Sentence examples for transformation operation from inspiring English sources

A physical entity is said to exhibit symmetry when it appears unchanged after undergoing a transformation operation.

Now, performing the transformation operation Q ⊗ R, we get the patient-diagnosis matrix D ∗ as follows: d 1.. d 2.. d 3... ⋅ ⋅ ⋅. d k.

Secondly, based on Lyapunov stability theory and transformation operation in complex plane, the sufficient condition of asymptotic stability for closed-loop power system with prescribed degree of stability α is derived.

Step I: Input the soft set ( F, P ) to obtain the patient-symptom matrix Q. Step II: Input the soft set ( G, S ) to obtain the symptom-disease matrix R. Step III: Perform the transformation operation Q ⊗ R to get the patient diagnosis matrix D ∗. Step IV: Defuzzify all the elements of the matrix D ∗ by (1) to obtain the matrix D ∗ ∗.

The present work substantially improves this last transformation operation by allowing the possibility of using rules belonging to different programs in a transformation sequence when performing a folding step, which is crucial to obtain better, recursive and elegant definitions of fuzzy predicates.

It comprises of three basic types of graph transformation operations: partition, selection, elimination and update.

The Thor system allows data transformation operations to be performed either locally on each physical node or globally across all nodes.

To preserve the structure of the raw data stored in the lake, we adopt two virtual transformation operations, Composition μ and Decomposition γ, to work on the schema of the raw data rather than on the data themselves.

The virtual transformation operations are based on [46, 47], and they allow SemLinker to virtually map an attribute in a source schema to a property in the global ontology.

In addition to the stretching operation, both global and local similarity transformation operations, which are critical problems in the case of watermarking embedding and extraction, are analyzed in detail.

This is in line with our expectation that the application of basic transformation operations can significantly improve the accuracy in recognizing similar brain images.