Yuanjun Guo

2.9k total citations
100 papers, 2.1k citations indexed

About

Yuanjun Guo is a scholar working on Electrical and Electronic Engineering, Control and Systems Engineering and Artificial Intelligence. According to data from OpenAlex, Yuanjun Guo has authored 100 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 54 papers in Electrical and Electronic Engineering, 22 papers in Control and Systems Engineering and 18 papers in Artificial Intelligence. Recurrent topics in Yuanjun Guo's work include Advanced Battery Technologies Research (16 papers), Electric Vehicles and Infrastructure (13 papers) and Smart Grid Energy Management (13 papers). Yuanjun Guo is often cited by papers focused on Advanced Battery Technologies Research (16 papers), Electric Vehicles and Infrastructure (13 papers) and Smart Grid Energy Management (13 papers). Yuanjun Guo collaborates with scholars based in China, United Kingdom and United States. Yuanjun Guo's co-authors include Zhile Yang, Juncheng Zhu, Chengyun Ning, Yanhong Gu, Chengke Wu, Kang Li, Shengzhong Feng, Cheng‐fu Chen, Sukumar Bandopadhyay and Kailong Liu and has published in prestigious journals such as PLoS ONE, Applied Catalysis B: Environmental and Expert Systems with Applications.

In The Last Decade

Yuanjun Guo

91 papers receiving 2.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yuanjun Guo China 26 1.1k 540 358 293 234 100 2.1k
Yan Chen United States 25 837 0.8× 1.8k 3.3× 949 2.7× 130 0.4× 602 2.6× 132 3.0k
Jing Deng United Kingdom 20 465 0.4× 291 0.5× 302 0.8× 168 0.6× 557 2.4× 54 1.5k
Lin Cheng China 38 2.7k 2.5× 1.1k 2.1× 1.1k 2.9× 120 0.4× 928 4.0× 278 4.4k
Yishen Wang United States 22 1.9k 1.8× 159 0.3× 820 2.3× 176 0.6× 79 0.3× 67 2.3k
Haobin Jiang China 29 960 0.9× 1.2k 2.2× 764 2.1× 147 0.5× 555 2.4× 192 2.9k
Jiaqi Ma United States 33 774 0.7× 1.9k 3.5× 1.8k 5.0× 326 1.1× 133 0.6× 177 4.3k
Rui Wang China 31 1.7k 1.5× 291 0.5× 1.6k 4.4× 179 0.6× 284 1.2× 228 3.3k
Xinming Zhang China 30 1.4k 1.3× 111 0.2× 108 0.3× 324 1.1× 167 0.7× 271 3.3k
Zhengmao Li China 35 2.7k 2.5× 288 0.5× 1.5k 4.1× 95 0.3× 135 0.6× 103 3.5k

Countries citing papers authored by Yuanjun Guo

Since Specialization
Citations

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

Fields of papers citing papers by Yuanjun Guo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yuanjun Guo

This figure shows the co-authorship network connecting the top 25 collaborators of Yuanjun Guo. A scholar is included among the top collaborators of Yuanjun Guo 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 Yuanjun Guo. Yuanjun Guo 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.
Yang, Zhenhuai, Long Xu, Yuanjun Guo, et al.. (2025). Influence of substrate bias voltage on tribological and corrosion properties of tetrahedral amorphous carbon films. Surface and Coatings Technology. 498. 131777–131777. 4 indexed citations
2.
Zhang, Ruisheng, Shi‐Jin Ding, Yuanjun Guo, et al.. (2025). Designing Laves-phase RFe2-type alloy with excellent magnetostrictive performance by physics-informed interpretable machine learning. Materials & Design. 252. 113799–113799. 3 indexed citations
3.
Guo, Yuanjun, Zhile Yang, Jun‐Zhe Yang, et al.. (2025). T2MFDF: An LLM-Enhanced Multimodal Fault Diagnosis Framework Integrating Time-Series and Textual Data. IEEE Transactions on Instrumentation and Measurement. 74. 1–11. 1 indexed citations
4.
Tian, Shilin, et al.. (2025). Investigation of the wake blending of wind turbines with different capacities in a tandem configuration using LES. Journal of Renewable and Sustainable Energy. 17(3).
5.
Guo, Yuanjun, Zhenhuai Yang, Lei Liu, et al.. (2024). Effect of oxygen flow rate on magnetron-sputtering-based preparation and photoelectric properties of vanadium dioxide films. Thin Solid Films. 809. 140595–140595. 2 indexed citations
6.
Liu, Yongqian, et al.. (2024). DivideMerge: A multi-vessel optimization approach for cooperative operation and maintenance scheduling in offshore wind farm. Renewable Energy. 229. 120758–120758. 7 indexed citations
7.
Zhang, Lidong, Tianyu Hu, Zhile Yang, et al.. (2023). A novel dynamic opposite learning enhanced Jaya optimization method for high efficiency plate–fin heat exchanger design optimization. Engineering Applications of Artificial Intelligence. 119. 105778–105778. 32 indexed citations
8.
Zhang, Lidong, Jiao Li, Xiandong Xu, et al.. (2023). High spatial granularity residential heating load forecast based on Dendrite net model. Energy. 269. 126787–126787. 25 indexed citations
9.
Yang, Zhile, et al.. (2023). A compatible detector based on improved YOLOv5 for hydropower device detection in AR inspection system. Expert Systems with Applications. 225. 120065–120065. 15 indexed citations
10.
Yang, Zhile, Tianyu Hu, Juncheng Zhu, et al.. (2022). Hierarchical High-Resolution Load Forecasting for Electric Vehicle Charging: A Deep Learning Approach. IEEE Journal of Emerging and Selected Topics in Industrial Electronics. 4(1). 118–127. 24 indexed citations
11.
Zhao, An, Lan Cheng, Yuanjun Guo, et al.. (2022). A Novel Principal Component Analysis-Informer Model for Fault Prediction of Nuclear Valves. Machines. 10(4). 240–240. 15 indexed citations
12.
Wu, Chengke, Yuanjun Guo, Rui Jiang, et al.. (2022). Transformer-based deep learning model and video dataset for unsafe action identification in construction projects. Automation in Construction. 146. 104703–104703. 46 indexed citations
13.
Zhang, Yanhui, Shili Lin, Haiping Ma, Yuanjun Guo, & Wei Feng. (2021). A Novel Pigeon‐Inspired Optimized RBF Model for Parallel Battery Branch Forecasting. Complexity. 2021(1). 1 indexed citations
14.
Yang, Zhile, et al.. (2021). An enhanced exploratory whale optimization algorithm for dynamic economic dispatch. Energy Reports. 7. 7015–7029. 27 indexed citations
15.
Cheng, Tingli, et al.. (2020). A Modified Social Spider Optimization for Economic Dispatch with Valve-Point Effects. Complexity. 2020. 1–13. 6 indexed citations
16.
Wang, Ying, et al.. (2019). Demand side management of plug-in electric vehicles and coordinated unit commitment: A novel parallel competitive swarm optimization method. Energy Conversion and Management. 196. 935–949. 63 indexed citations
17.
Wang, Ying, et al.. (2019). A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment. Applied Sciences. 9(9). 1776–1776. 10 indexed citations
18.
Zhu, Juncheng, Zhile Yang, Monjur Mourshed, et al.. (2019). Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches. Energies. 12(14). 2692–2692. 162 indexed citations
19.
Guo, Yuanjun, Zhile Yang, Shengzhong Feng, & Jinxing Hu. (2018). Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study. Complexity. 2018(1). 43 indexed citations
20.

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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