IEEE

A 3D Mesh Convolution-Based Autoencoder for Geometry Compression

Abstract: In this paper, we introduce a novel 3D mesh convolution-based autoencoder for geometry compression, able to deal with irregular mesh data without requiring neither preprocessing nor manifold ...
Scientific Research Publishing

Ma, J., Zhao, J. and Hou, Y. (2024) Spatial-Temporal Transformer Networks for Traffic Flow Forecasting Using a Pre-Trained Language Model. Sensors, 24, Article No. 5502.

ABSTRACT: Traffic prediction is the core of intelligent transportation system, and accurate traffic speed prediction is the key to optimize traffic management. Currently, the traffic speed prediction ...
Frontiers

Deep learning-based automated segmentation for the quantitative diagnosis of cerebral small vessel disease via multisequence MRI

Objective: Existing visual scoring systems for cerebral small vessel disease (CSVD) cannot assess the global lesion load accurately and quantitatively. We aimed to develop an automated segmentation ...
Frontiers

An abnormal traffic detection method for chain information management system network based on convolutional neural network

Chain information management system is widely used, providing convenience for the operation and management of enterprises. However, the problem of abnormal network traffic becomes increasingly ...
Scientific Research Publishing

A Combination Method of Stacked Convolutional Auto-Encoder and Selective Kernel Attention Mechanism for Image Classification ()

1 College of Information Engineering, Xinchuang Software Industry Base, Yancheng Teachers University, Yancheng, China. 2 Yancheng Agricultural College, Yancheng, China. Convolutional auto-encoders ...
Oak Ridge National Lab

Convolutional Variational Autoencoder-based Unsupervised Learning for Power Systems Faults

Classification of power system event data is a growing need, particularly where non-protective relaying-based sensors are used to monitor grid performance. Given the high burden of obtaining event ...
Visual Studio Magazine

Data Anomaly Detection Using a Neural Autoencoder with C#

Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that are different in some way from the majority of the source items. Data anomaly ...