Junghui Chen

5.9k total citations
287 papers, 4.8k citations indexed

About

Junghui Chen is a scholar working on Control and Systems Engineering, Mechanical Engineering and Analytical Chemistry. According to data from OpenAlex, Junghui Chen has authored 287 papers receiving a total of 4.8k indexed citations (citations by other indexed papers that have themselves been cited), including 240 papers in Control and Systems Engineering, 106 papers in Mechanical Engineering and 60 papers in Analytical Chemistry. Recurrent topics in Junghui Chen's work include Fault Detection and Control Systems (192 papers), Advanced Control Systems Optimization (102 papers) and Mineral Processing and Grinding (71 papers). Junghui Chen is often cited by papers focused on Fault Detection and Control Systems (192 papers), Advanced Control Systems Optimization (102 papers) and Mineral Processing and Grinding (71 papers). Junghui Chen collaborates with scholars based in Taiwan, China and United States. Junghui Chen's co-authors include Zhihuan Song, Zhengjiang Zhang, Zhiqiang Ge, Yi Liu, Lei Xie, Jialin Liu, Yi Liu, Li Zhu, Lihua Cheng and Tao Liu and has published in prestigious journals such as Renewable and Sustainable Energy Reviews, Bioresource Technology and Journal of Cleaner Production.

In The Last Decade

Junghui Chen

278 papers receiving 4.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Junghui Chen Taiwan 35 3.2k 1.7k 774 716 590 287 4.8k
Xuefeng Yan China 44 4.7k 1.5× 2.9k 1.7× 1.5k 1.9× 399 0.6× 1.3k 2.3× 278 6.8k
Christos Georgakis United States 35 3.8k 1.2× 1.5k 0.9× 754 1.0× 541 0.8× 238 0.4× 174 5.0k
Yalin Wang China 40 4.1k 1.3× 2.0k 1.2× 569 0.7× 371 0.5× 1.8k 3.0× 232 6.0k
Nina F. Thornhill United Kingdom 39 3.9k 1.2× 1.3k 0.8× 607 0.8× 279 0.4× 498 0.8× 183 5.2k
Weihua Gui China 44 3.9k 1.2× 1.5k 0.9× 364 0.5× 396 0.6× 1.3k 2.1× 293 6.5k
In−Beum Lee South Korea 41 5.3k 1.6× 3.1k 1.8× 1.9k 2.5× 484 0.7× 321 0.5× 202 7.0k
Raghunathan Rengaswamy United States 40 6.6k 2.1× 2.4k 1.4× 755 1.0× 431 0.6× 954 1.6× 190 8.6k
Xiaofeng Yuan China 43 4.4k 1.4× 2.0k 1.2× 678 0.9× 338 0.5× 1.8k 3.1× 140 6.0k
Chonghun Han South Korea 33 1.4k 0.4× 1.6k 0.9× 455 0.6× 612 0.9× 180 0.3× 177 3.5k
Jie Yu China 30 2.2k 0.7× 1.4k 0.8× 791 1.0× 138 0.2× 389 0.7× 123 3.5k

Countries citing papers authored by Junghui Chen

Since Specialization
Citations

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

Fields of papers citing papers by Junghui Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Junghui Chen

This figure shows the co-authorship network connecting the top 25 collaborators of Junghui Chen. A scholar is included among the top collaborators of Junghui Chen 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 Junghui Chen. Junghui Chen 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.
2.
Chen, Junghui, et al.. (2024). Developing a dynamic quality prediction model for limited samples target grade based on transfer learning. Measurement. 229. 114380–114380. 1 indexed citations
3.
Chen, Junghui, et al.. (2024). Enhancing predictive monitoring of ethylene oxychlorination reactor states through spatiotemporal coupling analysis. Process Safety and Environmental Protection. 191. 1301–1312.
4.
Shao, Weiming, et al.. (2024). A novel semi-supervised robust learning framework for dynamic generative latent variable models and its application to industrial virtual metrology. Advanced Engineering Informatics. 62. 102775–102775. 2 indexed citations
5.
Li, Zhu, et al.. (2024). Adaptive two-dimensional subspace identification for monitoring batch processes with limited batch data. ISA Transactions. 152. 408–426. 1 indexed citations
6.
Shao, Weiming, et al.. (2024). Semi-Supervised Robust Hidden Markov Regression for Large-Scale Time-Series Industrial Data Analytics and its Applications to Soft Sensing. IEEE Transactions on Automation Science and Engineering. 22. 5143–5157. 5 indexed citations
7.
Chen, Junghui, et al.. (2024). Meta-learning-based continuous state-space models for rapid monitoring using heterogeneous grade sources with uneven sampling. Chemical Engineering Science. 297. 120265–120265.
8.
Xu, Luping, et al.. (2024). Robust EKF based on the framework of dynamic data reconciliation for state estimation of chemical processes with Gaussian/non-Gaussian measurement noise. Chemical Engineering Science. 304. 121046–121046. 1 indexed citations
9.
Chen, Junghui, et al.. (2023). Fault diagnosis-based SDG transfer for zero-sample fault symptom. International Journal of Advances in Intelligent Informatics. 9(3). 551–551. 1 indexed citations
10.
Ren, Mifeng, Yan Liang, Junghui Chen, Xinying Xu, & Lan Cheng. (2023). Fault detection for NOx emission process in thermal power plants using SIP-PCA. ISA Transactions. 140. 46–54. 6 indexed citations
11.
Liu, Tao, et al.. (2023). Deep Learning-Based Binocular Image Analysis for In Situ Measurement of Particle Length Distribution During Crystallization Process. IEEE Transactions on Instrumentation and Measurement. 72. 1–14. 8 indexed citations
12.
13.
Liu, Tao, et al.. (2022). Variational PLS-Based Calibration Model Building With Semi-Supervised Learning for Moisture Measurement During Fluidized Bed Drying by NIR Spectroscopy. IEEE Transactions on Instrumentation and Measurement. 71. 1–13. 7 indexed citations
14.
Chen, Junghui, et al.. (2022). Developing a Conditional Variational Autoencoder to Guide Spectral Data Augmentation for Calibration Modeling. IEEE Transactions on Instrumentation and Measurement. 71. 1–8. 19 indexed citations
15.
Chen, Junghui, et al.. (2022). Accelerating reinforcement learning with case-based model-assisted experience augmentation for process control. Neural Networks. 158. 197–215. 9 indexed citations
16.
Chen, Junghui, et al.. (2022). Using source data to aid and build variational state–space autoencoders with sparse target data for process monitoring. Neural Networks. 154. 455–468. 5 indexed citations
17.
18.
Lu, P.-C., Junghui Chen, & Lei Xie. (2019). Disturbance-Based Alternate Feedback Control Scheme To Enhance Economic Performance of Batch Processes. Industrial & Engineering Chemistry Research. 58(10). 4143–4153. 1 indexed citations
19.
Chen, Junghui, et al.. (2018). Development of Self-Learning Kernel Regression Models for Virtual Sensors on Nonlinear Processes. IEEE Transactions on Automation Science and Engineering. 16(1). 286–297. 13 indexed citations
20.
Jiang, Li, Zhihuan Song, Zhiqiang Ge, & Junghui Chen. (2017). Robust Self-Supervised Model and Its Application for Fault Detection. Industrial & Engineering Chemistry Research. 56(26). 7503–7515. 25 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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