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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 64 Documents
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Search results for , issue "Vol 32, No 3: December 2023" : 64 Documents clear
Optimization of the operations and maintenance for wind farm using genetic algorithms Rachid Mkhaitari; Yamina Mir; Mimoun Zazoui
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.pp1257-1266

Abstract

In Morocco, with the growing wind installed capacity it becomes crucial to perform further the O&M process, the wind assets are exposed to several constraints and raindom of maintenances for incidents. The target of this work is to optimize the gross and net production while simultaneously adhering to the minimum turbine unavailability and minimizing all contractual power curtailments due to unforeseeable factors. The proposed system is modeled using technical and mathematical formulas composed of the main function and the associated constraints. The project's modularization ends with a complex mathematical system that is non-linear, non-differentiable and requires the genetic algorithm for optimal resolution. Using Matlab to determine the optimized solution that represents the number of operational turbines per day, allowing for maximum production, minimizing curtailment, and reducing the unavailability of turbines. The 365-vector containing the numbers of turbines per day will opimally define the long-term O&M strategy.
Brain computer interfaces in computer science and engineering areas: a systematic study Jozsef Katona; Attila Kovari; Tibor Guzsvinecz; Judit Szűcs; Robert Demeter; Veronika Szücs
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.pp1755-1765

Abstract

Brain-computer interfaces (BCI) are a channel that implements direct communication between the brain and some external unit. Developments of BCIs can provide new application opportunities in a large number of fields of use. In the development of BCI devices, the development of technology and digital technology represented a big change, as it provided the necessary computing power to implement and run the continuously developing signal processing algorithms that ensure processing and evaluation. The aim of this paper is to provide an overview of BCI research results which were published in the engineering field. In the present study, articles that had a greater impact, where the annual average number of citations is greater than 30, in the BCI field were reviewed and processed in a systematic way, in order to make individual research more comparable. The systematic processing was focused on the aims of application, used device/ dataset, applied data process and achieved best accuracy. This systematic study summarizes the most effective methods used in the BCI processing and highlights the future trends. The results showed an accuracy of 85% thanks to increasingly reliable, accurate and cost-effective signal detection and processing devices, as well as algorithms.
Design of routing protocol for enhancing quality of service in wireless ad hoc and sensor netw ork: LEQA Dawit Hadush Hailu; Berihu G. Gebrehaweria; Gebrehiwet Gebrekrstos Lema; Samrawit H. Kebede; Tole Sutikno
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.pp1589-1597

Abstract

Wireless sensor networks (WSNs) are now adopting mobile sensors due to their increased popularity in research and industry. To enhance WSNs' performance, mobility can be utilized to gather data. But, if the collector's route is fixed and movement is not manageable, current quality of service (QoS) strategies and protocols are ineffective in achieving timely data delivery while maintaining energy efficiency. In the real world, WSN networks use both actuator - actuator and sensor - actuator coordination. To conserve energy in communicati on tasks with heavy traffic and high volume, sensors/actuators can be relocated to desired locations. This study introduces a routing protocol that optimizes delivery latency and energy conservation in WSNs. The proposed latency, energy, and quality of ser vice aware (LEQA) protocol uses a cooperative approach to track the sink and coordinate communication between sensors and actuators. Each sensor schedules its time division multiple access (TDMA) to improve QoS metrics such as low energy consumption, low l atency, or packet loss. It also addresses sensor - actuator coordination and proposes a data communication protocol for efficient and fast communication with actuator nodes. This reduces energy consumption and minimizes latency.
Accuracy enhancement with artificial neural networks for bipolar disorder prediction Nisha Agnihotri; Sanjeev Kumar Prasad
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.pp1695-1702

