 doi: 10.1007/s11465-016-0376-z

Abstract: A multi-color space threshold segmentation and self-learning k-nearest neighbor algorithmthe unknown class sample Sr was classified by the k-NN algorithmseries of illuminant, indicator light, screen, and disturbances samples (a total of 21600 frames), the algorithm

Keywords： multi-color space     k-nearest neighbor algorithm (k-NN)     self-learning     surge test    

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A modified neural learning algorithm for online rotor resistance estimation in vector controlled induction

A. CHITRA,S. HIMAVATHI

Frontiers in Energy 2015, Volume 9, Issue 1,   Pages 22-30 doi: 10.1007/s11708-014-0339-1

Abstract: In this paper, a novel modified neural algorithm has been identified for the online estimation of rotorThe training algorithm of the neural network determines its learning speed, stability, weight convergenceIn this paper, the neural estimator has been studied with conventional and proposed learning algorithmsThe proposed learning algorithm is found to exhibit good estimation and tracking capabilities.

Keywords： neural networks     back propagation (BP)     rotor resistance estimators     vector control     induction motor    

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Max-margin basedBayesian classifier Article

Tao-cheng HU,Jin-hui YU

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 10,   Pages 973-981 doi: 10.1631/FITEE.1601078

Abstract: There is a tradeoff between generalization capability and computational overhead in multi-class learninggenerative probabilistic multi-class classifier, considering both the generalization capability and the learningBy convex and probabilistic analysis, an efficient online learning algorithm is developed.The algorithm aggregates rather than averages dualities, which is different from the classical situations

Keywords： Multi-class learning     Max-margin learning     Online algorithm    

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A Robust Transfer Dictionary Learning Algorithm for Industrial Process Monitoring Article

Chunhua Yang, Huiping Liang, Keke Huang, Yonggang Li, Weihua Gui

Engineering 2021, Volume 7, Issue 9,   Pages 1262-1273 doi: 10.1016/j.eng.2020.08.028

Abstract: online testing data that is induced by changeable operation environments, a robust transfer dictionary learning(RTDL) algorithm is proposed in this paper for industrial process monitoring.The RTDL is a synergy of representative learning and domain adaptive transfer learning.data and online testing data as the source domain and the target domain, respectively, in the transfer learningregularization and linear discriminant analysis-like regularization are then incorporated into the dictionary learning

Keywords： Process monitoring     Multimode process     Dictionary learning     Transfer learning    

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A new systematic firefly algorithm for forecasting the durability of reinforced recycled aggregate concrete

Wafaa Mohamed SHABAN; Khalid ELBAZ; Mohamed AMIN; Ayat gamal ASHOUR

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 3,   Pages 329-346 doi: 10.1007/s11709-022-0801-9

Abstract: This study presents a new systematic algorithm to optimize the durability of reinforced recycled aggregateThe proposed algorithm integrates machine learning with a new version of the firefly algorithm calledchaotic based firefly algorithm (CFA) to evolve a rational and efficient predictive model.k-fold cross validation algorithm is utilized to validate the hybrid algorithm.Results show that the developed CFA approach significantly outperforms the firefly algorithm on almost

Keywords： chloride penetrability     recycled aggregate concrete     machine learning     concrete components     durability    

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Learning and Applications of Procedure Neural Networks

He Xingui,Liang Jiuzhen,Xu Shaohua

Strategic Study of CAE 2001, Volume 3, Issue 4,   Pages 31-35

Abstract:

This paper deals with learning algorithms for procedure neural networks (PNN) and its applications

Keywords： procedure neural networks     learning algorithm     pattern recognition     chemical reaction     seepage    

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Research on An On-line Tracking Self-learning Algorithm for Fuzzy Basis Function Neural Network

Xu Feiyun,Zhong Binglin,Huang Ren

Strategic Study of CAE 2007, Volume 9, Issue 11,   Pages 48-53

Abstract:

An on-line tracking self-learning algorithm for fuzzy basis function Meanwhile,  a recursive algorithm for computing the sample mean and covariance matrix with

Keywords： fuzzy basis function     self-learning     fault diagnosis    

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Artificial intelligence algorithms for cyberspace security applications: a technological and status review Review

