The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Ilaria Papa - One of the best experts on this subject based on the ideXlab platform.
-
acoustic emission source location in unidirectional carbon fiber reinforced plastic plates with virtually trained artificial neural networks
Journal of Applied Polymer Science, 2011Co-Authors: G. Caprino, Vanni Lopresto, Claudio Leone, Ilaria PapaAbstract:Acoustic emission (AE) source location in a unidirectional carbon-fiber-reinforced plastic plate was attempted with artificial neural network (ANN) technology. The AE events were produced by a lead break, and the response wave was received by piezoelectric sensors. The time of arrival, determined through the conventional threshold-crossing technique, was used to measure the dependence of the wave velocity on the fiber orientation. A simple empirical formula, relying on classical lamination and suggested by wave-propagation theory, was able to accurately model the Experimental Trend. On the basis of the formula, virtual training and testing data sets were generated for the case of a plate monitored by three transducers and were adopted to select two potentially effective ANN architectures. For final validation, Experimental tests were carried out, with the source positioned at predetermined points evenly distributed within the plate area. A very satisfactory correlation was found between the actual source locations and those predicted by the virtually trained ANNs. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011
-
Acoustic emission source location in unidirectional carbon‐fiber‐reinforced plastic plates with virtually trained artificial neural networks
Journal of Applied Polymer Science, 2011Co-Authors: G. Caprino, Vanni Lopresto, Claudio Leone, Ilaria PapaAbstract:Acoustic emission (AE) source location in a unidirectional carbon-fiber-reinforced plastic plate was attempted with artificial neural network (ANN) technology. The AE events were produced by a lead break, and the response wave was received by piezoelectric sensors. The time of arrival, determined through the conventional threshold-crossing technique, was used to measure the dependence of the wave velocity on the fiber orientation. A simple empirical formula, relying on classical lamination and suggested by wave-propagation theory, was able to accurately model the Experimental Trend. On the basis of the formula, virtual training and testing data sets were generated for the case of a plate monitored by three transducers and were adopted to select two potentially effective ANN architectures. For final validation, Experimental tests were carried out, with the source positioned at predetermined points evenly distributed within the plate area. A very satisfactory correlation was found between the actual source locations and those predicted by the virtually trained ANNs. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011
-
Acoustic Emission Source Location in Unidirectional Carbon-Fibre-Reinforced Plastic Plates Using Virtually Trained Artificial Neural Networks
2010Co-Authors: G. Caprino, Vanni Lopresto, Claudio Leone, Ilaria PapaAbstract:Acoustic emission source location in a unidirectional carbon‐fibre‐reinforced plastic plate was attempted employing Artificial Neural Network (ANN) technology. The acoustic emission events were produced by a lead break, and the response wave received by piezoelectric sensors, type VS150‐M resonant at 150 kHz. The waves were detected by a Vallen AMSY4 eight‐channel instrumentation. The time of arrival, determined through the conventional threshold crossing technique, was used to measure the dependence of wave velocity on fibre orientation. A simple empirical formula, relying on classical lamination and suggested by wave propagation theory, was able to accurately model the Experimental Trend. Based on the formula, virtual training and testing data sets were generated for the case of a plate monitored by three transducers, and adopted to select two potentially effective ANN architectures. For final validation, Experimental tests were carried out, positioning the source at predetermined points evenly distribu...
G. Caprino - One of the best experts on this subject based on the ideXlab platform.
-
acoustic emission source location in unidirectional carbon fiber reinforced plastic plates with virtually trained artificial neural networks
Journal of Applied Polymer Science, 2011Co-Authors: G. Caprino, Vanni Lopresto, Claudio Leone, Ilaria PapaAbstract:Acoustic emission (AE) source location in a unidirectional carbon-fiber-reinforced plastic plate was attempted with artificial neural network (ANN) technology. The AE events were produced by a lead break, and the response wave was received by piezoelectric sensors. The time of arrival, determined through the conventional threshold-crossing technique, was used to measure the dependence of the wave velocity on the fiber orientation. A simple empirical formula, relying on classical lamination and suggested by wave-propagation theory, was able to accurately model the Experimental Trend. On the basis of the formula, virtual training and testing data sets were generated for the case of a plate monitored by three transducers and were adopted to select two potentially effective ANN architectures. For final validation, Experimental tests were carried out, with the source positioned at predetermined points evenly distributed within the plate area. A very satisfactory correlation was found between the actual source locations and those predicted by the virtually trained ANNs. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011
-
Acoustic emission source location in unidirectional carbon‐fiber‐reinforced plastic plates with virtually trained artificial neural networks
