Explainable artificial intelligence (XAI) is a method to understand “Why and How “outcomes created by machine learning algorithms are useful and applicable. Explainable AI describes an AI model, its significance and impact. It helps characterize model accuracy, fairness, transparency and outcomes in AI-powered decision making. As most of the ML/DL algorithms are experimented for various application, there is importance of justification of its outcomes. Explainable AI will add value to the research carried out in this area. Virtual: https://events.vtools.ieee.org/m/377915

This talk covers broad rules and guidelines for the design of transformers. In addition to describing important design principles and operational aspects, intricate considerations while choosing materials, and types of magnetic circuits and windings are elaborated. Conflicting requirements while choosing various design parameters are enumerated. Advanced numerical tools like the Finite Element Method need to be routinely used these days for estimating performance parameters accurately and for reliability enhancement. Insulation design and optimization can be tricky and the issues thereof are highlighted. Ensuring adequate short-circuit strength is an important design aspect that requires the deployment of complementary manufacturing practices. Power and distribution systems are increasingly becoming complex, and transformers need to be designed accordingly. The talk will end by enumerating challenges in transformer engineering, which include the requirements to make transformers green and efficient in emerging smart grids with increasing penetration of renewable energy sources. Speaker(s): Dr. S. V. Kulkarni, Virtual: https://events.vtools.ieee.org/m/383940

Department of Aerospace Engineering, MIT School of Engineering & Science and IEEE AESS, Pune Chapter organizes expert talk on Lightning Detection Sensors on 30 October 2023 at 3 pm in online mode. Link to join the expert session is meet.google.com/pfm-tiqc-grm Virtual: https://events.vtools.ieee.org/m/377903