A generic probabilistic framework for structural health prognostics and uncertainty management
Structural health prognostics can be broadly applied to various engineered artifacts in an
engineered system. However, techniques and methodologies for health prognostics become …
engineered system. However, techniques and methodologies for health prognostics become …
Propagation of uncertainty in bayesian kernel models-application to multiple-step ahead forecasting
The object of Bayesian modelling is predictive distribution, which, in a forecasting scenario,
enables evaluation of forecasted values and their uncertainties. We focus on reliably …
enables evaluation of forecasted values and their uncertainties. We focus on reliably …
Daily sea level prediction at Chiayi coast, Taiwan using extreme learning machine and relevance vector machine
The analysis and the prediction of sea level fluctuations are core requirements of marine
meteorology and operational oceanography. Estimates of sea level with hours-to-days …
meteorology and operational oceanography. Estimates of sea level with hours-to-days …
Modeling tunneling-induced ground surface settlement development using a wavelet smooth relevance vector machine
F Wang, B Gou, Y Qin - Computers and Geotechnics, 2013 - Elsevier
Accurate prediction of ground surface settlement is necessary for effectively controlling the
settlement that develops during tunneling. Many models have been established for this …
settlement that develops during tunneling. Many models have been established for this …
Multi-objective Optimization of water resources in real time based on integration of NSGA-II and support vector machines
One of the management strategies of water resources systems is the combination of
simulation and optimization models to achieve the optimal policies of reservoir operation in …
simulation and optimization models to achieve the optimal policies of reservoir operation in …
[PDF][PDF] Learning with uncertainty: Gaussian processes and relevance vector machines
J Quinonero-Candela - 2004 - pure.mpg.de
This thesis is concerned with Gaussian Processes (GPs) and Relevance Vector Machines
(RVMs), both of which are particular instances of probabilistic linear models. We look at both …
(RVMs), both of which are particular instances of probabilistic linear models. We look at both …
Probabilistic risk assessment of tunneling-induced damage to existing properties
F Wang, LY Ding, HB Luo, PED Love - Expert Systems with Applications, 2014 - Elsevier
There is an intrinsic risk associated with tunnel construction, particularly in urban areas
where a number of third party persons and properties are involved. Due to the limited …
where a number of third party persons and properties are involved. Due to the limited …
[HTML][HTML] Will Poland fulfill its coal commitment by 2030? An answer based on a novel time series prediction method
Y Li, H Zhang, Y Kang - Energy reports, 2020 - Elsevier
Coal accounted for around 80 percent of power production in Poland in 2018. Facing the
serious climate problem and pressure from all sides, Poland has laid out a long-term energy …
serious climate problem and pressure from all sides, Poland has laid out a long-term energy …
Gaussian kernel width optimization for sparse Bayesian learning
Sparse kernel methods have been widely used in regression and classification applications.
The performance and the sparsity of these methods are dependent on the appropriate …
The performance and the sparsity of these methods are dependent on the appropriate …
Sparse Bayesian modeling with adaptive kernel learning
Sparse kernel methods are very efficient in solving regression and classification problems.
The sparsity and performance of these methods depend on selecting an appropriate kernel …
The sparsity and performance of these methods depend on selecting an appropriate kernel …