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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,138 Documents
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A novel distributed generation integrated MFUPQC for active-power regulation with enhanced power quality features Seshu, Moturu; Sundaram, Kalyana; Ramesh, Maddukuri Venkata
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp26-40

Abstract

The distributed generation (DG) scheme has become significant and advanced energy generation corridor for present power distribution system. This advanced DG scheme offers several merits such as flexible active power transfer, low transmission losses, maximize power efficiency, reduce transmission cost, expanding grid capacity, so on. It is motivated that, integration of such DG system in to multi-parallel feeder distribution system with enhanced power-quality features is considered as major problem statement. The proposed multi-functional unified power-quality conditioner (MFUPQC) device has robust design, reliable performance; specifically for addressing the voltage-current affecting PQ issues, regulation of active-power in multi-parallel distribution system. The fundamental goal of the MFUPQC device has been to operate as both a PQ improvement device and a DG integration device by implementing a new universal fundamental vector reference (UFVR) control algorithm. The suggested innovative control algorithm extracts the fundamental voltage and current reference signals with low computational response delay, simple mathematical formulations and without additional transformations which are also major problems identified in classical control schemes. This work focuses on design, operation and performance of MFUPQC device has been evaluated in both PQ and DG operations in a multi-parallel feeder distribution system through MATLAB/Simulink computing platform. The simulation results are illustrated with possible interpretation and analysis.
Artificial intelligence detection of refractive eye diseases using certainty factor and image processing Rachman, Rizal; Susanti, Sari; Suhendi, Hendi; Satyanegara, Adi Karawinata
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.pp1787-1797

Abstract

Refractive errors are defined as an impairment in the eye’s capacity to focus light, resulting in the formation of blurred or unfocused images. These issues arise from alterations in the shape of the cornea, the length of the eyeball, or the aging of the crystalline lens. It is anticipated that the prevalence of visual impairment will increase in conjunction with global population growth. At present, a significant number of countries have not yet accorded sufficient priority to eye health within their healthcare systems. This has resulted in insufficient awareness and reluctance to seek costly specialized care. This study proposes the development of an advanced refractive eye disease detection system with the objective of improving diagnostic accuracy, disseminating disease information, and reducing financial barriers to specialist consultation. The research employs certainty factor (CF) methods and image processing with feature extraction. The initial results demonstrate the potential for identifying specific refractive eye diseases with high certainty through the analysis of symptoms and the examination of photographs of the eye. The proposed approach provides an alternative method for diagnosing refractive eye diseases, which could enhance access to refractive eye care services and reduce the economic burden on patients.
Golden jackal optimization-based clustering scheme for energy-aware vehicular ad-hoc networks Baladhandapani, Mahalakshmi; Kamal, Shoaib; Kumar, Chevella Anil; Balakrishnan, Jegajothi; Praveena, Segu; Puliyanjalil, Ezudheen
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp942-951

Abstract

Clustering in vehicular ad-hoc networks (VANETs) plays a pivotal role in enhancing the reliability and efficiency of transmission among vehicles. VANET is a dynamic and highly mobile network where vehicles form clusters to enable effective data exchange, resource allocation, and cooperative actions. Clustering algorithm, helps vehicles self-organize into clusters based on connectivity and proximity, thus improving scalability and reducing transmission overhead. This cluster enables critical applications such as traffic management, collision avoidance, and data dissemination in VANET, which contribute to more efficient and safer transportation systems. Effective clustering strategy remains an active area of research to address the unique challenges posed by the diverse and rapidly changing environments of VANET. Therefore, this article presents a golden jackal optimization-based energy aware clustering scheme (GJO-EACS) approach for VANET. The presented GJO-EACS technique uses a dynamic clustering approach which adapts to the varying network topologies and traffic conditions, intending to extend the network lifetime and improve energy utilization. The results highlight the potential of the GJO-EACS technique to contribute to the sustainable operation of VANETs, making it a valuable contribution to the field of vehicular networking and smart transportation systems.
Data mining implementation: a survey Widayat, Wisnu; Assiroj, Priati; Sohirin, Sohirin; Prabadhi, Isidorus Anung; Kautsar, Pasha Adelia
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.pp1960-1968

