5G and beyond is expected to enable various emerging use cases with diverse performance requirements from vertical industries. To serve these cost-effectively, network slicing plays a key role in dynamically creating virtual end-to-end networks according specific resource demands. A slice may have hundreds of configurable parameters over multiple technical domains that define the slice, which m...

Abstract Gold immunochromatographic strip (GICS) is a widely used lateral flow immunoassay technique. A novel image segmentation method developed in this paper for quantitative analysis of GICS based on the deep reinforcement learning (DRL), which can accurately distinguish test line and control images. The belief network (DBN) employed Q our DRL algorithm. Meanwhile, multi-factor curve introdu...

Abstract The vehicle-mounted missile vertical thermal emission not only has excellent maneuverability, randomness, and concealment, but also a short response time, good versatility, high reliability, so it is widely used. In order to explore the gas flow field of missile, firstly, this study introduces theoretical basis deep reinforcement learning (DRL) algorithm wireless sensor network (WSN). ...

Conventional simulations on multi-exit indoor evacuation focus primarily how to determine a reasonable exit based numerous factors in changing environment. Results commonly include some congested and other under-utilized exits, especially with large numbers of pedestrians. We propose simulation deep reinforcement learning (DRL), referred as the MultiExit-DRL, which involves neural network (DNN)...