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Toshihisa Funabashi

Toshihisa Funabashi

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
66
Citations
17478
World Ranking
1141
National Ranking
30

Toshihisa Funabashi publication distribution in Electronics and Electrical Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Electronics and Electrical Engineering in 2026. The highlighted bar marks where Toshihisa Funabashi sits on this spectrum.

34–53 publications: 24 scientists 54–73 publications: 52 scientists 74–93 publications: 114 scientists 94–113 publications: 203 scientists 114–133 publications: 269 scientists 134–153 publications: 355 scientists 154–173 publications: 403 scientists 174–193 publications: 445 scientists 194–213 publications: 430 scientists 214–233 publications: 431 scientists 234–253 publications: 399 scientists 254–273 publications: 366 scientists 274–293 publications: 335 scientists 294–313 publications: 300 scientists 314–333 publications: 276 scientists 334–353 publications: 250 scientists 354–373 publications: 214 scientists 374–393 publications: 187 scientists 394–413 publications: 152 scientists 414–433 publications: 169 scientists 434–453 publications: 147 scientists 454–473 publications: 111 scientists 474–493 publications: 117 scientists 494–513 publications: 103 scientists 514–533 publications: 99 scientists 534–553 publications: 92 scientists 554–573 publications: 75 scientists 574–593 publications: 58 scientists 594–613 publications: 69 scientists 614–633 publications: 50 scientists 634–653 publications: 62 scientists 654–673 publications: 54 scientists 674–693 publications: 44 scientists 694–713 publications: 37 scientists 714–733 publications: 28 scientists 734–753 publications: 26 scientists 754–773 publications: 26 scientists 774–793 publications: 19 scientists 794–813 publications: 23 scientists 814–833 publications: 20 scientists 834–853 publications: 16 scientists 854–873 publications: 20 scientists 874–893 publications: 11 scientists 894–913 publications: 11 scientists 914–933 publications: 16 scientists 934–953 publications: 13 scientists 954–973 publications: 10 scientists 974–993 publications: 11 scientists 994–1,013 publications: 9 scientists 1,014–1,033 publications: 9 scientists 1,034–1,053 publications: 10 scientists 1,054–1,064 publications: 6 scientists 1,065+ publications: 99 scientists
34 publications 1,065+

This scientist: 533 publications — 87th percentile

87% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,065 publications or more.

Toshihisa Funabashi D-index placement in Electronics and Electrical Engineering in 2026

The chart shows the D-index (discipline H-index) distribution of Electronics and Electrical Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where Toshihisa Funabashi sits on this spectrum.

30 D-Index: 178 scientists 31 D-Index: 257 scientists 32 D-Index: 263 scientists 33 D-Index: 262 scientists 34 D-Index: 244 scientists 35 D-Index: 236 scientists 36 D-Index: 211 scientists 37 D-Index: 220 scientists 38 D-Index: 214 scientists 39 D-Index: 214 scientists 40 D-Index: 205 scientists 41 D-Index: 187 scientists 42 D-Index: 194 scientists 43 D-Index: 201 scientists 44 D-Index: 155 scientists 45 D-Index: 189 scientists 46 D-Index: 148 scientists 47 D-Index: 160 scientists 48 D-Index: 134 scientists 49 D-Index: 130 scientists 50 D-Index: 141 scientists 51 D-Index: 156 scientists 52 D-Index: 108 scientists 53 D-Index: 130 scientists 54 D-Index: 112 scientists 55 D-Index: 97 scientists 56 D-Index: 111 scientists 57 D-Index: 102 scientists 58 D-Index: 108 scientists 59 D-Index: 120 scientists 60 D-Index: 103 scientists 61 D-Index: 93 scientists 62 D-Index: 92 scientists 63 D-Index: 74 scientists 64 D-Index: 77 scientists 65 D-Index: 73 scientists 66 D-Index: 64 scientists 67 D-Index: 69 scientists 68 D-Index: 60 scientists 69 D-Index: 39 scientists 70 D-Index: 57 scientists 71 D-Index: 59 scientists 72 D-Index: 46 scientists 73 D-Index: 49 scientists 74 D-Index: 38 scientists 75 D-Index: 35 scientists 76 D-Index: 32 scientists 77 D-Index: 35 scientists 78 D-Index: 31 scientists 79 D-Index: 22 scientists 80 D-Index: 34 scientists 81 D-Index: 31 scientists 82 D-Index: 34 scientists 83 D-Index: 23 scientists 84 D-Index: 18 scientists 85 D-Index: 30 scientists 86 D-Index: 19 scientists 87 D-Index: 19 scientists 88 D-Index: 20 scientists 89 D-Index: 8 scientists 90 D-Index: 17 scientists 91 D-Index: 7 scientists 92 D-Index: 14 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 12 scientists 97 D-Index: 10 scientists 98 D-Index: 10 scientists 99 D-Index: 12 scientists 100 D-Index: 16 scientists 101 D-Index: 5 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 8 scientists 105 D-Index: 9 scientists 106 D-Index: 13 scientists 107 D-Index: 4 scientists 108 D-Index: 5 scientists 109 D-Index: 10 scientists 110 D-Index: 8 scientists 111+ D-Index: 96 scientists
30 D-Index 111+

This scientist: 66 D-Index — 84th percentile

84% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 111 D-Index or more.

