Self-tuning varri method in preparing fatigue segment

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

An overlapping segmentation method on time series data is often used for preparing training dataset i.e. the population of instance, for classification data mining. Having large number of redundant instances would burden the training process with heavy computational operation. This would happen if practitioners fail to acknowledge an appropriate amount of overlap when performing the time series segmentation. Fortunately, the risk could be decreased if knowledge preferences can be determined to guide on overlapping criteria in the segmentation algorithm. Thus, this study aims to investigate how the Varri method is able to contribute for better understanding in preparing training dataset consists of irredundant fatigue segment from the loading history (fatigue signal). Generally, the method locates segment boundaries based on local maxima in the difference function which are above the assigned threshold. In the present study, the mean and standard deviation have been used to define the function due to the fact that predicting attributes are the key components in defining instance redundancy. The resulting dataset from the proposed method is trained by three classification algorithms under the supervision of the Genetic algorithms-based feature selection wrapper approach. The average performance index shows an additional advantage of the proposed method as compared to the conventional procedure in preparing training dataset.

Original languageEnglish
Pages (from-to)45-50
Number of pages6
JournalJurnal Teknologi (Sciences and Engineering)
Volume63
Issue number2
DOIs
Publication statusPublished - Jul 2013

Fingerprint

Time series
Tuning
Fatigue of materials
Redundancy
Data mining
Feature extraction
Genetic algorithms

Keywords

  • Classification
  • Fatigue segment
  • Fatigue signal
  • Segmentation
  • Varri method

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Self-tuning varri method in preparing fatigue segment. / Osman, Mohd Haniff; Mohd Nopiah, Zulkifli; Abdullah, Shahrum; Lennie, A.

In: Jurnal Teknologi (Sciences and Engineering), Vol. 63, No. 2, 07.2013, p. 45-50.

Research output: Contribution to journalArticle

@article{4fe519a5d7634dbc9d8c7a784d67638c,
title = "Self-tuning varri method in preparing fatigue segment",
abstract = "An overlapping segmentation method on time series data is often used for preparing training dataset i.e. the population of instance, for classification data mining. Having large number of redundant instances would burden the training process with heavy computational operation. This would happen if practitioners fail to acknowledge an appropriate amount of overlap when performing the time series segmentation. Fortunately, the risk could be decreased if knowledge preferences can be determined to guide on overlapping criteria in the segmentation algorithm. Thus, this study aims to investigate how the Varri method is able to contribute for better understanding in preparing training dataset consists of irredundant fatigue segment from the loading history (fatigue signal). Generally, the method locates segment boundaries based on local maxima in the difference function which are above the assigned threshold. In the present study, the mean and standard deviation have been used to define the function due to the fact that predicting attributes are the key components in defining instance redundancy. The resulting dataset from the proposed method is trained by three classification algorithms under the supervision of the Genetic algorithms-based feature selection wrapper approach. The average performance index shows an additional advantage of the proposed method as compared to the conventional procedure in preparing training dataset.",
keywords = "Classification, Fatigue segment, Fatigue signal, Segmentation, Varri method",
author = "Osman, {Mohd Haniff} and {Mohd Nopiah}, Zulkifli and Shahrum Abdullah and A. Lennie",
year = "2013",
month = "7",
doi = "10.11113/jt.v63.1911",
language = "English",
volume = "63",
pages = "45--50",
journal = "Jurnal Teknologi",
issn = "0127-9696",
publisher = "Penerbit Universiti Teknologi Malaysia",
number = "2",

}

TY - JOUR

T1 - Self-tuning varri method in preparing fatigue segment

AU - Osman, Mohd Haniff

AU - Mohd Nopiah, Zulkifli

AU - Abdullah, Shahrum

AU - Lennie, A.

PY - 2013/7

Y1 - 2013/7

N2 - An overlapping segmentation method on time series data is often used for preparing training dataset i.e. the population of instance, for classification data mining. Having large number of redundant instances would burden the training process with heavy computational operation. This would happen if practitioners fail to acknowledge an appropriate amount of overlap when performing the time series segmentation. Fortunately, the risk could be decreased if knowledge preferences can be determined to guide on overlapping criteria in the segmentation algorithm. Thus, this study aims to investigate how the Varri method is able to contribute for better understanding in preparing training dataset consists of irredundant fatigue segment from the loading history (fatigue signal). Generally, the method locates segment boundaries based on local maxima in the difference function which are above the assigned threshold. In the present study, the mean and standard deviation have been used to define the function due to the fact that predicting attributes are the key components in defining instance redundancy. The resulting dataset from the proposed method is trained by three classification algorithms under the supervision of the Genetic algorithms-based feature selection wrapper approach. The average performance index shows an additional advantage of the proposed method as compared to the conventional procedure in preparing training dataset.

AB - An overlapping segmentation method on time series data is often used for preparing training dataset i.e. the population of instance, for classification data mining. Having large number of redundant instances would burden the training process with heavy computational operation. This would happen if practitioners fail to acknowledge an appropriate amount of overlap when performing the time series segmentation. Fortunately, the risk could be decreased if knowledge preferences can be determined to guide on overlapping criteria in the segmentation algorithm. Thus, this study aims to investigate how the Varri method is able to contribute for better understanding in preparing training dataset consists of irredundant fatigue segment from the loading history (fatigue signal). Generally, the method locates segment boundaries based on local maxima in the difference function which are above the assigned threshold. In the present study, the mean and standard deviation have been used to define the function due to the fact that predicting attributes are the key components in defining instance redundancy. The resulting dataset from the proposed method is trained by three classification algorithms under the supervision of the Genetic algorithms-based feature selection wrapper approach. The average performance index shows an additional advantage of the proposed method as compared to the conventional procedure in preparing training dataset.

KW - Classification

KW - Fatigue segment

KW - Fatigue signal

KW - Segmentation

KW - Varri method

UR - http://www.scopus.com/inward/record.url?scp=84880738677&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84880738677&partnerID=8YFLogxK

U2 - 10.11113/jt.v63.1911

DO - 10.11113/jt.v63.1911

M3 - Article

AN - SCOPUS:84880738677

VL - 63

SP - 45

EP - 50

JO - Jurnal Teknologi

JF - Jurnal Teknologi

SN - 0127-9696

IS - 2

ER -