Abstract

In the current era, the relentless advancement of information technology necessitates efficient information acquisition, which relies on proper data processing. To address the challenges in data organization, data mining emerges as a pivotal solution. This study aims to delve into various methodologies for data grouping. Employing a survey approach, the research scrutinizes journals published from 2020 to 2024. The findings illuminate prevalent techniques, algorithms, and software tools utilized in similar research domains. Notably, the study reveals that the predominant approach entails clustering via K-Means leveraging RapidMiner. This insight underscores the significance of employing robust methodologies and tools to streamline data processing and analysis in the contemporary information landscape. By elucidating the prevalent techniques and tools, this study contributes to enhancing understanding and fostering advancements in data mining practices, thereby facilitating more efficient data utilization and decision-making processes.
Analyzing electroencephalogram signals with machine learning to comprehend online learning media Venu, Vasantha Sandhya; Moorthy, Chellapilla V. K. N. S. N.; Patil, Preeti S.; Kale, Navnath D.; Andhare, Chetan Vikram; Tripathi, Mukesh Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1876-1885

Abstract

In E-learning, evaluating students' comprehension of lecture video content is significant. The surge in online platform usage due to the pandemic has been remarkable, but the pressing issue is that learning outcomes still need to match the growth. Addressing this, a scientific system that gauges the comprehensibility of lecture videos becomes crucial for the effective design of future courses. This research paper is based on a cognitive approach utilizing EEG signals to determine student's level of comprehension. The study involves the design, evaluation, and comparison of multiple machines learning models, aiming to contribute to developing an efficient learning system. Fifteen distinct machine learning (ML) classifiers were implemented, among them AdaBoost (ADA), gradient boosting (GBC), extreme gradient boosting (XGboost), extra trees (ET), random forest (RF), light gradient boosting machine (light gum), and decision tree (DT) algorithms standouts. The DT exhibited exceptional performance across metrics such as area under the curve (AUC), accuracy, recall, F1 score, Kappa, precision, and matthews correlation coefficient (MCC). It achieved nearly 1.0 in these metrics while taking a short training time of only 1.7 seconds. This reveals its potential as an efficient classifier for electroencephalography (EEG) datasets and highlights its viability for practical implementation.
DoS attack detection and hill climbing based optimal forwarder selection Radhakrishnan, Palamalai; Seeni, Senthil Kumar; Devi, Dhamotharan Rukmani; Kanthimathi, Tumuluri; Neels Ponkumar, Devadhas David; Sankaran, Vikram Nattamai; Murugan, Subbiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp882-891

Abstract

Wireless networks are becoming a more and more common form of networking and communication, with several uses in many industries. However, the rising popularity has also increased security risks, such as Denial of Service (DoS) attacks. To solve these issues, Denial of Service Attack Detection and Hill Climbing (DDHC) based optimal forwarder selection in Wireless Network. The suggested method seeks to efficiently identify DoS attacks and enhance network performance by preventing the communication hiccups brought on by such attacks. Fuzzy learning method is suggested to analyze trends and find DoS threats. The node bandwidth, connectivity, packet received rate, utilized energy and response time parameters to detect the node abnormality. This abnormality decides the node's future state and detects the DoS attacker. A fuzzy learning algorithm is proposed to detect DoS attacks, which increases attack detection accuracy and lowers false alarm rates. Using the Hill Climbing (HC) procedure, the proposed system transmits data from sender to receiver. Simulation results illustrate the DDHC mechanism increases the DoS attacker detection ratio and minimizes the false positive ratio. Furthermore, it raises the network throughput and reduces the Delay in the network
Impacts of electric vehicle charging stations and DGs on RDS for improving voltage stability using honey badger algorithm Thiruveedula, Madhubabu; Asokan, K.; Subrahmanyam, JBV
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.pp1379-1388