Overview

Toshihisa Funabashi is affiliated with the University of the Ryukyus in Japan. Their research spans several fields and subfields, with a focus on engineering and physics, particularly within aerospace engineering and astronomy and astrophysics.

The main topics addressed in their work include:

  • Electromagnetic Launch and Propulsion Technology
  • Lightning and Electromagnetic Phenomena
  • Icing and De-icing Technologies

Funabashi's publication record includes recent contributions to the field in the form of scholarly articles. One notable paper is titled "Grounding System Design for Wind Power Generation System Considering the Effective Length of Grounding Conductor", published in 2024 in the IEEJ Transactions on Electrical and Electronic Engineering. This paper has received citations indicating engagement from the research community.

They frequently publish in the IEEJ Transactions on Electrical and Electronic Engineering, which represents a significant venue for their research dissemination. Their collaborations involve co-authors including Shozo Sekioka, M.I. Lorentzou, and Nikos Hatziargyriou, reflecting interdisciplinary and international scholarly interaction.

Best Publications

  • Multi-Agent Systems for Power Engineering Applications—Part I: Concepts, Approaches, and Technical Challenges

    S.D.J. McArthur;E.M. Davidson;V.M. Catterson;A.L. Dimeas

  • Multi-Agent Systems for Power Engineering Applications—Part II: Technologies, Standards, and Tools for Building Multi-agent Systems

    S.D.J. McArthur;E.M. Davidson;V.M. Catterson;A.L. Dimeas

  • A fast technique for unit commitment problem by extended priority list

    T. Senjyu;K. Shimabukuro;K. Uezato;T. Funabashi

  • Output power leveling of wind turbine Generator for all operating regions by pitch angle control

    T. Senjyu;R. Sakamoto;N. Urasaki;T. Funabashi

  • Optimal Distribution Voltage Control and Coordination With Distributed Generation

    T. Senjyu;Y. Miyazato;A. Yona;N. Urasaki

  • One-Hour-Ahead Load Forecasting Using Neural Networks

    T. Senjyu;H. Takara;K. Uezato;T. Funabashi

  • A hybrid power system using alternative energy facilities in isolated island

    T. Senjyu;T. Nakaji;K. Uezato;T. Funabashi

  • Application of neural network to 24-hour-ahead generating power forecasting for PV system

    A. Yona;T. Senjyu;A.Y. Saber;T. Funabashi

  • A Coordinated Control Method to Smooth Wind Power Fluctuations of a PMSG-Based WECS

    A Uehara;A Pratap;T Goya;T Senjyu

  • A Frequency-Control Approach by Photovoltaic Generator in a PV–Diesel Hybrid Power System

    M Datta;T Senjyu;A Yona;T Funabashi

  • A Novel Approach to Forecast Electricity Price for PJM Using Neural Network and Similar Days Method

    P. Mandal;T. Senjyu;N. Urasaki;T. Funabashi

  • A Hybrid Smart AC/DC Power System

    K Kurohane;T Senjyu;A Yona;N Urasaki

  • Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market

    Paras Mandal;Tomonobu Senjyu;Toshihisa Funabashi

  • A neural network based several-hour-ahead electric load forecasting using similar days approach

    Paras Mandal;Tomonobu Senjyu;Naomitsu Urasaki;Toshihisa Funabashi

  • An adaptive dead-time compensation strategy for voltage source inverter fed motor drives

    N. Urasaki;T. Senjyu;K. Uezato;T. Funabashi

  • Optimal configuration of power generating systems in isolated island with renewable energy

    Tomonobu Senjyu;Daisuke Hayashi;Atsushi Yona;Naomitsu Urasaki

  • A hybrid smart AC/DC power system

    Kyohei Kurohane;Tomonobu Senjyu;Akie Uehara;Atsushi Yona

  • A review of islanding detection methods for distributed resources

    T. Funabashi;K. Koyanagi;R. Yokoyama

  • A Coordinated Control Method for Leveling PV Output Power Fluctuations of PV–Diesel Hybrid Systems Connected to Isolated Power Utility

    M. Datta;T. Senjyu;A. Yona;T. Funabashi

  • Adaptive Dead-Time Compensation Strategy for Permanent Magnet Synchronous Motor Drive

    N. Urasaki;T. Senjyu;K. Uezato;T. Funabashi

  • A unit commitment problem by using genetic algorithm based on unit characteristic classification

    T. Senjyu;H. Yamashiro;K. Uezato;T. Funabashi

Frequent Co-Authors

Tomonobu Senjyu
Tomonobu Senjyu University of the Ryukyus
Atsushi Yona
Atsushi Yona University of the Ryukyus
Naomitsu Urasaki
Naomitsu Urasaki University of the Ryukyus
Chul-Hwan Kim
Chul-Hwan Kim Sungkyunkwan University
Katsumi Uezato
Katsumi Uezato University of the Ryukyus
Paras Mandal
Paras Mandal The University of Texas at El Paso
Akihiro Ametani
Akihiro Ametani University of Manitoba
Anurag K. Srivastava
Anurag K. Srivastava West Virginia University
Johan Driesen
Johan Driesen KU Leuven
Hiroshi Okamoto
Hiroshi Okamoto University of Tokyo

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Related Online Degrees & Career Pathways

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Moreover, competency based degrees and programs provide a skills-focused education model, allowing students to progress based on demonstrated abilities. This approach is ideal for self-motivated learners seeking practical expertise in engineering domains.

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