Deep neural networks (DNNs) offer a means of addressing the challenging task of clustering
high-dimensional data. DNNs can extract useful features, and so produce a lower …

In complex environments where occupied and unknown areas exceed the free space, it is
essential for robots to utilize efficient methods for environmental perception, trajectory …

Robotic calligraphy, as a typical application of robot movement planning, is of great
significance for the inheritance and education of calligraphy culture. The existing …

Deep clustering has attracted plentiful attention in various domains owning to the superior
performance. However, the previous deep clustering methods are guided by pre-specified …

This letter presents an integrated micromanipulation system suited for precise assembly of
micro-parts in intricate environments. By embedding the Global Attention Mechanism (GAM) …

Purpose The purpose of this paper is to enhance the performance of robots in peg-in-hole
assembly tasks, enabling them to swiftly and robustly accomplish the task. It also focuses on …

Traditional strength and conditioning training relies on the utilization of free weights, such as
weighted implements, to elicit external stimuli. However, this approach poses a significant …

Variational autoencoder (VAE) and generative adversarial networks (GAN) have found
widespread applications in clustering and have achieved significant success. However, the …

The Generative Adversarial Network (GAN) was recently introduced in the literature as a
novel machine learning method for training generative models. It has many applications in …

The divide between continuous and discrete data is a fundamental one in computer science
and mathematics, as well as related areas such as cognitive science. Historically, most of …