Liwei Lin (Advisor)

Soft actuators with unique mechanics have gained significant interests for unique capabilities and versatile applications. However, their actuation mechanisms (usually driven by light, heat, or chemical reactions) result in long actuation times. Reported magnetically actuated soft actuators can produce rapid and precise motions, yet their complex manufacturing processes may constrain their range of applications. Here, the “bone-in-flesh” is proposed that constructs combining rigid magnetic structures encapsulated within soft polymers to create untethered magnetic soft actuators. This...

Accurate and real-time gas detection is crucial for applications ranging from environmental monitoring to industrial processes. Traditional methods are often limited by low accuracy, slow response times, and high costs. This project introduces a scalable machine learning fusion system that integrates sensor fusion techniques to enhance detection performance. With encoder-decoder architectures and a decision fusion model, our approach significantly improves the accuracy of carbon dioxide sensing, achieving a mean absolute percentage error (MAPE) of 2.97% while reducing response and recovery...