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. The session is pre-recorded with a live Q&A session at the end, held by Christoph Stockhammer.
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 Presenters
Shang-Chuan Lee
Senior Application Engineer | MathWorks
Shang-Chuan 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.
Sagar Hukkire
Application Engineering | MathWorks
Sagar Hukkire works as an Application Engineer, focusing on the usages of MATLAB in Data Analytics, Text Analytics, and Image Analytics applications. Before joining MathWorks in 2020, Sagar worked extensively in the field of Machine Learning and Deep Learning, specializing in developing cutting-edge solutions for Text Analytics and Image Analytics. His passion for cloud technologies has allowed him to further enhance his skill set and stay at the forefront of technological advancements.
Sagar holds a Master of Science degree in Information Technology from the University of Stuttgart, Germany, further solidifying his technical foundation.