Journal of Applied Polymer Science, 2011Co-Authors: G. Caprino, Vanni Lopresto, Claudio Leone, Ilaria PapaAbstract:Acoustic emission (AE) source location in a unidirectional carbon-fiber-reinforced plastic plate was attempted with artificial neural network (ANN) technology. The AE events were produced by a lead break, and the response wave was received by piezoelectric sensors. The time of arrival, determined through the conventional threshold-crossing technique, was used to measure the dependence of the wave velocity on the fiber orientation. A simple empirical formula, relying on classical lamination and suggested by wave-propagation theory, was able to accurately model the Experimental Trend. On the basis of the formula, virtual training and testing data sets were generated for the case of a plate monitored by three transducers and were adopted to select two potentially effective ANN architectures. For final validation, Experimental tests were carried out, with the source positioned at predetermined points evenly distributed within the plate area. A very satisfactory correlation was found between the actual source locations and those predicted by the virtually trained ANNs. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011
-
Acoustic Emission Source Location in Unidirectional Carbon-Fibre-Reinforced Plastic Plates Using Virtually Trained Artificial Neural Networks
2010Co-Authors: G. Caprino, Vanni Lopresto, Claudio Leone, Ilaria PapaAbstract:Acoustic emission source location in a unidirectional carbon‐fibre‐reinforced plastic plate was attempted employing Artificial Neural Network (ANN) technology. The acoustic emission events were produced by a lead break, and the response wave received by piezoelectric sensors, type VS150‐M resonant at 150 kHz. The waves were detected by a Vallen AMSY4 eight‐channel instrumentation. The time of arrival, determined through the conventional threshold crossing technique, was used to measure the dependence of wave velocity on fibre orientation. A simple empirical formula, relying on classical lamination and suggested by wave propagation theory, was able to accurately model the Experimental Trend. Based on the formula, virtual training and testing data sets were generated for the case of a plate monitored by three transducers, and adopted to select two potentially effective ANN architectures. For final validation, Experimental tests were carried out, positioning the source at predetermined points evenly distribu...
Cláudio F. Tormena - One of the best experts on this subject based on the ideXlab platform.
-
Analysis of the electronic origin of the 1JCH spin-spin coupling Trend in 1-X-cyclopropanes: Experimental and DFT study.
The journal of physical chemistry. A, 2008Co-Authors: Álvaro Cunha Neto, Francisco P. Dos Santos, Rubén H. Contreras, Roberto Rittner, Cláudio F. TormenaAbstract:A conceptual analysis of the CLOPPA (Contributions from Localized Orbitals within the Polarization Propagator Approach) expressions that deconvolute NMR spin−spin coupling constants [Diz A. C.; Giribet C. G.; Ruiz de Azua, M. C.; Contreras, R. H. Int. J. Quantum Chem. 1990, 37, 663.] into orbital contributions can provide an in-depth insight into the features of the electronic molecular structure that originate a given 1JCH Experimental Trend. In this work, several 1-X-cyclopropane derivatives are taken as model compounds to apply such ideas to rationalize substituent effects on the Fermi contact term of 1JC1,H spin−spin coupling. It is shown that in this type of coupling, its Experimental Trend, as measured in this work, cannot be accounted for with only the “bond” and the “other bond” contributions, requiring the inclusion of “other antibonding contributions”. Such effect is discussed in terms of hyperconjugative interactions.
Vanni Lopresto - One of the best experts on this subject based on the ideXlab platform.
-
acoustic emission source location in unidirectional carbon fiber reinforced plastic plates with virtually trained artificial neural networks
Journal of Applied Polymer Science, 2011Co-Authors: G. Caprino, Vanni Lopresto, Claudio Leone, Ilaria PapaAbstract:Acoustic emission (AE) source location in a unidirectional carbon-fiber-reinforced plastic plate was attempted with artificial neural network (ANN) technology. The AE events were produced by a lead break, and the response wave was received by piezoelectric sensors. The time of arrival, determined through the conventional threshold-crossing technique, was used to measure the dependence of the wave velocity on the fiber orientation. A simple empirical formula, relying on classical lamination and suggested by wave-propagation theory, was able to accurately model the Experimental Trend. On the basis of the formula, virtual training and testing data sets were generated for the case of a plate monitored by three transducers and were adopted to select two potentially effective ANN architectures. For final validation, Experimental tests were carried out, with the source positioned at predetermined points evenly distributed within the plate area. A very satisfactory correlation was found between the actual source locations and those predicted by the virtually trained ANNs. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011
-
Acoustic emission source location in unidirectional carbon‐fiber‐reinforced plastic plates with virtually trained artificial neural networks
Journal of Applied Polymer Science, 2011Co-Authors: G. Caprino, Vanni Lopresto, Claudio Leone, Ilaria PapaAbstract:Acoustic emission (AE) source location in a unidirectional carbon-fiber-reinforced plastic plate was attempted with artificial neural network (ANN) technology. The AE events were produced by a lead break, and the response wave was received by piezoelectric sensors. The time of arrival, determined through the conventional threshold-crossing technique, was used to measure the dependence of the wave velocity on the fiber orientation. A simple empirical formula, relying on classical lamination and suggested by wave-propagation theory, was able to accurately model the Experimental Trend. On the basis of the formula, virtual training and testing data sets were generated for the case of a plate monitored by three transducers and were adopted to select two potentially effective ANN architectures. For final validation, Experimental tests were carried out, with the source positioned at predetermined points evenly distributed within the plate area. A very satisfactory correlation was found between the actual source locations and those predicted by the virtually trained ANNs. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011