Abstract

The intelligent computational technique used in this research handles the multi-objective voltage stability optimization (MOVSO) problem in radial distribution systems (RDS). The objectives of the proposed research are to minimize network loss, lower the average voltage deviation index (AVDI), and improve the voltage stability index (VSI) of RDS by taking into account the recently created distributed generators (DGs) and electric vehicle charging stations (EVCSs). To address the MOVSO problem, a novel and innovative honey badger algorithm (HBA) optimization technique is put forth. The two stages of HBA, known as the "digging" and "honey" phases, are responsible for effectively identifying the ideal position and appropriate quantity of EVCSs and DGs. The standard IEEE 33 node test system with different case studies is considered to validate the performance of HBA. The simulation results of improved voltage profile, minimized power loss, AVDI and improved VSI are tabulated. The proposed HBA fine-tunes the ideal position and size of the EVCSs to significantly enhance RDS performance under higher loading circumstances. To demonstrate the efficacy and originality of the suggested HBA, the numerical results are contrasted with those of earlier soft computing techniques.
Mathematical model enhancing flash memory reliability through DFT-driven error correction coding Sukanya, Poornima Huchegowda; Chowdaiah, Nagaraju
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1468-1479

Abstract

Flash memory, ubiquitous in diverse electronic devices, confronts persistent challenges stemming from inherent errors that jeopardize data integrity. This research situates itself at the intersection of these challenges and advancements, proposing an inventive error correction coding framework that harnesses the unique capabilities of analysis with a hybrid error control coding (HECC) approach. In the proposed work, a mathematical model aimed at enhancing the flash memory by identifying the error pattern within the pages using the discrete fourier transform (DFT). By incorporating distinctive DFT mathematical properties, the proposed technique intends to improve flash memory error correction beyond traditional methods. The flash storage defect detection and rectification results with hybrid error correction coding achieved bit error rate (BER) of 4.3e-6, latency 14.1, mean 15.1 and standard deviation 1.0. Error correction efficiency 98% and storage overhead 10%. With this approach results are significantly improving the error correction efficiency, reduce storage overhead and enhanced adaptability to diverse error patterns.
Improvement of electricity reliability on the 330 kV Nigeria transmission network with static synchronous compensators Omeje, Luke Uwakwe; Ohanu, Chibuike Peter; Anyaka, Boniface Onyemaechi; Sutikno, Tole
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp733-740

Abstract

The increasing demand for power has caused distortions in Nigeria’s 330 kV transmission network. This is a result of the bulk of the lines being heavily loaded at the moment, which leads to voltage drops and inconsistent electrical delivery. To ensure system reliability, it is therefore crucial to make sure that the system maintains a constant state under specific conditions. This research presents the use of static synchronous compensators (STATCOM) in the Nigerian 330 kV transmission network to reduce power loss and improve the voltage profile. To solve the problem of insufficient voltage and power losses, a three-phase network is simulated using the MATLAB/Simulink software. A three-level, 48- pulse STATCOM was employed to rectify the problem after weak buses were identified through load flow analysis. A 48- pulse converter that handled the STATCOM was used to control harmonic distortions in the system. The outcomes show how crucial the reactive power control mechanism is for regulating the system’s harmonics. However, the method was able to achieve real and reactive power losses of 12.5%. The STATCOM’s 3-level 48- pulse converter also resulted in a total 4.64% reduction in total harmonic distortion (THD).
Customized convolutional neural networks for Moroccan traffic signs classification Khalloufi, Fatima Ezzahra; Rafalia, Najat; Abouchabaka, Jaafar
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp469-476

Abstract

Recognition of traffic signs is a challenging task that can enhance road safety. Deep neural networks have demonstrated remarkable results in numerous applications, such as traffic signs recognition. In this paper, we propose an innovative and efficient system for recognizing traffic signs, based on customized convolutional neural network (CNN) developed through hyperparameters optimization. The effectiveness of the proposed system is assessed using a novel dataset, the Moroccan traffic signs dataset. The results show that the proposed design recognizes traffic signs with an accuracy of 0.9898, outperforming several CNN architectures such as VGGNet, DensNet, and ResNet.

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