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Your search keyword '"Liang, Chuan Zun"' showing total 11 results
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11 results on '"Liang, Chuan Zun"'

3. Optimized number of bats of binary bat algorithm for feature selection.

Author

Mustapa, Nur Atiqah, Senawi, Azlyna, and Liang, Chuan Zun

Subjects
FEATURE selection, BATS, K-nearest neighbor classification, METAHEURISTIC algorithms, ALGORITHMS, ERROR rates
Abstract

Due to its outstanding performance despite its sensitivity to input parameters, the bat algorithm (BA) is a popularly used metaheuristic algorithm in numerous applications. The algorithm's convergence behavior is affected by the algorithm's frequency, the number of bats, pulse emission rate, and loudness parameters. These parameters must be set to their optimal values to increase the outcome's quality. This research aims to find the optimal number of bats of the binary bat algorithm (BBA). In this situation, the fitness criterion for the fitness function will be a classifier error rate. K-nearest neighbors (KNN) are used as the classifier for error rate calculation and validation methods. This study is conducted using 13 datasets with various dimensional sizes. The analysis results indicate that the number of bats substantially impacts the classification accuracy. Based on the results, the optimal number of bats must be considered when tuning the algorithm's parameters to achieve adequate performance. [ABSTRACT FROM AUTHOR]

Published
2024
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6. A network topology approach to diagnose the shift of covariance structure

Author

Yusoff Nur Syahidah, Mohamad Noryanti, Liang Chuan Zun, Sharif Shamshuritawati, and Ken Tan Lit

Subjects
Engineering (General). Civil engineering (General), TA1-2040
Abstract

Understanding the shift of covariance matrices in any process is not an easy task. From the literatures, the most popular and widely used test for covariance shift is Jennrich’s test and Box’s M test. It is important to note that Box and also Jennrich have constructed their own test by involving sample covariance matrix determinant or, equivalently, generalized variance (GV) as multivariate variability measure. However, GV has serious limitations as a multivariate variability measure. Those limitations of GV motivate us to use a proposed test based on an alternative measure of multivariate variability called vector variance (VV). However, if after hypothesis testing the hypothesis of stable process covariance is rejected, then the next problem is to find the cause of that situation. In this paper, network topology approach will be used to understand the shift. A case study will be discussed and presented to illustrate the advantage of this approach.

Published
2018
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11. Analysis of stress intensity factor for fatigue crack using bootstrap S-version finite element model

Author

Mohd Noh, Muhamad Husnain, Mohd Romlay, Mohd Akramin, Liang, Chuan Zun, Shaari, Mohd Shamil, and Takahashi, Akiyuki

Abstract

Purpose: Failure of the materials occurs once the stress intensity factor (SIF) overtakes the material fracture toughness. At this level, the crack will grow rapidly resulting in unstable crack growth until a complete fracture happens. The SIF calculation of the materials can be conducted by experimental, theoretical and numerical techniques. Prediction of SIF is crucial to ensure safety life from the material failure. The aim of the simulation study is to evaluate the accuracy of SIF prediction using finite element analysis. Design/methodology/approach: The bootstrap resampling method is employed in S-version finite element model (S-FEM) to generate the random variables in this simulation analysis. The SIF analysis studies are promoted by bootstrap S-version Finite Element Model (BootstrapS-FEM). Virtual crack closure-integral method (VCCM) is an important concept to compute the energy release rate and SIF. The semielliptical crack shape is applied with different crack shape aspect ratio in this simulation analysis. The BootstrapS-FEM produces the prediction of SIFs for tension model. Findings: The mean of BootstrapS-FEM is calculated from 100 samples by the resampling method. The bounds are computed based on the lower and upper bounds of the hundred samples of BootstrapS-FEM. The prediction of SIFs is validated with Newman–Raju solution and deterministic S-FEM within 95 percent confidence bounds. All possible values of SIF estimation by BootstrapS-FEM are plotted in a graph. The mean of the BootstrapS-FEM is referred to as point estimation. The Newman–Raju solution and deterministic S-FEM values are within the 95 percent confidence bounds. Thus, the BootstrapS-FEM is considered valid for the prediction with less than 6 percent of percentage error. Originality/value: The bootstrap resampling method is employed in S-FEM to generate the random variables in this simulation analysis.

Published
2019
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