Yoichiro Maeda

463 total citations
78 papers, 320 citations indexed

About

Yoichiro Maeda is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Control and Systems Engineering. According to data from OpenAlex, Yoichiro Maeda has authored 78 papers receiving a total of 320 indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Computer Vision and Pattern Recognition, 26 papers in Artificial Intelligence and 25 papers in Control and Systems Engineering. Recurrent topics in Yoichiro Maeda's work include Reinforcement Learning in Robotics (16 papers), Robotic Path Planning Algorithms (14 papers) and Gaze Tracking and Assistive Technology (11 papers). Yoichiro Maeda is often cited by papers focused on Reinforcement Learning in Robotics (16 papers), Robotic Path Planning Algorithms (14 papers) and Gaze Tracking and Assistive Technology (11 papers). Yoichiro Maeda collaborates with scholars based in Japan, United States and China. Yoichiro Maeda's co-authors include Yasutake Takahashi, Morikazu Takegaki, Takayuki Nakamura, Tomohiro Takagi, Kazuyuki Murase, Kyohei Yoshida, Pintu Chandra Shill, Hiroshi Koyama, Satomi Kawaguchi and Masayuki Yamamoto and has published in prestigious journals such as Information Sciences, International Journal of Approximate Reasoning and Journal of Intelligent & Fuzzy Systems.

In The Last Decade

Yoichiro Maeda

70 papers receiving 275 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yoichiro Maeda Japan 8 113 100 98 57 52 78 320
Claudia Pérez-D’Arpino United States 8 165 1.5× 187 1.9× 110 1.1× 33 0.6× 48 0.9× 14 370
David St-Onge Canada 12 60 0.5× 103 1.0× 40 0.4× 108 1.9× 59 1.1× 44 323
Maurizio Ficocelli Canada 9 141 1.2× 73 0.7× 83 0.8× 34 0.6× 30 0.6× 17 322
Christoph Engels Germany 4 107 0.9× 77 0.8× 86 0.9× 35 0.6× 28 0.5× 9 284
Young-Jo Cho South Korea 10 177 1.6× 137 1.4× 54 0.6× 85 1.5× 27 0.5× 59 362
Çetin Meriçli United States 9 120 1.1× 92 0.9× 88 0.9× 22 0.4× 27 0.5× 25 255
Phillip Walker United States 11 100 0.9× 83 0.8× 120 1.2× 167 2.9× 34 0.7× 27 546
Dominik Joho Germany 9 170 1.5× 103 1.0× 99 1.0× 25 0.4× 21 0.4× 13 400
Gregory K. Tharp United States 10 142 1.3× 89 0.9× 33 0.3× 106 1.9× 47 0.9× 27 382

Countries citing papers authored by Yoichiro Maeda

Since Specialization
Citations

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

Fields of papers citing papers by Yoichiro Maeda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yoichiro Maeda

