Abstract The Internet of Things (IoTs) expanded quickly, giving rise to numerous services,
apps, electronic devices with integrated sensors, and associated protocols, which are still …

In recent years, brain science and neuroscience have greatly propelled the innovation of
computer science. In particular, knowledge from the neurobiology and neuropsychology of …

This study proposes an adaptive image augmentation scheme using deep reinforcement
learning (DRL) to improve the performance of a deep learning-based automated optical …

Along with the continuous progress of lithium-ion batteries and the automotive industry, the
safety of battery-pack systems (BPSs) is gradually becoming a hot topic of concern for …

Abstract Although deep Reinforcement Learning (RL) has proven successful in a wide range
of tasks, one challenge it faces is interpretability when applied to real-world problems …

This paper proposes a alternate physics-informed neural network (PINN) with a two-stage
training strategy to solve the thermoelastic coupling in the growth of Czochralski silicon …

Uncertainty estimation plays an important role for future reliable deployment of deep
segmentation models in safety-critical scenarios such as medical applications. However …

Human driving decisions are the leading cause of road fatalities. Autonomous driving
naturally eliminates such incompetent decisions and thus can improve traffic safety and …

Internet of Things (IoT) devices are increasingly popular due to their wide array of
application domains. In IoT networks, sensor nodes are often connected in the form of a …

In order to overcome the problems of object detection in complex scenes based on the
YOLOv4-tiny algorithm, such as insufficient feature extraction, low accuracy, and low recall …