@article{TAN2026, 
author = {Zhaoxiang TAN and Lingna YUE and Zijie GONG and Pengcheng YIN and Yanyu WEI and Chen GU and Lingfeng LAI},
title = {DEVELOPMENT AND VERIFICATION OF AN INTERACTIVE TEACHING SYSTEM FOR MICROWAVE IMPEDANCE TRANSFORMERS},
year = {2026},
journal = {Physics and Engineering},
volume = {36},
number = {3},
pages = {79-86},
keywords = {Impedance matching, Binomial impedance transformer, Chebyshev impedance transformer, Python, Educational tool},
url = {https://www.sciopen.com/article/10.26599/PHYS.2026.9320308},
doi = {10.26599/PHYS.2026.9320308},
abstract = {To address the limitations of traditional Coriolis-force experiments, such as coarse measurements and limited functionality, this study develops an intelligent platform for the demonstration and investigation of Coriolis dynamics. The platform adopts an end-cloud collaborative distributed architecture: the front end consists of an STM32 control unit and a K230 vision-acquisition module for multi-source data collection, while the PC-based back end integrates Bluetooth and Wi-Fi data transmission. By incorporating a YOLOv8-based object-detection model, the system enables real-time trajectory tracking and synchronous comparison between theoretical predictions and experimental measurements. Performance evaluation shows that the platform achieves a spatial resolution of 0.667mm/pixel, an end-to-end latency of less than 1.320s, and an R2 value above 0.99 between YOLO-extracted experimental trajectories and theoretical trajectories over 50 repeated trials under identical conditions, demonstrating high precision, real-time performance, and strong reliability. Through two representative experiments—ball-motion deflection and fluid erosion—the platform supports both intuitive visualization of classical physical laws and in-depth exploration of complex nonlinear phenomena. By integrating the Mamba model with physics-informed neural networks (PINNs), together with semi-supervised learning and self-attention mechanisms, the platform can predict the outcomes of fluid-erosion experiments and infer the dominant physical mechanisms governing the erosion process directly from experimental data. This work provides a complete and practical solution for the modernization and intelligent upgrading of traditional physics experimental platforms.}
}