Jun Lv

1.2k total citations · 1 hit paper
36 papers, 998 citations indexed

About

Jun Lv is a scholar working on Electrical and Electronic Engineering, Mechanical Engineering and Industrial and Manufacturing Engineering. According to data from OpenAlex, Jun Lv has authored 36 papers receiving a total of 998 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Electrical and Electronic Engineering, 9 papers in Mechanical Engineering and 7 papers in Industrial and Manufacturing Engineering. Recurrent topics in Jun Lv's work include Manufacturing Process and Optimization (6 papers), Advancements in Battery Materials (6 papers) and Advanced Measurement and Metrology Techniques (5 papers). Jun Lv is often cited by papers focused on Manufacturing Process and Optimization (6 papers), Advancements in Battery Materials (6 papers) and Advanced Measurement and Metrology Techniques (5 papers). Jun Lv collaborates with scholars based in China, Australia and United States. Jun Lv's co-authors include Shichang Du, Yafei Deng, Guilong Li, Delin Huang, Chen Zhao, Chenguang Zhao, Zunxian Yang, Lifeng Xi, Tailiang Guo and Zhanhu Guo and has published in prestigious journals such as Chemical Communications, Scientific Reports and Journal of Materials Chemistry A.

In The Last Decade

Jun Lv

33 papers receiving 982 citations

Hit Papers

A double-layer attention based adversarial network for pa... 2021 2026 2022 2024 2021 50 100 150
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 double-layer attention based adversarial network for partial transfer learning in machinery fault diagnosis
    2021 198 citations Yafei Deng, Shichang Du 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
Jun Lv China 16 272 251 238 176 112 36 998
Jinsong Yu China 20 203 0.7× 308 1.2× 438 1.8× 117 0.7× 23 0.2× 69 1.2k
Xiaogang Zhang China 22 480 1.8× 383 1.5× 383 1.6× 60 0.3× 204 1.8× 107 1.5k
Jin Cheng China 19 252 0.9× 109 0.4× 264 1.1× 109 0.6× 57 0.5× 139 1.4k
Jin Deng China 11 184 0.7× 326 1.3× 319 1.3× 63 0.4× 99 0.9× 27 830
Dong Gao China 19 587 2.2× 554 2.2× 432 1.8× 192 1.1× 57 0.5× 82 1.9k
Jong-Soo Kim South Korea 17 99 0.4× 180 0.7× 956 4.0× 146 0.8× 65 0.6× 115 1.5k
Matthias Weigold Germany 15 506 1.9× 250 1.0× 252 1.1× 345 2.0× 33 0.3× 138 1.1k
Wenbin Wang China 21 431 1.6× 239 1.0× 154 0.6× 75 0.4× 18 0.2× 92 1.4k
Tadahiro Shibutani Japan 20 274 1.0× 103 0.4× 435 1.8× 48 0.3× 45 0.4× 106 1.3k
Rui He China 17 203 0.7× 226 0.9× 197 0.8× 116 0.7× 24 0.2× 36 851

Countries citing papers authored by Jun Lv

Since Specialization
Citations

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

Fields of papers citing papers by Jun Lv

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jun Lv

This figure shows the co-authorship network connecting the top 25 collaborators of Jun Lv. A scholar is included among the top collaborators of Jun Lv 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 Jun Lv. Jun Lv 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.
Lv, Jun, et al.. (2025). Recent advances in oxygen reduction reaction catalysis under neutral conditions. Chemical Communications. 61(77). 14814–14822.
2.
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Wang, Bo, Lidan Wang, Guangqing Xu, et al.. (2024). Ag3PO4/n-SiMPs photoanode-peroxymonosulfate coupled system for efficient tetracycline degradation performance. Separation and Purification Technology. 359. 130516–130516. 8 indexed citations
4.
Yang, Yun, et al.. (2023). Enhanced modeling method of thermal behaviors in machine tool motorized spindles based on the mixture of thermal mechanism and machine learning. Journal of Intelligent Manufacturing. 36(1). 221–242. 15 indexed citations
5.
Sun, Yan, et al.. (2023). Bidding optimization of aggregators considering safety check in a distribution network. Electrical Engineering. 105(6). 3813–3824.
6.
Lv, Jun, et al.. (2022). Online Detection of Surface Defects Based on Improved YOLOV3. Sensors. 22(3). 817–817. 45 indexed citations
7.
Zhao, Chen, Shichang Du, Jun Lv, Yafei Deng, & Guilong Li. (2021). A novel parallel classification network for classifying three-dimensional surface with point cloud data. Journal of Intelligent Manufacturing. 34(2). 515–527. 35 indexed citations
8.
Li, Guilong, et al.. (2021). High Definition Metrology-Based Quality Improvement of Surface Texture in Face Milling of Workpieces With Discontinuous Surfaces. Journal of Manufacturing Science and Engineering. 144(3). 81 indexed citations
9.
Du, Shichang, et al.. (2020). A Hybrid Prognostics Deep Learning Model for Remaining Useful Life Prediction. Electronics. 10(1). 39–39. 30 indexed citations
10.
Qin, Xue, et al.. (2020). Boron-doped carbon microspheres as a new catalyst for rechargeable Li-CO2 batteries. Fullerenes Nanotubes and Carbon Nanostructures. 28(8). 680–685. 12 indexed citations
11.
Yang, Zunxian, Kun Qian, Jun Lv, et al.. (2016). Encapsulation of Fe3O4 Nanoparticles into N, S co-Doped Graphene Sheets with Greatly Enhanced Electrochemical Performance. Scientific Reports. 6(1). 27957–27957. 68 indexed citations
12.
Pang, Weiqiang, Fengqi Zhao, Huixiang Xu, et al.. (2015). Synthesis and Characterization of a High Energy Combustion Agent (BHN) and Its Effects on the Combustion Properties of Fuel Rich Solid Rocket Propellants. Central European Journal of Energetic Materials. 12(3). 2 indexed citations
13.
Tang, Mengqi, Xiaolang Chen, Jie Yu, et al.. (2015). Effect of Red Phosphorus Masterbatch on Flame Retardancy and Thermal Stability of Polypropylene/Thermoplastic Polyurethane Blends. Polymers and Polymer Composites. 23(2). 113–120. 7 indexed citations
14.
Yang, Zunxian, et al.. (2015). Facile Synthesis of Coaxial CNTs/MnOx-Carbon Hybrid Nanofibers and Their Greatly Enhanced Lithium Storage Performance. Scientific Reports. 5(1). 17473–17473. 59 indexed citations
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Lv, Jun, Weitao Sun, & Tong Li. (2013). Adaptive algorithm based on FFT for frequency estimation. 883–886. 2 indexed citations
18.
Lv, Jun, Qiancai Liu, Jie Tang, Franc Perdih, & Krištof Kranjc. (2012). A facile synthesis of indolo[3,2,1-jk]carbazoles via palladium-catalyzed intramolecular cyclization. Tetrahedron Letters. 53(39). 5248–5252. 44 indexed citations
19.
Yan, Huimin, et al.. (2012). Multireturn three-dimensional active imaging based on compressive sensing. Optics Letters. 37(23). 4904–4904. 12 indexed citations
20.
Du, Shichang, Jun Lv, & Lifeng Xi. (2011). Degradation process prediction for rotational machinery based on hybrid intelligent model. Robotics and Computer-Integrated Manufacturing. 28(2). 190–207. 29 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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