Jie CHEN, Dandan WU, Ruiyun XIE,chenjie1900@mail.nwpu.edu.cn,wudd@cetcsc.com

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 8,   Pages 1117-1142 doi: 10.1631/FITEE.2200314

Abstract: Three technical problems should be solved urgently in : the timeliness and accuracy of network attack detection, the credibility assessment and prediction of the security situation, and the effectiveness of security defense strategy optimization. algorithms have become the core means to increase the chance of security and improve the network attack and defense ability in the application of . Recently, the breakthrough and application of AI technology have provided a series of advanced approaches for further enhancing network defense ability. This work presents a comprehensive review of AI technology articles for applications, mainly from 2017 to 2022. The papers are selected from a variety of journals and conferences: 52.68% are from Elsevier, Springer, and IEEE journals and 25% are from international conferences. With a specific focus on the latest approaches in , , and some popular s, the characteristics of the algorithmic models, performance results, datasets, potential benefits, and limitations are analyzed, and some of the existing challenges are highlighted. This work is intended to provide technical guidance for researchers who would like to obtain the potential of AI technical methods for and to provide tips for the later resolution of specific issues, and a mastery of the current development trends of technology and application and hot issues in the field of network security. It also indicates certain existing challenges and gives directions for addressing them effectively.

Keywords： Artificial intelligence (AI)     Machine learning (ML)     Deep learning (DL)     Optimization algorithm     Hybridalgorithm     Cyberspace security    

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A Local Quadratic Embedding Learning Algorithm and Applications for Soft Sensing Article

Yaoyao Bao, Yuanming Zhu, Feng Qian

Engineering 2022, Volume 18, Issue 11,   Pages 186-196 doi: 10.1016/j.eng.2022.04.025

Abstract:

Inspired by the tremendous achievements of meta-learning in various fields, this paper proposes thelocal quadratic embedding learning (LQEL) algorithm for regression problems based on metric learningFirst, Mahalanobis metric learning is improved by optimizing the global consistency of the metrics betweenThen, we further prove that the improved metric learning problem is equivalent to a convex programmingBased on the hypothesis of local quadratic interpolation, the algorithm introduces two lightweight NNs

Keywords： Local quadratic embedding     Metric learning     Regression machine     Soft sensor    

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A hybrid machine learning model to estimate self-compacting concrete compressive strength

Hai-Bang LY; Thuy-Anh NGUYEN; Binh Thai PHAM; May Huu NGUYEN

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 8,   Pages 990-1002 doi: 10.1007/s11709-022-0864-7

Abstract: The results proved that the proposed ANN-GWO algorithm is a reliable predictor for SCC’s CS.

Keywords： artificial neural network     grey wolf optimize algorithm     compressive strength     self-compacting concrete    

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Data-driven approach to solve vertical drain under time-dependent loading

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 3,   Pages 696-711 doi: 10.1007/s11709-021-0727-7

Abstract: Currently, the vertical drain consolidation problem is solved by numerous analytical solutions, such as time-dependent solutions and linear or parabolic radial drainage in the smear zone, and no artificial intelligence (AI) approach has been applied. Thus, in this study, a new hybrid model based on deep neural networks (DNNs), particle swarm optimization (PSO), and genetic algorithms (GAs) is proposed to solve this problem. The DNN can effectively simulate any sophisticated equation, and the PSO and GA can optimize the selected DNN and improve the performance of the prediction model. In the present study, analytical solutions to vertical drains in the literature are incorporated into the DNN–PSO and DNN–GA prediction models with three different radial drainage patterns in the smear zone under time-dependent loading. The verification performed with analytical solutions and measurements from three full-scale embankment tests revealed promising applications of the proposed approach.

Keywords： vertical drain     artificial neural network     time-dependent loading     deep learning network     genetic algorithm    

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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 andTherefore, an AOD model based on a (DQN) with a novel training algorithm is proposed in this paper.In contrast to existing research, a novel training algorithm based on memory is designed for the proposedpresented method has better performance than the comparable methods and that the proposed training algorithmis more effective than the raw training algorithm.

