Titlebook: Data Science in Engineering, Volume 9; Proceedings of the 4 Ramin Madarshahian,Francois Hemez Conference proceedings 2022 The Society for E

沉积物

Synthesizing Dynamic Time-Series Data for Structures Under Shock Using Generative Adversarial Netwos chapter presents a methodology for synthesizing statistically indistinguishable time-series data for a structure under shock. Results show that generative adversarial networks are capable of producing material reminiscent of that obtained through experimental testing. The generated data is compare

Epidural-Space

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Ajay Krishan Gairola,Vidit Kumaralysis using Nengo, a large-scale neural network simulation package. In this work, we implement output-only modal identification techniques that rely on solving the blind source separation problem using spike neural networks to extract the natural frequencies, mode shapes, and damping ratios of a si

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Delude

Ritika Mehra,Phayung Meesad,Dhajvir S. Raihe corresponding areas of the selected damaged cases. The simulated dataset is used after for training of a Deep Learning (DL) Convolutional Neural Network (CNN) classifier. The presented methodology is tested on a lab scale CFRP truss structure for which different health scenarios are considered in

Ferritin

Monika Mital,Ashis K. Pani,Suma Damodaranaged condition. A parametric area is inserted into the FE model, changing stiffness and mas to simulate the effect of the physical damage. This area is controlled by the metaheuristic optimization algorithm, which is embedded in the proposed Damage Detection Framework. For effective damage localizat

ADAGE

René Palacios,Victor Morales-Rochadeemed to minimize the effect of the EOV. As such, extracting the mapped data from the original data, termed error signals, will remove the EOV effects and can be further used for damage detection. To this end, the Mahalanobis distances of the errors in the test set from the distribution of the erro

ALB

https://doi.org/10.1007/978-3-030-73819-8modulus which vary randomly from blade to blade. To identify the mistuning within each sector, this approach only uses physical-response data from an individual sector as well as forcing information from traveling-wave excitations. Unlike most previous approaches for blisk mistuning identification,

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