Gaussian process modelGuided waveThe particle filter (PF) has shown great potential for on-line fatigue crack growth prognosis by combining crack measurements from structural health monitoring (SHM) techniques. In this method, a key problem is to construct the mapping between the feature extracted ...
A new prognostics method for state of health estimation of lithium-ion batteries based on a mixture of Gaussian process models and particle filter Microelectron. Reliab., 55 (7) (2015), pp. 1035-1045 View PDFView articleView in ScopusGoogle Scholar [16] K.P. Murphy Machine Learning: a Pro...
Example comparison of interpolation techniques to reconstruct the radiation field from a point source at the origin from, irregularly spaced uncertain observation data (a), using linear interpolation (b), minimum curvature thin-plate splines (c), and Gaussian Process Regression (d). (All plots gene...
Gaussian process regression with neural network GPR: Gaussian process regression PF: Particle Filtering SVM: Support Vector Machine RVM: Relevant Vector Machine EKF: Extended Kalman filter RMSE: Root mean square error MAPE: Mean absolute percentage error. Conflicts of Interest The...
State of health (SOH) prediction of supercapacitors aims to provide reliable lifetime control and avoid system failure. Gaussian process regression (GPR) has emerged for SOH prediction because of its capability of capturing nonlinear relationships betwee
In this section, adaptive residual resample method is proposed which is simple but effective and compensates some support particles to improve residual resampling process. The steps of adaptive residual resample are: { }(1) For the particles ski , wki N i =1 , all weights are multiplied by ...
Radmehr, A., Ghasemi, A.: Error concealment via particle filter by Gaussian mixture modeling of motion vectors for H.264/AVC. Signal Image Video Process 10(2), 311–318 (2016) Article Google Scholar Lin, T.L., Wei, X., Wei, X., Su, T.H., Chiang, Y.L.: Novel pixel recovery...
This paper is concerned with a state-space approach to deep Gaussian process (DGP) regression. We construct the DGP by hierarchically putting transformed G
Gaussian process (GP) is a stochastic process that assumes a joint Gaussian distribution over all variables and, thus, a distribution over functions. From: Mechanical Systems and Signal Processing, 2023 About this pageSet alert Discover other topics On this page Definition Chapters and Articles Relat...
In [20], a recurrent neural network (RNN) with particle swarm optimization is also used to estimate the battery capacity. In [21], methods of Gaussian process regression (GPR) are used to estimate the battery capacity for which a GPR model is learned using the capacity, voltage and ...