The use of deep generative models (DGMs) such as variational autoencoders,
autoregressive models, flow-based models, energy-based models, generative adversarial …

In this paper, an autoencoder trained with non-standardized time series data and evaluated
using covariance-based residuals for generally applicable unsupervised damage …

Accurately assessing and monitoring the condition of structures is essential for ensuring the
safety and integrity of civil infrastructure. Over the past decade, image-based structural …

Structural health monitoring (SHM) is, without a doubt, one of the most important assets for
building resilient communities. The vast and rapidly advancing research in data science and …

Existing passive acoustics-based techniques for wind turbine blade damage detection lack
the robustness and adaptability necessary for an operational implementation due to their …

Single-cell RNA sequencing (scRNA-seq) provides the expression profiles of individual cells
to study cell-to-cell variation within a cell population and analyses single-cell RNA-seq data …

Condition assessment of civil engineering infrastructure systems is of growing importance as
they face aging and degradation due to both human-made activities and environmental …

This paper proposes a new in-situ damage detection approach for wind turbine blades,
which leverages blade-internal non-stationary acoustic pressure fluctuations caused by the …

We created an overall assessment metric using a deep learning autoencoder to directly
compare clinical outcomes in a comparison of lower limb amputees using two different …

This study delves into the application of Generative Adversarial Networks (GANs) within the
context of imbalanced datasets. Our primary aim is to enhance the performance and stability …