TRAJECTORY PLANNING OF 6-DOF MANIPULATOR BASED ON GAUSSIAN PROCESS REGRESSION METHOD

2020 ◽  
Vol 35 (3) ◽  
Author(s):  
Jinzhi Zhao ◽  
Shizhao Wang ◽  
Aibing Jiang ◽  
Jin Xiao ◽  
Bin Wang
Keyword(s):  
Gaussian Process ◽  
Trajectory Planning ◽  
Gaussian Process Regression ◽  
Regression Method

Partitioned Gaussian Process Regression for Online Trajectory Planning for Autonomous Vehicles

2021 ◽  
Author(s):  
Pavlo G. Vlastos ◽  
Aaron Hunter ◽  
Renwick Curry ◽  
Carlos Isaac Espinosa Ramirez ◽  
Gabriel Elkaim
Keyword(s):  
Gaussian Process ◽  
Trajectory Planning ◽  
Autonomous Vehicles ◽  
Gaussian Process Regression

scholarly journals Gaussian Process Regression Tuned by Bayesian Optimization for Seawater Intrusion Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
George Kopsiaftis ◽  
Eftychios Protopapadakis ◽  
Athanasios Voulodimos ◽  
Nikolaos Doulamis ◽  
Aristotelis Mantoglou
Keyword(s):  
Gaussian Process ◽  
Seawater Intrusion ◽  
Surrogate Model ◽  
Gaussian Process Regression ◽  
Variable Density ◽  
Regression Method ◽  
Bayesian Optimization ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Pumping Rate

Accurate prediction of the seawater intrusion extent is necessary for many applications, such as groundwater management or protection of coastal aquifers from water quality deterioration. However, most applications require a large number of simulations usually at the expense of prediction accuracy. In this study, the Gaussian process regression method is investigated as a potential surrogate model for the computationally expensive variable density model. Gaussian process regression is a nonparametric kernel-based probabilistic model able to handle complex relations between input and output. In this study, the extent of seawater intrusion is represented by the location of the 0.5 kg/m3 iso-chlore at the bottom of the aquifer (seawater intrusion toe). The initial position of the toe, expressed as the distance of the specific line from a number of observation points across the coastline, along with the pumping rates are the surrogate model inputs, whereas the final position of the toe constitutes the output variable set. The training sample of the surrogate model consists of 4000 variable density simulations, which differ not only in the pumping rate pattern but also in the initial concentration distribution. The Latin hypercube sampling method is used to obtain the pumping rate patterns. For comparison purposes, a number of widely used regression methods are employed, specifically regression trees and Support Vector Machine regression (linear and nonlinear). A Bayesian optimization method is applied to all the regressors, to maximize their efficiency in the prediction of seawater intrusion. The final results indicate that the Gaussian process regression method, albeit more time consuming, proved to be more efficient in terms of the mean absolute error (MAE), the root mean square error (RMSE), and the coefficient of determination (R2).

scholarly journals Research on Remaining Useful Life Prognostics Based on Fuzzy Evaluation-Gaussian Process Regression Method

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 71965-71973 ◽  
Author(s):  
Weijie Kang ◽  
Jiyang Xiao ◽  
Mingqing Xiao ◽  
Yangguang Hu ◽  
Haizhen Zhu ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Regression Method ◽  
Remaining Useful Life ◽  
Fuzzy Evaluation ◽  
Useful Life
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