-
Acoustic Emission Source Location in Unidirectional Carbon-Fibre-Reinforced Plastic Plates Using Virtually Trained Artificial Neural Networks
2010Co-Authors: G. Caprino, Vanni Lopresto, Claudio Leone, Ilaria PapaAbstract:Acoustic emission source location in a unidirectional carbon‐fibre‐reinforced plastic plate was attempted employing Artificial Neural Network (ANN) technology. The acoustic emission events were produced by a lead break, and the response wave received by piezoelectric sensors, type VS150‐M resonant at 150 kHz. The waves were detected by a Vallen AMSY4 eight‐channel instrumentation. The time of arrival, determined through the conventional threshold crossing technique, was used to measure the dependence of wave velocity on fibre orientation. A simple empirical formula, relying on classical lamination and suggested by wave propagation theory, was able to accurately model the Experimental Trend. Based on the formula, virtual training and testing data sets were generated for the case of a plate monitored by three transducers, and adopted to select two potentially effective ANN architectures. For final validation, Experimental tests were carried out, positioning the source at predetermined points evenly distribu...
Claudio Leone - One of the best experts on this subject based on the ideXlab platform.
-
acoustic emission source location in unidirectional carbon fiber reinforced plastic plates with virtually trained artificial neural networks
Journal of Applied Polymer Science, 2011Co-Authors: G. Caprino, Vanni Lopresto, Claudio Leone, Ilaria PapaAbstract:Acoustic emission (AE) source location in a unidirectional carbon-fiber-reinforced plastic plate was attempted with artificial neural network (ANN) technology. The AE events were produced by a lead break, and the response wave was received by piezoelectric sensors. The time of arrival, determined through the conventional threshold-crossing technique, was used to measure the dependence of the wave velocity on the fiber orientation. A simple empirical formula, relying on classical lamination and suggested by wave-propagation theory, was able to accurately model the Experimental Trend. On the basis of the formula, virtual training and testing data sets were generated for the case of a plate monitored by three transducers and were adopted to select two potentially effective ANN architectures. For final validation, Experimental tests were carried out, with the source positioned at predetermined points evenly distributed within the plate area. A very satisfactory correlation was found between the actual source locations and those predicted by the virtually trained ANNs. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011
-
Acoustic emission source location in unidirectional carbon‐fiber‐reinforced plastic plates with virtually trained artificial neural networks
Journal of Applied Polymer Science, 2011Co-Authors: G. Caprino, Vanni Lopresto, Claudio Leone, Ilaria PapaAbstract:Acoustic emission (AE) source location in a unidirectional carbon-fiber-reinforced plastic plate was attempted with artificial neural network (ANN) technology. The AE events were produced by a lead break, and the response wave was received by piezoelectric sensors. The time of arrival, determined through the conventional threshold-crossing technique, was used to measure the dependence of the wave velocity on the fiber orientation. A simple empirical formula, relying on classical lamination and suggested by wave-propagation theory, was able to accurately model the Experimental Trend. On the basis of the formula, virtual training and testing data sets were generated for the case of a plate monitored by three transducers and were adopted to select two potentially effective ANN architectures. For final validation, Experimental tests were carried out, with the source positioned at predetermined points evenly distributed within the plate area. A very satisfactory correlation was found between the actual source locations and those predicted by the virtually trained ANNs. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011
-
Acoustic Emission Source Location in Unidirectional Carbon-Fibre-Reinforced Plastic Plates Using Virtually Trained Artificial Neural Networks
2010Co-Authors: G. Caprino, Vanni Lopresto, Claudio Leone, Ilaria PapaAbstract:Acoustic emission source location in a unidirectional carbon‐fibre‐reinforced plastic plate was attempted employing Artificial Neural Network (ANN) technology. The acoustic emission events were produced by a lead break, and the response wave received by piezoelectric sensors, type VS150‐M resonant at 150 kHz. The waves were detected by a Vallen AMSY4 eight‐channel instrumentation. The time of arrival, determined through the conventional threshold crossing technique, was used to measure the dependence of wave velocity on fibre orientation. A simple empirical formula, relying on classical lamination and suggested by wave propagation theory, was able to accurately model the Experimental Trend. Based on the formula, virtual training and testing data sets were generated for the case of a plate monitored by three transducers, and adopted to select two potentially effective ANN architectures. For final validation, Experimental tests were carried out, positioning the source at predetermined points evenly distribu...
