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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 26, No 1: April 2022" : 64 Documents clear
Magnetic resonance coupling wireless power transfer for green technologies Reem Emad Nafiaa; Aws Zuheer Yonis
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp289-295

Abstract

Wireless power transfer (WPT) is a technology that is considered the focus of scientists' attention for its development and creation to be compatible with many devices that are used today and also consider one of the green technology apps which means any technology can reduce the effect of people on the environment which is today grow continuously. In this paper, a wireless power transfer for a mobile charger had been discussed to get a maximum power and efficiency power transfer. WPT is considered as a reliable technology, efficient, fast, not using wires, and can be used for short and long-range. There are three methods for WPT, electromagnetic induction, magnetic resonance coupling, and radio waves which are classified by the distance that sends the power. Magnetic resonance coupling is the method that has been focused on in this paper because of compatibility with short or medium distances as battery chargers which depend on the magnetic field to transfer power without wires that can protect devices from damages and heating. As result the effect of distance on efficiency has been discussed with reached to nearer distance can improve efficiency however by using magnetic resonance technique, acceptable efficiency can be obtained with appropriate distance.
Electronic datasheet parameter extractor and verifier system for printed circuit board development Ma. Chalina S. Cuntapay; Mark Joseph B. Enojas; Hohn Lois C. Bongao
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp143-151

Abstract

Library components are composed of devices, symbols, and footprints which are the fundamental parts to design and develop printed circuit boards (PCB). In archiving PCB libraries, the creation and checking of the component’s parameters are very important. The checking of footprints and pin configurations from datasheets are manually done which causes delays and downtime when the components do not fit the developed PCB. It was found in a survey that 61.5% of errors come from footprints dimensions. These cause delays in the production and development of PCBs. In this study, a parameter extractor and verifier system were developed to mitigate the error of manual process of archiving library parameters from datasheets. Text recognition and pattern matching algorithms were used for the processing of images from datasheets. The 10-trial tests conducted for each footprint for the extraction of text from the cropped images for standard operating procedures (SOP) series and heat sink thin shrink small outline package (HTSSOP) series were found successful without any error. The checking of footprints and pin configuration was reduced from 45 minutes to 25 minutes. The developed system was evaluated based on user perception by 10 PCB library users which resulted to agree that the system is functional, efficient, and convenient to use.
Vector support machine algorithm applied to the improvement of satisfaction levels in the acquisition of professional skills Omar Chamorro-Atalaya; Orlando Ortega-Galicio; Guillermo Morales-Romero; Darío Villar-Valenzuela; Yeferzon Meza-Chaupis; César León-Velarde; Lourdes Quevedo-Sánchez
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp597-604

Abstract

The study carried out identifies the metricss of the predictive model obtained through the support vector machine (VSM) algorithm, which will be applied in the satisfaction of the acquisition of professional skills of the students of the Professional Engineering Career. As part of the development, the statistical classification tool is used, during the development of the research, it was identified that the predictive model presents as general metrics an accuracy of 82.1%, a precision of 70.72%, a sensitivity of 91.06% and a specificity of 87.60%. Through this model, it contributes significantly to decision-making in relation to improving satisfaction related to the acquisition of professional skills in engineering students, since decision-making by university authorities will have a scientific basis, to take early and timely actions in relation to the predictive elements.
Convolutional neural network for the detection of coronavirus based on X-ray images Essam Hammodi Ahmed; Majid Razaq Mohamed Alsemawi; Mohammed Hasan Mutar; Hatem Oday Hanoosh; Ali Hashem Abbas
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp37-45

Abstract

Nowadays, the coronavirus disease (COVID-19) is considered an ongoing pandemic that spread quickly in most countries around the world. The COVID-19 causes severe acute respiratory syndrome. Moreover, the technique of chest computed tomography (CT) is a method used in the detection of COVID-19. However, the CT method consumes more time and higher-cost as compared with chest X-ray images. Therefore, this paper presents convolutional neural network (CNN) algorithm in the detection of COVID-19 by using X-ray images. In this method, we have used a balanced image database for the normal (healthy) and COVID-19 subjects. The total number of image database is 188 samples (94 healthy samples and 94 COVID-19 samples). Furthermore, there are several evaluation measurements are used to evaluate the proposed model such as accuracy, precision, specificity, sensitivity, F-measure, G-mean, and others. According to the experimental results, the proposed model obtains 98.68% accuracy, 100% precision, and 100% specificity. Besides, the proposed model achieves 97.37%, 98.67%, and 98.68% for sensitivity, F-measure, and G-mean, respectively. The performance of the proposed model by using CNN algorithm shows promising results in the detection of COVID-19. Also, it has outperformed all its comparatives in terms of detection accuracy.
Investigate the optimal power system by using hybrid optimization of multiple energy resources software Ghanim Thiab Hasan; Ali Hlal Mutlaq; Mohammad Omar Salih
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp9-19

