Yuan Yao

5.2k total citations · 2 hit papers
234 papers, 4.0k citations indexed

About

Yuan Yao is a scholar working on Control and Systems Engineering, Mechanical Engineering and Mechanics of Materials. According to data from OpenAlex, Yuan Yao has authored 234 papers receiving a total of 4.0k indexed citations (citations by other indexed papers that have themselves been cited), including 124 papers in Control and Systems Engineering, 87 papers in Mechanical Engineering and 60 papers in Mechanics of Materials. Recurrent topics in Yuan Yao's work include Fault Detection and Control Systems (112 papers), Mineral Processing and Grinding (61 papers) and Thermography and Photoacoustic Techniques (54 papers). Yuan Yao is often cited by papers focused on Fault Detection and Control Systems (112 papers), Mineral Processing and Grinding (61 papers) and Thermography and Photoacoustic Techniques (54 papers). Yuan Yao collaborates with scholars based in Taiwan, China and Hong Kong. Yuan Yao's co-authors include Furong Gao, Yi Liu, Zhengbing Yan, Kaixin Liu, Стефано Сфарра, Chao Yang, Jianguo Yang, Zengliang Gao, Kaiyi Zheng and Chunhui Zhao and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Applied Physics and IEEE Transactions on Power Electronics.

In The Last Decade

Yuan Yao

216 papers receiving 3.9k citations

Hit Papers

A review on autoencoder based representation learning for... 2022 2026 2023 2024 2022 2023 40 80 120
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. A review on autoencoder based representation learning for fault detection and diagnosis in industrial processes
    2022 147 citations Yuan Yao et al. Chemometrics and Intelligent Laboratory Systems profile →
  2. Graph convolutional network soft sensor for process quality prediction
    2023 82 citations Yi Liu, Yuan 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
Yuan Yao Taiwan 33 2.2k 1.5k 889 565 526 234 4.0k
Achmad Widodo Indonesia 20 2.2k 1.0× 1.2k 0.8× 676 0.8× 390 0.7× 194 0.4× 106 3.4k
Weihua Gui China 44 3.9k 1.8× 1.5k 1.0× 164 0.2× 1.3k 2.2× 364 0.7× 293 6.5k
Zhiwen Chen China 32 2.2k 1.0× 1.1k 0.7× 174 0.2× 545 1.0× 401 0.8× 212 3.6k
Minqiang Xu China 36 2.7k 1.2× 2.0k 1.3× 963 1.1× 441 0.8× 73 0.1× 136 4.1k
Junghui Chen Taiwan 35 3.2k 1.5× 1.7k 1.2× 102 0.1× 590 1.0× 774 1.5× 287 4.8k
Barry Lennox United Kingdom 32 1.9k 0.9× 1.0k 0.7× 126 0.1× 414 0.7× 330 0.6× 195 3.9k
B. Samanta India 27 1.7k 0.8× 1.5k 1.0× 1.1k 1.3× 560 1.0× 95 0.2× 110 3.5k
René–Vinicio Sánchez Ecuador 30 3.2k 1.5× 2.1k 1.4× 1.2k 1.3× 517 0.9× 78 0.1× 112 4.3k
Xiaofeng Yuan China 43 4.4k 2.0× 2.0k 1.4× 133 0.1× 1.8k 3.2× 678 1.3× 140 6.0k
Xihui Liang Canada 34 2.5k 1.1× 3.0k 2.0× 1.0k 1.1× 341 0.6× 97 0.2× 114 4.5k

Countries citing papers authored by Yuan Yao

Since Specialization
Citations

This map shows the geographic impact of Yuan 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 Yuan Yao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuan Yao more than expected).

Fields of papers citing papers by Yuan Yao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yuan 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 Yuan Yao. The network helps show where Yuan Yao may publish in the future.

