eprintid: 13128
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/31/28
datestamp: 2023-11-10 03:27:41
lastmod: 2023-11-10 03:27:41
status_changed: 2023-11-10 01:50:24
type: article
metadata_visibility: show
creators_name: Hamed, Y.
creators_name: Ibrahim Alzahrani, A.
creators_name: Shafie, A.
creators_name: Mustaffa, Z.
creators_name: Che Ismail, M.
creators_name: Kok Eng, K.
title: Two steps hybrid calibration algorithm of support vector regression and K-nearest neighbors
ispublished: pub
keywords: Errors; Magnetic leakage; Motion compensation; Nearest neighbor search; Nondestructive examination; Pipeline corrosion; Pipelines; Regression analysis; Systems engineering; Text processing, Calibration model; Fusion model; K-nearest neighbors; Magnetic flux leakage; Support vector regression (SVR), Ultrasonic testing
note: cited By 21
abstract: Errors in the measurements of pipeline nondestructive tools may lead to faulty decisions causing economic and environmental loss, or system failures. Calibration models are an effective tool that is used to enhance the quality of corrosion measurements affected by inline inspection tools sizing accuracy. Parametric calibration models are limited to datasets with Gaussian behavior. On the other hand, non-parametric calibration models can overcome the normality limitation, however, they provide only a local or general estimation. This paper presents a new hybrid calibration model that is based on two steps K nearest neighbor interpolation and support vector regression. The suggested hybrid model uses both general and local estimation behaviors for the calibration process, hence resulting in a better prediction. The hybrid algorithm was evaluated using a dataset of pipeline corrosion measurements collected by a Magnetic Flux Leakage (MFL) sensor (with an error margin of ±20 of the true values), and an Ultrasonic (UT) device (with an error margin of ±4). The suggested approach resulted in reducing the errors in MFL corrosion measurements to be only ±6.82 instead of the original ±20. © 2020 The Authors
date: 2020
publisher: Elsevier B.V.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078743031&doi=10.1016%2fj.aej.2020.01.033&partnerID=40&md5=2a8ac014cc025bbd9ec21b6dea7f14f9
id_number: 10.1016/j.aej.2020.01.033
full_text_status: none
publication: Alexandria Engineering Journal
volume: 59
number: 3
pagerange: 1181-1190
refereed: TRUE
issn: 11100168
citation:   Hamed, Y. and Ibrahim Alzahrani, A. and Shafie, A. and Mustaffa, Z. and Che Ismail, M. and Kok Eng, K.  (2020) Two steps hybrid calibration algorithm of support vector regression and K-nearest neighbors.  Alexandria Engineering Journal, 59 (3).  pp. 1181-1190.  ISSN 11100168