Sparse Gaussian process model with mixed covariance function for uncertainty quantification

2021 ◽  
Author(s):  
kai cheng ◽  
Zhenzhou Lu ◽  
Sinan Xiao ◽  
Sergey Oladyshkin ◽  
Wolfgang Nowak
Keyword(s):  
Gaussian Process ◽  
Uncertainty Quantification ◽  
Process Model ◽  
Covariance Function ◽  
Gaussian Process Model

scholarly journals Gaussian Process Model-Based Performance Uncertainty Quantification of a Typical Turboshaft Engine

2021 ◽  
Vol 11 (18) ◽  
pp. 8333
Author(s):  
Xuejun Liu ◽  
Hailong Tang ◽  
Xin Zhang ◽  
Min Chen
Keyword(s):  
Gaussian Process ◽  
Uncertainty Quantification ◽  
Process Model ◽  
Engine Performance ◽  
Latin Hypercube Sampling ◽  
Sampling Errors ◽  
Gaussian Process Model ◽  
Turboshaft Engine ◽  
The Impact ◽  
Performance Uncertainty

The gas turbine engine is a widely used thermodynamic system for aircraft. The demand for quantifying the uncertainty of engine performance is increasing due to the expectation of reliable engine performance design. In this paper, a fast, accurate, and robust uncertainty quantification method is proposed to investigate the impact of component performance uncertainty on the performance of a classical turboshaft engine. The Gaussian process model is firstly utilized to accurately approximate the relationships between inputs and outputs of the engine performance simulation model. Latin hypercube sampling is subsequently employed to perform uncertainty analysis of the engine performance. The accuracy, robustness, and convergence rate of the proposed method are validated by comparing with the Monte Carlo sampling method. Two main scenarios are investigated, where uncertain parameters are considered to be mutually independent and partially correlated, respectively. Finally, the variance-based sensitivity analysis is used to determine the main contributors to the engine performance uncertainty. Both approximation and sampling errors are explained in the uncertainty quantification to give more accurate results. The final results yield new insights about the engine performance uncertainty and the important component performance parameters.

scholarly journals Gaussian Process Autoregression for Joint Angle Prediction Based on sEMG Signals

2021 ◽  
Vol 9 ◽  
Author(s):  
Jie Liang ◽  
Zhengyi Shi ◽  
Feifei Zhu ◽  
Wenxin Chen ◽  
Xin Chen ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Probabilistic Model ◽  
Autoregressive Model ◽  
Process Model ◽  
Joint Angle ◽  
Gaussian Process Model ◽  
The Mean ◽  
Test Scenarios ◽  
Semg Signals ◽  
Non Parametric

There is uncertainty in the neuromusculoskeletal system, and deterministic models cannot describe this significant presence of uncertainty, affecting the accuracy of model predictions. In this paper, a knee joint angle prediction model based on surface electromyography (sEMG) signals is proposed. To address the instability of EMG signals and the uncertainty of the neuromusculoskeletal system, a non-parametric probabilistic model is developed using a Gaussian process model combined with the physiological properties of muscle activation. Since the neuromusculoskeletal system is a dynamic system, the Gaussian process model is further combined with a non-linear autoregressive with eXogenous inputs (NARX) model to create a Gaussian process autoregression model. In this paper, the normalized root mean square error (NRMSE) and the correlation coefficient (CC) are compared between the joint angle prediction results of the Gaussian process autoregressive model prediction and the actual joint angle under three test scenarios: speed-dependent, multi-speed and speed-independent. The mean of NRMSE and the mean of CC for all test scenarios in the healthy subjects dataset and the hemiplegic patients dataset outperform the results of the Gaussian process model, with significant differences (p < 0.05 and p < 0.05, p < 0.05 and p < 0.05). From the perspective of uncertainty, a non-parametric probabilistic model for joint angle prediction is established by using Gaussian process autoregressive model to achieve accurate prediction of human movement.

A hybrid Gaussian process model for system reliability analysis

2020 ◽  
Vol 197 ◽  
pp. 106816 ◽  
Author(s):  
Meng Li ◽  
Mohammadkazem Sadoughi ◽  
Zhen Hu ◽  
Chao Hu
Keyword(s):  
Gaussian Process ◽  
Reliability Analysis ◽  
System Reliability ◽  
Process Model ◽  
Gaussian Process Model ◽  
System Reliability Analysis
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