Sentence examples for multimodal models from inspiring English sources

Follow-up of these multimodal models using linear discriminant functions (LDA) is also discussed, with applications to future studies wishing to integrate multiple scanning techniques into investigating populations of interest.

A competitive selection mechanism is used to determine the most confident hypotheses, which are used on the system level to train multimodal models that link object identity to invariant hypothesis properties.

The top-down data flow originates at the system level, where the trained multimodal models are used to obtain space- and feature-based attentional modulation signals, providing biases for the competitive selection process at the hypothesis level.

Multimodal Model Integration for Sentence Unit Detection.

In support of such work, a multimodal model is developed to characterize the device and the liner material.

After the definition and validation of the multimodal model, the third part, an optimization model, has been developed, based on bi-level mathematical programming.

While the non-linear modelling of sloshing fluid is currently limited to tanks of simple geometries, this paper develops a non-linear multimodal model which describes the sloshing behaviour of a fluid in a flat-bottom tank of arbitrary geometry.

The multimodal model is validated against a finite element model, and also used in an optimization algorithm to estimate the material properties of a prototype liner using experimental transmission loss data.

Natural slosh modes/frequencies, hydrodynamic coefficients and Stokes-Joukowski potential associated with the multimodal model are then obtained by solving the eigenvalue problem of free fluid sloshing using a higher order boundary element method.

A multimodal model of fluid sloshing within a baffled cylindrical tank is initially formulated using potential flow equations and linearized free-surface boundary condition considering maneuver-induced vehicle motions along the lateral, vertical and roll axes.

The multimodal model with estimated material properties at 2.8 MPa achieves a root mean squared error of 1.7 dB or less for two different length devices over a frequency range of 50 2000 Hz.