Data-driven Semi-supervised Machine Learning with Surrogate Safety Measures for Abnormal Driving Behavior Detection

Detecting abnormal driving behavior is critical for road traffic safety and the evaluation of drivers' behavior. With the advancement of machine learning (ML) algorithms and the accumulation of naturalistic driving data, many ML models have been adopted for abnormal driving behavior detection. Most existing ML-based detectors rely on (fully)...

Data-Driven Fault Diagnosis of Lithium-Ion Battery Overdischarge in Electric Vehicles

The overdischarge can significantly degrade a lithium-ion (Li-ion) battery's lifetime. Therefore, it is important to detect the overdischarge and prevent severe damage of the Li-ion battery. Depending on the battery technology, there is a minimum voltage (cutoff voltage) that the battery is allowed to be discharged in common practice. Once the...

Tidal-flat reclamation aggravates potential risk from storm impacts

A better understanding of how tidal-flat reclamation changes the flood hazard is critical for climate-proofing coastal flood defense design of heavily urbanized areas. Since the 1950s, large-scale reclamation has been performed along the Shanghai coast, China, to fulfill the land demands of city expansion. We now show that the loss of tidal...

Scenario-based extreme flood risk of residential buildings and household properties in Shanghai

Extreme flooding usually causes huge losses of residential buildings and household properties, which is critical to flood risk analysis and flood resilience building in Shanghai. We developed a scenario-based multidisciplinary approach to analyze the exposure, losses and risks of residential buildings and household properties, and their...