Academic Field

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  1. Ohguchi Lab, Department of Electrical and Electronic Engineering, School of Engineering, Tokai University

    “Imagination Is More Important than Knowledge”Development of Human Resources with the Critical Thinking and Practical Skills to Contribute to Society

  2. Advanced Cooling Techniques for Automotive Electrical Machines: Thermal Modelling and Experimental Validation

    FENGYU ZHANG, University of Nottingham

  3. Challenges for Wide Frequency Band Operation of Inverter Drive Motors

    Kazumasa Ide, Nagoya University

  4. The Future of R&D and Design: What Can Electromagnetic Field Analysis and Artificial Intelligence Do for You?

    Hajime Igarashi, Hokkaido University

  5. Characteristic Analysis of 12/10 Switched Reluctance Motor Using Control Circuit Function of JMAG-Designer

    Nozomu Takemura, Osaka University / LG Japan Lab Inc.

  6. An Effect of Leakage Flux from the Traction Motor to the Resolver

    Kan Akatsu, Yokohama National University

  7. Design And Simulation Of High-Performance Permanent Magnet Synchronous Machines for Next Generation of Systems

    Bulent Sarlioglu, University of Wisconsin-Madison

  8. okayamauniv

    Investigation of Error Factors between Measurement and Analysis Results of Torque in a Permanent Magnet Synchronous Motor under High Speed Rotation

    Masatsugu Takemoto, Okayama University

  9. okayamauniv

    Aiming for Further Improved High Power Density Motor

    Masatsugu Takemoto, Okayama University

  10. Ritsumeikan University

    Automatic Design System for Permanent Magnet Synchronous Motors Using Deep Generative Model

    Yuki Shimizu, Ritsumeikan University

  11. Structure, Characteristics and Analysis of Poly-Phase Induction Motor for Automotive Traction Applications

    Akira Chiba, Tokyo Institute of Technology

  12. Analytical/Experimental Verification of Thermal Characteristics of Axial Gap Motors, and Comparison Between Axial and Radial Gap Motors

    Ren Tsunata, Okayama University

  13. AI Technology with Aid of Data-driven Method Makes EM Simulation and Optimization More Effective

    Hajime Igarashi, Hokkaido University

  14. Reduction of Optimal Design Time for IPMSMs for Automotive Applications Using Machine Learning

    Yuki Shimizu, Osaka Prefecture University

  15. New Opportunities for Motor Design – Electric and Hybrid Propulsion of Aircraft

    Bulent Sarlioglu, University of Wisconsin-Madison

  16. Model-Based Design Meets FEA Simulation – Improving Motor Control Development using High Fidelity Motor Models

    Tony Lennon, The MathWorks, Inc.

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