Due to rapid development of sensor technology, human activity recognition (HAR) using
wearable inertial sensors has recently become a new research hotspot. Deep learning …

Coal-fired power generation boilers are susceptible to low combustion efficiency, pollutant
exceedance, and over-temperature tube burst during deep peak shaving due to load …

We describe an explainable AI saliency map method for use with deep convolutional neural
networks (CNN) that is much more efficient than popular fine-resolution gradient methods. It …

A novel channel-selection convolutional neural network (CS-CNN) is proposed to predict
NOx emission from coal-fired boilers under steady-state and transient load conditions. First …

Recently, deep learning has achieved great success in visual tracking tasks, particularly in
single-object tracking. This paper provides a comprehensive review of state-of-the-art single …

Recently, the state-of-the-art performance in various sensor based human activity
recognition (HAR) tasks have been acquired by deep learning, which can extract …

As a pre-processing step of computer vision applications, single image dehazing remains
challenging due to existing inefficiencies in the restoration of content and details. In this …

Recent advances in off-policy deep reinforcement learning (RL) have led to impressive
success in complex tasks from visual observations. Experience replay improves sample …

Recently, learning-based image compression has attracted a lot of attention, leading to the
development of a new JPEG AI standard based on neural networks. Typically, this type of …

To address the low accuracy of current one-dimensional signal recognition for coal-rock
cutting vibration and the low efficiency of traditional static neural networks, this paper …