Search results for "PREDICT"
showing 10 items of 2174 documents
A Bayesian approach for predictive maintenance policy with imperfect monitoring
2009
In the traditional preventive maintenance policy, the periodic maintenance activities are scheduled on the basis of the a-priori information about the failure behaviour of the population which the component belongs to, by assuming a probability distribution function and by estimating the involved statistical parameters. On the contrary, with the predictive approach, the maintenance activity is scheduled on the basis of the real degradation level of the component. So, it is possible to reduce the failure probability and, at the same time, to use the component for almost all its useful life. For this reason, the predictive maintenance policy makes possible the reduction of the maintenance cos…
Finding Prediction Limits for a Future Number of Failures in the Prescribed Time Interval under Parametric Uncertainty
2012
Computing prediction intervals is an important part of the forecasting process intended to indicate the likely uncertainty in point forecasts. Prediction intervals for future order statistics are widely used for reliability problems and other related problems. In this paper, we present an accurate procedure, called ‘within-sample prediction of order statistics', to obtain prediction limits for the number of failures that will be observed in a future inspection of a sample of units, based only on the results of the first in-service inspection of the same sample. The failure-time of such units is modeled with a two-parameter Weibull distribution indexed by scale and shape parameters β and δ, …
Experimental observations of upstream overdeepening
2005
The issue of morphodynamic influence in meandering streams is investigated through a series of laboratory experiments on curved and straight flumes. Both qualitative and quantitative observations confirm the suitability of the recent theoretical developments (Zolezzi & Seminara 2001) that indicate the occurrence of two distinct regimes of morphodynamic influence, depending on the value of the width ratio of the channel β. The threshold value βR separating the upstream from the downstream influence regimes coincides with the resonant value discovered by Blondeaux & Seminara (1985). Indeed it is observed that upstream influence may occur only in relatively wide channels, while narrower stream…
Predicting mobile apps spread: An epidemiological random network modeling approach
2017
[EN] The mobile applications business is a really big market, growing constantly. In app marketing, a key issue is to predict future app installations. The influence of the peers seems to be very relevant when downloading apps. Therefore, the study of the evolution of mobile apps spread may be approached using a proper network model that considers the influence of peers. Influence of peers and other social contagions have been successfully described using models of epidemiological type. Hence, in this paper we propose an epidemiological random network model with realistic parameters to predict the evolution of downloads of apps. With this model, we are able to predict the behavior of an app…
New Reactions of Amino-Functionalized 3-Vinyl-1H-indoles and Tetrahydropyridin-4-yl Analogues with Dienophiles
1991
Reactions of 3-[2-(morpholin-4-yl)vinyl]-1H-indole (1), the 1,2-dihydro-9H-carbazole 2, as well as the 3-(tetrahydropyridin-4-yl)-1H-indoles 3a and 3b with some carbo- and heterodienophiles are described. The scope and limitations of the synthetic utility of these amino- (or homoamino)-functionalized 3-vinyl-1H-indoles are reported and some MO calculations for the qualitative prediction of their reactivities are presented. The reactions gave rise to substitution products, redox products, Diels-Alder adducts, ene adducts, and Michael-type adducts (Schemes 2 and 3).
Bayesian hierarchical models for analysing the spatial distribution of bioclimatic indices
2017
A methodological approach for modelling the spatial distribution of bioclimatic indices is proposed in this paper. The value of the bioclimatic index is modelled with a hierarchical Bayesian model that incorporates both structured and unstructured random effects. Selection of prior distributions is also discussed in order to better incorporate any possible prior knowledge about the parameters that could refer to the particular characteristics of bioclimatic indices. MCMC methods and distributed programming are used to obtain an approximation of the posterior distribution of the parameters and also the posterior predictive distribution of the indices. One main outcome of the proposal is the …
Measurement of lean body mass using bioelectrical impedance analysis: a consideration of the pros and cons
2017
The assessment of body composition has important applications in the evaluation of nutritional status and estimating potential health risks. Bioelectrical impedance analysis (BIA) is a valid method for the assessment of body composition. BIA is an alternative to more invasive and expensive methods like dual-energy X-ray absorptiometry, computerized tomography, and magnetic resonance imaging. Bioelectrical impedance analysis is an easy-to-use and low-cost method for the estimation of fat-free mass (FFM) in physiological and pathological conditions. The reliability of BIA measurements is influenced by various factors related to the instrument itself, including electrodes, operator, subject, a…
Assessment of Granger causality by nonlinear model identification: application to short-term cardiovascular variability.
2007
A method for assessing Granger causal relationships in bivariate time series, based on nonlinear autoregressive (NAR) and nonlinear autoregressive exogenous (NARX) models is presented. The method evaluates bilateral interactions between two time series by quantifying the predictability improvement (PI) of the output time series when the dynamics associated with the input time series are included, i.e., moving from NAR to NARX prediction. The NARX model identification was performed by the optimal parameter search (OPS) algorithm, and its results were compared to the least-squares method to determine the most appropriate method to be used for experimental data. The statistical significance of…
Mutual nonlinear prediction of cardiovascular variability series: Comparison between exogenous and autoregressive exogenous models
2007
A model-based approach to perform mutual nonlinear prediction of short cardiovascular variability series is presented. The approach is based on identifying exogenous (X) and autoregressive exogenous (ARX) models by K-nearest neighbors local linear approximation, and estimates the predictability of a series given the other as the squared correlation between original and predicted values of the series. The method was first tested on simulations reproducing different types of interaction between non-identical Henon maps, and then applied to heart rate (HR) and blood pressure (BP) variability series measured in healthy subjects at rest and after head-up tilt. Simulations showed that different c…
Bivariate nonlinear prediction to quantify the strength of complex dynamical interactions in short-term cardiovascular variability.
2005
A nonlinear prediction method for investigating the dynamic interdependence between short length time series is presented. The method is a generalization to bivariate prediction of the univariate approach based on nearest neighbor local linear approximation. Given the input and output series x and y, the relationship between a pattern of samples of x and a synchronous sample of y was approximated with a linear polynomial whose coefficients were estimated from an equation system including the nearest neighbor patterns in x and the corresponding samples in y. To avoid overfitting and waste of data, the training and testing stages of the prediction were designed through a specific out-of-sampl…