Results 71 to 80 of about 3,896 (184)

Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using AutoEncoder Models

Applied Sciences
This study presents a comparative analysis of three AutoEncoder (AE) models—Variational AutoEncoder (VAE), Sparse AutoEncoder (SAE), and Convolutional AutoEncoder (CAE)—to detect and quantify structural anomalies in railway vehicle wheels, such as ...
Renato Melo, Rafaelle Finotti, António Guedes, Vítor Gonçalves, Andreia Meixedo, Diogo Ribeiro, Flávio Barbosa, Alexandre Cury   +7 more
doaj   +1 more source

Attention and Geological Knowledge Guided Spectral‐Spatial Networks for Geochemical Anomalies Recognition

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract Achieving both accuracy and interpretability in deep learning models for geochemical anomaly recognition constitutes a significant challenge. To overcome this challenge, this study developed a novel interpretable dual‐branch network combining a spectral attention bidirectional RNN (BiRNN) branch and a spatial attention CNN branch guided with ...
Yihui Xiong, Zhixiang Liang, Zhiyi Chen, Yao Fan, Renguang Zuo   +4 more
wiley   +1 more source

AE-MoSE: an AutoEncoder Mixture of Spatial Experts for Geodemographic Classification

This work introduces a novel approach to geodemographic classification that combines an AutoEncoder architecture with a Mixture of Experts framework, incorporating a Graph Neural Network into the gating component to create a Mixture of Spatial Experts approach (AE-MoSE).
De Sabbata, Stef, Liu, Pengyuan, Goodwin, Owen, Singleton, Alex   +3 more
openaire   +2 more sources

Comparative analysis of autoencoder architectures for breast cancer detection using dynamic infrared thermography

Engineering Science and Technology, an International Journal
Breast cancer is the most diagnosed cancer among women worldwide. Early detection substantially improves treatment outcomes, especially when lesions are small and localized.
Burcu Acar Demirci, Mehmet Engin, Erkan Zeki Engin   +2 more
doaj   +1 more source

Intelligent Fault Diagnosis of Gas Pressure Regulator Based on AE-GWO-SVM Algorithm

Automation
A pressure regulator is essential for pressure control in a gas transmission system. The traditional maintenance approaches for pressure regulators involve equipment disassembly that disrupts normal production.
Shunyuan Hu, Zhixiang Dai, Keqing Zhang, Jingjian Liu, Qing Wen, Yanhua Qiu, Weidong Li   +6 more
doaj   +1 more source

Evaluating Reconstruction-Based and Proximity-Based Methods: A Four-Way Comparison (AE, LSTM-AE, OCSVM, IF) in SCADA Anomaly Detection Under Inverted Imbalance

Future Internet
This article investigates and compares four unsupervised anomaly detection algorithms: the Autoencoder (AE), LSTM-Autoencoder (LSTM-AE), One-Class SVM (OCSVM), and the Isolation Forest (IF). The analysis focuses on SCADA telemetry data from an urban wind
Lukasz Pawlik
doaj   +1 more source

Investigation of an Optimized Linear Regression Model with Nonlinear Error Compensation for Tool Wear Prediction

Machines
To solve the problem of insufficient accuracy in tool wear process modeling and Remaining Useful Life (RUL) estimation, this study proposes a two-stage prediction method.
Lihua Shen, Baorui Du, He Fan, Hailong Yang   +3 more
doaj   +1 more source

Spatially-aware unsupervised anomaly detection for soil stress monitoring using graph autoencoders in precision agriculture

Frontiers in Sustainable Food Systems
IntroductionEarly detection of crop stress in precision agriculture is hindered by complex multivariate sensor interactions, spatial variability across fields, and the scarcity of labeled anomaly data.
S. V. Sharvani, M. S. Suruthi, S. G. Rahul, T. M. Amirthalakshmi, M. Gayathri, D. Subitha   +5 more
doaj   +1 more source

Multi-view-AE: A Python package for multi-view autoencoder models

Journal of Open Source Software, 2023
Ana Lawry Aguila, Alejandra Jayme, Nina Montaña Brown, Vincent Heuveline, André Altmann   +4 more
openaire   +2 more sources

Ae$$^2$$I: A Double Autoencoder for Imputation of Missing Values

The most common strategy of imputing missing values in a table is to study either the column-column relationship or the row-row relationship of the data table, then use the relationship to impute the missing values based on the non-missing values from other columns of the same row, or from the other rows of the same column.
openaire   +2 more sources