World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
36
Citations
4198
World Ranking
8805
National Ranking
96

Yuan Yao publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Yuan Yao sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 179 publications — 39th percentile

39% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Yuan Yao D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Yuan Yao sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 36 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Yuan Yao is affiliated with National Tsing Hua University in Taiwan and has published extensively in the field of engineering, with a focus on control and systems engineering. Their research spans various subfields including mechanics of materials, mechanical engineering, artificial intelligence, and statistics, probability, and uncertainty.

Their recent publications cover topics such as fault detection, thermography, and industrial process monitoring. Key papers include:

  • "A review on autoencoder based representation learning for fault detection and diagnosis in industrial processes" (2022) published in Chemometrics and Intelligent Laboratory Systems
  • "Generative Principal Component Thermography for Enhanced Defect Detection and Analysis" (2020) published in IEEE Transactions on Instrumentation and Measurement
  • "Deep Autoencoder Thermography for Defect Detection of Carbon Fiber Composites" (2022) published in IEEE Transactions on Industrial Informatics
  • "Graph convolutional network soft sensor for process quality prediction" (2023) published in Journal of Process Control
  • "Development of Adversarial Transfer Learning Soft Sensor for Multigrade Processes" (2020) published in Industrial & Engineering Chemistry Research

The scientist frequently collaborates with several coauthors, notably:

  • Yi Liu
  • Kaixin Liu
  • Стефано Сфарра
  • Jianguo Wang
  • Jianguo Yang

Yuan Yao's work appears regularly in publication venues such as:

  • Computers & Chemical Engineering
  • 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)
  • Industrial & Engineering Chemistry Research
  • Quantitative InfraRed Thermography Journal
  • Polymers

The main topics of their research include:

  • Fault Detection and Control Systems
  • Thermography and Photoacoustic Techniques
  • Mineral Processing and Grinding
  • Industrial Vision Systems and Defect Detection
  • Advanced Control Systems Optimization
  • Advanced Statistical Process Monitoring
  • Advanced Data Processing Techniques

This body of work reflects a research agenda centered predominantly on engineering challenges associated with industrial processes, fault detection, and quality control through innovative applications of machine learning and data-driven techniques.

Best Publications

  • A survey on multistage/multiphase statistical modeling methods for batch processes

    Yuan Yao;Furong Gao

  • Ensemble deep kernel learning with application to quality prediction in industrial polymerization processes

    Yi Liu;Chao Yang;Zengliang Gao;Yuan Yao

  • Deep Autoencoder Thermography for Defect Detection of Carbon Fiber Composites

    Unknown

  • Domain adaptation transfer learning soft sensor for product quality prediction

    Yi Liu;Chao Yang;Kaixin Liu;Bocheng Chen

  • Two-dimensional dynamic PCA for batch process monitoring

    Ningyun Lu;Yuan Yao;Furong Gao;Fuli Wang

  • Statistical analysis and online monitoring for multimode processes with between-mode transitions

    Chunhui Zhao;Yuan Yao;Furong Gao;Fuli Wang

  • Phase and transition based batch process modeling and online monitoring

    Yuan Yao;Furong Gao

  • Sparse Principal Component Thermography for Subsurface Defect Detection in Composite Products

    Jin-Yi Wu;Stefano Sfarra;Yuan Yao

  • Generative Principal Component Thermography for Enhanced Defect Detection and Analysis

    Kaixin Liu;Yingjie Li;Jianguo Yang;Yi Liu

  • Variable selection method for fault isolation using least absolute shrinkage and selection operator (LASSO)

    Zhengbing Yan;Yuan Yao

  • Simplified Granger causality map for data-driven root cause diagnosis of process disturbances

    Yi Liu;Han-Sheng Chen;Haibin Wu;Yun Dai

  • Systematic Procedure for Granger-Causality-Based Root Cause Diagnosis of Chemical Process Faults

    Han-Sheng Chen;Zhengbing Yan;Yuan Yao;Tsai-Bang Huang

  • Development of Adversarial Transfer Learning Soft Sensor for Multigrade Processes

    Yi Liu;Chao Yang;Chao Yang;Mingtao Zhang;Yun Dai

  • Improved non-destructive testing of carbon fiber reinforced polymer (CFRP) composites using pulsed thermograph

    Kaiyi Zheng;Yu-Sung Chang;Kai-Hong Wang;Yuan Yao

  • Utilizing transition information in online quality prediction of multiphase batch processes

    Zhiqiang Ge;Zhiqiang Ge;Luping Zhao;Yuan Yao;Zhihuan Song

  • Statistical analysis and online monitoring for handling multiphase batch processes with varying durations

    Chunhui Zhao;Shengyong Mo;Furong Gao;Ningyun Lu

  • Defect detection in CFRP structures using pulsed thermographic data enhanced by penalized least squares methods

    Kaiyi Zheng;Yu-Sung Chang;Yuan Yao

  • Spatial-Neighborhood Manifold Learning for Nondestructive Testing of Defects in Polymer Composites

    Yi Liu;Kaixin Liu;Jianguo Yang;Yuan Yao

  • Active thermography testing and data analysis for the state of conservation of panel paintings

    Yuan Yao;Stefano Sfarra;Susana Lagüela;Susana Lagüela;Clemente Ibarra-Castanedo

  • Multilinear model decomposition of MIMO nonlinear systems and its implication for multilinear model-based control

    Jingjing Du;Chunyue Song;Yuan Yao;Ping Li

  • Independent component thermography for non-destructive testing of defects in polymer composites

    Yi Liu;Jin-Yi Wu;Kaixin Liu;Hsiu-Li Wen

  • The multi-dimensional ensemble empirical mode decomposition (MEEMD): An advanced tool for thermographic diagnosis of mosaics

    Yuan Yao;Stefano Sfarra;Stefano Sfarra;Stefano Sfarra;Clemente Ibarra-Castanedo;Renchun You

Frequent Co-Authors

Furong Gao
Furong Gao Hong Kong University of Science and Technology
Chunhui Zhao
Chunhui Zhao Zhejiang University
David Shan-Hill Wong
David Shan-Hill Wong National Tsing Hua University
Xavier Maldague
Xavier Maldague Université Laval
Minrui Fei
Minrui Fei Shanghai University
Zhiqiang Ge
Zhiqiang Ge Zhejiang University
Wenjing Hong
Wenjing Hong Xiamen University

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