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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6,301 Documents
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Multiple faults detection in doubly-fed induction generator wind turbine using artificial neural network Fadzail, Noor Fazliana; Zali, Samila Mat; Mid, Ernie Che
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3342-3349

Abstract

The development of fault detection methods in wind turbine (WT), especially for single fault detection, is continuously increasing. However, the rapid growth of fault detection in WT leads to another challenge where multiple faults can occur. The single fault detection method in WT is no longer reliable, especially when multiple faults occur simultaneously. Therefore, multiple faults detection in doubly-fed induction generators (DFIG) WT was proposed using an artificial neural networks (ANN) model. These multiple faults include internal and external stator faults happening simultaneously. Internal stator faults cover inter-turn short circuit faults and open circuit faults, while external stator faults cover loss of excitation and external short circuit faults. The performance of the developed multiple faults detection model was measured using accuracy and the root mean square error (RMSE) value. The results show that the developed model performs well with high accuracy and a low RMSE value. Thus, the developed model can accurately detect the coexistence of multiple faults in DFIG WT.
Evolution of the concept of device moving from internet of things to artificial intelligence of things Paolone, Gaetanino; Pilotti, Francesco; Piazza, Andrea
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7236-7243

Abstract

The internet of things (IoT) refers to a network of physical devices that are embedded with sensors, software, and network connectivity, allowing them to collect and share data. The devices are the core of any IoT ecosystem. Browsing the extant literature, it emerges that the meaning of the term device depends on the reference context. It follows that, it is an important topic to investigate the reasons behind such a degree of indeterminacy. This paper elaborates on the evolution of the concept of device moving from IoT to artificial intelligence of things (AIoT). The finding that comes from this study is that this evolution is a direct consequence of the evolution of the IoT computing paradigms.
Survey on electrocardiography signal analysis and diabetes mellitus: unraveling the complexities and complications Raja Rajeswari, Satuluri Venkata Kanaka; Vijayakumar, Ponnusamy
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1565-1571

Abstract

Electrocardiography (ECG) is crucial in the medical field to assess cardiovascular diseases. ECG signal generates information, i.e., QRS complexes that imply the cardiac health of the human body. It is depicted in the form of a graph with voltage versus time interval. A distorted, inverted, lagged, small waveform implies an abnormality in a cardiac system. This study highlights the generation of an ECG signal, QRS complexes undertoned towards different diseases, event detection, and signal processing methods. It has become crucial to highlight the possibilities and advances that can be derived from an ECG signal. Throughout this study, an instance of diabetes mellitus (DM) is considered for creating concrete awareness and understanding of an ECG signal in DM. This study focuses on finding the correlation between ECG and DM. Detection of DM from ECG signal is also studied. The findings of this survey paper conclude that the correlation between DM individuals with cardiovascular complications has autonomic neuropathy, which may lead to myocardial infarction. It is also found that the QRS complex and its abnormalities are not specific to complications in DM. However, non-invasive detection of diabetes through ECG signals demonstrates future research potential.
The feasibility of processing waste from religious ceremonies in Bali as clean energy Nugraha, I Made Aditya; Desnanjaya, I Gusti Made Ngurah
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp4921-4928

Abstract

Bali is one of the islands with the largest Hindu religion in Indonesia. This is of course an attraction for tourists to see the culture and natural beauty that the island of Bali has to offer. However, apart from that, there are many religious activities that occur on the island of Bali and the waste produced is something that needs special attention. If this waste is not handled properly, it will threaten environmental sustainability in Bali and indirectly the tourism offered. Therefore, there is a need for a solution to overcome the waste problem. By conducting observations, interviews and literature studies, a way to overcome this problem was found, namely by converting the waste into aromatherapy incense, vermicomposting, briquettes and biofuel. The results of this processing have been studied and of course have the potential to be carried out on the island of Bali. The application of this method also indirectly plays an important role in preserving the environment and economy on the island of Bali.
Real-time mask-wearing detection in video streams using deep convolutional neural networks for face recognition Suhirman, Suhirman; Saifullah, Shoffan; Hidayat, Ahmad Tri; Kusuma, M. Apriandi; Drezewski, Rafał
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp1005-1014

Abstract

This research aims to develop a real-time mask-wearing detection system using deep convolutional neural networks (CNNs). This is crucial in the coronavirus disease 2019 (COVID-19) pandemic to alert individuals who are not wearing masks early on, thereby reducing the spread of the virus. Since COVID-19 primarily spreads through respiratory droplets and mask-wearing is recommended, our proposed study utilizes computer vision techniques, specifically image processing, to detect masked and unmasked faces. We employ a customized CNN architecture consisting of five convolutional layers, followed by max-pooling layers and fully connected (FC) layers. The final output layer utilizes softmax activation for classification. The model is updated with optimized layer configurations and parameter values. We are developing an application that uses a digital camera as an input device. The application utilizes a dataset comprising 11,792 image samples, which are used for training and testing purposes with the 80:20 ratio. Real-time testing is conducted using human subjects captured by the camera. The experimental results demonstrate that the CNN method achieves a classification accuracy of 99% on the training data and 98.83% during real-time video testing. These findings suggest that the real-time mask detection system using CNN performs effectively.
Enhancement of detection accuracy for preventing iris presentation attack Venkatesh, Priyanka; Shyam, Gopal Krishna; Alam, Sumbul
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4376-4385

