Parallel Computing with the Thick Level Set Method

A parallel method for computing rough set approximations

Massive data mining and knowledge discovery present a tremendous challenge with the data volume growing at an unprecedented rate. Rough set theory has been successfully applied in data mining. The lower and upper approximations are basic concepts in rough set theory. The effective computation of approximations is vital for improving the performance of data mining or other related tasks. The rec...

Type-2 fuzzy set extension of DEMATEL method combined with perceptual computing for decision making

Most decision making methods used to evaluate a system or demonstrate the weak and strength points are based on fuzzy sets and evaluate the criteria with words that are modeled with fuzzy sets. The ambiguity and vagueness of the words and different perceptions of a word are not considered in these methods. For this reason, the decision making methods that consider the perceptions of decision...

Comparison between thick level set (TLS) and cohesive zone models

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Coupling local and non-local damage evolutions with the Thick Level Set model

It is well known that damage evolution models requires a length to avoid spurious localization. We gather these type of models under the name “non-local damage models”. In fact, non-locality is strictly needed only when the stress-strain curve starts to have a negative slope. Before this point, local models may be used. For instance, when damage exhibits some hardening, the stress-strain curve ...

Level Set Method for Thin