Inflation and the variability of relative prices in the Caribbean: evidence from panel threshold models

Includes bibliography

Risk-adjusted poverty in Argentina: measurement and determinants

Includes bibliography

Entendiendo las volatilidades cíclicas de la cuenta de capitales y del PIB: un estudio de panel para países latinoamericanos

Incluye Bibliografía

Dinámica del empleo y crisis en América Latina: resultados de estimaciones de panel

Incluye Bibliografía

Adaptive filter feature identification for structural health monitoring in aeronautical panel

This paper presents an approach for structural health monitoring (SHM) by using adaptive filters. The experimental signals from different structural conditions provided by piezoelectric actuators/sensors bonded in the test structure are modeled by a discrete-time recursive least square (RLS) filter. The biggest advantage to use a RLS filter is the clear possibility to perform an online SHM procedure since that the identification is also valid for non-stationary linear systems. An online damage-sensitive index feature is computed based on autoregressive (AR) portion of coefficients normalized by the square root of the sum of the square of them. The proposed method is then utilized in a laboratory test involving an aeronautical panel coupled with piezoelectric sensors/actuators (PZTs) in different positions. A hypothesis test employing the t-test is used to obtain the damage decision. The proposed algorithm was able to identify and localize the damages simulated in the structure. The results have shown the applicability and drawbacks the method and the paper concludes with suggestions to improve it. ©2010 Society for Experimental Mechanics Inc.

Presentación del panel "El rol de las tecnologías de la información y comunicación (TIC) en las experiencias de institucionalización de la política social"

Incluye Bibliografía

Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter

This paper proposes a filter based on a general regression neural network and a moving average filter, for preprocessing half-hourly load data for short-term multinodal load forecasting, discussed in another paper. Tests made with half-hourly load data from nine New Zealand electrical substations demonstrate that this filter is able to handle noise, missing data and abnormal data. © 2011 IEEE.