World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
37
Citations
4781
World Ranking
8467
National Ranking
89

Chao-Ton Su 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 Chao-Ton Su 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: 142 publications — 23rd percentile

23% of scientists in this discipline score the same or lower.

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

Chao-Ton Su 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 Chao-Ton Su 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: 37 D-Index — 16th percentile

16% of scientists in this discipline score the same or lower.

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

Overview

Chao-Ton Su is affiliated with National Tsing Hua University in Taiwan. Their research spans multiple fields, primarily focusing on engineering and computer science, with a strong emphasis on industrial and manufacturing engineering and computer vision and pattern recognition.

Their work covers various subfields, including artificial intelligence, management information systems, and the management of technology and innovation. The core topics in their research profile address industrial vision systems and defect detection, scheduling and optimization algorithms, face and expression recognition, digital media forensic detection, generative adversarial networks and image synthesis, supply chain and inventory management, and product development and customization.

Recent publications by Chao-Ton Su include:

  • "CNNs Combined With a Conditional GAN for Mura Defect Classification in TFT-LCDs," 2021, IEEE Transactions on Semiconductor Manufacturing
  • "A Reinforcement Learning Approach to Dynamic Scheduling in a Product-Mix Flexibility Environment," 2020, IEEE Access
  • "Enhancing ensemble diversity based on multiscale dilated convolution in image classification," 2022, Information Sciences
  • "Balancing accuracy and diversity in ensemble learning using a two-phase artificial bee colony approach," 2021, Applied Soft Computing
  • "Combination of Convolutional and Generative Adversarial Networks for Defect Image Demoiréing of Thin-Film Transistor Liquid-Crystal Display Image," 2020, IEEE Transactions on Semiconductor Manufacturing

Frequent coauthors collaborating with Su include Yeou-Ren Shiue, Hsueh-Ping Lu, Ken-Chuan Lee, Gui-Rong You, and Qing-Lan Huang.

Chao-Ton Su's publications have appeared in several recognized venues, notably:

  • IEEE Transactions on Semiconductor Manufacturing
  • IEEE Access
  • Information Sciences
  • Applied Soft Computing
  • International Journal of Computer Integrated Manufacturing

Best Publications

  • Multi-response robust design by principal component analysis

    Chao Ton Su;Lee-Ing Tong

  • Linking innovative product development with customer knowledge: a data-mining approach

    Chao-Ton Su;Yung-Hsin Chen;D.Y. Sha

  • Real-time scheduling for a smart factory using a reinforcement learning approach

    Yeou-Ren Shiue;Ken-Chuan Lee;Chao-Ton Su

  • A systematic methodology for the creation of Six Sigma projects: A case study of semiconductor foundry

    Chao-Ton Su;Chia-Jen Chou

  • An extended Chi2 algorithm for discretization of real value attributes

    Chao-Ton Su;Jyh-Hwa Hsu

  • Systematic layout planning: a study on semiconductor wafer fabrication facilities

    Ta-Ho Yang;Chao Ton Su;Yuan Ru Hsu

  • An Evaluation of the Robustness of MTS for Imbalanced Data

    Chao-Ton Su;Yu-Hsiang Hsiao

  • The optimization of multi‐response problems in the Taguchi method

    Unknown

  • A Kano-CKM model for customer knowledge discovery

    Yung-Hsin Chen;Chao-Ton Su

  • A neural-network approach for semiconductor wafer post-sawing inspection

    Chao-Ton Su;Taho Yang;Chir-Mour Ke

  • Lean production system design for fishing net manufacturing using lean principles and simulation optimization

    Taho Yang;Yiyo Kuo;Chao Ton Su;Chia Lin Hou

  • Precision parameter in the variable precision rough sets model: an application

    Chao-Ton Su;Jyh-Hwa Hsu

  • Feature selection for the SVM: An application to hypertension diagnosis

    Chao-Ton Su;Chien-Hsin Yang

  • Knowledge acquisition through information granulation for imbalanced data

    Chao-Ton Su;Long-Sheng Chen;Yuehwern Yih

  • Multiclass MTS for Simultaneous Feature Selection and Classification

    Chao-Ton Su;Yu-Hsiang Hsiao

  • Applying electromagnetism-like mechanism for feature selection

    Chao-Ton Su;Hung-Chun Lin

  • Particle swarm optimization for feature selection with application in obstructive sleep apnea diagnosis

    Li-Fei Chen;Chao-Ton Su;Kun-Huang Chen;Pa-Chun Wang;Pa-Chun Wang

  • Applying HFMEA to Prevent Chemotherapy Errors

    Chia-Hui Cheng;Chia-Jen Chou;Pa-Chun Wang;Hsi-Yen Lin

  • A case study on the application of Fuzzy QFD in TRIZ for service quality improvement

    Chao-Ton Su;Chin-Sen Lin

  • Multi-objective machine-part cell formation through parallel simulated annealing

    Unknown

  • Parameter optimization of continuous sputtering process based on Taguchi methods, neural networks, desirability function, and genetic algorithms

    Hung-Chun Lin;Chao-Ton Su;Chi-Ching Wang;Bing-Hung Chang

  • An empirical study of the Taiwan National Quality Award causal model

    Chao-Ton Su;Shao-Chang Li;Chin-Ho Su

  • Using the QFD concept to resolve customer satisfaction strategy decisions

    Te‐King Chien;Chao‐Ton Su

  • Applying neural network approach to achieve robust design for dynamic quality characteristics

    Chao‐Ton Su;Kun‐Lin Hsieh

  • Multi-objective machine-component grouping in cellular manufacturing: a genetic algorithm

    Chih-Ming Hsu;Chao-Ton Su

  • Applying lean six sigma to improve healthcare: An empirical study

    Hseng-Long Yeh;Chin-Sen Lin;Chao-Ton Su;Pa-Chun Wang

  • An improved particle swarm optimization for feature selection

    Li-Fei Chen;Chao-Ton Su;Kun-Huang Chen

Frequent Co-Authors

Taho Yang
Taho Yang National Cheng Kung University
Mu-Chen Chen
Mu-Chen Chen National Yang Ming Chiao Tung University
Xiaoyu Song
Xiaoyu Song Portland State University
Mohamad Sawan
Mohamad Sawan Polytechnique Montréal
Roberto Hornero
Roberto Hornero University of Valladolid
Nigel H. Lovell
Nigel H. Lovell University of New South Wales
Yuan-Ting Zhang
Yuan-Ting Zhang City University of Hong Kong
Eddie Y. K. Ng
Eddie Y. K. Ng Nanyang Technological University
Yuehwern Yih
Yuehwern Yih Purdue University West Lafayette

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