When Gaussian process meets big data: A review of scalable GPs
The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …
hardware encourages success stories in the machine learning community. In the …
Machinery health prognostics: A systematic review from data acquisition to RUL prediction
Machinery prognostics is one of the major tasks in condition based maintenance (CBM),
which aims to predict the remaining useful life (RUL) of machinery based on condition …
which aims to predict the remaining useful life (RUL) of machinery based on condition …
A review on prognostic techniques for non-stationary and non-linear rotating systems
MS Kan, ACC Tan, J Mathew - Mechanical Systems and Signal Processing, 2015 - Elsevier
The field of prognostics has attracted significant interest from the research community in
recent times. Prognostics enables the prediction of failures in machines resulting in benefits …
recent times. Prognostics enables the prediction of failures in machines resulting in benefits …
Extended target tracking using Gaussian processes
N Wahlström, E Özkan - IEEE Transactions on Signal …, 2015 - ieeexplore.ieee.org
In this paper, we propose using Gaussian processes to track an extended object or group of
objects, that generates multiple measurements at each scan. The shape and the kinematics …
objects, that generates multiple measurements at each scan. The shape and the kinematics …
COVID-19 mortality rate prediction for India using statistical neural network models
S Dhamodharavadhani, R Rathipriya… - Frontiers in Public …, 2020 - frontiersin.org
The primary aim of this study is to investigate suitable Statistical Neural Network (SNN)
models and their hybrid version for COVID-19 mortality prediction in Indian populations and …
models and their hybrid version for COVID-19 mortality prediction in Indian populations and …
A fast kriging-assisted evolutionary algorithm based on incremental learning
Kriging models, also known as Gaussian process models, are widely used in surrogate-
assisted evolutionary algorithms (SAEAs). However, the cubic time complexity of the …
assisted evolutionary algorithms (SAEAs). However, the cubic time complexity of the …
Online learning‐based model predictive control with Gaussian process models and stability guarantees
M Maiworm, D Limon… - International Journal of …, 2021 - Wiley Online Library
Abstract Model predictive control allows to provide high performance and safety guarantees
in the form of constraint satisfaction. These properties, however, can be satisfied only if the …
in the form of constraint satisfaction. These properties, however, can be satisfied only if the …
Modeling and interpolation of the ambient magnetic field by Gaussian processes
Anomalies in the ambient magnetic field can be used as features in indoor positioning and
navigation. By using Maxwell's equations, we derive and present a Bayesian nonparametric …
navigation. By using Maxwell's equations, we derive and present a Bayesian nonparametric …
Robust leak localization in water distribution networks using computational intelligence
The search for new strategies for leak detection, estimation and localization in Water
Distributions Networks (WDNs) is a state-of-the-art research topic. In this paper, a …
Distributions Networks (WDNs) is a state-of-the-art research topic. In this paper, a …
Gaussian process-based real-time learning for safety critical applications
The safe operation of physical systems typically relies on high-quality models. Since a
continuous stream of data is generated during run-time, such models are often obtained …
continuous stream of data is generated during run-time, such models are often obtained …