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p-Index From 2019 - 2024
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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Indonesian Journal of Electrical Engineering and Computer Science
Ali Sophian
International Islamic University Malaysia
Computer Science & IT Electrical & Electronics Engineering

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Pulsed Eddy Current Imaging of Inclined Surface Cracks Faris Nafiah; Ali Sophian
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 5, No 4: December 2017
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v5i4.359

Abstract

Inclined fatigue cracks can potentially cause severe damage to metallic structures as they affect larger region in the tested structure compared to crack perpendicular to the sample surface. The abilitiy to detect and characterize such cracks is paramount in non-destructive testing (NDT). Pulsed eddy current testing (PEC) is known to offer a broadband of excitation frequencies, which in conjuction with C-scan imaging, may offer discrimination of inclination angles of cracks. Finite element modelling (FEM) was carried out to study the effects of different crack inclination angles, while experimental results were used to verify the FEM results. Selection of both time and frequency domain features for C-scan image construction was also presented, where C-scan images of peak value and amplitude at 200 Hz were shown to be potentially capable in determining different inclination angles. Nevertheless, between these two signal teatures, the amplitude at 200 Hz was found to be more effective in the discriminataion of inclined cracks.
Modelling of scanning pulsed eddy current testing of normal and slanted surface cracks Faris Nafiah; Ali Sophian; Md Raisuddin Khan; Ilham Mukriz Zainal Abidin
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 3: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i3.pp1297-1302

Abstract

Thanks to its wide bandwidth, pulsed eddy current (PEC) has attracted researchers of various backgrounds in the attempt to exploit its benefits in Non-destructive Testing (NDT). The ability of modelling PEC problems would be a precious tool in this attempt as it would help improve the understanding of the interaction between the transient magnetic field and the specimen, among others. In this work, a Finite Element Modelling (FEM) has been developed and experimental test data have been gathered for its validation. The investigated cases were simulated surface cracks of different sizes and angles. The study involved looking at time-domain PEC signals at different spatial distances from the cracks’ faces, which would particularly be useful for modelling scanning PEC probes. The obtained results show a good agreement between the FEM and experiment, demonstrating that the modelling technique can be used with confidence for solving similar problems.
Visual-Based Fingertip Detection for Hand Rehabilitation Dayang Qurratu’aini; Ali Sophian; Wahju Sediono; Hazlina Md Yusof; Sud Sudirman
Indonesian Journal of Electrical Engineering and Computer Science Vol 9, No 2: February 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v9.i2.pp474-480

Abstract

This paper presents a visual detection of fingertips by using a classification technique based on the bag-of-words method. In this work, the fingertips are specifically of people who are are holding a therapy ball, as it is intended to be used in a hand rehabilitation project. Speeded Up Robust Features (SURF) descriptors are used to generate feature vectors and then the bag-of-feature model is constructed by K-mean clustering which reduces the number of features. Finally, a Support Vector Machine (SVM) is trained to produce a classifier that distinguishes whether the feature vector belongs to a fingertip or not. A total of 4200 images, 2100 fingertip images and 2100 non-fingertip images, were used in the experiment. Our results show that the success rates for the fingertip detection are higher than 94% which demonstrates that the proposed method produces a promising result for fingertip detection for therapy-ball-holding hands.
Fingertip Detection Using Histogram of Gradients and Support Vector Machine Ali Sophian; Dayang Qurratu’aini
Indonesian Journal of Electrical Engineering and Computer Science Vol 8, No 2: November 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v8.i2.pp482-486

Abstract

One important application in computer vision is detection of objects. One important application in computer vision is detection of objects. This paper discusses detection of fingertips by using Histogram of Gradients (HOG) as the feature descriptor and Support Vector Machines (SVM) as the classifier. The SVM is trained to produce a classifier that is able to distinguish whether an image contains a fingertip or not. A total of 4200 images were collected by using a commercial-grade webcam, consisting of 2100 fingertip images and 2100 non-fingertip images, were used in the experiment. Our work evaluates the performance of the fingertip detection and the effects of the cell’s size of the HOG and the number of the training data have been studied. It has been found that as expected, the performance of the detection is improved as the number of training data is increased. Additionally, it has also been observed that the 10 x 10 size gives the best results in terms of accuracy in the detection. The highest classification accuracy obtained was less than 90%, which is thought mainly due to the changing orientation of the fingertip and quality of the images. 
Efficient Pavement Crack Detection and Classification Using Custom YOLOv7 Model Arselan Ashraf; Ali Sophian; Amir Akramin Shafie; Teddy Surya Gunawan; Norfarah Nadia Ismail; Ali Aryo Bawono
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 1: March 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i1.4362

Abstract

It is crucial to detect and classify pavement cracks as part of maintaining road safety. The inspection process for identifying and classifying cracks manually is tedious, time-consuming, and potentially dangerous for inspectors. As a result, an efficient automated approach for detecting road cracks is essential for this development. Numerous issues, such as variations in intensity, uneven data availability, the inefficacy of traditional approaches, and others, make it challenging to accomplish. This research has been carried out to contribute towards developing an efficient pavement crack detection and classification system. This study uses state of the art deep learning algorithm, customized YOLOv7 model. Data from two sources, RDD2022, a publicly available online dataset, and the second set of data gathered from the roads of Malaysia have been used in this investigation. In order to have balanced data for training, many image preprocessing techniques have been applied to the data, such as augmentations, scaling, blurring, etc. Experimental results demonstrate that the detection accuracy of the YOLOv7 model is significant, 92% on the RDD2022 dataset and 88% on our custom dataset. This study reports the outcomes of experiments conducted on both datasets. RDD2022 achieved a precision of 0.9523 and a recall of 0.9545. On the custom dataset, the resulting values for precision and recall were 0.93 and 0.9158, respectively. The results of this study were compared to those of other recent studies in the same field in order toestablish a benchmark. Results from the proposed system were more encouraging and surpassed the benchmarking ones. 
Machine learning-based pavement crack detection, classification, and characterization: a review Arselan Ashraf; Ali Sophian; Amir Akramin Shafie; Teddy Surya Gunawan; Norfarah Nadia Ismail
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5345

Abstract

The detection, classification, and characterization of pavement cracks are critical for maintaining safe road conditions. However, traditional manual inspection methods are slow, costly, and pose risks to inspectors. To address these issues, this article provides a comprehensive overview of state-of-the-art machine vision and machine learning-based techniques for pavement crack detection, classification, and characterization. The paper explores the process flow of these systems, including both machine learning and traditional methodologies. The paper focuses on popular artificial intelligence (AI) techniques like support vector machines (SVM) and neural networks. It underscores the significance of utilizing image processing methods for feature extraction in order to detect cracks. The paper also discusses significant advancements made through deep learning strategies. The main objectives of this research are to improve efficiency and effectiveness in pavement crack detection, reduce inspection costs, and enhance safety. Additionally, the article presents data gathering approaches, various datasets for developing road crack detection models, and compares different models to demonstrate their advantages and limitations. Finally, the paper identifies open challenges in the field and provides valuable insights for future research and development efforts. Overall, this paper highlights the potential of AI-based techniques to revolutionize pavement maintenance practices and significantly improve road safety.
Co-Authors Ali Aryo Bawono Amir Akramin Shafie Arselan Ashraf Dayang Qurratu’aini Dayang Qurratu’aini Faris Nafiah Hazlina Md Yusof Ilham Mukriz Zainal Abidin Md Raisuddin Khan Norfarah Nadia Ismail Norfarah Nadia Ismail Sud Sudirman Teddy Surya Gunawan Wahju Sediono