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PRODID:IEEE vTools.Events//EN
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DTSTAMP:20250630T164646Z
UID:DE1EF270-D3C6-422D-992F-06D9E6F9592F
DTSTART;TZID=America/Los_Angeles:20250627T183000
DTEND;TZID=America/Los_Angeles:20250627T200000
DESCRIPTION:A talk by Prof. Min-Fu Hsieh of National Cheng Kung University 
 (NCKU)\, Tainan Taiwan\, exploring the integration of AI in diagnosing mot
 or faults and advancing motor design\, highlighting how AI can significant
 ly enhance the reliability and performance of electric motors in green tra
 nsportation. It will delve into the use of machine learning and deep learn
 ing models to predict and prevent motor failures (e.g.\, inter-turn short-
 circuits\, demagnetization\, and bearing faults)\, which is essential for 
 ensuring safety and reliability in transportation and industry. Furthermor
 e\, the talk will highlight AI-driven innovations in motor design\, such a
 s noise-reduction\, offering insights into how AI can revolutionize tradit
 ional motor systems and contribute to ongoing improvements in predictive m
 aintenance and design practices.\n\nAgenda: \n6:30 - 7:00	Socializing and 
 Networking at Quadrant\n6:55	Zoom session will be online with Waiting Room
 \n7:00 - 7:45	Lecture begins\, online and in person\n7:45 - 8:00	Questions
  and Answers\n\n1120 Ringwood Ct\, San Jose\, California\, United States\,
  95131\, Virtual: https://events.vtools.ieee.org/m/481023
LOCATION:1120 Ringwood Ct\, San Jose\, California\, United States\, 95131\,
  Virtual: https://events.vtools.ieee.org/m/481023
ORGANIZER:t.gardner@computer.org
SEQUENCE:50
SUMMARY:Artificial Intelligence-Assisted Design and Fault Diagnosis of Elec
 tric Motors for Green Transportation
URL;VALUE=URI:https://events.vtools.ieee.org/m/481023
X-ALT-DESC:Description: <br /><p>A talk by Prof. Min-Fu Hsieh of National C
 heng Kung University (NCKU)\, Tainan Taiwan\, exploring the integration of
  AI in diagnosing motor faults and advancing motor design\, highlighting h
 ow AI can significantly enhance the reliability and performance of electri
 c motors in green transportation. It will delve into the use of machine le
 arning and deep learning models to predict and prevent motor failures (e.g
 .\, inter-turn short-circuits\, demagnetization\, and bearing faults)\, wh
 ich is essential for ensuring safety and reliability in transportation and
  industry. Furthermore\, the talk will highlight AI-driven innovations in 
 motor design\, such as noise-reduction\, offering insights into how AI can
  revolutionize traditional motor systems and contribute to ongoing improve
 ments in predictive maintenance and design practices.</p><br /><br />Agend
 a: <br /><table style="border-collapse: collapse\; width: 100%\;" border="
 1"><colgroup><col style="width: 15.5951%\;"><col style="width: 84.4049%\;"
 ></colgroup>\n<tbody>\n<tr>\n<td>6:30 - 7:00</td>\n<td>Socializing and Net
 working at Quadrant</td>\n</tr>\n<tr>\n<td>6:55</td>\n<td>Zoom session wil
 l be online with Waiting Room</td>\n</tr>\n<tr>\n<td>7:00 - 7:45</td>\n<td
 >Lecture begins\, online and in person</td>\n</tr>\n<tr>\n<td>7:45 - 8:00<
 /td>\n<td>Questions and Answers</td>\n</tr>\n</tbody>\n</table>
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