Context-Aware Design of Cyber-Physical Human Systems (CPHS)
This addresses the challenge of designing more accurate cyber-physical systems for engineers and researchers, though it appears incremental as it builds on existing models.
The paper tackles the problem of performance gaps in cyber-physical systems due to unaddressed context factors in human-infrastructure interactions, proposing a CPHS framework that uses machine learning to integrate context-aware data and improve predictive accuracy.
Recently, it has been widely accepted by the research community that interactions between humans and cyber-physical infrastructures have played a significant role in determining the performance of the latter. The existing paradigm for designing cyber-physical systems for optimal performance focuses on developing models based on historical data. The impacts of context factors driving human system interaction are challenging and are difficult to capture and replicate in existing design models. As a result, many existing models do not or only partially address those context factors of a new design owing to the lack of capabilities to capture the context factors. This limitation in many existing models often causes performance gaps between predicted and measured results. We envision a new design environment, a cyber-physical human system (CPHS) where decision-making processes for physical infrastructures under design are intelligently connected to distributed resources over cyberinfrastructure such as experiments on design features and empirical evidence from operations of existing instances. The framework combines existing design models with context-aware design-specific data involving human-infrastructure interactions in new designs, using a machine learning approach to create augmented design models with improved predictive powers.