The field of cyber-physical systems is witnessing significant advancements in fault diagnosis, state estimation, and attack detection. Researchers are developing innovative methods to identify and mitigate faults in complex systems, such as those used in autonomous vehicles and power grids. Additionally, there is a growing focus on enhancing the resilience of power systems, including the development of robust state estimation techniques and dynamic voltage control schemes. Notable papers in this area include:
- A Compositional Approach to Diagnosing Faults in Cyber-Physical Systems, which presents a novel approach to diagnosing faults in cyber-physical systems using assume-guarantee contracts.
- Robust Power System State Estimation using Physics-Informed Neural Networks, which proposes a hybrid approach to enhance the accuracy and robustness of power system state estimation using physics-informed neural networks.
- Detection of Intelligent Tampering in Wireless Electrocardiogram Signals Using Hybrid Machine Learning, which demonstrates the effectiveness of hybrid machine learning models in detecting tampering in wireless electrocardiogram signals. These developments have significant implications for the reliability and security of critical infrastructure, such as power grids and transportation systems.