Lingjian Ye

1.7k total citations · 2 hit papers
103 papers, 1.2k citations indexed

About

Lingjian Ye is a scholar working on Control and Systems Engineering, Artificial Intelligence and Mechanical Engineering. According to data from OpenAlex, Lingjian Ye has authored 103 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 81 papers in Control and Systems Engineering, 25 papers in Artificial Intelligence and 18 papers in Mechanical Engineering. Recurrent topics in Lingjian Ye's work include Fault Detection and Control Systems (60 papers), Advanced Control Systems Optimization (46 papers) and Neural Networks and Applications (17 papers). Lingjian Ye is often cited by papers focused on Fault Detection and Control Systems (60 papers), Advanced Control Systems Optimization (46 papers) and Neural Networks and Applications (17 papers). Lingjian Ye collaborates with scholars based in China, United Kingdom and Norway. Lingjian Ye's co-authors include Xiaofeng Yuan, Yalin Wang, Kai Wang, Feifan Shen, Yi Cao, Zhihuan Song, Chunhua Yang, Weihua Gui, Chunhua Yang and Zhiqiang Ge and has published in prestigious journals such as IEEE Transactions on Industrial Electronics, Expert Systems with Applications and IEEE Access.

In The Last Decade

Lingjian Ye

90 papers receiving 1.1k citations

Hit Papers

align trajectories

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.

Quality Prediction Modeling for Industrial Processes Using Multiscale Attention-Based Convolutional Neural Network

2024 60 citations Xiaofeng Yuan, Lingjian Ye et al. profile →
Variable Correlation Analysis-Based Convolutional Neural Network for Far Topological Feature Extraction and Industrial Predictive Modeling

2024 48 citations Xiaofeng Yuan, Lingjian Ye et al. IEEE Transactions on Instrumentation and Measurement profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Lingjian Ye China	20	867	288	260	90	86	103	1.2k
Zheng Chai China	12	602 0.7×	287 1.0×	288 1.1×	40 0.4×	63 0.7×	23	842
Qingqiang Sun China	7	502 0.6×	213 0.7×	285 1.1×	58 0.6×	97 1.1×	17	765
Yuri A.W. Shardt Germany	21	1.3k 1.5×	563 2.0×	365 1.4×	187 2.1×	113 1.3×	89	1.7k
Francisco Souza Portugal	15	466 0.5×	136 0.5×	305 1.2×	57 0.6×	42 0.5×	47	820
Daoping Huang China	22	826 1.0×	234 0.8×	299 1.1×	118 1.3×	22 0.3×	76	1.2k
Jun Liang China	19	565 0.7×	207 0.7×	142 0.5×	109 1.2×	28 0.3×	109	937
Wenkai Hu China	18	586 0.7×	142 0.5×	374 1.4×	49 0.5×	58 0.7×	87	914
L.B. Jack United Kingdom	11	587 0.7×	341 1.2×	265 1.0×	64 0.7×	35 0.4×	15	920
Ashish Singhal India	15	643 0.7×	323 1.1×	141 0.5×	196 2.2×	25 0.3×	51	930

Countries citing papers authored by Lingjian Ye

Since Specialization

Citations

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

Fields of papers citing papers by Lingjian Ye

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lingjian Ye

This figure shows the co-authorship network connecting the top 25 collaborators of Lingjian Ye. A scholar is included among the top collaborators of Lingjian Ye 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 Lingjian Ye. Lingjian Ye is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Ye, Lingjian, Zeyu Yang, Feifan Shen, & Xiaofeng Yuan. (2025). Design of enhanced two-dimensional self-optimizing control system for batch process. Computers & Chemical Engineering. 195. 109029–109029.

