World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
59
Citations
10638
World Ranking
3490
National Ranking
471

Xinyu Shao 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 Xinyu Shao 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: 232 publications — 57th percentile

57% of scientists in this discipline score the same or lower.

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

Xinyu Shao 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 Xinyu Shao 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: 59 D-Index — 77th percentile

77% of scientists in this discipline score the same or lower.

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

Overview

Xinyu Shao is affiliated with Huazhong University of Science and Technology in China. Their research primarily focuses on engineering, with a significant number of publications in mechanical engineering, control and systems engineering, electrical and electronic engineering, mechanics of materials, and biomedical engineering.

The key topics in their work include machine fault diagnosis techniques, fault detection and control systems, engineering diagnostics and reliability, gear and bearing dynamics analysis, non-destructive testing techniques, advanced welding techniques analysis, and manufacturing process and optimization.

Their frequent co-authors include Jun Wu, Yiwei Cheng, Haiping Zhu, Ping Jiang, and Leshi Shu, indicating active collaboration within their research community.

Xinyu Shao has published extensively in several venues, with multiple papers appearing in arXiv (Cornell University), IEEE/ASME Transactions on Mechatronics, IEEE Transactions on Instrumentation and Measurement, International Journal of Biological Macromolecules, and Frontiers in Genetics.

Notable published papers include:

  • Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network, 2021, Knowledge-Based Systems
  • A convolutional neural network based degradation indicator construction and health prognosis using bidirectional long short-term memory network for rolling bearings, 2021, Advanced Engineering Informatics
  • Lamb wave-based damage detection of composite structures using deep convolutional neural network and continuous wavelet transform, 2021, Composite Structures
  • Stacked pruning sparse denoising autoencoder based intelligent fault diagnosis of rolling bearings, 2020, Applied Soft Computing
  • Remaining Useful Life Prognosis Based on Ensemble Long Short-Term Memory Neural Network, 2020, IEEE Transactions on Instrumentation and Measurement

Best Publications

  • An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem

    Guohui Zhang;Xinyu Shao;Peigen Li;Liang Gao

  • Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network.

    Jun Wu;Kui Hu;Yiwei Cheng;Haiping Zhu

  • Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network

    Yiwei Cheng;Manxi Lin;Jun Wu;Haiping Zhu

  • Integration of process planning and scheduling-A modified genetic algorithm-based approach

    Xinyu Shao;Xinyu Li;Liang Gao;Chaoyong Zhang

  • Laser beam oscillating welding of 5A06 aluminum alloys: Microstructure, porosity and mechanical properties

    Zhimin Wang;J.P. Oliveira;Zhi Zeng;Xianzheng Bu

  • A multi-objective genetic algorithm based on immune and entropy principle for flexible job-shop scheduling problem

    Xiaojuan Wang;Liang Gao;Chaoyong Zhang;Xinyu Shao

  • Degradation Data-Driven Time-To-Failure Prognostics Approach for Rolling Element Bearings in Electrical Machines

    Jun Wu;Chaoyong Wu;Shuai Cao;Siu Wing Or

  • MILP models for energy-aware flexible job shop scheduling problem

    Leilei Meng;Chaoyong Zhang;Xinyu Shao;Yaping Ren

  • Machine Health Monitoring Using Adaptive Kernel Spectral Clustering and Deep Long Short-Term Memory Recurrent Neural Networks

    Yiwei Cheng;Haiping Zhu;Jun Wu;Xinyu Shao

  • Multi-sensor information fusion for remaining useful life prediction of machining tools by adaptive network based fuzzy inference system

    Jun Wu;Yongheng Su;Yiwei Cheng;Xinyu Shao

  • Mathematical modeling and evolutionary algorithm-based approach for integrated process planning and scheduling

    Xinyu Li;Liang Gao;Xinyu Shao;Chaoyong Zhang

  • A local Kriging approximation method using MPP for reliability-based design optimization

    Xiaoke Li;Haobo Qiu;Zhenzhong Chen;Liang Gao

  • A convolutional neural network based degradation indicator construction and health prognosis using bidirectional long short-term memory network for rolling bearings

    Yiwei Cheng;Kui Hu;Jun Wu;Haiping Zhu

  • Hybrid discrete particle swarm optimization for multi-objective flexible job-shop scheduling problem

    Xinyu Shao;Weiqi Liu;Weiqi Liu;Qiong Liu;Chaoyong Zhang

  • Mathematical modelling and optimisation of energy-conscious hybrid flow shop scheduling problem with unrelated parallel machines

    Leilei Meng;Chaoyong Zhang;Xinyu Shao;Yaping Ren

  • Lamb wave-based damage detection of composite structures using deep convolutional neural network and continuous wavelet transform

    Jun Wu;Xuebing Xu;Cheng Liu;Chao Deng

  • Surrogate Model-Based Engineering Design and Optimization

    Ping Jiang;Qi Zhou;Xinyu Shao

  • An agent-based approach for integrated process planning and scheduling

    Xinyu Li;Chaoyong Zhang;Liang Gao;Weidong Li

  • A review on Integrated Process Planning and Scheduling

    Xinyu Li;Liang Gao;Chaoyong Zhang;Xinyu Shao

  • Integrating data mining and rough set for customer group-based discovery of product configuration rules

    X.-Y Shao;Z.-H Wang;P.-G Li;C.-X. J Feng

  • An effective hybrid algorithm for integrated process planning and scheduling

    Xinyu Li;Xinyu Shao;Liang Gao;Weirong Qian

  • Optimization of laser welding process parameters of stainless steel 316L using FEM, Kriging and NSGA-II

    Ping Jiang;Chaochao Wang;Qi Zhou;Xinyu Shao

Frequent Co-Authors

Liang Gao
Liang Gao Huazhong University of Science and Technology
Chunming Wang
Chunming Wang Huazhong University of Science and Technology
Chaoyong Zhang
Chaoyong Zhang Huazhong University of Science and Technology
Peigen Li
Peigen Li Huazhong University of Science and Technology
Hui Zhou
Hui Zhou Huazhong University of Science and Technology
Xinyu Li
Xinyu Li Huazhong University of Science and Technology
Weidong Li
Weidong Li Wuhan University of Technology
Guangdong Tian
Guangdong Tian Shandong University
Jun Wu
Jun Wu The University of Texas Southwestern Medical Center
Wei Liu
Wei Liu Huazhong University of Science and Technology

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