Jun Lv

2.1k citations
42 papers · 852 · 1 hit paper · h-index 16

Impact in

Papers in

Jun Lv

36 papers receiving 841 citations

Jun Lv's Hit Papers

Combining the theoretical bound and deep adversarial network for machinery open-set diagnosis transfer 2023 · 90 citations
900+1+2Years since publication255075

Peers

Jun Lv
Comparison fields: 5 of 119
  • Sensory Systems 90
  • Statistics, Probability and Uncertainty 64
  • Medical Laboratory Technology 11
  • Control and Systems Engineering 180
  • Cancer Research 93
Replace Hsiuying Wang with:
Hsiuying Wang Taiwan
Jong Woo Kim South Korea
Bingfeng Zhang China
Zhaohui Zeng China
Chenxin Li China
Li Nie China
Zhonghua Wu China
Jian Ma China
Yingsheng Zhang China
Jun Lv relative to Hsiuying Wang Taiwan Hsiuying Wang's profile →
Citations per field
00.5×10×15×
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 42 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2021121
2
Combining the theoretical bound and deep adversarial network for machinery open-set diagnosis transfer
Hit paper breakdown →
202390
3 201584
4 201872
5 202250
6 201246
7 201242
8 202140
9 202238
10 200835
11 202031
12 202228
13 201019
14 202418
15 202018
16 202416
17 202314
18 202513
19 202011
20 20199

About Jun Lv

Jun Lv is a scholar working on Molecular Biology, Control and Systems Engineering, Sensory Systems, Statistics, Probability and Uncertainty and Infectious Diseases, having authored 42 papers that have together received 852 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), RNA regulation and disease (4 papers), Advanced Statistical Process Monitoring (4 papers), COVID-19 epidemiological studies (3 papers), SARS-CoV-2 and COVID-19 Research (3 papers) and CRISPR and Genetic Engineering (3 papers). The work is most often cited by research in Sensory Systems (90 citations), Statistics, Probability and Uncertainty (64 citations), Medical Laboratory Technology (11 citations), Control and Systems Engineering (180 citations) and Cancer Research (93 citations). Jun Lv has collaborated with scholars based in China, United States and Finland. Frequent co-authors include Shichang Du, Yafei Deng, Delin Huang, Meichun Liu, Pengfei Zhu, Lifeng Xi, Yanmin Liu, Zujiang Yu, Bowen Li and Jun Li. Their work appears in journals such as Molecular Therapy — Nucleic Acids, Computers & Industrial Engineering, International Journal of Production Economics, Measurement and Journal of Nanobiotechnology.

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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