Notice of RetractionImproved Least Square Support Vector Machine for structural damage detection

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

Structural damage detection and health monitoring is very important in many applications, and its key related issue is the method of damage detection. Due to its coefficient matrices being not positive, the KKT system of Least Square Support Vector Machine (LS-SVM) can´t be solved directly by Conjugate Gradient (CG) method. To enhance the convexity and increase further the computational speed, an improved LS-SVM is proposed. Based on the active monitoring technology, the improved LS-SVM combined with Hilbert transform used to extract the characteristics of monitoring signals, is applied to detect damage locations for the composite laminated plate. The testing results show that, the improved LS-SVM not only ensures the detection accuracy but also reduces largely the computation amount compared with LS-SVM under the same conditions.