World's Best Scientists 2026 revealed!
Zhihuan Song

Zhihuan Song

D-Index & Metrics

Computer Science

D-Index
60
Citations
12155
World Ranking
3285
National Ranking
441

Zhihuan Song 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 Zhihuan Song 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: 277 publications — 69th percentile

69% of scientists in this discipline score the same or lower.

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

Zhihuan Song 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 Zhihuan Song 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: 60 D-Index — 78th percentile

78% of scientists in this discipline score the same or lower.

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

Overview

Zhihuan Song is affiliated with Zhejiang University in China and has contributed extensively to the field of engineering, with a focus on control and systems engineering, mechanical engineering, and artificial intelligence.

Their research topics encompass a range of specialized areas, including:

  • Fault Detection and Control Systems
  • Mineral Processing and Grinding
  • Advanced Control Systems Optimization
  • Spectroscopy and Chemometric Analyses
  • Machine Fault Diagnosis Techniques
  • Advanced Statistical Process Monitoring
  • Industrial Vision Systems and Defect Detection

Zhihuan Song's published work spans several major venues, frequently contributing to:

  • IEEE Transactions on Industrial Informatics
  • IEEE Transactions on Instrumentation and Measurement
  • IEEE Sensors Journal
  • IEEE Transactions on Neural Networks and Learning Systems
  • 2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)

Recent papers authored or co-authored by Zhihuan Song include:

  • "Imbalanced Sample Selection With Deep Reinforcement Learning for Fault Diagnosis," 2021, IEEE Transactions on Industrial Informatics
  • "Multirate Mixture Probability Principal Component Analysis for Process Monitoring in Multimode Processes," 2023, IEEE Transactions on Automation Science and Engineering
  • "Data-driven modelling methods in sintering process: Current research status and perspectives," 2022, The Canadian Journal of Chemical Engineering
  • "Dynamic Process Monitoring Based on Variational Bayesian Canonical Variate Analysis," 2021, IEEE Transactions on Systems Man and Cybernetics Systems
  • "Kernel Generalization of Multi-Rate Probabilistic Principal Component Analysis for Fault Detection in Nonlinear Process," 2021, IEEE/CAA Journal of Automatica Sinica

Collaborations are an important aspect of Zhihuan Song's scientific work. Frequent co-authors include:

  • Xinmin Zhang
  • Zhiqiang Ge
  • Chihang Wei
  • Jinchuan Qian
  • Le Yao

Best Publications

  • Review of Recent Research on Data-Based Process Monitoring

    Zhiqiang Ge;Zhihuan Song;Furong Gao

  • Data Mining and Analytics in the Process Industry: The Role of Machine Learning

    Zhiqiang Ge;Zhihuan Song;Steven X. Ding;Biao Huang

  • Process Monitoring Based on Independent Component Analysis - Principal Component Analysis ( ICA - PCA ) and Similarity Factors

    Zhiqiang Ge;Zhihuan Song

  • Distributed Parallel PCA for Modeling and Monitoring of Large-Scale Plant-Wide Processes With Big Data

    Jinlin Zhu;Zhiqiang Ge;Zhihuan Song

  • Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data

    Jinlin Zhu;Jinlin Zhu;Zhiqiang Ge;Zhihuan Song;Furong Gao

  • Distributed PCA Model for Plant-Wide Process Monitoring

    Zhiqiang Ge;Zhihuan Song

  • Improved kernel PCA-based monitoring approach for nonlinear processes

    Zhiqiang Ge;Chunjie Yang;Zhihuan Song

  • A comparative study of just-in-time-learning based methods for online soft sensor modeling

    Zhiqiang Ge;Zhihuan Song

  • Online monitoring of nonlinear multiple mode processes based on adaptive local model approach

    Zhiqiang Ge;Zhihuan Song

  • Global–Local Structure Analysis Model and Its Application for Fault Detection and Identification

    Muguang Zhang;Zhiqiang Ge;Zhihuan Song;Ruowei Fu

  • Mixture Bayesian regularization method of PPCA for multimode process monitoring

    Zhiqiang Ge;Zhihuan Song

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

    Xiaofeng Yuan;Yalin Wang;Chunhua Yang;Zhiqiang Ge

  • Locally Weighted Kernel Principal Component Regression Model for Soft Sensing of Nonlinear Time-Variant Processes

    Xiaofeng Yuan;Zhiqiang Ge;Zhihuan Song

  • Nonlinear process monitoring based on linear subspace and Bayesian inference

    Zhiqiang Ge;Muguang Zhang;Zhihuan Song

  • Multimode process monitoring based on Bayesian method

    Zhiqiang Ge;Zhihuan Song

  • Semisupervised JITL Framework for Nonlinear Industrial Soft Sensing Based on Locally Semisupervised Weighted PCR

    Xiaofeng Yuan;Zhiqiang Ge;Biao Huang;Zhihuan Song

  • Batch process monitoring based on support vector data description method

    Zhiqiang Ge;Zhiqiang Ge;Furong Gao;Zhihuan Song

  • Hilbert–Huang transform based signal analysis for the characterization of gas–liquid two-phase flow

    Hao Ding;Zhiyao Huang;Zhihuan Song;Yong Yan

  • Soft sensor model development in multiphase/multimode processes based on Gaussian mixture regression

    Xiaofeng Yuan;Zhiqiang Ge;Zhihuan Song

  • Semi-supervised fault classification based on dynamic Sparse Stacked auto-encoders model

    Li Jiang;Zhiqiang Ge;Zhihuan Song

  • Mixture probabilistic PCR model for soft sensing of multimode processes

    Zhiqiang Ge;Zhiqiang Ge;Furong Gao;Zhihuan Song

Frequent Co-Authors

Zhiqiang Ge
Zhiqiang Ge Zhejiang University
Junghui Chen
Junghui Chen Chung Yuan Christian University
Xiaofeng Yuan
Xiaofeng Yuan Central South University
Furong Gao
Furong Gao Hong Kong University of Science and Technology
Biao Huang
Biao Huang University of Alberta
Yi Cao
Yi Cao Lund University
Steven X. Ding
Steven X. Ding University of Duisburg-Essen
Uwe Kruger
Uwe Kruger Rensselaer Polytechnic Institute
Ahmet Palazoglu
Ahmet Palazoglu University of California, Davis
S. Joe Qin
S. Joe Qin Lingnan University

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