Multi-q pattern classification of polarization curves

Author(s)

Fabbri, Ricardo ^[1] ; Bastos, Ivan N. ^[1] ; Moura Neto, Francisco D. ^{[2, 1]} ; Lopes, Francisco J. P. ; Goncalves, Wesley N. ^[3] ; Bruno, Odemir M. ^[3]

Affiliation

^[1] Univ Estado Rio de Janeiro, Inst Politecn, BR-28625570 Nova Friburgo, RJ - Brazil

^[2] Univ Fed Rio de Janeiro, Inst Biofis Carlos Chagas Filho, BR-21941902 Rio De Janeiro, RJ - Brazil

^[3] Univ Sao Paulo, Inst Fis Sao Carlos, BR-13560970 Sao Carlos, SP - Brazil

• Document type: Journal article
• Source: PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS; v. 395, p. 332-339, FEB 1 2014.
• Web of Science Citations: 1

Abstract

Several experimental measurements are expressed in the form of one-dimensional profiles, for which there is a scarcity of methodologies able to classify the pertinence of a given result to a specific group. The polarization curves that evaluate the corrosion kinetics of electrodes in corrosive media are applications where the behavior is chiefly analyzed from profiles. Polarization curves are indeed a classic method to determine the global kinetics of metallic electrodes, but the strong nonlinearity from different metals and alloys can overlap and the discrimination becomes a challenging problem. Moreover, even finding a typical curve from replicated tests requires subjective judgment. In this paper, we used the so-called multi-q approach based on the Tsallis statistics in a classification engine to separate the multiple polarization curve profiles of two stainless steels. We collected 48 experimental polarization curves in an aqueous chloride medium of two stainless steel types, with different resistance against localized corrosion. Multi-q pattern analysis was then carried out on a wide potential range, from cathodic up to anodic regions. An excellent classification rate was obtained, at a success rate of 90%, 80%, and 83% for low (cathodic), high (anodic), and both potential ranges, respectively, using only 2% of the original profile data. These results show the potential of the proposed approach towards efficient, robust, systematic and automatic classification of highly nonlinear profile curves. (C) 2013 Elsevier B.V. All rights reserved. (AU)

• FAPESP's process: 11/01523-1 - Computer vision methods applied to the identification and analysis of plants
• Grantee: Odemir Martinez Bruno
• Support Opportunities: Regular Research Grants
• FAPESP's process: 10/08614-0 - Static and Dynamic Texture Analysis and their Applications in Biology and Nanotechnology
• Grantee: Wesley Nunes Gonçalves
• Support Opportunities: Scholarships in Brazil - Doctorate