Co-authorship network of co-authors of Yuan Yao

This figure shows the co-authorship network connecting the top 25 collaborators of Yuan Yao. A scholar is included among the top collaborators of Yuan 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 Yuan Yao. Yuan 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
1.
Dai, Yun, Chao Yang, Zu-Han Gu, Yuan Yao, & Yi Liu. (2025). Hybrid factors latent Gaussian process modeling with wasserstein distance for soft sensing of extruder processes. Chemometrics and Intelligent Laboratory Systems. 261. 105387–105387. 2 indexed citations
2.
Wang, Fumin, et al.. (2025). Spatio-temporal feature extraction for infrared thermal imaging by three-dimensional convolutional autoencoder. Quantitative InfraRed Thermography Journal. 1–14.
3.
Kang, Jia‐Lin, et al.. (2024). Deep learning model predictive control of a high-density polyethylene reactor with a physics-guided sequence-to-sequence model with memory. Computers & Chemical Engineering. 189. 108790–108790. 2 indexed citations
4.
Yao, Yuan, Yucheng Zhong, Zhoucheng Su, et al.. (2024). Micromechanical analyses of unidirectional (UD) discontinuous flax fiber reinforced composites. Composites Science and Technology. 258. 110893–110893. 6 indexed citations
5.
Wang, Fumin, et al.. (2024). Defect Detection of Composite Materials Using Channel Attention and Convolutional Autoencoder. 1895–1899. 1 indexed citations
6.
Zhang, Xu, et al.. (2024). Autofocusing and self-healing properties of circular Mathieu beam carrying orbital angular momentum. Optics & Laser Technology. 177. 111186–111186. 6 indexed citations
7.
Liu, Yi, et al.. (2024). Stable 3D Deep Convolutional Autoencoder Method for Ultrasonic Testing of Defects in Polymer Composites. Polymers. 16(11). 1561–1561. 4 indexed citations
8.
Liu, Yi, et al.. (2023). Generative Deep Learning-Based Thermographic Inspection of Artwork. Sensors. 23(14). 6362–6362. 8 indexed citations
9.
Ma, Tengfei, Jin Wu, Yichang Yu, et al.. (2023). The Inhibition of Engineered Nano-ZnO in the Biological Nitrogen Removal Process: A Review. Water. 16(1). 17–17. 2 indexed citations
10.
Liu, Yi, et al.. (2023). Deep convolutional autoencoder thermography for artwork defect detection. Quantitative InfraRed Thermography Journal. 21(6). 367–383. 31 indexed citations
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12.
Liu, Yi, Yuxin Jiang, Zengliang Gao, Kaixin Liu, & Yuan Yao. (2023). Convolutional Neural Network-Based Machine Vision for Non-Destructive Detection of Flooding in Packed Columns. Sensors. 23(5). 2658–2658. 2 indexed citations
13.
Zheng, Junhua, et al.. (2023). Semi-Supervised Process Data Regression and Application Based on Latent Factor Analysis Model. IEEE Transactions on Instrumentation and Measurement. 72. 1–11. 6 indexed citations
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Сфарра, Стефано, et al.. (2021). Exploratory factor analysis for defect identification with active thermography. Measurement Science and Technology. 32(11). 114010–114010. 8 indexed citations
17.
Liu, Kaixin, Stefano Perilli, A. O. Chulkov, et al.. (2021). Defining the Thermal Features of Sub-Surface Reinforcing Fibres in Non-Polluting Thermo–Acoustic Insulating Panels: A Numerical–Thermographic–Segmentation Approach. Infrastructures. 6(9). 131–131. 2 indexed citations
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Сфарра, Стефано, et al.. (2020). Multiscale Analysis of Solar loading Thermographic Data for the Inspection of Civil Engineering Structures. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
20.
Wu, Haibin, et al.. (2019). Physically Consistent Soft-Sensor Development Using Sequence-to-Sequence Neural Networks. IEEE Transactions on Industrial Informatics. 16(4). 2829–2838. 26 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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