Abstract: Radio interference is one of the major concerns in
using the global positioning system (GPS) for civilian and military
applications. Interference signals are produced not only through
all electronic systems but also illegal jammers. Among different
types of interferences, continuous wave (CW) interference has strong
adverse impacts on the quality of the received signal. In this paper,
we make more detailed analysis for CW interference effects on
GPS signal quality. Based on the C/A code spectrum lines, the
influence of CW interference on the acquisition performance of
GPS receivers is further analysed. This influence is supported by
simulation results using GPS software receiver. As the most important
user parameter of GPS receivers, the mathematical expression of bit
error probability is also derived in the presence of CW interference,
and the expression is consistent with the Monte Carlo simulation
results. The research on CW interference provides some theoretical
gist and new thoughts on monitoring the radio noise environment and
improving the anti-jamming ability of GPS receivers.
Abstract: Slope stability analyses are largely carried out by deterministic methods and evaluated through a single security factor. Although it is known that the geotechnical parameters can present great dispersal, such analyses are considered fixed and known. The probabilistic methods, in turn, incorporate the variability of input key parameters (random variables), resulting in a range of values of safety factors, thus enabling the determination of the probability of failure, which is an essential parameter in the calculation of the risk (probability multiplied by the consequence of the event). Among the probabilistic methods, there are three frequently used methods in geotechnical society: FOSM (First-Order, Second-Moment), Rosenblueth (Point Estimates) and Monte Carlo. This paper presents a comparison between the results from deterministic and probabilistic analyses (FOSM method, Monte Carlo and Rosenblueth) applied to a hypothetical slope. The end was held to evaluate the behavior of the slope and consequent risk analysis, which is used to calculate the risk and analyze their mitigation and control solutions. It can be observed that the results obtained by the three probabilistic methods were quite close. It should be noticed that the calculation of the risk makes it possible to list the priority to the implementation of mitigation measures. Therefore, it is recommended to do a good assessment of the geological-geotechnical model incorporating the uncertainty in viability, design, construction, operation and closure by means of risk management.
Abstract: This paper presents a novel algorithm for modeling
photovoltaic based distributed generators for the purpose of optimal
planning of distribution networks. The proposed algorithm utilizes
sequential Monte Carlo method in order to accurately consider the
stochastic nature of photovoltaic based distributed generators. The
proposed algorithm is implemented in MATLAB environment and
the results obtained are presented and discussed.
Abstract: Inferring the network structure from time series data
is a hard problem, especially if the time series is short and noisy.
DNA microarray is a technology allowing to monitor the mRNA
concentration of thousands of genes simultaneously that produces
data of these characteristics. In this study we try to investigate the
influence of the experimental design on the quality of the result.
More precisely, we investigate the influence of two different types of
random single gene perturbations on the inference of genetic networks
from time series data. To obtain an objective quality measure for
this influence we simulate gene expression values with a biologically
plausible model of a known network structure. Within this framework
we study the influence of single gene knock-outs in opposite to
linearly controlled expression for single genes on the quality of the
infered network structure.
Abstract: to simulate the phenomenon of electronic transport in semiconductors, we try to adapt a numerical method, often and most frequently it’s that of Monte Carlo. In our work, we applied this method in the case of a ternary alloy semiconductor GaInP in its cubic form; The Calculations are made using a non-parabolic effective-mass energy band model. We consider a band of conduction to three valleys (ΓLX), major of the scattering mechanisms are taken into account in this modeling, as the interactions with the acoustic phonons (elastic collisions) and optics (inelastic collisions). The polar optical phonons cause anisotropic collisions, intra-valleys, very probable in the III-V semiconductors. Other optical phonons, no polar, allow transitions inter-valleys. Initially, we present the full results obtained by the simulation of Monte Carlo in GaInP in stationary regime. We consider thereafter the effects related to the application of an electric field varying according to time, we thus study the transient phenomenon which make their appearance in ternary material
Abstract: This paper presents an online method that learns the
corresponding points of an object from un-annotated grayscale images
containing instances of the object. In the first image being
processed, an ensemble of node points is automatically selected
which is matched in the subsequent images. A Bayesian posterior
distribution for the locations of the nodes in the images is formed.
The likelihood is formed from Gabor responses and the prior assumes
the mean shape of the node ensemble to be similar in a translation
and scale free space. An association model is applied for separating
the object nodes and background nodes. The posterior distribution is
sampled with Sequential Monte Carlo method. The matched object
nodes are inferred to be the corresponding points of the object
instances. The results show that our system matches the object nodes
as accurately as other methods that train the model with annotated
training images.