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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
 785   786   787   788   789 
Performance analysis of voltage source converter based high voltage direct current line under small control perturbations Reem Ahmed Mostafa; Adel Emary Salem; Ahmed Sayed Abdelhamid; Mohamed EL-Shimy Mahmoud Bekhet
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1224-1235

Abstract

High voltage direct current (HVDC) systems provide important advantages; among them the ability to transmit enormous amounts of electrical power over great distances at low cost. As a result, planners of power systems consider it as a viable choice   for power transmission and interconnection of asynchronous networks. Depending on HVDC grids, continental/super grids have been recently constructed to promote global economic development. The study described in the paper focuses on the behavior of  a voltage sourced converter (VSC) based HVDC  transmission system comprising three arms-neutral point  clamped (NPC) converters interconnecting two asynchronous alternating current (AC)networks. In addition, the system components, and the vector control strategy of active/reactive powers and direct current (DC)bus voltage are simulated in MATLAB/Simulink under varying situations by adjusting the  controller’s  settings.  The study records and analyzes AC/DC voltages and active/reactive powers at two converter stations undervarying power and voltage conditions. The results of the study provide key performance indicators, such as settling time (tsett), steady state error (SSE), overshot/undershoot (OS%/US%), and correlation factor (CF), which demonstrate the robustness of thesystem’s control.
Sanskrit to Hindi language translation using multimodal neural machine translation Prashanth Kammar; Parashuram Baraki; Sunil Kumar Ganganayaka; Manjunath Swamy Byranahalli Eraiah; Kolakaluri Lakshman Arun Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1235-1245

Abstract

Machine translation (MT) is a subfield of computer features that focuses on the automatic translation from one natural language into another without any human involvement. Due to native people interacting in a variety of languages, there is a great need for translating information between languages to send and communicate thoughts. However, they disregard the significance of semantic data encoded in the text features. In this paper, multimodal neural machine translation (MNMT) is proposed for Sanskrit-Hindi translation. The main goal of the proposed method is to fully utilize semantic text features on NMT architecture and to minimize testing and training time. The MNMT is validated on two different NMT architectures: recurrent neural network (RNN) and self-attention network (SAN). The MNMT method’s efficacy is demonstrated by employing the dataset of Sanskrit-Hindi Corpora. Extensive experimental outcomes represent the proposed method’s enhancement over baselines on both architectures. The existing methods, namely, English-to-Indian MT system, Sanskrit-Hindi MT system, and hybrid MT system are used to justify the efficacy of the MNMT method. When compared to the above-mentioned existing methods, RA-RNN respectively achieves a superior BLEU and METEOR of 80.5% and 75.3%, while the RA-SAN respectively achieves a superior BLEU and METEOR of 78.2% and 77.1%.
Performance analysis of photovoltaic panel using machine learning method Ganesh S. Wahile; Srikant Londhe; Shivshankar Trikal; Chandrakant Kothare; Prateek D. Malwe; Nitin P. Sherje; Prasad D. Kulkarni; Uday Aswalekar; Chandrakant Sonawane; Mustak Maher Abdul Zahra; Abhinav Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp19-30

Abstract

Demand for energy is increasing as the world’s population grows, fossil fuels deplete on a daily basis, and climate conditions change. Renewable energy is more important than ever. Solar energy is the most accessible and cost-effective renewable energy source available today. Photovoltaic (PV) cells are the most promising way to convert solar energy into electricity. Wind speed, ambient temperature, incident radiation rate, and dust deposition are some of the internal and external variables that affect photovoltaic panel performance. Unwanted heat from the sun’s rays raises panel temperatures, reduces the amount of energy that solar cells can produce, and lowers conversion efficiency. Solar panels must be adequately cooled. The current research is focused on improving photovoltaic panel performance. The experimental system includes a fully automated photovoltaic panel, a microcontroller (NodeMCU8266), a DC pump, voltage and temperature sensors. The experiment was carried out with and without cooling of the PV panel. The findings suggest that keeping PV panel temperatures close to ambient temperatures improves performance. The Wi-Fi module collects real-time data on PV panel temperature, irradiation, ambient temperature, water temperature, and PV panel power output. The collected data was analyzed using machine learning. The PV panel’s performance was analyzed using the linear regression method.
Advancing medical imaging with GAN-based anomaly detection Ounasser, Nabila; Rhanoui, Maryem; Mikram, Mounia; El Asri, Bouchra
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp570-582

