BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Asia/Tokyo BEGIN:STANDARD DTSTART:19510909T000000 TZOFFSETFROM:+1000 TZOFFSETTO:+0900 TZNAME:JST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260219T075404Z UID:7E89D7ED-02EB-4536-82C4-27BBCAE7533C DTSTART;TZID=Asia/Tokyo:20251114T160000 DTEND;TZID=Asia/Tokyo:20251114T170000 DESCRIPTION:Professor Min-Fu Hsieh of National Chen Kung University\, one o f this year's IEEE Distinguished Lecturers\, will give a talk on Artificia l Intelligence-Assisted Design and Fault Diagnosis of Electric Motors for Green Transportation. Everyone is welcome\, no pre-registration necessary. \n\nAbstract of the talk:\n\nThe impact of artificial intelligence (AI) is rapidly growing and is increasingly pivotal across a wide range of discip lines\, from innovative scientific research to practical\, everyday applic ations. The powerful capabilities of AI—spanning data analysis\, predict ive modeling\, and beyond—equip researchers and professionals with unpar alleled tools to tackle complex problems\, push the boundaries of scientif ic discovery\, and elevate productivity to unprecedented levels. This talk will explore the integration of AI in diagnosing motor faults and advanci ng motor design\, highlighting how AI can significantly enhance the reliab ility and performance of electric motors in green transportation. It will delve into the use of machine learning and deep learning models to predict and prevent motor failures (e.g.\, inter-turn short-circuits\, demagnetiz ation\, and bearing faults) [1]-[3]\, which is essential for ensuring safe ty and reliability in transportation and industry. Furthermore\, the talk will highlight AI-driven innovations in motor design [4]\, such as noise-r eduction\, offering insights into how AI can revolutionize traditional mot or systems and contribute to ongoing improvements in predictive maintenanc e and design practices.\n\n[1] A. Mohammad-Alikhani\, B. Nahid-Mobarakeh\, and M. F. Hsieh\, “One-Dimensional LSTM-Regulated Deep Residual Network for Data-Driven Fault Detection in Electric Machines\,” IEEE Trans. Ind ustrial Elect. vol. 71\, no. 3\, pp. 3083-3092\, Mar 2024.\n[2] A. Mohamma d-Alikhani\, B. Nahid-Mobarakeh\, and M. F. Hsieh\, “Diagnosis of Mechan ical and Electrical Faults in Electric Machines Using a Lightweight Freque ncy-Scaled Convolutional Neural Network\,” IEEE Trans. Energy Conver.\, early access\, Nov 2024\, doi: 10.1109/TEC.2024.3490736.\n[3] K. J. Shih\, M. F. Hsieh\, B. J. Chen\, and S. F. Huang\, “Machine Learning for Inte r-Turn Short-Circuit Fault Diagnosis in Permanent Magnet Synchronous Motor s\,” IEEE Trans. Magn.\, vol. 58\, no. 8\, 8204307\, Apr 2022.\n[4] M. F . Hsieh\, L. H. Lin\, T. A. Huynh\, and D. Dorrell\, “Development of Mac hine Learning-Based Design Platform for Permanent Magnet Synchronous Motor Toward Simulation Free\,” IEEE Trans. Magn.\, vol. 59\, no. 11\, 820430 7\, Aug 2023.\n\nRoom: M331\, Bldg: 本館\, 仙台市青葉区片平2-1-1 \, 東北大学電気通信研究所\, 仙台\, Miyagi\, Japan LOCATION:Room: M331\, Bldg: 本館\, 仙台市青葉区片平2-1-1\, 東北 大学電気通信研究所\, 仙台\, Miyagi\, Japan ORGANIZER:simon@riec.tohoku.ac.jp SEQUENCE:14 SUMMARY:Artificial Intelligence-Assisted Design and Fault Diagnosis of Elec tric Motors for Green Transportation URL;VALUE=URI:https://events.vtools.ieee.org/m/512313 X-ALT-DESC:Description: <br /><p>Professor Min-Fu Hsieh of National Chen Ku ng University\, one of this year's IEEE Distinguished Lecturers\, will giv e a talk on Artificial Intelligence-Assisted Design and Fault Diagnosis of Electric Motors for Green Transportation. Everyone is welcome\, no pre-re gistration necessary.</p>\n<p>&nbsp\;</p>\n<p>Abstract of the talk:</p>\n< p>The impact of artificial intelligence (AI) is rapidly growing and is inc reasingly pivotal across a wide range of disciplines\, from innovative sci entific research to practical\, everyday applications. The powerful capabi lities of AI&mdash\;spanning data analysis\, predictive modeling\, and bey ond&mdash\;equip researchers and professionals with unparalleled tools to tackle complex problems\, push the boundaries of scientific discovery\, an d elevate productivity to unprecedented levels. This talk will explore the integration of AI in diagnosing motor faults and advancing motor design\, highlighting how AI can significantly enhance the reliability and perform ance of electric motors in green transportation. It will delve into the us e of machine learning and deep learning models to predict and prevent moto r failures (e.g.\, inter-turn short-circuits\, demagnetization\, and beari ng faults) [1]-[3]\, which is essential for ensuring safety and reliabilit y in transportation and industry. Furthermore\, the talk will highlight AI -driven innovations in motor design [4]\, such as noise-reduction\, offeri ng insights into how AI can revolutionize traditional motor systems and co ntribute to ongoing improvements in predictive maintenance and design prac tices.</p>\n<p class="text-sm">[1] &nbsp\;A. Mohammad-Alikhani\, B. Nahid- Mobarakeh\, and M. F. Hsieh\, &ldquo\;One-Dimensional LSTM-Regulated Deep Residual Network for Data-Driven Fault Detection in Electric Machines\,&rd quo\; IEEE Trans. Industrial Elect. vol. 71\, no. 3\, pp. 3083-3092\, Mar 2024.<br>[2] &nbsp\;A. Mohammad-Alikhani\, B. Nahid-Mobarakeh\, and M. F. Hsieh\, &ldquo\;Diagnosis of Mechanical and Electrical Faults in Electric Machines Using a Lightweight Frequency-Scaled Convolutional Neural Network \,&rdquo\; IEEE Trans. Energy Conver.\, early access\, Nov 2024\, doi: 10. 1109/TEC.2024.3490736.<br>[3] &nbsp\;K. J. Shih\, M. F. Hsieh\, B. J. Chen \, and S. F. Huang\, &ldquo\;Machine Learning for Inter-Turn Short-Circuit Fault Diagnosis in Permanent Magnet Synchronous Motors\,&rdquo\; IEEE Tra ns. Magn.\, vol. 58\, no. 8\, 8204307\, Apr 2022.<br>[4] &nbsp\;M. F. Hsie h\, L. H. Lin\, T. A. Huynh\, and D. Dorrell\, &ldquo\;Development of Mach ine Learning-Based Design Platform for Permanent Magnet Synchronous Motor Toward Simulation Free\,&rdquo\; IEEE Trans. Magn.\, vol. 59\, no. 11\, 82 04307\, Aug 2023.</p> END:VEVENT END:VCALENDAR