K. Kawamura

159 papers receiving 1.9k citations

Peers

K. Kawamura
Comparison fields: 5 of 135
  • Control and Systems Engineering 580
  • Artificial Intelligence 472
  • Electrical and Electronic Engineering 401
  • Computer Vision and Pattern Recognition 388
  • Biomedical Engineering 372
Replace Fulvio Mastrogiovanni with:
Fulvio Mastrogiovanni Italy
Miguel Á. Salichs Spain
William D. Smart United States
Guillem Alenyà Spain
Jim Tørresen Norway
Yixin Zhu China
Shen Li China
Minho Lee South Korea
Michael Barnes United States
Serena Ivaldi France
K. Kawamura relative to Fulvio Mastrogiovanni Italy Fulvio Mastrogiovanni's profile →
Citations per field
00.5×3.7×
Fulvio Mastrogiovanni · 1×
Citations per year

Countries citing papers authored by K. Kawamura

Since Specialization
Citations

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

Fields of papers citing papers by K. Kawamura

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of K. Kawamura

This figure shows the co-authorship network connecting the top 25 collaborators of K. Kawamura. A scholar is included among the top collaborators of K. Kawamura 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 K. Kawamura. K. Kawamura 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
#WorkIndexed citations
1 5
2 8
3 1
4 29
5 9
6
Promotion of Mouse Preimplantation Embryo Development by Vascular Endothelial Growth Factor
1
7
A Biologically Inspired Adaptive Working Memory for Robots.
19
8
Toward perception-based navigation using EgoSphere.
4
9 8
10 2
11 1
12 19
13 5
14
Exploring Problem-Specific Recombination Operators for Job Shop Scheduling.
64
15 4
16
MODULE: a modular programming environment in Prolog
1
17
A model-based intelligence tutoring system for power distribution systems
2
18 0
19
A Novel Two-Phase Shift Register using Si Tunnel MIS Switching Diodes
0
20 1

About K. Kawamura

K. Kawamura is a scholar working on Control and Systems Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 162 papers that have together received 2.1k indexed citations. Recurring topics across this work include Robotics and Automated Systems (28 papers), Robot Manipulation and Learning (22 papers) and AI-based Problem Solving and Planning (22 papers). The work is most often cited by research in Human-Computer Interaction (147 citations), Control and Systems Engineering (580 citations) and Computer Vision and Pattern Recognition (388 citations). K. Kawamura has collaborated with scholars based in United States, Japan and Türkiye. Frequent co-authors include Sugato Bagchi, Gautam Biswas, Richard Alan Peters, D.M. Wilkes, Julie A. Adams, Erdem Erdemir, Stephen M. Gordon, Noboru Ohtani, Serdar Uckun and Mitch Wilkes. Their work appears in journals such as Applied Physics Letters, Journal of Food Engineering and Technological Forecasting and Social Change.

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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