This paper investigates the theoretical foundations of the t-distributed stochastic neighbor
embedding (t-SNE) algorithm, a popular nonlinear dimension reduction and data …

This paper presents an intelligent human PC intuitive framework. In this proposed work,
artificial intelligence is utilized for home mechanization, which perceives human motions …

Intelligent fault diagnosis methods, especially convolutional neural network (CNN), have
made significant progress in gas-insulated switchgear (GIS) partial discharge (PD) …

Abstract Software Defined Networking (SDN) decouples the control plane and the data
plane and solves the difficulty of new services deployment. However, the threat of a single …

It is a great challenge to differentiate partial discharge (PD) induced by different types of
insulation defects in high-voltage cables. Some types of PD signals have very similar …

Graph convolutional networks (GCNs) emerge as the most successful learning models for
graph-structured data. Despite their success, existing GCNs usually ignore the entangled …

Dimension reduction is an indispensable part of modern data science, and many algorithms
have been developed. However, different algorithms have their own strengths and …

A novel method for human action recognition using a deep learning network with features
optimized using particle swarm optimization is proposed. The binary histogram, Harris …

Recently, numerous data‐driven fault diagnosis methods have been developed, and the
tasks involving the same distribution of training and test data have been well solved …

Random splitting strategy is a common approach for training, testing, and validating object
detection algorithms based on deep learning. Is common for datasets to have images …