Keywords
Abstract
AI-driven infrastructure and engineering systems require reliable sensing, diagnostics, simulation, and model interpretation. Continual de-raining and rainy-condition segmentation improve outdoor visual monitoring under adverse weather, while LiDAR-guided hyperspectral fusion and infrared-visible image fusion support multimodal image classification and target-aware supervision. Battery state-of-health prediction and AI applications in power electronics contribute diagnostic methods for energy systems that support infrastructure monitoring. Pavement friction prediction links image-based indicators to road safety, and hemodynamic modeling of aortic arch aneurysm treatment provides a biomedical simulation example where virtual modeling supports decision-making. LLM confidence research is relevant across these systems because automated outputs must be interpreted with appropriate uncertainty. This literature cluster connects visual sensing, energy reliability, road safety, biomedical simulation, and confidence-aware AI in safety-critical environments.
References
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