Effective utilization of heterogeneous multi-modal data for Alzheimer's Disease (AD) diagnosis and prognosis has always been hampered by incomplete data. One method to deal with this is low-rank matrix completion (LRMC), which simultaneous imputes missing data features and target values of interest. Although LRMC yields reasonable results, it implicitly weights features from all the modalities ...

A new Bayesian model updating approach is presented for linear structural models. It is based on the Gibbs sampler, a stochastic simulation method that decomposes the uncertain model parameters into three groups, so that the direct sampling from any one group is possible when conditional on the other groups and the incomplete modal data. This means that even if the number of uncertain parameter...

This paper presents an application of a two-step finite element model updating methodology to a 3D truss bridge model, with the inertial moment and elastic modulus as variables. First, the sensitivity of the change in modal parameters to variations in the physical parameters were analyzed. Based on these sensitivity analyses, the parameters that can be updated were determined. Next, the optimiz...