High Quality Final Gathering for Hierarchical Monte Carlo Radiosity for General Environments

2002 ◽  
pp. 425-437 ◽  
Author(s):  
Frederic Pérez ◽  
Ignacio Martín ◽  
Xavier Pueyo
Keyword(s):  
Monte Carlo ◽  
High Quality ◽  
Final Gathering

High Quality 3C-SiC Substrate for MOSFET Fabrication

2012 ◽  
Vol 711 ◽  
pp. 91-98 ◽  
Author(s):  
Hiroyuki Nagasawa ◽  
Takamitsu Kawahara ◽  
Kuniaki Yagi ◽  
Naoki Hatta ◽  
Hidetsugu Uchida ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Reduction Method ◽  
Stacking Fault ◽  
High Quality ◽  
Finite Area ◽  
Side Walls ◽  
Mc Simulation ◽  
Annihilation Probability

Quantitative efficacies of several methods for stacking fault (SF) reduction are evaluated using Monte Carlo (MC) simulation. SF density on a 3C–SiC {001} surface depends on interactions of adjoining SFs: annihilation between counter pairs of SFs and termination by orthogonal SF pairs. However, SFs are not entirely eliminated when growth occurs on undulant-Si and switch back epitaxy (SBE) due to spontaneous SF collimation that suppresses the annihilation probability of counter SFs. The MC simulation also reveals the efficacy of SF reduction method which includes the growth of 3C–SiC on finite area bounded by side walls. One can theoretically reduce the SF density below 100 cm-1on 3C–SiC {001} surface. A practical way for eliminating the SF by termination at side walls is demonstrated, and it clearly exhibits that the SF density can be reduced under 120 cm-1.

scholarly journals Application of the Monte-Carlo Method to the Description of the Dynamics of the Spread of Salvo Pollution Complicated by Adsorption

2021 ◽  
Author(s):  
Serghei Travin
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Specific Adsorption ◽  
Underlying Surface ◽  
High Quality ◽  
Kinetic Curves ◽  
The Monte Carlo Method

The possibilities of application of the Monte-Carlo method for simulating the consequences of pollutants emissions with specific adsorption on the underlying surface were considered. Effective methods of obtaining kinetic curves for the concentration of a pollutant for a selected square on the field and constructing contamination profiles for a specified time are analysed. The estimation of the necessary parameters of the model for obtaining high-quality kinetic curves was performed and recommendations for their optimization are given. Specific fronts for the spot propagation were obtained and visualised.

A high-quality multilayer structure characterization method based on X-ray fluorescence and Monte Carlo simulation

2014 ◽  
Vol 118 (2) ◽  
pp. 497-504 ◽  
Author(s):  
Antonio Brunetti ◽  
Bruno Golosio ◽  
Maria Grazia Melis ◽  
Stefania Mura
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Multilayer Structure ◽  
Structure Characterization ◽  
High Quality ◽  
X Ray

scholarly journals Modelling the Uncertainty of Slope Estimation from a Lidar-Derived Dem: a Case Study from a Large-Scale Area in the Czech Republic / Modelovanie Neistoty Vo Výpočte Sklonov Z Lidarových Dmr; Prípadová Štúdia Vybraného Malého Územia V Čr

2013 ◽  
Vol 59 (2) ◽  
pp. 25-39 ◽  
Author(s):  
Ivan Mudron ◽  
Michal Podhoranyi ◽  
Juraj Cirbus ◽  
Branislav Devečka ◽  
Ladislav Bakay
Keyword(s):  
Monte Carlo ◽  
Error Propagation ◽  
Input Data ◽  
Large Scale ◽  
Extreme Values ◽  
High Quality ◽  
Significant Difference ◽  
Error Propagation Analysis ◽  
Slope Estimation ◽  
Slope Error

