Dynamic texture modeling and synthesis using multi-kernel Gaussian process dynamic model

2016 ◽  
Vol 124 ◽  
pp. 63-71 ◽  
Author(s):  
Ziqi Zhu ◽  
Xinge You ◽  
Shujian Yu ◽  
Jixin Zou ◽  
Haiquan Zhao
Keyword(s):  
Gaussian Process ◽  
Dynamic Model ◽  
Dynamic Texture ◽  
Process Dynamic ◽  
Texture Modeling

Spatially recursive estimation and Gaussian process dynamic models of bat flapping flight

2018 ◽  
Vol 95 (1) ◽  
pp. 217-237 ◽  
Author(s):  
Matt Bender ◽  
Li Tian ◽  
Xiaozhou Fan ◽  
Andrew Kurdila ◽  
Rolf Müller
Keyword(s):  
Gaussian Process ◽  
Dynamic Models ◽  
Flapping Flight ◽  
Recursive Estimation ◽  
Process Dynamic

Dynamic Texture Modeling Applied on Computer Vision Based Fire Recognition

2011 ◽  
pp. 545-553
Author(s):  
Yang Zhao ◽  
Jianhui Zhao ◽  
Erqian Dong ◽  
Bingyu Chen ◽  
Jun Chen ◽  
...  
Keyword(s):  
Computer Vision ◽  
Dynamic Texture ◽  
Texture Modeling ◽  
Fire Recognition

AXIAL-FLOW BLOOD PUMP ACCELERATION CONTROL METHOD BASED ON A START-UP PROCESS DYNAMIC MODEL

2017 ◽  
Vol 41 (5) ◽  
pp. 691-705
Author(s):  
Xu Yan ◽  
Pang Youxia ◽  
Cheng Lizhi ◽  
Liang Liang
Keyword(s):  
Dynamic Model ◽  
Control Method ◽  
Axial Flow ◽  
Test System ◽  
Blood Pump ◽  
Theoretical Simulation ◽  
Process Dynamic ◽  
Axial Flow Blood Pump ◽  
Start Up ◽  
Set Up

The principle of a large gap magnetic drive system was used to achieve control of an axial-flow blood pump. A dynamic model of the start-up process of the axial-flow blood pump was established. It was analyzed and simulated. An acceleration control method for the blood pump was proposed based on the start-up process dynamic model. A corresponding parameter measurement test system was set up, and experimental data were compared with the results of the theoretical simulation. Results indicated that the experimental values obtained for the blood pump outlet pressure and flow rate changed similarly with the values obtained using theoretical simulation. These changes occurred simultaneously with the change in speed of the blood pump over time, and the driving control target value was reached within 4 seconds.

scholarly journals Avoiding tipping points in fisheries management through Gaussian process dynamic programming

2015 ◽  
Vol 282 (1801) ◽  
pp. 20141631 ◽  
Author(s):  
Carl Boettiger ◽  
Marc Mangel ◽  
Stephan Munch
Keyword(s):  
Dynamic Programming ◽  
Model Selection ◽  
Gaussian Process ◽  
Model Uncertainty ◽  
Single Species ◽  
Stochastic Dynamic ◽  
Tipping Points ◽  
Limited Data ◽  
Process Dynamic ◽  
Access To Data

Model uncertainty and limited data are fundamental challenges to robust management of human intervention in a natural system. These challenges are acutely highlighted by concerns that many ecological systems may contain tipping points, such as Allee population sizes. Before a collapse, we do not know where the tipping points lie, if they exist at all. Hence, we know neither a complete model of the system dynamics nor do we have access to data in some large region of state space where such a tipping point might exist. We illustrate how a Bayesian non-parametric approach using a Gaussian process (GP) prior provides a flexible representation of this inherent uncertainty. We embed GPs in a stochastic dynamic programming framework in order to make robust management predictions with both model uncertainty and limited data. We use simulations to evaluate this approach as compared with the standard approach of using model selection to choose from a set of candidate models. We find that model selection erroneously favours models without tipping points, leading to harvest policies that guarantee extinction. The Gaussian process dynamic programming (GPDP) performs nearly as well as the true model and significantly outperforms standard approaches. We illustrate this using examples of simulated single-species dynamics, where the standard model selection approach should be most effective and find that it still fails to account for uncertainty appropriately and leads to population crashes, while management based on the GPDP does not, as it does not underestimate the uncertainty outside of the observed data.

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