Le Yao

4.0k total citations · 1 hit paper
107 papers, 3.2k citations indexed

About

Le Yao is a scholar working on Control and Systems Engineering, Mechanical Engineering and Artificial Intelligence. According to data from OpenAlex, Le Yao has authored 107 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 61 papers in Control and Systems Engineering, 24 papers in Mechanical Engineering and 20 papers in Artificial Intelligence. Recurrent topics in Le Yao's work include Fault Detection and Control Systems (59 papers), Advanced Control Systems Optimization (25 papers) and Mineral Processing and Grinding (20 papers). Le Yao is often cited by papers focused on Fault Detection and Control Systems (59 papers), Advanced Control Systems Optimization (25 papers) and Mineral Processing and Grinding (20 papers). Le Yao collaborates with scholars based in China, Hong Kong and Taiwan. Le Yao's co-authors include Zhiqiang Ge, Jiaguo Yu, Bei Cheng, Xiaofeng Zhu, Weiquan Cai, Chunsheng Lei, Bicheng Zhu, Bingbing Shen, Chuanjia Jiang and Zhiqiang Ge and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Hazardous Materials and IEEE Transactions on Industrial Electronics.

In The Last Decade

Le Yao

92 papers receiving 3.1k citations

Hit Papers

Superb adsorption capacity of hierarchical calcined Ni/Mg... 2016 2026 2019 2022 2016 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Superb adsorption capacity of hierarchical calcined Ni/Mg/Al layered double hydroxides for Congo red and Cr(VI) ions
    2016 455 citations Le Yao et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Le Yao China 29 1.1k 902 656 593 478 107 3.2k
Heng Zhang China 31 1.0k 0.9× 678 0.8× 731 1.1× 1.4k 2.3× 150 0.3× 135 4.0k
Jianbo Lǚ United States 33 837 0.8× 375 0.4× 409 0.6× 785 1.3× 207 0.4× 154 3.4k
Vinay Prasad Canada 31 539 0.5× 796 0.9× 642 1.0× 108 0.2× 169 0.4× 136 3.7k
Luis Ricardez‐Sandoval Canada 41 1.8k 1.7× 1.6k 1.8× 1.3k 1.9× 138 0.2× 117 0.2× 248 5.7k
Babu Joseph United States 34 1.4k 1.2× 1.4k 1.6× 810 1.2× 97 0.2× 141 0.3× 148 4.2k
Min Dai China 28 198 0.2× 492 0.5× 794 1.2× 353 0.6× 97 0.2× 102 2.8k
Fang Liao China 30 1.2k 1.1× 972 1.1× 150 0.2× 522 0.9× 50 0.1× 121 3.6k
Artur M. Schweidtmann Netherlands 26 628 0.6× 558 0.6× 319 0.5× 140 0.2× 229 0.5× 65 2.1k
Iqbal M. Mujtaba United Kingdom 38 1.1k 1.0× 608 0.7× 1.5k 2.2× 1.9k 3.2× 93 0.2× 255 5.0k
Günter Wozny Germany 32 1.9k 1.7× 705 0.8× 771 1.2× 290 0.5× 60 0.1× 264 4.0k

Countries citing papers authored by Le Yao

Since Specialization
Citations

This map shows the geographic impact of Le Yao's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Le Yao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Le Yao more than expected).

Fields of papers citing papers by Le Yao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Le Yao. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Le Yao. The network helps show where Le Yao may publish in the future.

