87 citations · 136 across the 9 of their papers we have counts for
9 papers
Generative adversarial networks with Monte Carlo dropout uncertainty quantification for structural health monitoring: Fibre Bragg grating data augmentation comparison in Hawk T1A aircraft
Alvaro Peiro, Marios Impraimakis
The signal generation capabilities of a novel data augmentation framework are examined in aerospace structures. First, the framework learns class-conditional temporal and spectral…
Transformer self-attention encoder-decoder with multimodal deep learning for response time series forecasting and digital twin support in wind structural health monitoring
Feiyu Zhou, Marios Impraimakis
The wind-induced structural response forecasting capabilities of a novel transformer methodology are examined here. The model also provides a digital twin component for bridge stru…
An Information-Theoretic Method for Dynamic System Identification With Output-Only Damping Estimation
Marios Impraimakis, Feiyu Zhou, Andrew Plummer
The system identification capabilities of a novel information-theoretic method are examined here. Specifically, this work uses information-theoretic metrics and vibration-based mea…
YOLO with Kolmogorov-Arnold networks and vision-language foundation models for interpretable object detection with trustworthy multimodal AI in computer vision perception
Marios Impraimakis, Daniel Vazquez, Feiyu Zhou
The trustworthy object detection capabilities of a novel Kolmogorov-Arnold network framework are examined here. The approach addresses a key limitation in computer vision for vehic…
An unscented Kalman filter method for real time input-parameter-state estimation
Marios Impraimakis, Andrew W. Smyth
The input-parameter-state estimation capabilities of a novel unscented Kalman filter is examined herein on both linear and nonlinear systems. The unknown input is estimated in two…
A Kullback-Leibler divergence method for input-system-state identification
Marios Impraimakis
The capability of a novel Kullback-Leibler divergence method is examined herein within the Kalman filter framework to select the input-parameter-state estimation execution with the…
A convolutional neural network deep learning method for model class selection
Marios Impraimakis
The response-only model class selection capability of a novel deep convolutional neural network method is examined herein in a simple, yet effective, manner. Specifically, the resp…
Deep recurrent-convolutional neural network learning and physics Kalman filtering comparison in dynamic load identification
Marios Impraimakis
The dynamic structural load identification capabilities of the gated recurrent unit, long short-term memory, and convolutional neural networks are examined herein. The examination…
A generative adversarial network optimization method for damage detection and digital twinning by deep AI fault learning: Z24 Bridge structural health monitoring benchmark validation
Marios Impraimakis, Evangelia Nektaria Palkanoglou
The optimization-based damage detection and damage state digital twinning capabilities are examined here of a novel conditional-labeled generative adversarial network methodology.…