@article{ZHANG2026, 
author = {Wenhao ZHANG and Zhihao MAO and Zhonglong XIONG and Yan WU},
title = {DEVELOPMENT OF AN INTELLIGENT EXPERIMENTAL RESEARCH PLATFORM FOR CORIOLIS FORCE DYNAMICS},
year = {2026},
journal = {Physics and Engineering},
volume = {36},
number = {2},
pages = {211-222},
keywords = {intelligent experimental platform, Coriolis force, YOLO object detection, attention mechanism, PINN, Mamba},
url = {https://www.sciopen.com/article/10.26599/PHYS.2026.9320232},
doi = {10.26599/PHYS.2026.9320232},
abstract = {To address the limitations of traditional Coriolis-force experiments—such as coarse measurements and single-function setups—this study develops an intelligent demonstration and investigation platform for 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 back-end host computer integrates Bluetooth and Wi-Fi dual-channel data transmission. By incorporating a YOLOv8-based object detection model, the system enables real-time tracking of dynamic processes and simultaneous comparison between theoretical predictions and experimental measurements. Performance evaluations show that the platform achieves a spatial resolution of 0.667 mm/pixel, an end-to-end latency of less than 1.320s, and a trajectory-fit goodness（R2）above 0.99 between YOLO-extracted experimental trajectories and theoretical trajectories over 50 repeated trials under identical conditions, demonstrating high precision, low latency, and strong reliability.Through two representative experiments—ball motion deflection and fluid-erosion patterns—the platform is shown to support both intuitive visualization of classical physical laws and in-depth investigation of complex nonlinear phenomena. By integrating the Mamba model with Physics-Informed Neural Networks（PINN）, and further introducing semi-supervised learning and self-attention mechanisms, the platform is capable of predicting the outcomes of fluid erosion experiments and inferring the dominant physical mechanisms governing the erosion process directly from data. This work provides a complete and practical solution for the modernization and intelligent upgrading of traditional physics experimentation systems.}
}