The Problem of Planning with Three Sources of Uncertainty

2003 ◽  
pp. 73-84
Author(s):  
Paolo Traverso
Keyword(s):  
Sources Of Uncertainty

Glacial Contributions to 21st Century Sea Level Rise

Eos ◽  
2020 ◽  
Vol 101 ◽  
Author(s):  
Kate Wheeling
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
21St Century ◽  
Mass Change ◽  
Sources Of Uncertainty

Researchers identify the main sources of uncertainty in projections of global glacier mass change, which is expected to add about 8–16 centimeters to sea level, through this century.

Sources of uncertainty in knowledge

1951 ◽  
Vol 37 (2) ◽  
pp. 156-156
Author(s):  
Alexander Hamilton
Keyword(s):  
Sources Of Uncertainty

scholarly journals Sensor Modeling for Underwater Localization Using a Particle Filter

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1549
Author(s):  
Humberto Martínez-Barberá ◽  
Pablo Bernal-Polo ◽  
David Herrero-Pérez
Keyword(s):  
Sequential Monte Carlo ◽  
Sensory Information ◽  
Dead Reckoning ◽  
Underwater Vehicle ◽  
Position Estimation ◽  
Sources Of Uncertainty ◽  
Sensor Signals ◽  
Underwater Navigation ◽  
Underwater Localization ◽  
Sensor Modeling

This paper presents a framework for processing, modeling, and fusing underwater sensor signals to provide a reliable perception for underwater localization in structured environments. Submerged sensory information is often affected by diverse sources of uncertainty that can deteriorate the positioning and tracking. By adopting uncertain modeling and multi-sensor fusion techniques, the framework can maintain a coherent representation of the environment, filtering outliers, inconsistencies in sequential observations, and useless information for positioning purposes. We evaluate the framework using cameras and range sensors for modeling uncertain features that represent the environment around the vehicle. We locate the underwater vehicle using a Sequential Monte Carlo (SMC) method initialized from the GPS location obtained on the surface. The experimental results show that the framework provides a reliable environment representation during the underwater navigation to the localization system in real-world scenarios. Besides, they evaluate the improvement of localization compared to the position estimation using reliable dead-reckoning systems.

Analysis of Late Phase Severe Accident Phenomena in CANDU Plant

2017 ◽  
Vol 3 (2) ◽  
Author(s):  
D. Dupleac
Keyword(s):  
Heat Transfer ◽  
Fluid Dynamics ◽  
Late Phase ◽  
Severe Accident ◽  
Ansys Fluent ◽  
Water Depletion ◽  
Sources Of Uncertainty ◽  
Parametric Studies ◽  
Computational Fluid Dynamics Cfd ◽  
Containment Pressure

The paper overviews the analytical studies performed at Politehnica University of Bucharest on the analysis of late phase severe accident phenomena in a Canada Deuterium Uranium (CANDU) plant. The calculations start from a dry debris bed at the bottom of calandria vessel. Both SCDAPSIM/RELAP code and ansys-fluent computational fluid dynamics (CFD) code are used. Parametric studies are performed in order to quantify the effect of several identified sources of uncertainty on calandria vessel failure: metallic fraction of zirconium inside the debris, containment pressure, timing of water depletion inside calandria vessel, steam circulation in calandria vessel above debris bed, debris temperature at moment of water depletion inside calandria vessel, calandria vault nodalization, and the gap heat transfer coefficient.

Analysis of the sources of uncertainty for portfolio-level earthquake loss estimation

2012 ◽  
Vol 41 (11) ◽  
pp. 1549-1568 ◽  
Author(s):  
Hesaam Aslani ◽  
Carlos Cabrera ◽  
Mohsen Rahnama
Keyword(s):  
Loss Estimation ◽  
Earthquake Loss Estimation ◽  
Sources Of Uncertainty ◽  
Earthquake Loss

Gaussian Process Based Model to Optimize Additively Manufactured Powder Microstructures From Phase Field Modeling

2021 ◽  
Author(s):  
Arunabha Batabyal ◽  
Sugrim Sagar ◽  
Jian Zhang ◽  
Tejesh Dube ◽  
Xuehui Yang ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Phase Field ◽  
Bayesian Optimization ◽  
Expected Improvement ◽  
Neck Size ◽  
Sources Of Uncertainty ◽  
Surface Diffusivity ◽  
Input Parameters ◽  
Optimal Values ◽  
Probability Of Improvement

Abstract A persistent problem in the selective laser sintering process is to maintain the quality of additively manufactured parts, which can be attributed to the various sources of uncertainty. In this work, a two-particle phase-field microstructure model has been analyzed. The sources of uncertainty as the two input parameters were surface diffusivity and inter-particle distance. The response quantity of interest (QOI) was selected as the size of the neck region that develops between the two particles. Two different cases with equal and unequal sized particles were studied. It was observed that the neck size increased with increasing surface diffusivity and decreased with increasing inter-particle distance irrespective of particle size. Sensitivity analysis found that the inter-particle distance has more influence on variation in neck size than that of surface diffusivity. The machine learning algorithm Gaussian Process Regression was used to create the surrogate model of the QOI. Bayesian Optimization method was used to find optimal values of the input parameters. For equal-sized particles, optimization using Probability of Improvement provided optimal values of surface diffusivity and inter-particle distance as 23.8268 and 40.0001, respectively. The Expected Improvement as an acquisition function gave optimal values 23.9874 and 40.7428, respectively. For unequal sized particles, optimal design values from Probability of Improvement were 23.9700 and 33.3005, respectively, while those from Expected Improvement were 23.9893 and 33.9627, respectively. The optimization results from the two different acquisition functions seemed to be in good agreement.

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