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Rising Stars

D-Index
42
Citations
5831
World Ranking
577
National Ranking
199

Computer Science

D-Index
47
Citations
7675
World Ranking
6577
National Ranking
881

Xiaofeng Yuan 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 Xiaofeng Yuan 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: 151 publications — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Xiaofeng Yuan 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 Xiaofeng Yuan 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: 47 D-Index — 56th percentile

56% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Xiaofeng Yuan is affiliated with Central South University in China and has contributed extensively to the fields of engineering and computer science. Their research spans multiple subfields including control and systems engineering, artificial intelligence, mechanical engineering, analytical chemistry, and industrial and manufacturing engineering.

The scholar's publication record includes numerous papers in key venues such as:

  • IEEE Sensors Journal
  • IEEE Transactions on Industrial Informatics
  • IEEE Transactions on Instrumentation and Measurement
  • Engineering Applications of Artificial Intelligence
  • Journal of Process Control

Xiaofeng Yuan's primary research topics focus on fault detection and control systems, mineral processing and grinding, neural networks and applications, advanced control systems optimization, spectroscopy and chemometric analyses, advanced data processing techniques, and machine learning and extreme learning machines.

Notable recent publications include:

  • Deep Learning With Spatiotemporal Attention-Based LSTM for Industrial Soft Sensor Model Development (2020), IEEE Transactions on Industrial Electronics
  • A dynamic CNN for nonlinear dynamic feature learning in soft sensor modeling of industrial process data (2020), Control Engineering Practice
  • Soft sensor model for dynamic processes based on multichannel convolutional neural network (2020), Chemometrics and Intelligent Laboratory Systems
  • Deep learning for fault-relevant feature extraction and fault classification with stacked supervised auto-encoder (2020), Journal of Process Control
  • Learning Deep Multimanifold Structure Feature Representation for Quality Prediction With an Industrial Application (2021), IEEE Transactions on Industrial Informatics

Their collaborative work includes frequent partnerships with coauthors such as:

  • Yalin Wang
  • Kai Wang
  • Chenliang Liu
  • Chunhua Yang
  • Lingjian Ye

Xiaofeng Yuan's research contributions demonstrate a focus on developing advanced sensor models and feature learning methods for industrial process data and fault detection. The integration of deep learning techniques, such as convolutional neural networks and long short-term memory networks, plays a significant role in their work. This work is situated primarily at the intersection of engineering applications and AI-driven process control.

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

  • Nonlinear Dynamic Soft Sensor Modeling With Supervised Long Short-Term Memory Network

    Xiaofeng Yuan;Lin Li;Yalin Wang

  • 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

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

    Xiaofeng Yuan;Jiao Zhou;Biao Huang;Yalin Wang

  • 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

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

    Xiaofeng Yuan;Zhiqiang Ge;Biao Huang;Zhihuan Song

  • 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 dynamic CNN for nonlinear dynamic feature learning in soft sensor modeling of industrial process data

    Xiaofeng Yuan;Shuaibin Qi;Yalin Wang;Haibing Xia

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

    Xiaofeng Yuan;Zhiqiang Ge;Zhihuan Song

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

    Xiaofeng Yuan;Yongjie Gu;Yalin Wang;Chunhua Yang

  • A Probabilistic Just-in-Time Learning Framework for Soft Sensor Development With Missing Data

    Xiaofeng Yuan;Zhiqiang Ge;Biao Huang;Zhihuan Song

  • Deep quality-related feature extraction for soft sensing modeling: A deep learning approach with hybrid VW-SAE

    Xiaofeng Yuan;Chen Ou;Yalin Wang;Chunhua Yang

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

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

  • Learning deep multi-manifold structure feature representation for quality prediction with an industrial application

    Chenliang Liu;Kai Wang;Yalin Wang;Xiaofeng Yuan

  • Deep learning for fault-relevant feature extraction and fault classification with stacked supervised auto-encoder

    Yalin Wang;Haibing Yang;Xiaofeng Yuan;Yuri A.W. Shardt

  • Deep learning for quality prediction of nonlinear dynamic processes with variable attention‐based long short‐term memory network

    Xiaofeng Yuan;Lin Li;Yalin Wang;Chunhua Yang

  • Soft Sensor Modeling of Nonlinear Industrial Processes Based on Weighted Probabilistic Projection Regression

    Xiaofeng Yuan;Zhiqiang Ge;Zhihuan Song;Yalin Wang

  • A novel semi-supervised pre-training strategy for deep networks and its application for quality variable prediction in industrial processes

    Xiaofeng Yuan;Chen Ou;Yalin Wang;Chunhua Yang

Frequent Co-Authors

Chunhua Yang
Chunhua Yang Central South University
Weihua Gui
Weihua Gui Central South University
Zhihuan Song
Zhihuan Song Zhejiang University
Zhiqiang Ge
Zhiqiang Ge Zhejiang University
Biao Huang
Biao Huang University of Alberta
Yi Cao
Yi Cao Lund University
Heikki N. Koivo
Heikki N. Koivo Aalto University
Steven X. Ding
Steven X. Ding University of Duisburg-Essen
Junghui Chen
Junghui Chen Chung Yuan Christian University

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