Jun Imai

531 citations
94 papers · 337 · h-index 8

Impact in

Papers in

Jun Imai

80 papers receiving 319 citations

Peers

Jun Imai
Comparison fields: 5 of 61
  • Energy Engineering and Power Technology 36
  • Control and Systems Engineering 147
  • Industrial and Manufacturing Engineering 47
  • Public Administration 16
  • Automotive Engineering 25
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Logan Beal United States
Jingwei Zhang China
Daniil Panasetsky Russia
Vlad Mureşan Romania
Faizal Hafiz New Zealand
Kazuyuki Mori Japan
Souvik Ganguli India
Nihat Öztürk Türkiye
Ajit Gopalakrishnan United States
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Citations per field
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Citations per year

Countries citing papers authored by Jun Imai

Since Specialization
Citations

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

Fields of papers citing papers by Jun Imai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jun Imai, 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 Imai Line = papers co-authored together Jun Imai links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 94 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201028
2 200724
3 202320
4 200319
5 200310
6 201510
7 201410
8 20138
9 20047
10 20137
11 20247
12 20097
13 20096
14 20156
15 20226
16 20026
17 20185
18 20035
19 20145
20 20005

About Jun Imai

Jun Imai is a scholar working on Control and Systems Engineering, Electrical and Electronic Engineering, Industrial and Manufacturing Engineering, Mechanical Engineering and Computer Vision and Pattern Recognition, having authored 94 papers that have together received 337 indexed citations. Recurring topics across this work include Electric Motor Design and Analysis (12 papers), Advanced Manufacturing and Logistics Optimization (11 papers), Robotic Path Planning Algorithms (10 papers), Magnetic Bearings and Levitation Dynamics (10 papers), Probabilistic and Robust Engineering Design (7 papers), Structural Health Monitoring Techniques (7 papers), Metallurgy and Material Forming (6 papers) and Control Systems and Identification (6 papers). The work is most often cited by research in Energy Engineering and Power Technology (36 citations), Control and Systems Engineering (147 citations), Industrial and Manufacturing Engineering (47 citations), Public Administration (16 citations) and Automotive Engineering (25 citations). Jun Imai has collaborated with scholars based in Japan and United States. Frequent co-authors include Masami Konishi, Shigeyuki Funabiki, Tatsushi Nishi, Masatsugu Takemoto, Kiyoshi Wada, Tomoyuki Ueno, Tatsuya Saito, Yoshihiro Abe, Yuichi Yoshida and Yoshinori Takei. Their work appears in journals such as IEEE Transactions on Industry Applications, Tetsu-to-Hagane, IEEE Access, ISIJ International and Japanese Studies.

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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