A5018164186- Anomaly Detection Techniques and Applications
- Fault Detection and Control Systems
- Autonomous Vehicle Technology and Safety
- Adversarial Robustness in Machine Learning
- Machine Fault Diagnosis Techniques
- Traffic and Road Safety
- Risk and Safety Analysis
- Vehicle Dynamics and Control Systems
- Underground infrastructure and sustainability
- Traffic control and management
- Fire Detection and Safety Systems
- Fire effects on ecosystems
- Network Security and Intrusion Detection
Chang'an University
2023-2024
To guarantee the safety and reliability of autonomous driving applications, it is indispensable to construct a proper fault diagnosis framework tailored vehicles. Fault aims provide essential information about system operational status its interpretation facilitates decision-making mitigates potential operation risks. In present work, interpretability issue in for vehicles discussed from sensor data analytics perspective. Environmental impact first evaluated using noise energy as measure...
Abstract With the continuous development of society, power grid has become a major guarantee for social and economic development, its coverage is also expanding. The not only covers some developed areas, but extends to remote mountainous areas with complex changes in geological climatic conditions. However, at same time, line failures outages caused by mountain fires are becoming increasingly frequent, which poses significant threat safety stable operation system. Therefore, how solve...
Road vehicle safety is of major importance for autonomous vehicles. Complexity deriving from both the system itself and its external environment can lead to failures, i.e., breakdowns in adaptation coping with such complexity. Resilience engineering provides a distinctive sight measuring maintaining complex systems, where seen as something positive adaptability view, emphasizing ability proactively adjust hazards not lose functionality. In present work, resilience evaluation framework...
Connected and automated vehicles (CAVs) are revolutionizing the development of transportation, while reliability safety issues for CAVs remain to be improved. Sensor data observations internal state CAV its external environment, fault diagnosis sensor system aims provide available information about operation status sensors decision-making unit, avoiding potential risks. This paper presents a self-fault framework with environmental impact quantification. We attribute occurrence abnormal...