IEEE

PI-Based Set-Point Learning Control for Batch Processes With Unknown Dynamics and Nonrepetitive Uncertainties

Abstract: For industrial batch processes with unknown dynamics subject to nonrepetitive initial conditions and disturbances, this article develops a novel adaptive data-driven set-point learning ...
IEEE

GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Point Cloud Understanding via Self-supervised Learning

Abstract: Self-supervised learning of point cloud aims to leverage unlabeled 3D data to learn meaningful representations without reliance on manual annotations. However, current approaches face ...
IEEE

Clutter Detection in Automotive Radar Point Clouds Based on Deep Learning with Self-attention

Abstract: As radar can directly provide the velocity of the targets in autonomous driving and is known for the robustness against adverse weather conditions, it plays an important role in contrast to ...
IEEE

Eliminating Rogue Access Point Attacks in IoT: A Deep Learning Approach With Physical-Layer Feature Purification and Device Identification

Abstract: Wi-Fi plays an essential role in various emerging Internet of Things (IoT) services and applications in smart cities and communities, such as IoT access, data transmission, and intelligent ...
IEEE

A Content-Adaptive Hierarchical Deep Learning Model for Detecting Arbitrary-Oriented Road Surface Elements Using MLS Point Clouds

Abstract: Accurate and automatic detection of road surface element (such as road marking or manhole cover) information is the basis and key to many applications. To efficiently obtain the information ...
IEEE

SiT-MLP: A Simple MLP With Point-Wise Topology Feature Learning for Skeleton-Based Action Recognition

Abstract: Graph convolution networks (GCNs) have achieved remarkable performance in skeleton-based action recognition. However, previous GCN-based methods rely on elaborate human priors excessively ...
IEEE

Implementation of Cascade Learning using Apache Spark

Abstract: With the exponential growth of data, many technologies have also been developed to cope with the need to process such big dataset and generate meaningful information out of those dataset. To ...
IEEE

Learning Semantic-Aware Point-Line Features for Localization and Reconstruction

Abstract: High-precision image matching and localization technology in a 3D environment map is essential for many tasks, such as marine engineering detection, robotics, and autonomous navigation.
IEEE

Sonata: Self-Supervised Learning of Reliable Point Representations

Abstract: In this paper, we question whether we have a reliable self-supervised point cloud model that can be used for diverse 3D tasks via simple linear probing, even with limited data and minimal ...