Results 1 to 10 of about 65,018 (270)

An Autoencoder‐Based Deep‐Learning Method for Augmenting the Sensing Capability of Piezoelectric Microelectromechanical System Sensors in a Fluid‐Dynamic System

Advanced Intelligent Systems
Herein, an innovative deep‐learning architecture is proposed to enhance the sensing capabilities of a microelectromechanical system (MEMS) used in fluid dynamic applications.
Mohammadrahim Kazemzadeh, Iman Mehdipour, Massimo De Vittorio, Ferruccio Pisanello   +3 more
doaj   +1 more source

Convolution Neural Network Development for Identifying Damage in Vibrating Pylons with Mass Attachments

Sensors
A convolution neural network (CNN) is developed in this work to detect damage in pylons by measuring their vibratory response. More specifically, damage detection through testing relies on the development of damage-sensitive indicators, which are then ...
George D. Manolis, Georgios I. Dadoulis
doaj   +1 more source

Reconstructing attractors with autoencoders

Chaos: An Interdisciplinary Journal of Nonlinear Science
We propose a method based on autoencoders to reconstruct attractors from recorded footage, preserving the topology of the underlying phase space. We provide theoretical support and test the method with (i) footage of the temperature and stream function fields involved in the Lorenz atmospheric convection problem and (ii) a time series obtained by ...
F. Fainstein, G. B. Mindlin, P. Groisman
openaire   +4 more sources

Knee Osteoarthritis Detection and Classification Using Autoencoders and Extreme Learning Machines

AI
Background/Objectives: Knee osteoarthritis (KOA) is a prevalent disorder affecting both older adults and younger individuals, leading to compromised joint function and mobility.
Jarrar Amjad, Muhammad Zaheer Sajid, Ammar Amjad, Muhammad Fareed Hamid, Ayman Youssef, Muhammad Irfan Sharif   +5 more
doaj   +1 more source

Autoencoder

, 2021
Dimitrios Toumpanakis, Edgar Lorente, Candace Moore   +2 more
openaire   +1 more source

Unsupervised Real-Time Anomaly Detection in Hydropower Systems via Time Series Clustering and Autoencoders

Technologies
Hydropower plants generate large volumes of data with high-dimensional time series, making early anomaly detection essential for monitoring, preventive maintenance and cost reduction. This study addresses the challenge of detecting anomalies in real time
Ana I. Oviedo, John F. Vargas, Roberto C. Hincapie, Andres F. Molina, Edimerk A. Vergara, Diana M. Tello   +5 more
doaj   +1 more source

MONEY LAUNDERING DETECTION USING GRAPH NEURAL NETWORKS ENHANCED WITH AUTOENCODER COMPONENTS

Studia Universitatis Babes-Bolyai: Series Informatica
The paper addresses the topic of detecting money laundering operations in transaction data represented as graph data-structures. We propose the integration of autoencoder components in Graph Neural Networks (GNN) architectures, in order to incorporate a
Tudor-Ionuț GRAMA
doaj   +1 more source

Optimizing IoMT Security: Performance Trade-Offs Between Neural Network Architectural Design, Dimensionality Reduction, and Class Imbalance Handling

IoT
The proliferation of Internet of Medical Things (IoMT) devices in healthcare requires robust intrusion detection systems to protect sensitive data and ensure patient safety.
Heyfa Ammar, Asma Cherif
doaj   +1 more source

Machine learning approach to reconstruct density matrices from quantum marginals

Machine Learning: Science and Technology
In this work, we propose a machine learning (ML)-based approach to address a specific aspect of the Quantum Marginal Problem: reconstructing a global density matrix compatible with a given set of quantum marginals.
Daniel Uzcategui-Contreras, Antonio Guerra, Sebastian Niklitschek, Aldo Delgado   +3 more
doaj   +1 more source

Gaussian AutoEncoder

, 2018
Generative AutoEncoders require a chosen probability distribution in latent space, usually multivariate Gaussian. The original Variational AutoEncoder (VAE) uses randomness in encoder - causing problematic distortion, and overlaps in latent space for distinct inputs.
openaire   +2 more sources