ID: 176
/ PC-M1: 1
Topics:
Optimization and Design, AI and Machine Learning Technologies
Keywords:
Design optimization, Induction motors, Reinforcement learning
A Data-driven Automatic Design Method of Induction Motors Based on Tree Search and Reinforcement Learning Considering Multiple Objectives
Takahiro Sato
, Kota Watanabe
Muroran Institute of Technology, Japan
ID: 233
/ PC-M1: 2
Topics:
Optimization and Design, AI and Machine Learning Technologies
Keywords:
AC motors, permanet magnet motors, traction motors, design optimization, data-driven modeling
A data-driven approach to the design of traction electric motors
Francesco Moraglio, Paolo Ragazzo, Gaetano Dilevrano, Simone Ferrari, Gianmario Pellegrino,
Maurizio Repetto
Politecnico di Torino, Italy
ID: 470
/ PC-M1: 3
Topics:
AI and Machine Learning Technologies
Keywords:
Convolutional neural networks, data visualization, topology optimization, explainable artificial intelligence.
Visual Interpretation of Topology Optimization Results Based on Deep Learning
Hayaho Sato
, Hajime Igarashi
Hokkaido University, Japan
ID: 292
/ PC-M1: 4
Topics:
Mathematical Modelling and Formulations, AI and Machine Learning Technologies
Keywords:
neural networks, computational electromagnetics, method of moments
Towards Physics Informed Neural Network Generalised Polygonal Vector Basis Function Model
Marijana Krivic
1,2
, Jeannick Sercu
1
, Filip Demuynck
1
, Tom De Muer
1
, Thomas Zwick
2
1
Keysight Technologies, Belgium;
2
Institute of Radio Frequency Engineering and Electronics, Karlsruhe Institute of Technology, Karlsruhe, Germany
ID: 166
/ PC-M1: 5
Topics:
AI and Machine Learning Technologies
Keywords:
Analytical models, Fault detection, Induction motors, Machine learning
Classification of Electrical Faults in Induction Machines using Multiple Coupled Circuit Modeling and a Neural Network
Moritz Benninger
1
, Marcus Liebschner
1
, Christian Kreischer
2
1
University of Applied Sciences Aalen, Germany;
2
Helmut-Schmidt-University, Germany
ID: 334
/ PC-M1: 6
Topics:
AI and Machine Learning Technologies
Keywords:
Lightning Localization, Machine Learning, Transmission Lines.
Neural Network Based Procedure for Lightning Localization
Sami Barmada
1
, Mauro Tucci
1
, Massimo Brignone
2
, Martino Nicora
2
, Renato Procopio
2
1
Universita di Pisa, Italy;
2
University of Genoa, Italy
ID: 128
/ PC-M1: 7
Topics:
AI and Machine Learning Technologies
Keywords:
Neural network, alternative flux model, synchronous machines, hybrid-field motor, Bayesian approach
Alternative Flux Model Generation Method for Hybrid-Field Motors Based on Bayesian Approach and Neural Networks
ZHAO TIEYANG
1
, HIDAKA YUKI
1
, HIRUMA SHINGO
2
, KAIMORI HIROYUKI
3
, EGAWA MICHI
4
, MATSUSHITA YOSHIKO
4
1
Department of Electrical, Electronics and Information Engineering,Nagaoka University of Technology;
2
Graduate School of Engineering,Kyoto University;
3
Science Solutions International Laboratory, Inc.;
4
MSC Software Corporation
ID: 144
/ PC-M1: 8
Topics:
Multi-Physics and Coupled Problems, AI and Machine Learning Technologies
Keywords:
Electrostatic discharges, Numerical simulation, Plasma simulation, Neural networks, Deep learning.
Numerical Simulation of Streamer Discharge Using Physics-Informed Neural Networks
Changzhi Peng
1
, Ruth V. Sabariego
2
, Xuzhu Dong
1
, Jiangjun Ruan
1
1
School of Electrical Engineering and Automation, Wuhan University,47000 Wuhan, China;
2
Dept. of Electrical Engineering (ESAT), KU Leuven, Campus EnergyVille, 3600 Genk, Belgium
|
