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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Hanafi, Ainain Nur
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Electrical & Electronics Engineering

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Pre-slippage detection and counter-slippage for e-pattern omniwheeled cellular conveyor Keek, Joe Siang; Loh, Ser Lee; Hanafi, Ainain Nur; Cheong, Tau Han
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.6996

Abstract

This paper presents continuation work of e-pattern omniwheeled cellular conveyor (EOCC) since its first introduction. EOCC is a conveyor that is modular and is made up of omniwheels arranged horizontally and vertically. Although in the last published paper, the EOCC had been proven to be capable of transporting box omnidirectionally and achieving yaw control concurrently, however, due to the natural properties of omniwheel, the performance is jeopardized by slippage. While minor slippage can be negligible, but a major slippage can eventually destroy the whole trajectory tracking performance. Therefore, counter-slippage methods are proposed in this paper. The simulation results show that the proposed counter-slippage method significantly improves the trajectory tracking performance up to 42% of reduction in integral of absolute error. Moreover, in this paper, pre-slippage detection method, which aims to perform early detection of slippage, is being presented as well. Although these proposed methods are simple, but they are proven to have achieved improved tracking performance than conventional controller, as presented in this paper.
Hybrid feature selection of microarray prostate cancer diagnostic system Ali, Nursabillilah Mohd; Hanafi, Ainain Nur; Karis, Mohd Safirin; Shamsudin, Nur Hazahsha; Shair, Ezreen Farina; Abdul Aziz, Nor Hidayati
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1884-1894

Abstract

DNA microarray prostate cancer diagnosis systems are widely used, and hybrid feature selection methods are applied to select optimal features to address the high dimensionality of the dataset. This work proposes a new hybrid feature selection method, namely the relief-F (RF)-genetic algorithm (GA) with support vector machine (SVM) classification method. The aim is to evaluate the performance of the proposed method in terms of accuracy, computation time, and the number of selected features. The method is implemented using Python in PyCharm and is evaluated on a DNA microarray prostate cancer. The outcome of this work is a performance comparison table for the proposed methods on the dataset. The performance of GA, particle swarm optimization (PSO), and whale optimization algorithm (WOA) is compared in terms of accuracy, computation time, and the number of selected features. Results show that GA has the highest average accuracy (91.17%) compared to PSO (90.52%) and WOA (85.74%). GA outperforms PSO and WOA due to its superior convergence properties and better alignment with complex problems.
Co-Authors Abdul Aziz, Nor Hidayati Ali, Nursabillilah Mohd Cheong, Tau Han Karis, Mohd Safirin Keek, Joe Siang Loh, Ser Lee Shair, Ezreen Farina Shamsudin, Nur Hazahsha