World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
38
Citations
6993
World Ranking
4855
National Ranking
196

Katsumi Uezato 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 Katsumi Uezato 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: 226 publications — 38th percentile

38% of scientists in this discipline score the same or lower.

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

Katsumi Uezato 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 Katsumi Uezato 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: 38 D-Index — 30th percentile

30% of scientists in this discipline score the same or lower.

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

Overview

Katsumi Uezato is affiliated with the University of the Ryukyus in Japan. Their academic career is primarily associated with this institution.

There is no available detailed information on their recent papers, frequent co-authors, publication venues, book publications, main fields or subfields of study, or specific topics of work.

No awards or distinctions have been recorded in the available data. There is no indication that Katsumi Uezato is deceased.

The profile is limited to basic biographical and institutional affiliation details due to the absence of publications or other academic contributions in the provided information.

Best Publications

  • Neural-network-based maximum-power-point tracking of coupled-inductor interleaved-boost-converter-supplied PV system using fuzzy controller

    M. Veerachary;T. Senjyu;K. Uezato

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

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

  • 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

  • Voltage-based maximum power point tracking control of PV system

    M. Veerachary;T. Senjyu;K. Uezato

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

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

  • Feedforward maximum power point tracking of PV systems using fuzzy controller

    M. Veerachary;T. Senjyu;K. Uezato

  • Maximum power point tracking control of IDB converter supplied PV system

    M. Veerachary;T. Senjyu;K. Uezato

  • Next day load curve forecasting using hybrid correction method

    T. Senjyu;P. Mandal;K. Uezato;T. Funabashi

  • A novel calculation method for iron loss resistance suitable in modeling permanent-magnet synchronous motors

    N. Urasaki;T. Senjyu;K. Uezato

  • Maximum power point tracking of coupled inductor interleaved boost converter supplied PV system

    M. Veerachary;T. Senjyu;K. Uezato

  • Wind velocity and rotor position sensorless maximum power point tracking control for wind generation system

    Tomonobu Senjyu;Satoshi Tamaki;Endusa Muhando;Naomitsu Urasaki

  • Position control of ultrasonic motors using MRAC and dead-zone compensation with fuzzy inference

    T. Senjyu;T. Kashiwagi;K. Uezato

  • Output power control of wind turbine generator by pitch angle control using minimum variance control

    Tomonobu Senjyu;Ryosei Sakamoto;Naomitsu Urasaki;Hiroki Higa

  • Speed control of ultrasonic motors using neural network

    T. Senjyu;H. Miyazato;S. Yokoda;K. Uezato

  • Next day load curve forecasting using recurrent neural network structure

    T. Senjyu;P. Mandal;K. Uezato;T. Funabashi

  • Relationship of parallel model and series model for permanent magnet synchronous motors taking iron loss into account

    N. Urasaki;T. Senjyu;K. Uezato

  • Future load curve shaping based on similarity using fuzzy logic approach

    T. Senjyu;S. Higa;K. Uezato

Frequent Co-Authors

Tomonobu Senjyu
Tomonobu Senjyu University of the Ryukyus
Toshihisa Funabashi
Toshihisa Funabashi University of the Ryukyus
Naomitsu Urasaki
Naomitsu Urasaki University of the Ryukyus
Mummadi Veerachary
Mummadi Veerachary Indian Institute of Technology Delhi
Paras Mandal
Paras Mandal The University of Texas at El Paso
Sanjib Kumar Panda
Sanjib Kumar Panda National University of Singapore
Marta Molinas
Marta Molinas Norwegian University of Science and Technology

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Related Online Degrees & Career Pathways

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Additionally, pursuing a master's in instructional design can open roles in educational technology and corporate training. This path suits engineers interested in developing training modules or e-learning tools within technical industries.

Best Scientists Citing Katsumi Uezato