Yasuaki Kuroe

1.5k total citations
124 papers, 858 citations indexed

About

Yasuaki Kuroe is a scholar working on Artificial Intelligence, Control and Systems Engineering and Electrical and Electronic Engineering. According to data from OpenAlex, Yasuaki Kuroe has authored 124 papers receiving a total of 858 indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Artificial Intelligence, 39 papers in Control and Systems Engineering and 33 papers in Electrical and Electronic Engineering. Recurrent topics in Yasuaki Kuroe's work include Neural Networks and Applications (23 papers), Reinforcement Learning in Robotics (13 papers) and Stability and Control of Uncertain Systems (13 papers). Yasuaki Kuroe is often cited by papers focused on Neural Networks and Applications (23 papers), Reinforcement Learning in Robotics (13 papers) and Stability and Control of Uncertain Systems (13 papers). Yasuaki Kuroe collaborates with scholars based in Japan and Germany. Yasuaki Kuroe's co-authors include Hitoshi Iima, Taketoshi Mori, Tohru Nitta, Eckhard Hitzer, Yuichi Mori, Takehiro Mori, Toru Maruhashi, Shinji Hayashi, Mitsuo Yoshida and Yoshihiro Mori and has published in prestigious journals such as IEEE Transactions on Automatic Control, IEEE Transactions on Power Electronics and IEEE Access.

In The Last Decade

Yasuaki Kuroe

109 papers receiving 815 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Yasuaki Kuroe Japan 15 289 288 223 179 115 124 858
Li Xu Japan 19 727 2.5× 164 0.6× 182 0.8× 153 0.9× 137 1.2× 175 1.5k
Tamal Bose United States 20 303 1.0× 251 0.9× 372 1.7× 418 2.3× 130 1.1× 160 1.4k
Tianqi Zhang China 18 150 0.5× 140 0.5× 247 1.1× 184 1.0× 183 1.6× 138 1.0k
Gonzalo Joya Spain 13 184 0.6× 329 1.1× 190 0.9× 312 1.7× 62 0.5× 59 791
Saïd Djennoune Algeria 20 718 2.5× 96 0.3× 253 1.1× 117 0.7× 161 1.4× 83 1.3k
William A. Porter United States 15 288 1.0× 182 0.6× 67 0.3× 135 0.8× 76 0.7× 121 830
Lin Shi China 21 669 2.3× 139 0.5× 617 2.8× 73 0.4× 210 1.8× 72 1.5k
W. Steven Gray United States 17 840 2.9× 229 0.8× 272 1.2× 154 0.9× 32 0.3× 151 1.4k
M.M. Fahmy Canada 22 339 1.2× 182 0.6× 91 0.4× 127 0.7× 471 4.1× 95 1.3k

Countries citing papers authored by Yasuaki Kuroe

Since Specialization
Citations

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

Fields of papers citing papers by Yasuaki Kuroe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yasuaki Kuroe

This figure shows the co-authorship network connecting the top 25 collaborators of Yasuaki Kuroe. A scholar is included among the top collaborators of Yasuaki Kuroe 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 Yasuaki Kuroe. Yasuaki Kuroe 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.
2.
Kuroe, Yasuaki, et al.. (2013). Swarm Reinforcement Learning Method for Multi-agent Tasks. Transactions of the Society of Instrument and Control Engineers. 49(3). 370–377. 1 indexed citations
3.
Mori, Yoshihiro & Yasuaki Kuroe. (2012). A synthesis method of gene regulatory networks having cyclic expression pattern sequences and its evaluation. International Conference on Control, Automation and Systems. 1694–1699.
4.
Kuroe, Yasuaki, et al.. (2011). Swarm reinforcement learning method for multi-agent tasks — Solution of dilemma problems. Society of Instrument and Control Engineers of Japan. 905–910. 3 indexed citations
5.
Mori, Yoshihiro & Yasuaki Kuroe. (2010). A Synthesis Method of Gene Networks Having Cyclic Expression Pattern Sequences by Network Learning. Transactions of the Society of Instrument and Control Engineers. 46(11). 713–722. 1 indexed citations
6.
Mori, Yoshihiro & Yasuaki Kuroe. (2010). Gene expression pattern based controller design for gene regulatory networks. Society of Instrument and Control Engineers of Japan. 1451–1457. 1 indexed citations
7.
Iima, Hitoshi & Yasuaki Kuroe. (2008). Particle Swarm Optimization with Enhanced Autonomous Search Ability of Each Particle. Transactions of the Society of Instrument and Control Engineers. 44(1). 61–70. 2 indexed citations
8.
Mori, Yoshihiro, Yasuaki Kuroe, & Takehiro Mori. (2008). A Synthesis Method of Gene Networks Based on Gene Expression Patterns by Network Learning. Transactions of the Society of Instrument and Control Engineers. 44(11). 936–945. 2 indexed citations
9.
Iima, Hitoshi & Yasuaki Kuroe. (2006). Swarm Reinforcement Learning Algorithm Based on Exchanging Information among Agents. Transactions of the Society of Instrument and Control Engineers. 42(11). 1244–1251. 6 indexed citations
10.
Kuroe, Yasuaki. (2004). Learning and identifying finite state automata with recurrent high-order neural networks. Society of Instrument and Control Engineers of Japan. 3. 2241–2246. 3 indexed citations
11.
Mori, Takehiro, et al.. (2004). Relations between Common Quadratic Lyapunov Functions and Common Infinity-Norm Lyapunov Functions. Transactions of the Society of Instrument and Control Engineers. 40(10). 1067–1069. 4 indexed citations
12.
Mori, Yoshihiro, et al.. (2003). QE Approach to Common Lyapunov Function Problem (特集 Quantifier Elimination). 10(1). 52–62. 1 indexed citations
13.
Kuroe, Yasuaki & Taketoshi Mori. (2003). Neural network representation and identification of finite state automata. Society of Instrument and Control Engineers of Japan. 3. 2328–2332. 1 indexed citations
14.
Kuroe, Yasuaki. (1998). Simulation Technology on Power Electronic Systems. IEEJ Transactions on Industry Applications. 118(7-8). 822–827. 1 indexed citations
15.
KIMURA, Ichirô, et al.. (1998). Estimation of Flow Velocity Vector Fields Using Neural Networks. Transactions of the Society of Instrument and Control Engineers. 34(12). 1800–1805. 1 indexed citations
16.
KIMURA, Ichirô, et al.. (1998). Analysis of a Human Sensibility Model for Musical Chord Motion Using Neural Networks. Transactions of the Society of Instrument and Control Engineers. 34(7). 823–829. 1 indexed citations
17.
KIMURA, Ichirô, et al.. (1996). Realization of Color Sensibility Using Neural Networks. Transactions of the Society of Instrument and Control Engineers. 32(2). 224–230. 4 indexed citations
18.
Kuroe, Yasuaki, Toru Maruhashi, & Kazuo Okamura. (1989). Linearizing control of synchronous motors through decoupling of d-q axes and its application to design of optimal speed-servo systems.. IEEJ Transactions on Industry Applications. 109(11). 817–824. 3 indexed citations
19.
Kuroe, Yasuaki, et al.. (1988). Optimal speed control of synchronous motors based on feedback linearization. 328–331. 6 indexed citations
20.
Kuroe, Yasuaki. (1980). An efficient digital simulation program NETCAP-IM for power-electronic induction motor drive systems by means of decomposed tableau approach. International Symposium on Circuits and Systems. 704–707. 5 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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