Abstract

Anomaly detection in medical imaging is a complex challenge, exacerbated by limited annotated data. Recent advancements in generative adversarial networks (GANs) offer potential solutions, yet their effectiveness in medical imaging remains largely uncharted. We conducted a targeted exploration of the benefits and constraints associated with GAN-based anomaly detection techniques. Our investigations encompassed experiments employing eight anomaly detection methods on three medical imaging datasets representing diverse modalities and organ/tissue types. These experiments yielded notably diverse results. The results exhibited significant variability, with metrics spanning a wide range (area under the curve (AUC): 0.475-0.991; sensitivity: 0.17-0.98; specificity: 0.14-0.97). Furthermore, we offer guidance for implementing anomaly detection models in medical imaging and anticipate pivotal avenues for future research. Results unveil varying performances, influenced by factors like dataset size, anomaly subtlety, and dispersion. Our findings provide insights into the complex landscape of anomaly detection in medical imaging, offering recommendations for future research and deployment.
Automatic question generation using extended dependency parsing Walelign Tewabe Sewunetie; Laszlo Kovacs
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp1108-1115

Abstract

The importance of automatic question generation (AQG) systems in education is recognized for automating tasks and providing adaptive assessments. Recent research focuses on improving quality with advanced neural networks and machine learning techniques. However, selecting the appropriate target sentences and concepts remains challenging in AQG systems. To address this problem, the authors created a novel system that combined sentence structure analysis, dependency parsing approach, and named entity recognition techniques to select the relevant target words from the given sentence. The main goal of this paper is to develop an AQG system using syntactic and semantic sentence structure analysis. Evaluation using manual and automatic metrics shows good performance on simple and short sentences, with an overall score of 3.67 out of 5.0. As the field of AQG continues to evolve rapidly, future research should focus on developing more advanced models that can generate a wider range of questions, especially for complex sentence structures.
Patient data management using blockchain technology Vijaykumar Bidve; Kiran Kakakde; Pakiriswamy Sarasu; Shailesh Kediya; Pradip Tamkhade; Suprakash Sudarsanan Nair
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1746-1754

Abstract

The patient data management is an essential component of healthcare systems, the secure and efficient data processing is important for the medical data. Data security, interoperability and privacy are the key requirements of data storage systems of healthcare organizations. The electronic medical records have become a key technique to maintain patient information in hospitals due to the technology revolution. Some hospital systems are also using server-based patient detail management systems, they require considerable storage to record all of the patient's medical reports, limiting scalability. They are facing difficulties, including interoperability, security and privacy worries, cyberattacks on centralized storage, and maintaining medical policy compliance simultaneously. The blockchain technology has come up with solution having decentralized and irreversible data storage. A distributed secure ledger of blockchain is the solution, enabling safe storage and retrieval of data. The proposed work yields effectively deployed smart contracts based on the system's functions, real-time patient health monitoring. The main goal of this system is to bring the whole medical data together on a single platform, employing a secured decentralized approach to store and retrieve medical information effectively.
Multi-modal fusion deep transfer learning for accurate brain tumor classification using magnetic resonance imaging images Srinivas Babu Gottipati; Gowri Thumbur
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp825-834

