The Monte Carlo framework for adaptive IIR filter & ARMA parameters estimation

2007 ◽  
Author(s):  
Li-xin Wang ◽  
Wei Xiang ◽  
Jing-yuan Zhang
Keyword(s):  
Monte Carlo ◽  
Parameters Estimation ◽  
Iir Filter ◽  
Monte Carlo Framework

Coarse-grained modeling of the nucleation of polycyclic aromatic hydrocarbons into soot precursors

2019 ◽  
Vol 21 (9) ◽  
pp. 5123-5132 ◽  
Author(s):  
J. Hernández-Rojas ◽  
F. Calvo
Keyword(s):  
Monte Carlo ◽  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
Physical Growth ◽  
Coarse Grained ◽  
Soot Precursors ◽  
Polycyclic Aromatic ◽  
Hydrocarbon Molecules ◽  
A Minor ◽  
Monte Carlo Framework

The aggregation and physical growth of polycyclic aromatic hydrocarbon molecules was simulated using a coarse-grained potential and a stochastic Monte Carlo framework. In agreement with earlier studies, homomolecular nucleation of pyrene, coronene and circumcoronene is found to be limited at temperatures in the 500–1000 K range. Heteromolecular nucleation is found to occur with a minor spontaneous segregation toward pure and equi concentrations.

RISK ANALYSIS OF OFFSHORE TRANSPORTATION ACCIDENT IN ARCTIC WATERS

2021 ◽  
Vol 159 (A3) ◽  
Author(s):  
R Abbassi ◽  
F Khan ◽  
N Khakzad ◽  
B Veitch ◽  
S Ehlers
Keyword(s):  
Monte Carlo ◽  
Risk Analysis ◽  
Historical Data ◽  
Fault Trees ◽  
Northern Sea Route ◽  
Accident Scenarios ◽  
Oil Tanker ◽  
Harsh Conditions ◽  
Monte Carlo Framework ◽  
Transportation Accident

A methodology for risk analysis applicable to shipping in arctic waters is introduced. This methodology uses the Bowtie relationship to represent an accident causes and consequences. It is further used to quantify the probability of a ship accident and also the related accident consequences during navigation in arctic waters. Detailed fault trees for three possible ship accident scenarios in arctic transits are developed and represented as bowties. Factors related to cold and harsh conditions and their effects on grounding, foundering, and collision are considered as part of this study. To illustrate the application of the methodology, it is applied to a case of an oil-tanker navigating on the Northern Sea Route (NSR). The methodology is implemented in a Markov Chain Monte Carlo framework to assess the uncertainties arisen from historical data and expert judgments involved in the risk analysis.

Introduction — A Monte Carlo Framework

2015 ◽  
pp. 105-106
Author(s):  
Roland Lichters ◽  
Roland Stamm ◽  
Donal Gallagher
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Framework

Erratum to “Dosimetric parameters estimation using PENELOPE-Monte-Carlo simulation code: Model 6711 a 123I brachytherapy seed”

2006 ◽  
Vol 64 (2) ◽  
pp. 290
Author(s):  
Edgar A.V. Rodríguez ◽  
Elmer P.Q. Alcón ◽  
Miguel L. Rodriguez ◽  
F. Gutt ◽  
Carlos E. de Almeida
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Parameters Estimation ◽  
Simulation Code ◽  
Code Model

scholarly journals Flood risk assessment and associated uncertainty

2004 ◽  
Vol 4 (2) ◽  
pp. 295-308 ◽  
Author(s):  
H. Apel ◽  
A. H. Thieken ◽  
B. Merz ◽  
G. Blöschl
Keyword(s):  
Risk Assessment ◽  
Monte Carlo ◽  
Flood Risk ◽  
Economic Damage ◽  
Aleatory Uncertainty ◽  
Process Chain ◽  
Risk And Uncertainty ◽  
Sources Of Uncertainty ◽  
Model Components ◽  
Monte Carlo Framework

Abstract. Flood disaster mitigation strategies should be based on a comprehensive assessment of the flood risk combined with a thorough investigation of the uncertainties associated with the risk assessment procedure. Within the "German Research Network of Natural Disasters" (DFNK) the working group "Flood Risk Analysis" investigated the flood process chain from precipitation, runoff generation and concentration in the catchment, flood routing in the river network, possible failure of flood protection measures, inundation to economic damage. The working group represented each of these processes by deterministic, spatially distributed models at different scales. While these models provide the necessary understanding of the flood process chain, they are not suitable for risk and uncertainty analyses due to their complex nature and high CPU-time demand. We have therefore developed a stochastic flood risk model consisting of simplified model components associated with the components of the process chain. We parameterised these model components based on the results of the complex deterministic models and used them for the risk and uncertainty analysis in a Monte Carlo framework. The Monte Carlo framework is hierarchically structured in two layers representing two different sources of uncertainty, aleatory uncertainty (due to natural and anthropogenic variability) and epistemic uncertainty (due to incomplete knowledge of the system). The model allows us to calculate probabilities of occurrence for events of different magnitudes along with the expected economic damage in a target area in the first layer of the Monte Carlo framework, i.e. to assess the economic risks, and to derive uncertainty bounds associated with these risks in the second layer. It is also possible to identify the contributions of individual sources of uncertainty to the overall uncertainty. It could be shown that the uncertainty caused by epistemic sources significantly alters the results obtained with aleatory uncertainty alone. The model was applied to reaches of the river Rhine downstream of Cologne.

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