IEEE

Three-Dimensional Urban Airflow Prediction Method Based on Space-Filling Curve Autoencoder

Abstract: Air flow prediction in urban is of great significance to various industries, and it is also a complex engineering problem. Using the traditional computational fluid dynamics method to ...
IEEE

A Hybrid Prognostic Approach Combining a Stacked Multihead Self-Attention Autoencoder and Multimodal Relevance Sampling Particle Filter

Abstract: Accurate prediction of the remaining useful life (RUL) of bearings is critical for optimizing maintenance strategies, preventing unexpected failures, and enhancing repair management. Despite ...
IEEE

Learning Causal Representations Based on a GAE Embedded Autoencoder

Abstract: Traditional machine-learning approaches face limitations when confronted with insufficient data. Transfer learning addresses this by leveraging knowledge from closely related domains. The ...
IEEE

CAN: Classification-Based Autoencoder Network for Enhanced Out-of-Distribution Detection in Radar-Based Hand Gesture Recognition

Abstract: Out-of-distribution (OOD) detection is essential for the robustness of radar-based gesture recognition systems. This paper proposes Classification-Based Autoencoder Network (CAN), as a novel ...
IEEE

Autoencoder for 4-Dimensional Fiber Orientation Distributions from Diffusion MRI

Abstract: Fiber orientation distributions (FODs) are widely used in connectome analysis based on diffusion MRI. Spherical har-monics (SPHARMs) are often used for the efficient repre-sentation of FODs; ...
IEEE

Reinforcement Learning and Spatio-Temporal Graph Neural Networks for Alzheimer's Disease Progression Prediction with Variational Autoencoder-Based Data Imputation

Abstract: Early detection and accurate modeling of Alzheimer's disease (AD) progression are still significant challenges because of its intricate neurodegenerative patterns. Traditional deep learning ...
IEEE

A Radio Map Estimation Method Based on Improved Conditional Variational Autoencoder

Abstract: A radio map is a specialized representation that depicts the power spectral density across a specific region. By leveraging electromagnetic maps, the efficient utilization of spectrum ...
IEEE

Autoencoder Aided Semi-supervised Incremental Learning of Handwritten Characters

Abstract: Incremental learning is an important feature of next generation machine learning systems - it is about learning new classes incrementally by training on labeled images of the new classes.
IEEE

A Reconstruction-Inversion Method of Atmospheric Duct Parameters Using ADS-B Signals Based on Autoencoder and ResNet18

Abstract: Low-altitude atmospheric ducts are anomalous atmospheric phenomena in the troposphere, significantly affecting the operation of microwave or ultrashort-wave radio systems. Therefore, an ...
IEEE

Denoising Autoencoder for ECG Signals Based on Frequency Domain Alignment and Multi-Scale Transformer

Abstract: Electrocardiogram (ECG) signals are widely utilized for cardiovascular disease monitoring. However, these signals are often susceptible to various types of noise during acquisition, which ...
IEEE

Unsupervised Contrastive Hashing With Autoencoder Semantic Similarity for Cross-Modal Retrieval in Remote Sensing

Abstract: In large-scale multimodal remote sensing data archives, the application of cross-modal technology to achieve fast retrieval between different modalities has attracted great attention. In ...
IEEE

Advancing Handwritten Digit Recognition in Defense Systems: Comparative Analysis of Autoencoder-Based Transfer Learning

Abstract: The presence of labelled data is limited while the unlabelled data is present in abundance. Developing well annotated datasets is a challenging task and it requires lot of computation. These ...