This figure shows the co-authorship network connecting the top 25 collaborators of Yoichiro Maeda. A scholar is included among the top collaborators of Yoichiro Maeda 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 Yoichiro Maeda. Yoichiro Maeda 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.
Takahashi, Yasutake, et al.. (2015). Motion Segmentation and Recognition for Imitation Learning and Influence of Bias for Learning Walking Motion of Humanoid Robot Based on Human Demonstrated Motion. Journal of Advanced Computational Intelligence and Intelligent Informatics. 19(4). 532–543. 1 indexed citations
2.
Takahashi, Yasutake, et al.. (2015). Fuzzy Control for a Kite-Based Tethered Flying Robot. Journal of Advanced Computational Intelligence and Intelligent Informatics. 19(3). 349–358. 2 indexed citations
3.
Maeda, Yoichiro, et al.. (2014). Visual Attention Region Prediction Based on Eye Tracking Using Fuzzy Inference. Journal of Advanced Computational Intelligence and Intelligent Informatics. 18(4). 499–510. 6 indexed citations
4.
Maeda, Yoichiro, et al.. (2012). Generation Method of Mixed Emotional Behavior by Self-Organizing Maps in Interactive Emotion Communication. Journal of Japan Society for Fuzzy Theory and Intelligent Informatics. 24(5). 933–943. 3 indexed citations
5.
Zhang, Kun, Yoichiro Maeda, & Yasutake Takahashi. (2012). Learning Model Considering the Interaction among Heterogeneous Multi-Agents. Journal of Japan Society for Fuzzy Theory and Intelligent Informatics. 24(5). 1002–1011. 1 indexed citations
6.
Takahashi, Yasutake, et al.. (2010). Inverted-pendulum Mobile Robot Motion Learning from Human Player Observation. 2010. 211–216. 3 indexed citations
7.
Zhang, Kun, Yoichiro Maeda, & Yasutake Takahashi. (2010). Group Behavior Learning in Multi-agent Systems Based on Social Interaction among Agents. 2010. 193–198. 3 indexed citations
8.
Maeda, Yoichiro, et al.. (2010). Effective Emotional Model of Pet-type Robot in Interactive Emotion Communication. 2010. 199–204. 2 indexed citations
9.
Maeda, Yoichiro, et al.. (2010). Relaxation Effect Measurement Method Used Brain Wave Feature Extraction. 2010. 205–210. 2 indexed citations
10.
Maeda, Yoichiro, et al.. (2010). Task Assignment Based on Fuzzy Contract Net for Multi-Agent Robot System. 26. 53–53. 1 indexed citations
11.
Maeda, Yoichiro, et al.. (2010). Personal Preference Analysis for Emotional Behavior Response of Autonomous Robot in Interactive Emotion Communication. Journal of Advanced Computational Intelligence and Intelligent Informatics. 14(7). 852–859. 7 indexed citations
12.
Maeda, Yoichiro, et al.. (2007). Behavior Acquisition Supporting Method for Autonomous Mobile Robot Based on Animal Training. 23. 230–230.
13.
Maeda, Yoichiro, et al.. (2006). Interactive Chaotic Sound Generation System by Using Globally Coupled Map and Incorporation of Musical Factors. Journal of Japan Society for Fuzzy Theory and Intelligent Informatics. 18(4). 507–518. 6 indexed citations
14.
Maeda, Yoichiro, et al.. (2006). Basic Study on Interactive Emotional Communication by Pet-type Robot. Transactions of the Society of Instrument and Control Engineers. 42(4). 359–366. 6 indexed citations
15.
Maeda, Yoichiro, et al.. (2005). Parallel genetic algorithm with adaptive genetic parameters tuned by fuzzy reasoning. International journal of innovative computing, information & control. 1(1). 95–107. 13 indexed citations
16.
Maeda, Yoichiro, et al.. (2005). Fuzzy adaptive search method for parallel genetic algorithm with island combination process. International Journal of Approximate Reasoning. 41(1). 59–73. 15 indexed citations
17.
Maeda, Yoichiro. (2001). Multi-Agent Simulation by Using Modified Q-Learning Method with Fuzzy States and Rewards. 17. 419–422. 1 indexed citations
18.
Maeda, Yoichiro & Satomi Kawaguchi. (2000). Redundant node pruning and adaptive search method for Genetic Programming. Genetic and Evolutionary Computation Conference. 535–535. 3 indexed citations
19.
Maeda, Yoichiro. (1999). Multi-Agent Robot Simulation for Evolutionary Learning of Cooperative Behavior. The Florida AI Research Society. 118–123. 1 indexed citations
20.
Miyazaki, Fumio, Suguru Arimoto, Morikazu Takegaki, & Yoichiro Maeda. (1985). Sensory Feedback Control Based on the Artificial Potential for Robot Manipulators. Transactions of the Society of Instrument and Control Engineers. 21(1). 71–77. 4 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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