Co-authorship network of co-authors of Le Yao

This figure shows the co-authorship network connecting the top 25 collaborators of Le Yao. A scholar is included among the top collaborators of Le Yao based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Le Yao. Le Yao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
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Shen, Bingbing, et al.. (2024). Multirate Nonlinear Process Fault Detection Based on Multiscale Hierarchical Variational Autoencoder. IEEE Sensors Journal. 24(10). 16467–16477. 6 indexed citations
4.
Chen, Zhichao, et al.. (2024). Analyzing and Improving Supervised Nonlinear Dynamical Probabilistic Latent Variable Model for Inferential Sensors. IEEE Transactions on Industrial Informatics. 20(11). 13296–13307. 6 indexed citations
5.
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Wu, Yanzi, et al.. (2023). A mixed‐attention‐based multi‐scale autoencoder algorithm for fabric defect detection. Coloration Technology. 140(3). 451–466. 1 indexed citations
7.
Yao, Le, et al.. (2023). Input Factor Selection Based on Interpretable Neural Network for Industrial Virtual Sensing Application. IEEE Transactions on Instrumentation and Measurement. 72. 1–12. 8 indexed citations
8.
Shen, Bingbing, Le Yao, Zeyu Yang, & Zhiqiang Ge. (2023). Mode Information Separated β-VAE Regression for Multimode Industrial Process Soft Sensing. IEEE Sensors Journal. 23(9). 10231–10240. 16 indexed citations
9.
Yao, Le & Zhiqiang Ge. (2022). Causal variable selection for industrial process quality prediction via attention-based GRU network. Engineering Applications of Artificial Intelligence. 118. 105658–105658. 36 indexed citations
10.
Kong, Xiangyin, et al.. (2022). Neural Network Weight Comparison for Industrial Causality Discovering and Its Soft Sensing Application. IEEE Transactions on Industrial Informatics. 19(8). 8817–8828. 19 indexed citations
11.
Yao, Le, et al.. (2022). Semi-Supervised Deep Dynamic Probabilistic Latent Variable Model for Multimode Process Soft Sensor Application. IEEE Transactions on Industrial Informatics. 19(4). 6056–6068. 45 indexed citations
12.
Shen, Bingbing, Le Yao, & Zhiqiang Ge. (2022). Predictive Modeling With Multiresolution Pyramid VAE and Industrial Soft Sensor Applications. IEEE Transactions on Cybernetics. 53(8). 4867–4879. 44 indexed citations
13.
Yang, Zeyu, et al.. (2022). Probabilistic Fusion Model for Industrial Soft Sensing Based on Quality-Relevant Feature Clustering. IEEE Transactions on Industrial Informatics. 19(8). 9037–9047. 7 indexed citations
14.
Yao, Le & Zhiqiang Ge. (2021). Dynamic Features Incorporated Locally Weighted Deep Learning Model for Soft Sensor Development. IEEE Transactions on Instrumentation and Measurement. 70. 1–11. 32 indexed citations
15.
Yao, Le & Zhiqiang Ge. (2020). Industrial Big Data Modeling and Monitoring Framework for Plant-Wide Processes. IEEE Transactions on Industrial Informatics. 17(9). 6399–6408. 42 indexed citations
16.
Zhu, Jinlin, et al.. (2020). Scalable Soft Sensor for Nonlinear Industrial Big Data via Bagging Stochastic Variational Gaussian Processes. IEEE Transactions on Industrial Electronics. 68(8). 7594–7602. 11 indexed citations
17.
Yao, Le & Zhiqiang Ge. (2019). Scalable learning and probabilistic analytics of industrial big data based on parameter server: Framework, methods and applications. Journal of Process Control. 78. 13–33. 19 indexed citations
18.
Shao, Weiming, Zhiqiang Ge, Le Yao, & Zhihuan Song. (2019). Bayesian Nonlinear Gaussian Mixture Regression and its Application to Virtual Sensing for Multimode Industrial Processes. IEEE Transactions on Automation Science and Engineering. 17(2). 871–885. 26 indexed citations
19.
Yao, Le, Weiming Shao, & Zhiqiang Ge. (2019). Hierarchical Quality Monitoring for Large-Scale Industrial Plants With Big Process Data. IEEE Transactions on Neural Networks and Learning Systems. 32(8). 3330–3341. 33 indexed citations
20.
Yao, Le & Zhiqiang Ge. (2018). Nonlinear Gaussian Mixture Regression for Multimode Quality Prediction With Partially Labeled Data. IEEE Transactions on Industrial Informatics. 15(7). 4044–4053. 20 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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