Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
Authors
 
Session Overview
Session
PC-M1: AI and machine learning technologies/Software methodology 2
Time:
Thursday, 25/May/2023:
11:00am - 12:30pm

Session Chair: Prof. Soichiro Ikuno, Tokyo University of Technology, Japan

Presentations
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 Krivic1,2, Jeannick Sercu1, Filip Demuynck1, Tom De Muer1, Thomas Zwick2

1Keysight Technologies, Belgium; 2Institute 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 Benninger1, Marcus Liebschner1, Christian Kreischer2

1University of Applied Sciences Aalen, Germany; 2Helmut-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 Barmada1, Mauro Tucci1, Massimo Brignone2, Martino Nicora2, Renato Procopio2

1Universita di Pisa, Italy; 2University 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 TIEYANG1, HIDAKA YUKI1, HIRUMA SHINGO2, KAIMORI HIROYUKI3, EGAWA MICHI4, MATSUSHITA YOSHIKO4

1Department of Electrical, Electronics and Information Engineering,Nagaoka University of Technology; 2Graduate School of Engineering,Kyoto University; 3Science Solutions International Laboratory, Inc.; 4MSC 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 Peng1, Ruth V. Sabariego2, Xuzhu Dong1, Jiangjun Ruan1

1School of Electrical Engineering and Automation, Wuhan University,47000 Wuhan, China; 2Dept. of Electrical Engineering (ESAT), KU Leuven, Campus EnergyVille, 3600 Genk, Belgium