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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Mechatronics, Electrical Power, and Vehicular Technology Bulletin of Electrical Engineering and Informatics
Che Ani, Adi Izhar
Unknown Affiliation
Economics, Econometrics & Finance Electrical & Electronics Engineering

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Enhancing efficiency of magnetic energy by implementing square-shaped materials adjacent to induction machine windings Habibi, Muhammad Afnan; Mustika, Soraya Norma; Aripriharta, Aripriharta; Che Ani, Adi Izhar
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 14, No 2 (2023)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2023.v14.158-165

Abstract

This study provides a worthwhile method for increasing the magnetic field energy and induction machine (IM) effectiveness. The coupling between the transmitter and receiver windings in the IM system can be improved by creating materials with specific electromagnetic properties. This added material has altered the magnetic flow as well as the energy of the magnetic field. Eventually, it is possible to calculate the efficiency of the magnetic field, or the ratio of primary to secondary magnetic energy. With the use of two-dimensional finite element analysis, numerical results on five cases with various configurations of a magnetic substance have been produced. This material, which varies in length or breadth, is positioned close to the windings of the transmitter, receiver, or both. Case 3, in which the transmitter generates a magnetic field on the receiver side with a minimum energy of 0.05 J and a maximum energy of 0.015 J, is the ideal material configuration for DC current. Currently, the system efficiency is 0.29 on average. A 1 kHz transmitter's energy is constant under all conditions, but its counterpart's energy fluctuates significantly, with case 5 receiving the most energy. Therefore, case 5 turns into the optimal structural arrangement. It can be inferred that case 5 similarly dominates the other with an efficiency of 0.0026, which is much greater than that of 1 kHz efficiency, while the windings are operating at 1 MHz. This leads to stronger magnetic field coupling and increased power transfer effectiveness.
Performance evaluation of generative adversarial networks for generating mugshot images from text description Bahrum, Nur Nabilah; Setumin, Samsul; Othman, Nor Azlan; Fitri Maruzuki, Mohd Ikmal; Abdullah, Mohd Firdaus; Che Ani, Adi Izhar
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5895

Abstract

The process of identifying photos from a sketch has been explored by many researchers, and the performance of the identification process is almost perfect, particularly for viewed sketches. Suspect identification based on sketches is one of the applications in forensic science. To identify the suspect using these kinds of methods, a face sketch is required. Hence, the methods require skilled artists to sketch the suspect based on descriptions provided by eyewitnesses. However, the skills of these artists are different from one another, which results in different rendered sketches. Therefore, this work attempts to propose a new identification method based only on forensic face-written descriptions. To investigate the feasibility of the proposed method, this study has evaluated the performance of some text-to-photo generators on both viewed and forensic datasets using three different models of GAN which are SAGAN, DFGAN, and DCGAN. Then, the generated images are compared to the real photo contained within those datasets to evaluate how well the proposed method recognizes the faces. The results demonstrated that the recognition rate for the generated photos by the DCGAN models is better than the other two models which achieve a 38.3% recognition rate at rank-10 for mugshot identification.
A dataset for computer-vision-based fig fruit detection in the wild with benchmarking you only look once model detector Che Ani, Adi Izhar; Hamdani Mohammad Farid, Mohammad Afiq; Firdhaus Kamaruzaman, Ahmad Shukri; Ahmad, Sharaf; Hadi, Mokh Sholihul
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.v13i4.5705

Abstract

The image datasets that are most widely used for training deep learning models are specifically developed for applications. This study introduces a novel dataset aimed at augmenting the existing data for the identification of figs in their natural habitats, specifically in the wilderness. In the present study, researchers have generated numerous image datasets specifically for object detection focus on applications in agriculture. Regrettably, it is exceedingly difficult for us to obtain a specialized dataset specifically designed for detecting figs. To tackle this issue, a grand total of 462 photographs of fig fruits were gathered. The augmentation technique was utilized to substantially increase the size of the dataset. Ultimately, we conduct an examination of the dataset by doing a baseline performance study for bounding-box detection using established object detection methods, specifically you only look once (YOLO) version 3 and YOLOv4. The performance obtained on the test photos of our dataset is satisfactory. For farmers, the capacity to identify and oversee fig fruits in their natural or developed environments can be highly advantageous. The detecting device offers instantaneous data regarding the quantity of mature figs, facilitating decision-making procedures.
Systematic review of a lightweight convolutional neural network architectures on edge devices Abu Talib, Muhammad Abbas; Setumin, Samsul; Abu Bakar, Siti Juliana; Che Ani, Adi Izhar; Cahyani, Denis Eka
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i2.pp339-352

Abstract

A lightweight convolutional neural network (CNN) has become one of the major studies in machine learning field to optimize its potential for employing it on the resource-constrained devices. However, a benchmark for fair comparison is still missing and thus, this paper aims to identify the recent studies regarding the lightweight CNN architectures including the types of CNN, its applications, edge devices usage, evaluation types and matrices, and performance comparison. The preferred reporting items for systematic reviews and meta-analysis (PRISMA) framework was used as the main approach to collect and interpret the literature. In the process, 37 papers were identified as meeting the criteria for lightweight CNNs aimed at image classification or regression tasks. Of these, only 20 studies explored the use of these models on edge devices. To conclude, MobileNet appeared as the most used architecture, while the types of CNN focused on image classification for the general-purpose application. Following that, the NVIDIA Jetson Nano was the most utilized edge device in recent research. Additionally, performance evaluation commonly included measures like accuracy and time, along with metrics such as recall, precision, F1-Score, and other similar indicators. Finally, the average accuracy for performance comparison can serve as threshold value for future research in this scope of study.
Co-Authors Abdullah, Mohd Firdaus Abu Bakar, Siti Juliana Abu Talib, Muhammad Abbas Ahmad, Sharaf Aripriharta - Bahrum, Nur Nabilah Denis Eka Cahyani Firdhaus Kamaruzaman, Ahmad Shukri Fitri Maruzuki, Mohd Ikmal Hamdani Mohammad Farid, Mohammad Afiq Mokh Sholihul Hadi Muhammad Afnan Habibi Othman, Nor Azlan Setumin, Samsul Soraya Norma Mustika