Yingze Yang

Abstract Accurate prediction of battery capacity degradation is critical for the continuous operation of wearable medical devices and patient safety. However, batteries in these devices often show complex, nonlinear degradation patterns caused by irregular charging cycles and intermittent high loads. To address this, we propose a novel framework that integrates deep temporal decomposition with autoregressive fine-tuning.

1. Introduction The electropneumatic brake system is crucial for ensuring the safe operation of trains. One critical issue is implementing the fast and precise pressure tracking control of the brake cylinder in the electropneumatic brake system [1]. However, some intrinsic characteristics significantly challenge the reliable and robust pressure control of the electropneumatic brake system.

1. Introduction Virtual coupling systems have emerged as a promising technology to improve the transportation efficiency and capacity of heavy-haul trains []. Cooperative control has been extensively used in virtual coupling systems to coordinate the movements of multiple trains and maintain safe separation distances and avoid potential conflicts [,].