Real-Time and Accurate Segmentation of 3-D Point Clouds Based on Gaussian Process Regression

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2017.2685523 ◽

2017 ◽

Vol 18 (12) ◽

pp. 3363-3377 ◽

Author(s):

Myung-Ok Shin ◽

Gyu-Min Oh ◽

Seong-Woo Kim ◽

Seung-Woo Seo

Keyword(s):

Gaussian Process ◽

Real Time ◽

Gaussian Process Regression ◽

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Real-time property prediction for an industrial rubber-mixing process with probabilistic ensemble Gaussian process regression models

Journal of Applied Polymer Science ◽

10.1002/app.41432 ◽

2014 ◽

Vol 132 (6) ◽

pp. n/a-n/a ◽

Author(s):

Yi Liu ◽

Zengliang Gao

Keyword(s):

Gaussian Process ◽

Real Time ◽

Regression Models ◽

Gaussian Process Regression ◽

Mixing Process ◽

Property Prediction ◽

Time Property ◽

Industrial Rubber

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Sparse Gaussian process regression for compliant, real-time robot control

2015 IEEE International Conference on Robotics and Automation (ICRA) ◽

10.1109/icra.2015.7139547 ◽

2015 ◽

Author(s):

Jens Schreiter ◽

Peter Englert ◽

Duy Nguyen-Tuong ◽

Marc Toussaint

Keyword(s):

Gaussian Process ◽

Real Time ◽

Robot Control ◽

Gaussian Process Regression

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Real-time determination of optimal switching times for a H2 production process with CO2 capture using Gaussian Process Regression models

Computer Aided Chemical Engineering - 29th European Symposium on Computer Aided Process Engineering ◽

10.1016/b978-0-12-818634-3.50204-6 ◽

2019 ◽

pp. 1219-1224

Author(s):

Luca Zanella ◽

Marcella Porru ◽

Giulio Bottegal ◽

Fausto Gallucci ◽

Martin van Sint Annaland ◽

...

Keyword(s):

Gaussian Process ◽

Production Process ◽

Real Time ◽

Co2 Capture ◽

Regression Models ◽

Gaussian Process Regression ◽

H2 Production ◽

Optimal Switching ◽

Time Determination

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Real-time prediction and gating of respiratory motion in 3D space using extended Kalman filters and Gaussian process regression network

Physics in Medicine and Biology ◽

10.1088/0031-9155/61/5/1947 ◽

2016 ◽

Vol 61 (5) ◽

pp. 1947-1967 ◽

Author(s):

W Bukhari ◽

S-M Hong

Keyword(s):

Gaussian Process ◽

Real Time ◽

Kalman Filters ◽

Respiratory Motion ◽

Gaussian Process Regression ◽

Extended Kalman Filters ◽

Time Prediction ◽

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Real-time prediction and gating of respiratory motion using an extended Kalman filter and Gaussian process regression

Physics in Medicine and Biology ◽

10.1088/0031-9155/60/1/233 ◽

2014 ◽

Vol 60 (1) ◽

pp. 233-252 ◽

Author(s):

W Bukhari ◽

S-M Hong

Keyword(s):

Kalman Filter ◽

Gaussian Process ◽

Real Time ◽

Extended Kalman Filter ◽

Respiratory Motion ◽

Gaussian Process Regression ◽

Time Prediction

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Real-time rotor effective wind speed estimation using Gaussian process regression and Kalman filtering

Renewable Energy ◽

10.1016/j.renene.2021.01.040 ◽

2021 ◽

Vol 169 ◽

pp. 670-686

Author(s):

Wai Hou Lio ◽

Ang Li ◽

Fanzhong Meng

Keyword(s):

Gaussian Process ◽

Real Time ◽

Kalman Filtering ◽

Gaussian Process Regression ◽

Speed Estimation

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Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD '19 ◽

10.1145/3292500.3330695 ◽

2019 ◽

Author(s):

Ming Lin ◽

Xiaomin Song ◽

Qi Qian ◽

Hao Li ◽

Liang Sun ◽

...

Keyword(s):

Gaussian Process ◽

Real Time ◽

High Precision ◽

Gaussian Process Regression ◽

Signal Enhancement

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Communication-aware Distributed Gaussian Process Regression Algorithms for Real-time Machine Learning

2020 American Control Conference (ACC) ◽

10.23919/acc45564.2020.9147886 ◽

2020 ◽

Author(s):

Zhenyuan Yuan ◽

Minghui Zhu

Keyword(s):

Machine Learning ◽

Gaussian Process ◽

Real Time ◽

Gaussian Process Regression ◽

Time Machine ◽

Regression Algorithms

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Real-time model learning using Incremental Sparse Spectrum Gaussian Process Regression

Neural Networks ◽

10.1016/j.neunet.2012.08.011 ◽

2013 ◽

Vol 41 ◽

pp. 59-69 ◽

Author(s):

Arjan Gijsberts ◽

Giorgio Metta

Keyword(s):

Gaussian Process ◽

Real Time ◽

Gaussian Process Regression ◽

Model Learning ◽

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Data-driven method for real-time prediction and uncertainty quantification of fatigue failure under stochastic loading using artificial neural networks and Gaussian process regression

International Journal of Fatigue ◽

10.1016/j.ijfatigue.2021.106415 ◽

2021 ◽

pp. 106415

Author(s):

Maor Farid

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Gaussian Process ◽

Uncertainty Quantification ◽

Real Time ◽

Fatigue Failure ◽

Gaussian Process Regression ◽

Data Driven ◽

Time Prediction ◽

Stochastic Loading

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