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A method for reducing errors of downwind sail simulations is presented. This method can be used to improve the results of any simulation making use of Computational Fluid Dynamics (CFD) models and is particularly useful when simplified CFD models are considered, as in these cases the error of the results achieved can be large. In the present approach, in order to reduce the error the results are corrected...
The objective of this paper is to report on new developments in the project we are working on for the development of a mobile sensor based opportunistic urban pollution monitoring network. This work follows from the implementation of a single pollution sensor based sensing node prototype which was used for testing an opportunistic communications network and which was reported elsewhere. Here we concentrate...
A study of the possibility of approximating the Neyman-Pearson detector using supervised learning machines is presented. Two error functions are considered for training: the sum-of-squares error and the Minkowski error with R = 1. The study is based on the calculation of the function the learning machine approximates to during training, and the application of a sufficient condition previously formulated...
Logistic regression (LR) has become a widely used and accepted method to analyse binary or multiclass outcome variables, since it is a flexible tool that can predict the probability for the state of a dichotomous variable. A recently proposed LR method is based on the hybridisation of a linear model and evolutionary product-unit neural network (EPUNN) models for binary classification. This produces...
Detecting changes in the behavior of users can serve as an indicator of malicious or damaging misuse in many services; including the possible usurpation of a regular user identity by an intruder. For these purposes, approaches based on the profiling of users are not as common as those based on the analysis of the system behavior. This paper presents a method for automatically profiling and subsequently...
An approach for considering spatio-spectral information when classifying inhomogeneous materials in industrial environments is proposed. Its main application would be in the inspection and quality control tasks. They system core is an ANN based hyperspectral processing unit able to perform the online determination of the quality of the material based on its composition and grain size. A training adviser...
The application of importance sampling to train neural networks which approximates the Neyman-Pearson detector is considered in this paper. A comparative study with two different error functions is carried out. These two error functions are selected to make the Neyman-Pearson detector approximation possible. The importance sampling technique is used to estimate the error function for training. Some...
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