Recently, a chat generative pre-trained transformer (ChatGPT) attracts widespread attention
in the academies and industries because of its powerful conversational ability with human …

Deep learning (DL) has demonstrated splendid performance in fault diagnosis with sufficient
samples and ideal operating environments. However, in practice, it is hard to acquire …

Modular supervisory control may lead to conflicts among the modular supervisors for large-
scale discrete event systems. The existing methods for ensuring nonblocking control of …

Deep Reinforcement Learning (DRL) has recently been focused on extracting more
knowledge from the reward signal to improve sample efficiency. The Multi-Reward …

Although deep reinforcement learning (DRL) methods are promising for making behavioral
decisions in autonomous vehicles (AVs), their low training efficiency and difficulty to adapt to …

Blood Glucose (BG) control involves keeping an individual's BG within a healthy range
through extracorporeal insulin injections is an important task for people with type 1 diabetes …

The emergence of sophisticated applications and vertical services results in ever more
complex mobile networks. Hence radio access network (RAN) slicing based on the …

In the cloud environment, task scheduling has always been a challenge. Failure to use a
proper scheduling approach in cloud computing may cause high energy consumption and …

Reinforcement learning (RL) is an experience-driven and data-driven learning method that
can well solve the lane-changing problems. However, because traditional RL methods rely …

Based on the background of a multi-agent game with limited attack and defense capabilities
and communication range, a game model is established to study the encirclement-combat …