Abstract

The perfect physical health and mental wellbeing is an important aspect of human kind. Healthcare sectors involving machine learning and deep learning is providing good healthcare services is helping people for safeguarding them from being exploited with extra and unnecessary expenditures on medical check-ups. This gives treatments and many health services on time when needed. In this paper, different performance metrics are applied on online bipolar dataset named “Theory of mind in remitted bipolar disorder dataset” from Kaggle to evaluate the diagnosis for bipolar disorder feature prediction and analysis. In this study the proposed accuracy is better as compared to previous traditional models. As a result, artificial neural networks reduce the time taken in training and classification of dataset in prediction as given in result by optimal combination of epoch and hyperperameters.
Multiple object tracking using space-time adaptive correlation tracking Kusuma Sriram; Kiran Purushotham
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.pp1805-1815

Abstract

In application of tracking and detecting the suspicious activities, multiple object tracking (MOT) has been given fine attention due to its application as it provides the parallel task of identification and tracking of human. MOT ensures the identification and trajectory for each object frame as they interact, despite the changes in its appearance, occlusion and various other tasks involved. Recent adoption of deep learning has given a new perspective but still achieving high metrics remains a major issue to overcome such issues, this research work presents the integrated architecture of deep convolutional covariance networks (DCCNs) and space-time adaptive correlation tracking (STACT) algorithm with similarity map function (SMF). Moreover, in proposed work, DCCNs is utilized for feature extractions through each frame capturing the distinctive information, STACT is tracking approaches that utilizes the SMF for locating and tracking objects. SMFs are updated for any changes in human appearances and motion, also it deals with occlusion. Here the proposed model is evaluated on MOT17 and MOT20 dataset. Performance analysis is carried out through comparing the existing model and Integrated-DCCN achieves higher metrics.
Automatic detection of solar cell surface defects in electroluminescence images based on YOLOv8 algorithm Drir Nadia; Chekired Fathia
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.pp1392-1404

Abstract

In the last few years, the development of renewable energies has increased on a large scale. At least, to guarantee the security and stability of the photovolataic system's production, it is imperative that the photovoltaic modules exhibit a high level of reliability. Therefore, the development of an intelligent detection environment to enable the identification of defects in solar cells during manufacturing has become an important issue for the growth of the photovoltaic (PV) sector. This work proposed a fault diagnosis of surface solar cells using deep learning methods for computer vision, using the eighth version of the you only look once (YOLOv8) algorithm. This detection method was applied to a dataset of electroluminescence (EL) images containing twelve PV cell defects on a publicly available heterogeneous background. Then, using this dataset, we trained, validated, and tested the YOLOv8, YOLOv5 models. The results show that YOLOv8 provides a high level of accuracy in fault diagnosis compared with YOLOv5, and also improves the detection speed of the model. Indeed, the average precision achieves 90.5% This suggested approach ensures high accuracy in fault identification which demonstrates the effectiveness of computer vision to identify multi-object cell defects.
Performance improvement in photovoltaic-grid system using genetic algorithm Rangasamy Sankar; Durairaj Chandrakala; Rengaraj Hema; Dakshnamurthy Padmapriya
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.pp1327-1336

Abstract

In recent, photov oltaic (PV) power generation has increased in importance. The growing significance of PV power production has generated the demand for enhancing energy efficiency via continuous operation at the maximum power point (MPP). To enable effective MPP trac king, the suggested system integrates a proportional - integral (PI) controller with the p erturb and observe (P&O) technique. In order to improve performance in a PV grid system, this work provides a unique method using a proportional - integral - derivative (PI D) controller optimized using a genetic algorithm (GA). The proposed controller architecture integrates the GA algorithm with a PID controller in the voltage source inverter (VSI) of the PV system. To enable effective grid integration, the GA is used to co ntinually optimize the PID controller settings. The converter’ s design criteria and computations are discussed, and MATLAB simulations are used to assess the system’ s performance. Compared to traditional PID controllers, the observed findings show increas ed efficiency, cheaper cost, and enhanced controllability. The suggested GA - PID controller offers opportunities for more study and development in this area while showing potential for improving PV grid system performance.
Cloud computing: an efficient load balancing and scheduling of task method using a hybrid optimization algorithm Ravinder Ravinder; Naresh Vurukonda
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.pp1545-1556

