@inproceedings{6aca58867b3945ec8b6b5e449bd5e1bc,
title = "Power Electronics Turing Test: A Path Toward Strong AI in Power Electronics",
abstract = "This paper presents a hypothetical Turing test in power electronics, leveraging structured computer vision as a step towards domain-specific artificial general intelligence (AGI). To illustrate the key principles of such a power electronics Turing test, we developed PowerVision, a computer vision framework designed to teach machines to understand schematic drawings. PowerVision comprises four key components: 1) ComponentNet: an image database for component recognition; 2) CircuitNet: an image database for schematic recognition; 3) NetlistMaker: a schematic recognition tool that converts human-readable schematics into netlists for SPICE simulations; and 4) NetlistClassifier: a circuit classification tool that can categorize different power electronics circuits based on machine-generated netlists. The PowerVision platform can facilitate the learning of power electronics fundamental principles by large-scale AGI models through human-accessible information including texts, schematics, computer simulations, and experimental results, ultimately enabling machines to comprehend power electronics.",
keywords = "artificial general intelligence, computer vision, machine learning, netlists, SPICE simulation, Turing test",
author = "Minjie Chen and Cheng, {Dak C.}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 25th IEEE Workshop on Control and Modeling for Power Electronics, COMPEL 2024 ; Conference date: 24-06-2024 Through 27-06-2024",
year = "2024",
doi = "10.1109/COMPEL57542.2024.10613961",
language = "English (US)",
series = "2024 IEEE 25th Workshop on Control and Modeling for Power Electronics, COMPEL 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2024 IEEE 25th Workshop on Control and Modeling for Power Electronics, COMPEL 2024",
address = "United States",
}