This paper presents a fast approach for vision-based self-localization in RoboCup. The vision system extracts the features required for localization without processing the whole image and is a first step towards independence of lighting conditions. In the field of self-localization, some new ideas are added to the well-known MonteCarlo localization approach that increase both stability and reac...

In this paper a procedure is established for solving the Probabilistic Load Flow in an electrical power network, considering correlation between power generated by power plants, loads demanded on each bus and power injected by wind farms. The method proposed is based on the generation of correlated series of power values, which can be used in a MonteCarlo simulation, to obtain the probability d...

Mechatronic automotive systems are hybrid systems. Modelling and simulation of the interactions between continuous and discrete parts is essential to evaluate dependability. In this paper we show how a simple mechatronic system can be modelled in the CPN formalism. Quantitative dependability evaluation is obtained thanks to MonteCarlo simulation. We use the DesignCPN Occurrence Graph tool to va...

This paper introduces an extension of the balanced augmented empirical likelihood method for estimating simulation models. We analyze its performance empirically using MonteCarlo methods, and demonstrate that our new method increases the flexibility and accuracy of the empirical likelihood approach, while preserving both its limit distribution and its consistency for moment condition models. We...

In this work we focus on the effects of aggregation on parameters estimation and Value-at-Risk computation if the data generator follow a FIGARCH model. We present a Montecarlo experiment which shows that the memory structure is affected. In a second simulation study we compare Value-at-Risk estimates obtained by high frequency and aggregated data. We verify that aggregated data have a better p...

We propose a novel approach to intranuclear cascades which takes as input quantum MonteCarlo nuclear configurations and uses semi-classical, impact-parameter based algorithm modelthe propagation of protons neutrons in the medium. successfully compare oursimulations available proton-carbon scattering data nuclear-transparency measurements. Byanalyzing dependence simulated observables upon ingred...

Starting from an agent-based interpretation of the well-known Bass innovation diffusion model, we perform a Montecarlo analysis of the performance of a method of simulated moment (MSM) estimator. We show that nonlinearities of the moments lead to a small bias in the estimates in small populations, although our estimates are consistent and converge to the true values as population size increases...

We study the effect of mutations in a simple model of colonization, based on Montecarlo simulations. When the population colonizes the whole available habitat, a maximum population density is reached, which depends on the mutation rate. Depending on the values of other parameters, such as selection pressure, fecundity and mobility, there is an optimal value for the mutation rate for which the c...

This paper proposes a Monte Carlo based statistical simulation technique in place of the difficult mathematical analysis to obtain an estimate of the error probability of a log-normal Raleigh faded channel in a digital wireless communication network taking into account the users random positions and the modulation type. The results obtained are consistent with those in the literature. Index Ter...

The objective of this paper is to develop the method for selection of the optimal structure of simultaneous equation system from the given model set in the condition of the limited sample of observations. The identification purpose is the model selection with the best predictive possibility. The information criteria for selecting the best model of nonlinear simultaneous equation system from the...