Jun Lv

2.0k citations
40 papers · 791 · 1 hit paper · h-index 15

Impact in

Papers in

Jun Lv

36 papers receiving 779 citations

Jun Lv's Hit Papers

Combining the theoretical bound and deep adversarial network for machinery open-set diagnosis transfer 2023 · 85 citations
850+1+2Years since publication255075

Peers

Jun Lv
Comparison fields: 5 of 116
  • Sensory Systems 79
  • Statistics, Probability and Uncertainty 62
  • Control and Systems Engineering 172
  • Industrial and Manufacturing Engineering 69
  • Cancer Research 103
Replace Hsiuying Wang with:
Hsiuying Wang Taiwan
Bingfeng Zhang China
Zhaohui Zeng China
Yingsheng Zhang China
Li Nie China
Don Hur South Korea
Jian Ma China
Jingjing Zhou China
Pin Wan China
Jun Lv relative to Hsiuying Wang Taiwan Hsiuying Wang's profile →
Citations per field
00.5×10×13.2×
Hsiuying Wang · 1×
Citations per year

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-authors

The 25 scholars most cited alongside Jun Lv, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jun Lv Line = papers co-authored together Jun Lv links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 40 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2021112
2
Combining the theoretical bound and deep adversarial network for machinery open-set diagnosis transfer
Hit paper breakdown →
202385
3 201584
4 201872
5 202244
6 201244
7 201242
8 202140
9 202236
10 200833
11 202030
12 202226
13 201018
14 202017
15 202415
16 202413
17 202011
18 20239
19 20258
20 20198

About Jun Lv

Jun Lv is a scholar working on Molecular Biology, Control and Systems Engineering, Sensory Systems, Infectious Diseases and Statistics, Probability and Uncertainty, having authored 40 papers that have together received 791 indexed citations. Recurring topics across this work include Hearing, Cochlea, Tinnitus, Genetics (6 papers), Fault Detection and Control Systems (5 papers), Protein Structure and Dynamics (5 papers), Advanced Statistical Process Monitoring (4 papers), RNA regulation and disease (4 papers), SARS-CoV-2 and COVID-19 Research (4 papers), CRISPR and Genetic Engineering (3 papers) and MicroRNA in disease regulation (3 papers). The work is most often cited by research in Sensory Systems (79 citations), Statistics, Probability and Uncertainty (62 citations), Control and Systems Engineering (172 citations), Industrial and Manufacturing Engineering (69 citations) and Cancer Research (103 citations). Jun Lv has collaborated with scholars based in China, Australia and Finland. Frequent co-authors include Shichang Du, Yafei Deng, Delin Huang, Meichun Liu, Pengfei Zhu, Lifeng Xi, Zujiang Yu, Bowen Li, Jun Li and Yanmin Liu. Their work appears in journals such as Molecular Therapy — Nucleic Acids, Virus Research, Measurement, Chemical Communications and The International Journal of Biochemistry & Cell Biology.

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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