Tag: sensors fault
-
Generative AI-Enhanced Sensor Fusion: Robust LiDAR-Radar Inpainting and Restoration in Extreme Weather
Introduction This thesis investigates the application of Generative AI for the intelligent fusion of heterogeneous sensor data. The goal is to develop a Generative AI framework, specifically leveraging Conditional Diffusion Models, for real-time point cloud restoration. The research focuses on using Radar data, which is inherently weather-resilient, as a “semantic and physical prior” to denoise
-
Resilient Autonomous Navigation via Generative AI: Virtual Sensor Synthesis for Real-Time Fault Recovery
Introduction What happens when an autonomous vehicle’s camera is covered by mud or a LiDAR module suffers a hardware failure? Currently, the car performs a “safe stop.” This thesis aims to replace this passive safety with Generative AI-driven resilience, creating a system that can “imagine” missing data. Goals The project focuses on building a Virtual
-
Fault Detection and Management in Sensors for Structural Health Monitoring of Railway Bridges
fault detection, permanent monitoring system, Railway bridges, sensors fault, structural health monitoringStructural health monitoring (SHM) of railway bridges heavily relies on data collected from sensor networks (accelerometers, strain gauges, data acquisition systems). However, technical faults in sensors – such as electrical failures, connection errors, drift, or hardware malfunctions – may compromise the reliability of the measurements. In some cases, fault-induced signals can be mistakenly interpreted as