AI-Powered Virtual Sensors in Embedded Applications
Overview
This webinar will explore the integration of artificial intelligence (AI) with MBD to create virtual sensors that replicate the behavior of physical sensors. These virtual sensors are particularly useful when direct measurement is not feasible or when adding physical sensors becomes too costly or complex. Real-world examples such as Battery Management System (BMS) State of Charge (SOC) estimation and motor position tracking for control applications illustrate how AI models can replace traditional sensor technologies
The webinar will focus on the design, validation, and deployment of AI-based virtual sensor models, emphasizing AI model verification, system integration, and performance optimization on resource-constrained embedded devices.
Highlights
- Design and train AI-based virtual sensors using MATLAB.
- Import models from TensorFlow and integrate them into Simulink for system-level simulation and verification.
- Apply formal neural network verification techniques and conduct simulation-based testing.
- Explore AI model compression techniques to reduce memory footprint and enhance execution speed.
- Generate library-free C code for processor-in-the-loop (PIL) testing and production deployment.
- Profile code performance and evaluate trade-offs in design and model selection.
About the Presenter
Shang-Chuan Lee
Senior Application Engineer | MathWorks
Shang-Chuan Lee is a senior application engineer working at The MathWorks. She received her PhD in mechanical engineering from the University of Wisconsin-Madison (WEMPEC). Her specialty is control of power electronics and motor drives in industrial automation applications. Prior to joining MathWorks, her graduate study focus was on real-time simulation and testing of motor control applications using Simulink Real-Time and Speedgoat target hardware.
This event is part of a series of related topics. View the full list of events in this series.
