29,701 research outputs found
Strict bounding of quantities of interest in computations based on domain decomposition
This paper deals with bounding the error on the estimation of quantities of
interest obtained by finite element and domain decomposition methods. The
proposed bounds are written in order to separate the two errors involved in the
resolution of reference and adjoint problems : on the one hand the
discretization error due to the finite element method and on the other hand the
algebraic error due to the use of the iterative solver. Beside practical
considerations on the parallel computation of the bounds, it is shown that the
interface conformity can be slightly relaxed so that local enrichment or
refinement are possible in the subdomains bearing singularities or quantities
of interest which simplifies the improvement of the estimation. Academic
assessments are given on 2D static linear mechanic problems.Comment: Computer Methods in Applied Mechanics and Engineering, Elsevier,
2015, online previe
A traffic classification method using machine learning algorithm
Applying concepts of attack investigation in IT industry, this idea has been developed to design
a Traffic Classification Method using Data Mining techniques at the intersection of Machine
Learning Algorithm, Which will classify the normal and malicious traffic. This classification will
help to learn about the unknown attacks faced by IT industry. The notion of traffic classification
is not a new concept; plenty of work has been done to classify the network traffic for
heterogeneous application nowadays. Existing techniques such as (payload based, port based
and statistical based) have their own pros and cons which will be discussed in this
literature later, but classification using Machine Learning techniques is still an open field to explore and has provided very promising results up till now
On the role of pre and post-processing in environmental data mining
The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed
Strict lower bounds with separation of sources of error in non-overlapping domain decomposition methods
This article deals with the computation of guaranteed lower bounds of the
error in the framework of finite element (FE) and domain decomposition (DD)
methods. In addition to a fully parallel computation, the proposed lower bounds
separate the algebraic error (due to the use of a DD iterative solver) from the
discretization error (due to the FE), which enables the steering of the
iterative solver by the discretization error. These lower bounds are also used
to improve the goal-oriented error estimation in a substructured context.
Assessments on 2D static linear mechanic problems illustrate the relevance of
the separation of sources of error and the lower bounds' independence from the
substructuring. We also steer the iterative solver by an objective of precision
on a quantity of interest. This strategy consists in a sequence of solvings and
takes advantage of adaptive remeshing and recycling of search directions.Comment: International Journal for Numerical Methods in Engineering, Wiley,
201
Regression Monte Carlo for Microgrid Management
We study an islanded microgrid system designed to supply a small village with
the power produced by photovoltaic panels, wind turbines and a diesel
generator. A battery storage system device is used to shift power from times of
high renewable production to times of high demand. We introduce a methodology
to solve microgrid management problem using different variants of Regression
Monte Carlo algorithms and use numerical simulations to infer results about the
optimal design of the grid.Comment: CEMRACS 2017 Summer project - proceedings
Probabilistic estimation of microarray data reliability and underlying gene expression
Background: The availability of high throughput methods for measurement of
mRNA concentrations makes the reliability of conclusions drawn from the data
and global quality control of samples and hybridization important issues. We
address these issues by an information theoretic approach, applied to
discretized expression values in replicated gene expression data.
Results: Our approach yields a quantitative measure of two important
parameter classes: First, the probability that a gene is in the
biological state in a certain variety, given its observed expression
in the samples of that variety. Second, sample specific error probabilities
which serve as consistency indicators of the measured samples of each variety.
The method and its limitations are tested on gene expression data for
developing murine B-cells and a -test is used as reference. On a set of
known genes it performs better than the -test despite the crude
discretization into only two expression levels. The consistency indicators,
i.e. the error probabilities, correlate well with variations in the biological
material and thus prove efficient.
Conclusions: The proposed method is effective in determining differential
gene expression and sample reliability in replicated microarray data. Already
at two discrete expression levels in each sample, it gives a good explanation
of the data and is comparable to standard techniques.Comment: 11 pages, 4 figure
Extracting user spatio-temporal profiles from location based social networks
Report de RecercaLocation Based Social Networks (LBSN) like Twitter or Instagram are a good source for user spatio-temporal behavior. These social network provide a low rate sampling of user's location information during large intervals of time that can be used to discover complex behaviors, including mobility profiles, points of interest or unusual events. This information is important for different domains like mobility route planning, touristic recommendation systems or city planning.
Other approaches have used the data from LSBN to categorize areas of a city depending on the categories of the places that people visit or to discover user behavioral patterns from their visits. The aim of this paper is to analyze how the spatio-temporal behavior of a large number of users in a well limited geographical area can be segmented in different profiles. These behavioral profiles are obtained by means of clustering algorithms that show the different behaviors that people have when living and visiting a city.
The data analyzed was obtained from the public data feeds of Twitter and Instagram inside the area of the city of Barcelona for a period of several months. The analysis of these data shows that these kind of algorithms can be successfully applied to data from any city (or any general area) to discover useful profiles that can be described on terms of the city singular places and areas and their temporal relationships. These profiles can be used as a basis for making decisions in different application domains, specially those related with mobility inside and outside a city.Preprin
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