Sentence examples for representation model based on from inspiring English sources

A semantic network is a knowledge representation model based on a structured graph.

The Qualitative Rectilinear Projection Calculus (QRPC), a new representation model based on planar trajectories, is presented in this work for describing qualitatively motion patterns.

Then, we propose a structured sparse representation model based on this new transformation matrix for image restoration, taking advantage of the sparse nature of the transform coefficients of image patches over the corresponding transformation matrices.

To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies.

By first developing a conceptual representation of a network model based on the literature, and then formalizing this model into a numerical representation, simulations can be done for almost any kind of religion and person, showing different behaviours for persons with different religious backgrounds and characters.

A new discrete/continuous time representation mixed-integer linear programming model, based on the definition of families of products, is presented.

Additionally, one of the limitations of a model based on a graph representation is that depending on the criteria used to identify the co-substrates and co-products in the reactions, the networks obtained are different.

In order to avoid producing the artifact, this paper presents a new de-noising model based on sparse representation and dictionary learning.

This was in contrast with the behavior of the Control group, which is inconsistent with the model based on explicit representation of the delay.

In the Methods section, we describe a symbolic algorithm for finding all the attractors of a model, based on compact representation of sets of states using Binary Decision Diagrams (BDDs) [ 29].

The proposed technique considers a covariance matrix model based on overcomplete basis representation and tries to fit the unknown signal powers to the sample covariance matrix.