Abstract

A system that recognizes the iris is susceptible to presentation attacks (PAs), in which a malicious party shows artefacts such as printed eyeballs, patterned contact lenses, or cosmetics to obscure their personal identity or manipulate someone else’s identity. In this study, we suggest the dual channel DenseNet presentation attack detection (DC-DenseNetPAD), an iris PA detector based on convolutional neural network architecture that is dependable and effective and is known as DenseNet. It displays generalizability across PA datasets, sensors, and artifacts. The efficiency of the suggested iris PA detection technique has been supported by tests performed on a popular dataset which is openly accessible (LivDet-2017 and LivDet-2015). The proposed technique outperforms state-of-the-art techniques with a true detection rate of 99.16% on LivDet-2017 and 98.40% on LivDet-2015. It is an improvement over the existing techniques using the LivDet-2017 dataset. We employ Grad-CAM as well as t-SNE plots to visualize intermediate feature distributions and fixation heatmaps in order to demonstrate how well DC-DenseNetPAD performs.
Research design and production of ambient atmosphere monitoring control system internet of things technology application Chuyen, Tran Duc; Hoa, Doan Van
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2554-2561

Abstract

In this paper, presents solutions to application internet of things (IoT) technology in the field of high-tech agriculture, livestock in Vietnam is currently a new problem. The system includes an application on a smartphone; access the parameters via the web, on the computer. The product has a monitoring function: the ambient atmosphere indicators of the pig farm (temperature, humidity, CO2, NH3, H2S gas, and Biogas pressure), on the website 24/24 hour and control automatic control monitoring system. This is a problem of researching, designing and manufacturing products to automatically control the quality of the ambient atmosphere to improve productivity and quality of pig raising in practice at Bach Khoa Production, Trade and Service Joint Stock Company Bach Phuong (Address: Vinh Hao Commune, Vu Ban District, Nam Dinh Province, Vietnam).
Effective ethernet controller protocol architecture verification strategy using system Verilog Parameshwara, Shubha; Sagar, Ganapathi Vithoba; Dasappa, Hamsa Rekha Sorekunte; Nagaraj, Sheetal
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6195-6203

Abstract

The pre-silicon verification is typically more significant than post-silicon verification, which produces an algorithm with the correct functionality and timing parameters. In this paper we propose innovative pre-silicon verification methodology focused on the Ethernet controller architecture as the design under test (DUT). The methodology employs a layered verification architecture implemented using the system Verilog language, aiming to streamline the testing process. A novel test pattern test generator, interfaces and blocks are used to perform the verification. The test patterns are generated based on the operational principles of the ethernet controller block, ensuring comprehensive verification coverage. Additionally, the paper combines different verification parameters with existing approaches to demonstrate the effectiveness of the proposed methodology. It is observed that the performance of the proposed method is better compared to existing methods.
The performance of artificial intelligence in prostate magnetic resonance imaging screening Abu Owida, Hamza; R. Hassan, Mohammad; Ali, Ali Mohd; Alnaimat, Feras; Al Sharah, Ashraf; Abuowaida, Suhaila; Alshdaifat, Nawaf
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2234-2241

Abstract

Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Optimizing electric vehicle charging station placement integrates distributed generations and network reconfiguration Bukit, Ferry Rahmat Astianta; Zulkarnain, Hendra; Kusuma, Choirul Purnama
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp4929-4939

Abstract

The surge in adoption of electric vehicles (EVs) within the transportation sector can be attributed to the growing interest in sustainable transportation initiatives. It is imperative to position electric vehicle charging stations (EVCS) strategically and distribute generations (DGs) to mitigate the effects of electric vehicle loads. This research employs the whale optimization algorithm (WOA) to optimize the placement of EVCS and DGs alongside network reconfiguration. The backward-forward sweep (BFS) power flow technique is utilized to compute load flow under varying load conditions. The primary objective of this investigation is to minimize power losses and enhance the voltage profile within the system. The proposed approach was tested on IEEE-33 and 69 bus systems and compared with particle swarm optimization (PSO) and genetic algorithm (GA) techniques. The simulation outcomes affirm the effectiveness of whale optimization algorithm in determining that integrating 3 EVCS with 3 DGs yields optimal outcomes following network reconfiguration, resulting in a 56.22% decrease in power losses for the IEEE-33 bus system and a 76.13% reduction for the IEEE-69 bus system. The simulation results indicate that the proposed approach enhances system performance across all metrics, showcasing the superior performance of WOA compared to PSO and GA in accomplishing set objectives.

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