World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
57
Citations
8910
World Ranking
2740
National Ranking
548

Chengliang Liu 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 Chengliang Liu 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: 253 publications — 65th percentile

65% of scientists in this discipline score the same or lower.

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

Chengliang Liu 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 Chengliang Liu 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: 57 D-Index — 74th percentile

74% of scientists in this discipline score the same or lower.

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

Overview

Chengliang Liu is affiliated with Shanghai Jiao Tong University in China, focusing primarily on engineering research. Their work spans multiple interconnected subfields, with notable activity in Control and Systems Engineering, Cardiology and Cardiovascular Medicine, Mechanical Engineering, Civil and Structural Engineering, and Plant Science.

Their research addresses a range of topics, including:

  • Machine Fault Diagnosis Techniques
  • ECG Monitoring and Analysis
  • Tunneling and Rock Mechanics
  • EEG and Brain-Computer Interfaces
  • Smart Agriculture and AI
  • Drilling and Well Engineering
  • Rock Mechanics and Modeling

Chengliang Liu has published extensively, with a strong presence in several academic journals. The most frequent venues for their publications include:

  • Science China Technological Sciences
  • Knowledge-Based Systems
  • Computers and Electronics in Agriculture
  • Mechanical Systems and Signal Processing
  • Biomedical Signal Processing and Control

Some recent papers highlight the scope and focus of their research:

  • Precise cutterhead torque prediction for shield tunneling machines using a novel hybrid deep neural network, 2020, Mechanical Systems and Signal Processing
  • Actual bearing compound fault diagnosis based on active learning and decoupling attentional residual network, 2020, Measurement
  • A VMD-EWT-LSTM-based multi-step prediction approach for shield tunneling machine cutterhead torque, 2021, Knowledge-Based Systems
  • Multi-domain modeling of atrial fibrillation detection with twin attentional convolutional long short-term memory neural networks, 2020, Knowledge-Based Systems
  • A high-precision arrhythmia classification method based on dual fully connected neural network, 2020, Biomedical Signal Processing and Control

Collaboration is a significant component of their research activity. Frequent co-authors include:

  • Chengjin Qin
  • Yanrui Jin
  • Jianfeng Tao
  • Yixiang Huang
  • Zhiyuan Li

The body of work by Chengliang Liu reflects an intersection of advanced engineering methodologies applied to complex systems, including medical signal processing, fault diagnosis in machinery, and applications in agricultural technology. The research demonstrates a multidisciplinary approach involving computational intelligence methods such as neural networks and deep learning techniques applied across diverse technical problems.

Best Publications

  • A Manufacturing Big Data Solution for Active Preventive Maintenance

    Jiafu Wan;Shenglong Tang;Di Li;Shiyong Wang

  • A review of key techniques of vision-based control for harvesting robot

    Yuanshen Zhao;Liang Gong;Yixiang Huang;Chengliang Liu

  • Fog Computing for Energy-Aware Load Balancing and Scheduling in Smart Factory

    Jiafu Wan;Baotong Chen;Shiyong Wang;Min Xia

  • Adaptive Transmission Optimization in SDN-Based Industrial Internet of Things With Edge Computing

    Xiaomin Li;Di Li;Jiafu Wan;Chengliang Liu

  • Deep Learning-Based Segmentation and Quantification of Cucumber Powdery Mildew Using Convolutional Neural Network.

    Ke Lin;Liang Gong;Yixiang Huang;Chengliang Liu

  • An enhanced empirical wavelet transform for noisy and non-stationary signal processing

    Unknown

  • Adaptive feature extraction using sparse coding for machinery fault diagnosis

    Haining Liu;Chengliang Liu;Yixiang Huang

  • Context-Aware Cloud Robotics for Material Handling in Cognitive Industrial Internet of Things

    Jiafu Wan;Shenglong Tang;Qingsong Hua;Di Li

  • A novel methodology to explain and evaluate data-driven building energy performance models based on interpretable machine learning

    Cheng Fan;Cheng Fan;Fu Xiao;Chengchu Yan;Chengliang Liu

  • Toward Dynamic Resources Management for IoT-Based Manufacturing

    Jiafu Wan;Baotong Chen;Muhammad Imran;Fei Tao

  • Wavelet leaders multifractal features based fault diagnosis of rotating mechanism

    Wenliao Du;Wenliao Du;Jianfeng Tao;Yanming Li;Chengliang Liu

  • A hierarchical method based on weighted extreme gradient boosting in ECG heartbeat classification.

    Haotian Shi;Haoren Wang;Yixiang Huang;Liqun Zhao

  • Detecting tomatoes in greenhouse scenes by combining AdaBoost classifier and colour analysis

    Yuanshen Zhao;Liang Gong;Bin Zhou;Yixiang Huang

  • Computer vision detection of defective apples using automatic lightness correction and weighted RVM classifier

    Baohua Zhang;Wenqian Huang;Liang Gong;Jiangbo Li

  • Precise cutterhead torque prediction for shield tunneling machines using a novel hybrid deep neural network

    Chengjin Qin;Gang Shi;Jianfeng Tao;Honggan Yu

  • Actual bearing compound fault diagnosis based on active learning and decoupling attentional residual network

    Yanrui Jin;Chengjin Qin;Yixiang Huang;Chengliang Liu

  • Dual-arm cooperation and implementing for robotic harvesting tomato using binocular vision

    Xiao Ling;Yuanshen Zhao;Liang Gong;Chengliang Liu

  • Domain Adaptive Motor Fault Diagnosis Using Deep Transfer Learning

    Dengyu Xiao;Yixiang Huang;Lujie Zhao;Chengjin Qin

  • Reconfigurable Smart Factory for Drug Packing in Healthcare Industry 4.0

    Jiafu Wan;Shenglong Tang;Di Li;Muhammad Imran

  • An adaptive hierarchical decomposition-based method for multi-step cutterhead torque forecast of shield machine

    Unknown

  • A VMD-EWT-LSTM-based multi-step prediction approach for shield tunneling machine cutterhead torque

    Gang Shi;Chengjin Qin;Jianfeng Tao;Chengliang Liu

  • Dual-arm Robot Design and Testing for Harvesting Tomato in Greenhouse

    Yuanshen Zhao;Liang Gong;Chengliang Liu;Yixiang Huang

  • Study on a piezoelectric micropump for the controlled drug delivery system

    Unknown

  • Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion.

    Yuanshen Zhao;Liang Gong;Yixiang Huang;Chengliang Liu

Frequent Co-Authors

Jiafu Wan
Jiafu Wan South China University of Technology
Jay Lee
Jay Lee University of Maryland, College Park
Dabing Zhang
Dabing Zhang Shanghai Jiao Tong University
Michael Pecht
Michael Pecht University of Maryland, College Park
Jaime Lloret
Jaime Lloret Universitat Politècnica de València
Jinhong Yuan
Jinhong Yuan University of New South Wales
Fei Tao
Fei Tao Beihang University
Athanasios V. Vasilakos
Athanasios V. Vasilakos University of Agder
Jianwei Zhang
Jianwei Zhang Universität Hamburg
Valeriy Vyatkin
Valeriy Vyatkin Aalto University

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