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Deep variational auto-encoders: A promising tool for dimensionality reduction and ball bearing elements fault diagnosis
(SAGE Publications Ltd, 2019)
© The Author(s) 2018.One of the main challenges that the industry faces when dealing with massive data for failure diagnosis is high dimensionality of such data. This can be tackled by dimensionality reduction method such ...
Modelo para identificación de modos de falla de máquinas en base a variational Auto-Encoders
(Universidad de Chile, 2018)
Dentro del campo de la ingeniería mecánica, una de las áreas que más crecimiento ha mostrado en los últimos años es la de la gestión de activos físicos y confiabilidad. Junto con la capacidad de construir máquinas y sistemas ...
On the usage of generative models for network anomaly detection in multivariate time-series.
(ACM, 2020)
Despite the many attempts and approaches for anomaly detection explored over the years, the automatic detection of rare events in data communication networks remains a complex problem. In this paper we introduce Net-GAN, ...
DC-VAE, Fine-grained anomaly detection in multivariate time-series with dilated convolutions and variational auto encoders
(IEEE, 2022)
Due to its unsupervised nature, anomaly detection plays a central role in cybersecurity, in particular on the detection of unknown attacks. A major source of cybersecurity data comes in the form of multivariate time-series ...
Mining multivariate time-series for anomaly detection in mobile networks on the usage of variational auto encoders and dilated convolutions
(ACM, 2022)
The automatic detection of anomalies in communication networks plays a central role in network management. Despite the many attempts and approaches for anomaly detection explored in the past, the detection of rare events ...
Semi-supervised learning with temporal variational auto-encoders for the diagnosis of failure severities and the prognosis of remaining useful life
(Universidad de Chile, 2020)
Dentro del manejo de activos físicos, una de las áreas que más ha crecido en los últimos años
es la investigación y aplicación de técnicas de relacionadas a la inteligencia artificial y modelos
de aprendizaje profundo ...
Steps towards continual learning in multivariate time-series anomaly detection using variational autoencoders
(ACM, 2022)
We present DC-VAE, an approach to network anomaly detection in multivariate time-series (MTS), using Variational Auto Encoders (VAEs) and Dilated Convolutional Neural Networks (CNN). DC-VAE detects anomalies in MTS data ...
Sistema de controle de velocidade sincronizada entre dois veículos agrícolasSynchronized speed control system between two agricultural vehicles
(Universidade Federal de Santa Maria, 2012)
Deep Learning Methods for Forecasting COVID-19 Time-Series Data: A Comparative Study
The novel coronavirus (COVID-19) has significantly spread over the world and comes up with new challenges to the research community. Although governments imposing numerous containment and social distancing measures, the ...
Bayesian plug & play methods for inverse problems in imaging.
(Université de Paris : Udelar.FI., 2021)
This thesis deals with Bayesian methods for solving ill-posed inverse problems in imaging with learnt image priors. The first part of this thesis (Chapter 3) concentrates on two particular problems, namely joint denoising ...