Abstract This paper summarizes the methods and results of error modelling and propagation analyses in the Olše and Stonávka confluence area. In terrain analyses, the outputs of the aforementioned analysis are always a function of input. Two approaches according to the input data were used to generate field elevation errors which subsequently entered the error propagation analysis. The main goal solved in this research was to show the importance of input data in slope estimation and to estimate the elevation error propagation as well as to identify DEM errors and their consequences. Dependencies were investigated as well to achieve a better prediction of slope errors. Four different digital elevation model (DEM) resolutions (0.5, 1, 5 and 10 meters) were examined with the Root Mean Square Error (RMSE) rating up to 0.317 meters (10 m DEM). They all originated from a LIDAR survey. In the analyses, a stochastic Monte Carlo simulation was performed with 250 iterations. The article focuses on the error propagation in a large-scale area using high quality input DEM and Monte Carlo methods. The DEM uncertainty (RMSE) was obtained by sampling and ground research (RTK GPS) and from subtraction of two DEMs. According to empirical error distribution a semivariogram was used to model spatially autocorrelated uncertainty in elevation. The second procedure modelled the uncertainty without autocorrelation using a random N(0,RMSE) error generator. Statistical summaries were drawn to investigate the expected hypothesis. As expected, the error in slopes increases with the increasing vertical error in the input DEM. According to similar studies the use of different DEM input data, high quality LIDAR input data decreases the output uncertainty. Errors modelled without spatial autocorrelation do not result in a greater variance in the resulting slope error. In this case, although the slope error results (comparing random uncorrelated and empirical autocorrelated error fields) did not show any statistical significant difference, the input elevation error pattern was not normally distributed and therefore the random error generator realization is not a suitable interpretation of the true state of elevation errors. The normal distribution was rejected because of the high kurtosis and extreme values (outliners). On the other hand, it can show an important insight into the expected elevation and slope errors. Geology does not influence the slope error in the study area.

scholarly journals The LUMBA UVES stellar parameter pipeline

2019 ◽  
Vol 629 ◽  
pp. A74
Author(s):  
Alvin Gavel ◽  
Pieter Gruyters ◽  
Ulrike Heiter ◽  
Andreas J. Korn ◽  
Karin Lind ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Systematic Errors ◽  
Parameter Determination ◽  
Parameter Estimates ◽  
Red Giants ◽  
Chemical Abundances ◽  
High Quality ◽  
Stellar Parameter ◽  
Astrometric Data ◽  
General Method

Context. The Gaia-ESO Survey has taken high-quality spectra of a subset of 100 000 stars observed with the Gaia spacecraft. The goal for this subset is to derive chemical abundances for these stars that will complement the astrometric data collected by Gaia. Deriving the chemical abundances requires that the stellar parameters be determined. Aims. We present a pipeline for deriving stellar parameters from spectra observed with the FLAMES-UVES spectrograph in its standard fibre-fed mode centred on 580 nm, as used in the Gaia-ESO Survey. We quantify the performance of the pipeline in terms of systematic offsets and scatter. In doing so, we present a general method for benchmarking stellar parameter determination pipelines. Methods. Assuming a general model of the errors in stellar parameter pipelines, together with a sample of spectra of stars whose stellar parameters are known from fundamental measurements and relations, we use a Markov chain Monte Carlo method to quantitatively test the pipeline. Results. We find that the pipeline provides parameter estimates with systematic errors on effective temperature below 100 K, on surface gravity below 0.1 dex, and on metallicity below 0.05 dex for the main spectral types of star observed in the Gaia-ESO Survey and tested here. The performance on red giants is somewhat lower. Conclusions. The pipeline performs well enough to fulfil its intended purpose within the Gaia-ESO Survey. It is also general enough that it can be put to use on spectra from other surveys or other spectrographs similar to FLAMES-UVES.

Probability and Sensitivity Analysis of Plate

2014 ◽  
Vol 617 ◽  
pp. 193-196 ◽  
Author(s):  
Katarina Tvrdá
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Flat Plate ◽  
Concrete Structures ◽  
Probability Analysis ◽  
Triangular Distribution ◽  
High Quality ◽  
Input Parameters ◽  
Concrete Solution

This paper deals with some problems of the ceiling plate, made of the Cobiax-system. Cobiax provides a system to produce voided, biaxial, flat plate slabs as a high-quality concrete solution for large spans and slim slabs. Plastic voids in the shape of spheres or flattened spheres are contained in steel cages and put into concrete structures to create longer spans and reduce vertical loads. The presented plate is made of cobiax balls with a diameter of 27 cm located outside the area of columns. Probability analysis of Monte-Carlo method in Ansys is presented. Input parameters are changing according to Gauss or triangular distribution.

scholarly journals Simulasi Monte Carlo dalam Prediksi Jumlah Penumpang Angkutan Massal Bus Rapid Transit Kota Padang

2020 ◽  
Author(s):  
Khaliq Alfikrizal ◽  
Sarjon Defit ◽  
Yuhandri Yunus
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Real Data ◽  
Bus Rapid Transit ◽  
Rapid Transit ◽  
High Quality ◽  
Simulation Data ◽  
The Monte Carlo Method ◽  
Average Accuracy ◽  
High Level

