Posts By: ITeN Editorial Board

[WiE Webinar] Distributed Secure Cooperative Control, Optimization, and Learning

Thursday 14 November 2024 at 8:00 PM CST, 1:00 PM CET, 7:00 AM EST
By Wangli He (East China University of Science and Technology, Shanghai, China)
Joint Webinar with IEEE Women in IES

Register now using the link below:

https://attendee.gotowebinar.com/register/4935455251698636119

Abstract:
Cooperative collective behaviors in networks of autonomous agents have attracted …

Insulation System Solution for Oil-cooled 800 V+ Electrical Machines: Development of High-Performance Slot Liner Solution

Wednesday 27 November 2024 at 3:00 PM CET, 9:00 AM EST
By Shafigh Nategh (Sedrive, Sweden) & James Bonnett (Victrex, United Kingdom)

 
Register now using the link below:

https://attendee.gotowebinar.com/register/4349598072875749728

Abstract:
With the growing demand for Battery-driven Electric Vehicles (BEV) to reduce the CO2 footprint and GHG emissions from the transportation …

An Innovative Sustainable Conductor Insulation for Oil-cooled 800 V+ Electrical Machines for Automotive Sector (Canceled)

Friday 29 November 2024 at 4:00 PM CET, 10:00 AM EST  Canceled – Will be rescheduled soon. Sorry for the inconvenience.
By Dr. Jahirul (SEDRIVE, Sweden) and Alessandro Cadel (De Angeli Prodotti, Italy)

Register now using the link below:

https://attendee.gotowebinar.com/register/8089951912855019351

Abstract:
With the growing demand for electric vehicles to fulfil …

2024 ONCON – The 3rd IEEE Industrial Electronics Society Annual Online Conference

ONCON 2024 will take place virtually on 8-10 December 2024.
The IEEE Industrial Electronics Society Annual Online Conference is the third online conference organized by IEEE IES.

ONCON 2024 has focuses on theory and applications of industrial electronic systems. The conference provides a flexible and convenient platform …

Feature Mode Decomposition: New Decomposition Theory for Rotating Machinery Fault Diagnosis

Authors: Yonghao Miao; Boyao Zhang; Chenhui Li; Jing Lin; Dayi Zhang

Abstract:
Decomposition methods can separate the different components from the original signal into several regular simple modes. They are the most effective tool for signal multicomponent analysis. However, the current decomposition methods do not exhibit the …