Abstract

The cloud computing trend on the internet is vital as it allows data and applications to be managed over the internet instead of requiring personal devices. The job of users is scheduled in the resources of the cloud in order to improve performance. Schedu ling tasks is an non - deterministic polynomial (NP) - hard problem, as it may have multiple solutions. Various researchers have proposed different load balancing and job scheduling algorithms to optimize the scheduling process in cloud environments, each with disadvantages. Therefore, this research proposes a novel hybrid load balancing and scheduling of tasks by the whale optimization algo rithm (WOA) and seagull optimization algorithm (SOA) in the cloud. This hybrid proposed whale - seagull optimization algorithm (WSOA) optimizes task scheduling in the cloud b y reducing processing time, response, and execution time, maximizing central processing unit (CPU) utilization, memory utilization, throughput, reliability, and balancing the load. The algorithm is simulated using the CloudSim toolkit package. As compared with existing approaches, simulation results showed better performance in terms of response time, processing time, execution time, CPU utilization, memory utilization, throughput, and reliability and is analyzed by comparing with the harries hawks optimiza tion (HHO), hybrid dragonfly and firefly algorithm (ADA), spider monkey algorithm (SMA) and bird swarm optimization (BSO).
A new approach to solve the problem of partial shading in a photovoltaic system Abdessamad, Benlafkih; El Idrissi Mohamed, Chafik; Hadjoudja, Abdelkader; El Moujahid, Yassine; El Maliki, Anas; Othmane, Echarradi; Mounir, Fahoume
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.pp1298-1308

Abstract

This paper introduces a novel global maximum power point (GMPP) tracking method that addresses the challenges of efficiency and power quality degradation in photovoltaic (PV) systems caused by inadequate tracking of the GMPP. The proposed approach employs a cuckoo search algorithm with proportional, integral, and derivative (CSPID). A bio-inspired optimization technique, to effectively track the GMPP under varying weather conditions. To demonstrate its effectiveness, the CSPID algorithm is comprehensively evaluated against two well-established methods, particle swarm optimization (PSO), and cuckoo search algorithm traditional (CSA). The evaluation includes three different scenarios with gradual changes in irradiance and temperature, these tests show the ability of the algorithm to handle the condition of partial shading. The results reveal that the CSPID method achieves an average tracking time of 0.098s and an average tracking efficiency of 99.62%, thereby significantly improving the efficiency and quality of photovoltaic energy production.
Enhancing lung cancer disease diagnosis by employing ensemble deep learning approaches Manmath Nath Das; Niranjan Panda; Rasmita Rautray
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.pp1766-1773

Abstract

Cancer is a disease that results from the unnatural proliferation of aberrant cells that infest the body’s healthy cells and spread throughout the body. Lung cancer is characterized by an imbalance in the cells of the affected organs, namely the lungs. The prediction of lung cancer at an early stage is very important, particularly in countries that are densely populated and have lower incomes. Clinically conventional approaches, such as blood tests and other types of treatments, are used by specialists. The age of artificial intelligence (AI) has begun, and today, it is feasible to construct a computer-aided diagnostic mechanism with the assistance of machine learning and deep learning algorithms. In this particular piece of research, one deep learning algorithm, an artificial neural network (ANN), has been investigated to determine whether or not lung cancer could be detected at an earlier stage. In addition to conventional ANN, ensemble ANN with weighted averaging and soft and hard voting ensemble techniques are also considered. In order to achieve this effectiveness, the state-of-the-art parameters for the proposed method using ANN are assessed and evaluated using the lung cancer dataset. The empirical analysis shows that hard voting-enabled ANN shows the highest accuracy at 97.47%.

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