In response to the 2023 mandate requiring electronic stability control (ESC) for trucks in South Korea, domestic manufacturers have called for a relaxation of the maximum safe slope angle to reduce production costs. However, limited research exists on the quantitative relationship between ESC implementation and vehicle rollover stability under relaxed safety standards. This study addresses this gap by conducting dynamic simulations of standardized rollover tests to evaluate the static stability factor (SSF) and by developing a machine-learning-based model for predicting rollover risk. The model incorporates planned path curvature and driving speed to compute lateral acceleration, which serves as a key input for predicting the lateral load transfer ratio (LTR), a critical indicator of vehicle stability. Among several models tested, the recurrent neural network (RNN) achieved the highest accuracy in LTR prediction. The results highlight the effectiveness of integrating data-driven models into dynamic stability assessment frameworks, offering practical insights for optimizing route planning and speed control—particularly in autonomous freight vehicle applications.
Rootkits are malicious programs designed to conceal their activities on compromised systems, making them challenging to detect using conventional methods. As the threat landscape continually evolves, rootkits pose a serious threat by stealthily concealing malicious activities, ma…
Predicting innovation outcomes at the firm level continues to be an important but challenging goal for researchers and practitioners alike. In this study, multiple machine learning models, encompassing both ensemble-based and single-model approaches, were applied to data from the…
The significant role of Li-ion batteries (LIBs) in electric vehicles (EVs) emphasizes their advantages in terms of energy density, being lightweight, and being environmentally sustainable. Despite their obstacles, such as costs, safety concerns, and recycling challenges, LIBs are…
Breast cancer is among the most prevalent cancers in the female population globally. Therefore, screening campaigns as well as approaches to identify patients at risk are particularly important for the early detection of suspect lesions. This study aims to propose a workflow for…
This study aimed to investigate whether running kinematics can be accurately estimated through an artificial neural network (ANN) model containing GPS-based accelerometer variables and anthropometric data. Thirteen male participants with extensive running experience completed tre…
Phubbing, defined as the disruption of social relationships and interactions due to excessive cell phone use, is becoming an increasing concern in modern society. Since one of the primary motivations for cell phone use is internet access, it is crucial to assess the time that ind…