Abstract

Early identification and treatment of brain tumors depend critically on accurate classification. Accurate brain tumor classification in medical imaging is essential for clinical decisions and individualized treatment plans. This paper introduces a novel method for classifying brain tumors called multimodal fusion deep transfer learning (MMFDTL) using original, contoured, and annotated magnetic resonance imaging (MRI) images to showcase its capabilities. The MMFDTL can capture complex tumor features frequently missed in analyzing individual modalities. The MMFDTL model employs three deep learning models for extracting features VGG16, Inception V3, and ResNet 50. The accuracy rate improves when combined with decision based multimodal fusion. It produces impressive outcomes of sensitivity 92.96%, specificity 98.54%, precision 93.60%, accuracy 98.80%, F1-score 93.26%, and kappa 91.86%. This research can improve medical imaging and brain tumor analysis through its multi modal fusion approach. It could give healthcare practitioners vital insights for personalized treatment plans and informed decision making.
Education and awareness: keys to solid waste reduction Laberiano Andrade-Arenas; Elizabeth Liñan Espinoza
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp520-533

Abstract

In the research, education, and awareness were focused on as essential pillars to successfully address the problem of reducing solid waste. The objective is to implement educational and awareness solutions within the community to encourage a substantial change in behavior toward the reduction of solid waste. The design thinking methodology was applied to develop effective solutions. To measure the level of public awareness, we conducted interviews using the Atlas ti22 which allowed us to triangulate with the surveys that revealed that 60% of those surveyed agreed with the policies regarding environmental impact, 55% agreed that the authorities take preventive measures regarding public health and 58% stated that the participation of the citizens in recycling programs. Then, innovative prototypes were developed that satisfied the real needs of users and experts in their evaluation, thus laying a solid foundation. It was concluded that citizens as well as authorities must be aware that by working collaboratively, we can contribute to society.
A time-efficient nonlinear control method for the hyperchaotic finance system synchronization Haris, Muhammad; Shafiq, Muhammad; Ahmad, Israr; Ali, Zulfiqar; Manickam, Geethalakshmi; Ghaffar, Abid
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp834-843

Abstract

Irregular and complex behavior in the financial system can disrupt stability and smooth economic growth. It causes randomness within the system, generating chaos; hindering synchronization behaviour. Achieving smooth and rapid synchronization between two coupled hyperchaotic finance (HF) systems with lessened fluctuation of input and output signals is vital for continuing financial stability and fostering economic growth, a challenge addressed in this article. The paper proposes a novel time-efficient nonlinear control (TENLC) technique and investigates HF systems synchronization using the drive-response system (DRS) arrangement. The proposed TENLC strategy realizes fast and smooth synchronization behaviour between two coupled HF systems, reducing closed-loop state-variable trajectory oscillations. The controller is designed to retain the nonlinear components within the closed-loop system and does not depend on the system's parameters, simplifying the design and analysis process. The Lyapunov stability technique confirms the closed-loop's global stability at the origin. Proofs of mathematical analysis and computer-based simulation results validate the theoretical findings, showing that the presented TENLC strategy converges the state error trajectories to zero in a short transient time with lessened fluctuations for all signals. The comparative computer-based simulation analysis confirms that the presented TENLC approach outperforms other synchronization control techniques.
AES-128 reduced-round permutation by replacing the MixColumns function Jerico S. Baladhay; Edjie M. De Los Reyes
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1641-1652

Abstract

Ensuring the protection of digital data is of utmost importance in our current reliance on network operations. However, security measures such as data encryption often result in decreased performance speed. This paper enhanced the 128-bit version of the advanced encryption standard (AES) by substituting the MixColumns function with a permutation-based approach and decreasing the overall number of rounds. The evaluation results indicate a substantial enhancement in the speed of encryption and decryption, with a 76.76% improvement in encryption time and a 55.46% improvement in decryption time. Furthermore, it is important to mention that the modifications implemented in the standard AES did not compromise its security in relation to the strict avalanche criterion. The avalanche effect of the modified AES is 52.92%, surpassing the minimum requirement of 50%. Finally, the modified AES demonstrated a 31.12% increase in throughput for encryption and a 25.50% increase for decryption when compared to the original AES, using the sample dataset.

Page 787 of 914 | Total Record : 9138

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