Text-to-image generative models such as Stable Diffusion and DALL• E raise many ethical
concerns due to the generation of harmful images such as Not-Safe-for-Work (NSFW) ones …

In this paper, an event-triggered control scheme with periodic characteristic is developed for
nonlinear discrete-time systems under an actor–critic architecture of reinforcement learning …

In recent years, the integration of intelligent industrial process monitoring, quality prediction,
and predictive maintenance solutions has garnered significant attention, driven by rapid …

Safety and stability are common requirements for robotic control systems; however,
designing safe, stable controllers remains difficult for nonlinear and uncertain models. We …

This paper investigates the deep reinforcement learning based secure control problem for
cyber–physical systems (CPS) under false data injection attacks. We describe the CPS …

Reinforcement learning with safety constraints is promising for autonomous vehicles, of
which various failures may result in disastrous losses. In general, a safe policy is trained by …

The rise of mass production and the resulting accumulation of end-of-life (EoL) products
present a growing challenge in waste management and highlight the need for efficient …

The problem of learning-based control for robots has been extensively studied, whereas the
security issue under malicious adversaries has not been paid much attention to. Malicious …

This paper presents a novel model-reference reinforcement learning algorithm for the
intelligent tracking control of uncertain autonomous surface vehicles with collision …

In this paper, we introduce a new reinforcement learning (RL) based neural architecture
search (NAS) methodology for effective and efficient generative adversarial network (GAN) …