22,746 research outputs found
Extraction of the beam elastic shape from uncertain FBG strain measurement points
Aim of the present paper is the analysis of the strain along the beam that is equipped with Glass Fibers Reinforced Polymers (GFRP) with an embedded set of optical Fiber Bragg Grating sensors (FBG), in the context of a project to equip with these new structural elements an Italian train bridge. Different problems are attacked, and namely: (i)during the production process [1] it is difficult to locate precisely the FBG along the reinforcement bar, therefore the following question appears: How can we associate the strain measurements to the points along the bar? Is it possible to create a signal analysis procedure such that this correspondence is found?(ii)the beam can be inflected and besides the strain at some points, we would like to recover the elastic shape of the deformed beam that is equipped with the reinforcement bars. Which signal processing do we use to determine the shape of the deformed beam in its inflection plane?(iii)if the beam is spatially inflected, in two orthogonal planes, is it possible to recover the beam spatial elastic shape? Object of the paper is to answer to these questions
House Price Prediction: Hedonic Price Model vs. Artificial Neural Network
The objective of this paper is to empirically compare the predictive power of the hedonic model with an artificial neural network model on house price prediction. A sample of 200 houses in Christchurch, New Zealand is randomly selected from the Harcourt website. Factors including house size, house age, house type, number of bedrooms, number of bathrooms, number of garages, amenities around the house and geographical location are considered. Empirical results support the potential of artificial neural network on house price prediction, although previous studies have commented on its black box nature and achieved different conclusions.Hedonic Model, Artificial Neural Network (ANN), House Price., Environmental Economics and Policy, Land Economics/Use, Research Methods/ Statistical Methods, C53, L74,
Neural network determination of parton distributions: the nonsinglet case
We provide a determination of the isotriplet quark distribution from
available deep--inelastic data using neural networks. We give a general
introduction to the neural network approach to parton distributions, which
provides a solution to the problem of constructing a faithful and unbiased
probability distribution of parton densities based on available experimental
information. We discuss in detail the techniques which are necessary in order
to construct a Monte Carlo representation of the data, to construct and evolve
neural parton distributions, and to train them in such a way that the correct
statistical features of the data are reproduced. We present the results of the
application of this method to the determination of the nonsinglet quark
distribution up to next--to--next--to--leading order, and compare them with
those obtained using other approaches.Comment: 46 pages, 18 figures, LaTeX with JHEP3 clas
Supervised ANN vs. unsupervised SOM to classify EEG data for BCI: why can GMDH do better?
Construction of a system for measuring the brain activity (electroencephalogram (EEG)) and recognising thinking patterns comprises significant challenges, in addition to the noise and distortion present in any measuring technique. One of the most major applications of measuring
and understanding EGG is the brain-computer interface (BCI) technology. In this paper, ANNs (feedforward back
-prop and Self Organising Maps) for EEG data classification will be implemented and compared to abductive-based networks, namely GMDH (Group Methods of Data Handling) to show how GMDH can optimally (i.e. noise and accuracy) classify a given set of BCIâs EEG signals. It is shown that GMDH provides such improvements. In this endeavour, EGG classification based on GMDH will be researched for
comprehensible classification without scarifying accuracy.
GMDH is suggested to be used to optimally classify a given
set of BCIâs EEG signals. The other areas related to BCI will
also be addressed yet within the context of this purpose
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