Yuan, Xiaofeng, Lingjian Ye, Feifan Shen, et al.. (2025). Deep learning-based model predictive control with exponential weighting strategy and its application in energy management systems. Journal of Process Control. 155. 103542–103542. 1 indexed citations

Zheng, Junhua, Le Zhou, Lingjian Ye, & Zhiqiang Ge. (2025). Deep Co-Training Partial Least Squares Model for Semi-Supervised Industrial Soft Sensing. IEEE Transactions on Systems Man and Cybernetics Systems. 55(5). 3363–3371. 2 indexed citations

Zheng, Junhua, Jian Wang, Lingjian Ye, & Yue Zhuo. (2024). Semi-supervised convolutional generative adversarial networks for dynamic fault classification with manifold regularization. Process Safety and Environmental Protection. 193. 550–557. 2 indexed citations

Zheng, Junhua, Lingjian Ye, & Zhiqiang Ge. (2024). Semi-supervised process monitoring based on self-training PCA model. Process Safety and Environmental Protection. 187. 1311–1321. 5 indexed citations

Ye, Lingjian, et al.. (2024). Identification of sparse nonlinear controlled variables for near‐optimal operation of chemical processes. The Canadian Journal of Chemical Engineering. 103(5). 2281–2296.

Yuan, Xiaofeng, et al.. (2024). Semantic segmentation model based on edge information for rock structural surface traces detection. Engineering Applications of Artificial Intelligence. 140. 109706–109706.

Yuan, Xiaofeng, Jiale Zhang, Kai Wang, et al.. (2024). Missing Data Imputation for Industrial Time Series With Adaptive Median Iteration Based on Generative Adversarial Networks. IEEE Sensors Journal. 24(21). 35081–35091. 12 indexed citations

Wang, Yurun, Yi Huang, Dongsheng Chen, et al.. (2024). Prox-STA-LSTM: A Sparse Representation for the Attention-Based LSTM Networks for Industrial Soft Sensor Development. IEEE Access. 12. 80633–80645. 2 indexed citations

10.

Tang, X., et al.. (2024). Generalized Global Self-Optimizing Control for Chemical Processes: Part II Objective-Guided Controlled Variable Learning Approach. Industrial & Engineering Chemistry Research. 64(1). 535–550. 1 indexed citations

11.

Wang, Yun, Yuchen He, Lijuan Qian, et al.. (2024). A Novel Multiscale Gated Structure Model for Soft Sensing of Nonstationary Process With Randomly Missing Data. IEEE Transactions on Industrial Informatics. 21(2). 1269–1278. 1 indexed citations

12.

Huang, Yi, Longyan Wang, Lingjian Ye, & Zeyu Yang. (2024). A Bidirectional Attention-Based LSTM Model for Multi-Step Ahead Prediction in Dynamic Soft Sensors. 1964–1969. 1 indexed citations

13.

Shen, Feifan, Jiayang Wu, Lingjian Ye, & Bin Wang. (2024). Memory-Adaptive Supervised LSTM Networks for Deep Soft Sensor Development of Industrial Processes. IEEE Sensors Journal. 24(13). 21641–21654. 9 indexed citations

14.

Ye, Lingjian, et al.. (2024). Adversarial Weight Prediction Networks for Defense of Industrial FDC Systems. IEEE Transactions on Industrial Informatics. 20(11). 13201–13211. 1 indexed citations

15.

Zhu, Hongqiu, Yuri A.W. Shardt, Lingjian Ye, et al.. (2024). Missing-Data Imputation With Position-Encoding Denoising Autoencoders for Industrial Processes. IEEE Transactions on Instrumentation and Measurement. 73. 1–11. 24 indexed citations

16.

Ye, Lingjian, et al.. (2023). Generalized Global Self-Optimizing Control for Chemical Processes Part I. The Existence of Perfect Controlled Variables and Numerical Design Methods. Industrial & Engineering Chemistry Research. 62(37). 15051–15069. 5 indexed citations

17.

Wu, Yi, et al.. (2022). Nonlinear Dynamic Soft Sensor Development with a Supervised Hybrid CNN-LSTM Network for Industrial Processes. ACS Omega. 7(19). 16653–16664. 20 indexed citations

18.

Yuan, Xiaofeng, et al.. (2021). Online Adaptive Modeling Framework for Deep Belief Network-Based Quality Prediction in Industrial Processes. Industrial & Engineering Chemistry Research. 60(42). 15208–15218. 17 indexed citations

19.

Wang, Kai, et al.. (2019). Accelerated Kernel Canonical Correlation Analysis with Fault Relevance for Nonlinear Process Fault Isolation. Industrial & Engineering Chemistry Research. 58(39). 18280–18291. 15 indexed citations

20.

Ye, Lingjian & Sigurd Skogestad. (2018). Dynamic self-optimizing control for unconstrained batch processes. Computers & Chemical Engineering. 117. 451–468. 14 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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