World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
64
Citations
14773
World Ranking
2626
National Ranking
359

Chunhua Yang publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Chunhua Yang sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 527 publications — 94th percentile

94% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Chunhua Yang D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Chunhua Yang sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 64 D-Index — 82nd percentile

82% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Chunhua Yang is currently affiliated with Central South University in China and has a substantial body of research primarily within the field of Engineering. Their work spans multiple subfields including Control and Systems Engineering, Mechanical Engineering, Artificial Intelligence, Industrial and Manufacturing Engineering, and Computer Vision and Pattern Recognition.

The scientist's research topics emphasize areas such as Fault Detection and Control Systems, Advanced Control Systems Optimization, Mineral Processing and Grinding, Water Quality Monitoring and Analysis, Spectroscopy and Chemometric Analyses, Neural Networks and Applications, and Process Optimization and Integration.

Frequently publishing in established venues, Chunhua Yang has contributed notably to the following journals and platforms:

  • SSRN Electronic Journal
  • IEEE Transactions on Instrumentation and Measurement
  • Control Engineering Practice
  • IFAC-PapersOnLine
  • arXiv (Cornell University)

Some of their recent papers include:

  • Deep Learning With Spatiotemporal Attention-Based LSTM for Industrial Soft Sensor Model Development, 2020, IEEE Transactions on Industrial Electronics
  • Graph Convolutional Network-Based Method for Fault Diagnosis Using a Hybrid of Measurement and Prior Knowledge, 2021, IEEE Transactions on Cybernetics
  • A Just-In-Time-Learning-Aided Canonical Correlation Analysis Method for Multimode Process Monitoring and Fault Detection, 2020, IEEE Transactions on Industrial Electronics
  • Automated Visual Defect Classification for Flat Steel Surface: A Survey, 2020, IEEE Transactions on Instrumentation and Measurement
  • Soft sensor model for dynamic processes based on multichannel convolutional neural network, 2020, Chemometrics and Intelligent Laboratory Systems

The scientist has collaborated frequently with several coauthors, including:

  • Weihua Gui
  • Yonggang Li
  • Bei Sun
  • Keke Huang
  • Xiaojun Zhou

Best Publications

  • Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE

    Xiaofeng Yuan;Biao Huang;Yalin Wang;Chunhua Yang

  • Automated Visual Defect Detection for Flat Steel Surface: A Survey

    Qiwu Luo;Xiaoxin Fang;Li Liu;Chunhua Yang

  • Deep Learning With Spatiotemporal Attention-Based LSTM for Industrial Soft Sensor Model Development

    Xiaofeng Yuan;Lin Li;Yuri A. W. Shardt;Yalin Wang

  • A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network.

    Yalin Wang;Zhuofu Pan;Xiaofeng Yuan;Chunhua Yang

  • A Distributed Dynamic Event-Triggered Control Approach to Consensus of Linear Multiagent Systems With Directed Networks

    Wenfeng Hu;Chunhua Yang;Tingwen Huang;Weihua Gui

  • Fault Detection for Non-Gaussian Processes Using Generalized Canonical Correlation Analysis and Randomized Algorithms

    Zhiwen Chen;Steven X. Ding;Tao Peng;Chunhua Yang

  • Set stability and set stabilization of Boolean control networks based on invariant subsets

    Yuqian Guo;Pan Wang;Weihua Gui;Chunhua Yang

  • STATE TRANSITION ALGORITHM

    Xiaojun Zhou;Chunhua Yang;Weihua Gui

  • Non-ferrous metals price forecasting based on variational mode decomposition and LSTM network

    Yishun Liu;Chunhua Yang;Keke Huang;Weihua Gui

  • Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy

    Xiaofeng Yuan;Jiao Zhou;Biao Huang;Yalin Wang

  • Graph Convolutional Network-Based Method for Fault Diagnosis Using a Hybrid of Measurement and Prior Knowledge.

    Zhiwen Chen;Jiamin Xu;Tao Peng;Chunhua Yang

  • Passivity-Based Asynchronous Sliding Mode Control for Delayed Singular Markovian Jump Systems

    Fanbiao Li;Chenglong Du;Chunhua Yang;Weihua Gui

  • Stability and Set Stability in Distribution of Probabilistic Boolean Networks

    Yuqian Guo;Rongpei Zhou;Yuhu Wu;Weihua Gui

  • Weighted Linear Dynamic System for Feature Representation and Soft Sensor Application in Nonlinear Dynamic Industrial Processes

    Xiaofeng Yuan;Yalin Wang;Chunhua Yang;Zhiqiang Ge

  • Fault Diagnosis of Hydraulic Systems Based on Deep Learning Model With Multirate Data Samples.

    Keke Huang;Shujie Wu;Fanbiao Li;Chunhua Yang

  • A Layer-Wise Data Augmentation Strategy for Deep Learning Networks and Its Soft Sensor Application in an Industrial Hydrocracking Process

    Xiaofeng Yuan;Chen Ou;Yalin Wang;Chunhua Yang

  • A Distributed Canonical Correlation Analysis-Based Fault Detection Method for Plant-Wide Process Monitoring

    Zhiwen Chen;Yue Cao;Steven X. Ding;Kai Zhang

  • A Just-In-Time-Learning-Aided Canonical Correlation Analysis Method for Multimode Process Monitoring and Fault Detection

    Zhiwen Chen;Chang Liu;Steven X. Ding;Tao Peng

  • A Deep Supervised Learning Framework for Data-Driven Soft Sensor Modeling of Industrial Processes

    Xiaofeng Yuan;Yongjie Gu;Yalin Wang;Chunhua Yang

  • A Fault-Injection Strategy for Traction Drive Control Systems

    Chunhua Yang;Chao Yang;Tao Peng;Xiaoyue Yang

  • Distributed Consensus of Second-Order Multiagent Systems With Nonconvex Velocity and Control Input Constraints

    Peng Lin;Wei Ren;Chunhua Yang;Weihua Gui

  • Automated Visual Defect Classification for Flat Steel Surface: A Survey

    Qiwu Luo;Xiaoxin Fang;Jiaojiao Su;Jian Zhou

  • Soft sensor model for dynamic processes based on multichannel convolutional neural network

    Xiaofeng Yuan;Shuaibin Qi;Yuri A.W. Shardt;Yalin Wang

  • Color co-occurrence matrix based froth image texture extraction for mineral flotation

    Weihua Gui;Jinping Liu;Chunhua Yang;Ning Chen

Frequent Co-Authors

Weihua Gui
Weihua Gui Central South University
Xiaofeng Yuan
Xiaofeng Yuan Central South University
Tingwen Huang
Tingwen Huang Shenzhen Institutes of Advanced Technology
Kok Lay Teo
Kok Lay Teo Sunway University
Steven X. Ding
Steven X. Ding University of Duisburg-Essen
Wei Ren
Wei Ren University of California, Riverside
Peng Shi
Peng Shi University of Adelaide
David Yang Gao
David Yang Gao Federation University Australia
Ryan Loxton
Ryan Loxton Curtin University

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