Research Index

Develop an intelligent digital twin system that combines safety-critical control theory with real-time hydrogen storage monitoring using adaptive learning algorithms. The system would integrate multiple data streams to predict and prevent hazardous conditions while optimizing storage efficiency through reinforcement learning approaches.

A novel framework combining digital twin technology, human-state-aware robotics, and safety-critical control for collaborative maintenance of hydrogen storage systems. The system adapts robot behavior based on real-time human state monitoring and safety constraints while maintaining a digital twin for training, simulation, and risk assessment.

A novel approach combining digital twin technology with bio-inspired reinforcement learning to create self-healing protection systems for microgrids. The system learns from both simulated and real-world scenarios to adaptively optimize protection settings while maintaining grid stability, drawing inspiration from biological immune systems' ability to learn and adapt to new threats.

A novel approach combining quantum annealing, bio-inspired algorithms, and digital twin technology to optimize microgrid formation and restoration during natural disasters. The system uses real-time sensor data and quantum-classical hybrid computing to dynamically reconfigure microgrids while maintaining frequency stability and minimizing energy losses.