Results 41 to 50 of about 30,442 (292)

Unsupervised Deep Learning for Structural Health Monitoring

Big Data and Cognitive Computing, 2023
In the last few decades, structural health monitoring has gained relevance in the context of civil engineering, and much effort has been made to automate the process of data acquisition and analysis through the use of data-driven methods.
Roberto Boccagna, Maurizio Bottini, Massimo Petracca, Alessia Amelio, Guido Camata   +4 more
doaj   +1 more source

AutoEncoder by Forest

Proceedings of the AAAI Conference on Artificial Intelligence, 2018
Auto-encoding is an important task which is typically realized by deep neural networks (DNNs) such as convolutional neural networks (CNN). In this paper, we propose EncoderForest (abbrv. eForest), the first tree ensemble based auto-encoder.
Ji Feng, Zhi-Hua Zhou
openaire   +3 more sources

Robust reduced-order machine learning modeling of high-dimensional nonlinear processes using noisy data

Digital Chemical Engineering
Autoencoder-based reduced-order machine learning models have been developed for modeling and predictive control of nonlinear chemical processes with high dimensionality such as discretization of reaction–diffusion processes.
Wallace Gian Yion Tan, Ming Xiao, Zhe Wu
doaj   +1 more source

Feature Extraction from Building Submetering Networks Using Deep Learning

Sensors, 2020
The understanding of the nature and structure of energy use in large buildings is vital for defining novel energy and climate change strategies. The advances on metering technology and low-cost devices make it possible to form a submetering network ...
Antonio Morán, Serafín Alonso, Daniel Pérez, Miguel A. Prada, Juan José Fuertes, Manuel Domínguez   +5 more
doaj   +1 more source

SAFEPA: An Expandable Multi-Pose Facial Expressions Pain Assessment Method

Applied Sciences, 2023
Accurately assessing the intensity of pain from facial expressions captured in videos is crucial for effective pain management and critical for a wide range of healthcare applications.
Thoria Alghamdi, Gita Alaghband
doaj   +1 more source

A Manifold Learning Perspective on Representation Learning: Learning Decoder and Representations without an Encoder

Entropy, 2021
Autoencoders are commonly used in representation learning. They consist of an encoder and a decoder, which provide a straightforward method to map n-dimensional data in input space to a lower m-dimensional representation space and back.
Viktoria Schuster, Anders Krogh
doaj   +1 more source

Multiresolution convolutional autoencoders

Journal of Computational Physics, 2023
20 pages, 11 ...
Yuying Liu 0010, Colin Ponce, Steven L. Brunton, J. Nathan Kutz   +3 more
openaire   +2 more sources

The Optimally Designed Deep Autoencoder-Based Compressive Sensing Framework for 1D and 2D Signals

IEEE Access
The capacity of Compressive Sensing (CS) to recreate original data from a limited number of samples has led to a surge in attention in recent years.
Irfan Ahmed, Lunchakorn Wuttisittikulkij, Aftab Khan, Abid Iqbal   +3 more
doaj   +1 more source

An Introduction to Autoencoders

CoRR, 2022
In this article, we will look at autoencoders. This article covers the mathematics and the fundamental concepts of autoencoders. We will discuss what they are, what the limitations are, the typical use cases, and we will look at some examples. We will start with a general introduction to autoencoders, and we will discuss the role of the activation ...
openaire   +3 more sources

Fast and Effective Techniques for LWIR Radiative Transfer Modeling: A Dimension-Reduction Approach

Remote Sensing, 2019
The increasing spatial and spectral resolution of hyperspectral imagers yields detailed spectroscopy measurements from both space-based and airborne platforms.
Nicholas Westing, Brett Borghetti, Kevin C. Gross   +2 more
doaj   +1 more source