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

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Multi-scale UDCT dictionary learning based highly undersampled MR image reconstruction using patch-basedconstraint splitting augmented Lagrangian shrinkage algorithm

Min YUAN,Bing-xin YANG,Yi-de MA,Jiu-wen ZHANG,Fu-xiang LU,Tong-feng ZHANG

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 12,   Pages 1069-1087 doi: 10.1631/FITEE.1400423

Abstract: Recently, dictionary learning (DL) based methods have been introduced to compressed sensing magneticto this brand-new sparsity model, we modify the constraint splitting augmented Lagrangian shrinkage algorithm

Keywords： Magnetic resonance imaging (MRI)     Uniform discrete curvelet transform (UDCT)     Multi-scale dictionary learning(MSDL)     Patch-based constraint splitting augmented Lagrangian shrinkage algorithm (PB C-SALSA)    

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Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges Review

Xinbo Qi, Guofeng Chen, Yong Li, Xuan Cheng, Changpeng Li

Engineering 2019, Volume 5, Issue 4,   Pages 721-729 doi: 10.1016/j.eng.2019.04.012

Abstract: Today, the machine learning (ML) method has been demonstrated to be a valid way to perform complex patterndue to the large dataset that is currently available, strong computational power, and sophisticated algorithmThis paper overviews the progress of applying the NN algorithm to several aspects of the AM whole chain

Keywords： Additive manufacturing     3D printing     Neural network     Machine learning     Algorithm    

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Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Article

Houfa Wu,Jianyun Zhang,Zhenxin Bao,Guoqing Wang,Wensheng Wang,Yanqing Yang,Jie Wang

Engineering 2023, Volume 28, Issue 9,   Pages 93-104 doi: 10.1016/j.eng.2021.12.014

Abstract: LR-based approach, the accuracy of the runoff modeling in ungauged catchments was improved by the machine learningperformances of different approaches were similar in humid regions, while the advantages of the machine learning

Keywords： Parameters estimation     Ungauged catchments     Regionalization scheme     Machine learning algorithms     Soil and    

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Title Author Date Type Operation

Multi-color space threshold segmentation and self-learning k-NN algorithm for surge test EUT status

Jian HUANG,Gui-xiong LIU

Journal Article

A modified neural learning algorithm for online rotor resistance estimation in vector controlled induction

A. CHITRA,S. HIMAVATHI

Journal Article

Max-margin basedBayesian classifier

Tao-cheng HU,Jin-hui YU

Journal Article

A Robust Transfer Dictionary Learning Algorithm for Industrial Process Monitoring

Chunhua Yang, Huiping Liang, Keke Huang, Yonggang Li, Weihua Gui

Journal Article

A new systematic firefly algorithm for forecasting the durability of reinforced recycled aggregate concrete

Wafaa Mohamed SHABAN; Khalid ELBAZ; Mohamed AMIN; Ayat gamal ASHOUR

Journal Article

Learning and Applications of Procedure Neural Networks

He Xingui,Liang Jiuzhen,Xu Shaohua

Journal Article

Research on An On-line Tracking Self-learning Algorithm for Fuzzy Basis Function Neural Network

Xu Feiyun,Zhong Binglin,Huang Ren

Journal Article

Artificial intelligence algorithms for cyberspace security applications: a technological and status review

Jie CHEN, Dandan WU, Ruiyun XIE,chenjie1900@mail.nwpu.edu.cn,wudd@cetcsc.com

Journal Article

A Local Quadratic Embedding Learning Algorithm and Applications for Soft Sensing

Yaoyao Bao, Yuanming Zhu, Feng Qian

Journal Article

A hybrid machine learning model to estimate self-compacting concrete compressive strength

Hai-Bang LY; Thuy-Anh NGUYEN; Binh Thai PHAM; May Huu NGUYEN

Journal Article

Data-driven approach to solve vertical drain under time-dependent loading

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

Multi-scale UDCT dictionary learning based highly undersampled MR image reconstruction using patch-basedconstraint splitting augmented Lagrangian shrinkage algorithm

Min YUAN,Bing-xin YANG,Yi-de MA,Jiu-wen ZHANG,Fu-xiang LU,Tong-feng ZHANG

Journal Article

Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges

Xinbo Qi, Guofeng Chen, Yong Li, Xuan Cheng, Changpeng Li

Journal Article

Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization

Houfa Wu,Jianyun Zhang,Zhenxin Bao,Guoqing Wang,Wensheng Wang,Yanqing Yang,Jie Wang

Journal Article