Chuankun Li

Learn More
Motivated by the promising performance achieved by deep learning, an effective yet simple method is proposed to encode the spatio-temporal information of skeleton sequences into color texture images, referred to as joint distance maps (JDMs), and convolutional neural networks are employed to exploit the discriminative features from the JDMs for human action(More)
Convolutional Neural Networks (ConvNets) have recently shown promising performance in many computer vision tasks, especially image-based recognition. How to effectively apply ConvNets to sequence-based data is still an open problem. This paper proposes an effective yet simple method to represent spatio-temporal information carried in 3D skeleton sequences(More)
Recent methods based on 3D skeleton data have achieved outstanding performance due to its conciseness, robustness, and view-independent representation. With the development of deep learning, Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM)-based learning methods have achieved promising performance for action recognition. However, for(More)
In view of mechanism principles, dynamic model of rectifying tower, one of the most important unit operations in chemical industry, was built in this work, and abnormal simulation for some typical faults was performed. The constant-volume flash model was proposed and utilized to model each distillation stage. During the modeling procedure, MATLAB software(More)
  • 1