Abstract: Edgeworth Approximation, Bootstrap and Monte Carlo Simulations have a considerable impact on the achieving certain results related to different problems taken into study. In our paper, we have treated a financial case related to the effect that have the components of a Cash-Flow of one of the most successful businesses in the world, as the financial activity, operational activity and investing activity to the cash and cash equivalents at the end of the three-months period. To have a better view of this case we have created a Vector Autoregression model, and after that we have generated the impulse responses in the terms of Asymptotic Analysis (Edgeworth Approximation), Monte Carlo Simulations and Residual Bootstrap based on the standard errors of every series created. The generated results consisted of the common tendencies for the three methods applied, that consequently verified the advantage of the three methods in the optimization of the model that contains many variants.
Abstract: With the rapid development of national modern industry, people begin to pay attention to environmental pollution and harm caused by industrial dust. Based on above, a numerical study on the dedusting technology of industrial environment was conducted. The dynamic models of multicomponent particles collision and coagulation, breakage and deposition are developed, and the interaction of water droplet and aerosol particle in 2-Dimension flow field was researched by Eulerian-Lagrangian method and Multi-Monte Carlo method. The effects of the droplet scale, movement speed of droplet and the flow field structure on scavenging efficiency were analyzed. The results show that under the certain condition, 30μm of droplet has the best scavenging efficiency. At the initial speed 1m/s of droplets, droplets and aerosol particles have more time to interact, so it has a better scavenging efficiency for the particle.
Abstract: Photoacoustic imaging (PAI) is a non-invasive and
non-ionizing imaging modality that combines the absorption contrast
of light with ultrasound resolution. Laser is used to deposit optical
energy into a target (i.e., optical fluence). Consequently, the target
temperature rises, and then thermal expansion occurs that leads to
generating a PA signal. In general, most image reconstruction
algorithms for PAI assume uniform fluence within an imaging object.
However, it is known that optical fluence distribution within the
object is non-uniform. This could affect the reconstruction of PA
images. In this study, we have investigated the influence of optical
fluence distribution on PA back-propagation imaging using finite
element method. The uniform fluence was simulated as a triangular
waveform within the object of interest. The non-uniform fluence
distribution was estimated by solving light propagation within a
tissue model via Monte Carlo method. The results show that the PA
signal in the case of non-uniform fluence is wider than the uniform
case by 23%. The frequency spectrum of the PA signal due to the
non-uniform fluence has missed some high frequency components in
comparison to the uniform case. Consequently, the reconstructed
image with the non-uniform fluence exhibits a strong smoothing
effect.
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.