Safety and reliability assurance of next generation AI-enabled cyber physical systems for energy systems

Recently, data-driven Artificial Intelligence (AI) techniques are increasingly being adopted for intelligent optimization and control of power and energy systems. The system status data (e.g., temperature) are collected and used to learn an AI system for intelligent control, management, and optimization towards energy efficiency. This research makes a special focus on innovating the safety and reliability assurance for AI systems applied in the context of energy sectors, the current research of which is still at a very early stage. For example, in this project, we intend to propose an automated framework for safety issue detection of AI-enabled energy systems (e.g., power plant, power grid). We would also explore and propose novel techniques to enhance the safety and reliability of next-generation AI-enabled energy systems.

AutoRepair: Automated Repair for AI-Enabled Cyber- Physical Systems under Safety-Critical Conditions

Lei Ma, Jiayang Song

Article in professional or trade journals

Mosaic: Model-based Safety Analysis Framework for AI-enabled Cyber-Physical Systems

Lei Ma, Jiayang Song, Xuan Xie

Conference Proceedings

SIEGE: A Semantics-Guided Safety Enhancement Framework for AI-enabled Cyber-Physical Systems

Lei Ma, Jiayang Song, Xuan Xie

Article in professional or trade journals

When Cyber-Physical Systems Meet AI: A Benchmark, an Evaluation, and a Way Forward

Lei Ma, Jiayang Song

Conference Proceedings