Bus Rapid Transit is a system of bus facilities, services and comfort which is used to increase speed and reliability and is integrated with a strong transit identity through high quality services. Trans Padang is a land transportation based on Bus Rapid Transit in Padang City which is managed by the Transportation Agency which started operating in January 2014 with a total bus fleet of 10 units on the Lubuk Buaya-Pasar Raya Padang route. Currently it has 2 corridors operating out of 6 corridors designed. This study aims to predict the number of Bus Rapid Transit passengers in Padang City and determine the level of accuracy of simulation data with real data using the Monte Carlo method. The data used to predict the number of passengers is data on the number of passengers from January 2017 to December 2019. From the simulations carried out, simulated accuracy is obtained for predicting the number of passengers with an average accuracy of above 80%. Based on a fairly high level of accuracy, the application of the Monte Carlo method to predict the number of Bus Rapid Transit passengers in Padang City is considered to be able to predict the number of passengers in the following year.

scholarly journals Uncertainty Analysis of Two Methods in Hydrocarbon Prediction under Different Water Saturation and Noise Conditions

2019 ◽  
Vol 9 (23) ◽  
pp. 5239
Author(s):  
Changcheng Liu ◽  
Deva Ghosh ◽  
Ahmed Mohamed Ahmed Salim
Keyword(s):  
Monte Carlo ◽  
Input Data ◽  
Water Saturation ◽  
Geophysical Methods ◽  
High Water ◽  
High Quality ◽  
Traditional Methods ◽  
Hydrocarbon Prediction ◽  
Fluid Factor ◽  
Noise Energy

The uncertainty of two recently proposed methods, “new fluid factor” and “delta K”, is analyzed under different water saturation and noise conditions through Monte Carlo modelling. The new fluid factor performs reliably (all metric parameters are above 0.9) when the water saturation is up to 95%. The delta K has better performance (all metric parameters are close to 1) such that it is able to distinguish hydrocarbon from brine without the interference of high water saturation. The results prove the performances of the two methods are stable in a high water-saturation scenario. The analysis of noise indicates the methods are sensitive to noise in the input data in that the performance is excellent when the noise is relatively low (−20 dB) and decreases with increasing noise energy. The new fluid factor, which is in the interface domain, is more sensitive than delta K in the impedance domain. The metric parameters of the new fluid factor and delta K are in the range of 0.5 to 0.8 when the noise is high (−7 dB). High-quality input data and integration with other geophysical methods can effectively reduce these risks. In addition, two widely used traditional methods (fluid factor and Lambda-Rho) are analyzed as comparisons. It turns out the new fluid factor and delta K have better performance than traditional methods in both high water saturation and noise conditions.

scholarly journals MONTE CARLO SIMULATION OF DEFFUANT OPINION DYNAMICS WITH QUALITY DIFFERENCES

2004 ◽  
Vol 15 (10) ◽  
pp. 1439-1447 ◽  
Author(s):  
PATRICK ASSMANN
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
High Probability ◽  
Opinion Dynamics ◽  
High Quality

In this work, the consequences of different opinion qualities in the Deffuant model were examined. If these qualities are randomly distributed, no different behavior was observed. In contrast to that, systematically assigned qualities had strong effects to the final opinion distribution. There was a high probability that the strongest opinion was one with a high quality. Furthermore, under the same conditions, this major opinion was much stronger than in the models without systematic differences. Finally, a society with systematic quality differences needed more tolerance to form a complete consensus than one without or with unsystematic ones.

scholarly journals ANALOG-DIGITAL CONVERSION MONTE-CARLO IN FACILITIES OF MEASURING AND TREATMENT OF VIBRATION SIGNALS

2014 ◽  
pp. 43-50
Author(s):  
M. V. Lavriv ◽  
L. B. Petryshyn
Keyword(s):  
Monte Carlo ◽  
High Quality ◽  
Digital Conversion ◽  
Vibration Signals ◽  
Effective Application ◽  
Pseudorandom Signals

Application of method of analog-digital conversion of Monte Carlo is grounded as such, that allows considerably to extend the spectral bar of signal of conversion n in the class of integrating converters. Application of methods of generation of pseudorandom signals is offered on the basis of recursive and with the mirror mutual reflection of binary digits generators. High-quality descriptions and type of distributing are definite as even. Methods are developed and the structures of analog-digital converters are resulted, and also sentinel diagrams of their functioning. Directions and applied tasks of effective application of method of Monte Carlo are definite.

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