Sentence examples for methods we propose from inspiring English sources

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By investigating the connection between probabilistic and theoretical assumptions in commonly used logN-logS methods, we propose a new class of models with a more realistic physical interpretation.

In this paper, different from defragmentation methods, we propose multi-flow virtual concatenation (MFVC) in Flexi-Grid optical networks.

To alleviate the combinatorial problems associated with such methods, we propose new representational and computational techniques for MDPs that exploit certain types of problem structure.

To overcome the limitations of unsupervised and supervised methods, we propose a new method, which utilizes the class labels to a less important role so as to perform class discovery and classification simultaneously.

To overcome this difficulty and enable the integration of computational analysis and symmetry methods, we propose an automated detection method for engineering structures with cyclic symmetries.

To address the problems with existing keyword-based and semantic-enable methods, we propose an ontology-based semantic retrieval scheme for knowledge search and retrieval from domain documents.

Different with the traditional shortest-path and shortest-time routing methods, we propose a new routing choice with the lowest fuel cost for PHEV drivers.

Based on this methods we propose an architecture approach for the integrated processing of real-time data and historic learning data.

Based on the Lax Friedrichs and Nessyahu–Tadmor one-dimensional central finite difference schemes, the numerical methods we propose involve an original and a staggered grid in order to avoid the resolution of the Riemann problems at the cell interfaces.

Using the matrix perturbation theory and the equivalent modified equation approach for finite difference methods, we propose a class of minimization problems to optimize the free-parameters in the MRT-LBM.

To model problem solvers that use speedup learning methods, we propose a two-component linear model that captures how learned knowledge may accelerate the solution of some problems while leaving the solution of others relatively unchanged.

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