Abstract

Increasing the effects of global pollution and the availability of renewable energy sources has push many countries to use reasonable energy sources such as wind and solar energy. This paper presents a case study of evaluating a hybrid renewable energy system by using a hybrid optimization of multiple energy resources (HOMER) software program based on the entered data available from the net for the considered location. The hybrid system consisting of a wind turbine, a photovoltaic system, a battery and a diesel generator. The simulation results are presented in a graphical curves n HOMER software. The obtained results indicate that by using the HOMER simulation program, the optimal design of the hybrid electrical power system for the considered location can be achieved which can help the designer to decide the types and number of the competent required for conducting the intending hybrid electrical power system which results in optimum output power in addition to reducing the overall operating costs.
Adaptive neuro-fuzzy controller trained by genetic-particle swarm for active queue management in internet congestion Mohammed I. Berbek; Ahmed A. Oglah
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp229-242

Abstract

Routers are vital during network congestion. All routers have input and output packet buffers. VVarious congestion control strategies have been suggested. Some controller-based proportional-integral derivative (PIDs) have recently been offered as active queue management (AQM) solutions to alleviate the deterioration of transmission control protocol (TCP) congestion management system performance. However, the time delay is large, the data retention decreases, and oscillation occurs, suggesting that the present PID-controller is unable to fulfill quality of service (QoS) criteria. Some research is developed on new control technologies such as neural networks and fuzzy logic. This paper proposes the adaptive neuro-fuzzy inference system (ANFIS) like PID controller for AQM. This model employs genetic algorithms (GAs) and particle swarm optimization (PSO) to learn and optimize all variables for ANFIS like PID controller. Simulations were used to investigate the effects of using fuzzy like PID based on single sign-on (SSO), and (ANFIS like PI, ANFIS like PID with GA-PSO) controllers on the length of the queue for an AQM router, respectively. Then we compared the findings to see which approach should be utilized to manage the queue length for AQM routers. In simulations, ANFIS like PID has superior stability, convergence, resilience, loss ratio, goodput, lowest rising time, overshoot, and settling time.
Two phase secure data collection technique for wireless sensor networks Gousia Thahniyath; Priti Mishra; Sundar Raj Moorthy
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp512-520

Abstract

Wireless sensor networks (WSNs) are the sensors that are dispersed in a different location that can sense the accumulated data in real-time and send it to the central location for the process of data aggregation. During the transfer of the information using the data nodes from the WSNs to the central location, there may be chances that the data node could be compromised by sensor failure or by an attack from a malicious user. To overcome this problem, we propose a two-phase secure data collection (TPSDC) technique for wireless sensor networks which provides confidentiality and integrity for the data nodes that are being sent to the central location and also during the data aggregation in the central location. Various existing methods have been proposed to secure the data when sent from the WSNs to the internet of things (IoT) devices but they lack to provide both confidentiality and integrity at the same time. Hence our model provides both integrity and confidentiality by providing security to the data nodes. Experimental results show that our model TPSDC performs better in terms of misclassification rate, detection rate, throughput, network lifetime analysis of the node, and communication overhead of the node when compared with the existing methods.
Real-time twitter data analytics of mental illness in COVID-19: sentiment analysis using deep neural network Poonkuzhali Sugumaran; Anu Barathi Bhagavathi Kannu Uma
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp560-567

Abstract

The World Health Organization (WHO) states that the COVID-19 epidemic is being treated as a pandemic, with thousands of individuals infected and dead worldwide. School and college students are suffering from their online classes without any physical activities. Working men and women are also suffering from their working situations, as lots of people have lost their jobs and unemployment rates have become high due to the pandemic, and people are also losing physical contact with other family members, friends, and colleagues. The main objective of the proposed model is to monitor and analyse the real-time Twitter data-related tweets, such as coronavirus mental illness that are commonly used while referencing the pandemic. We have compared three deep learning approaches to sentiment analysis and found them to be useful. The first deep learning technique is to use a basic recurrent neural network (RNN), and the second is to use a deep learning RRN with long short-term memory (LSTM), followed by a gated recurrent unit (GRU). The experiment results indicate that the recurrent neural network built using GRU has the maximum accuracy of 99.47% for positive, negative, and neutral words and statements in Twitter data.
Identifying significant elements of the digital transformation of organizations in Kuwait Abdullah Alshehab; Thalaya Alfozan; Hesham F. Gaderrab; Mohammad Abdulateef Alahmad; Abdulrahman Alkandari
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp318-325

Abstract

The adoption and implementation of digital transformation (DT) plans and initiatives have become compulsory for organizations worldwide during this flourishing era of advanced technology. However, it is estimated that 70% of all DT initiatives do not achieve their goals, with billions of dollars going to waste. This study identified the main elements of digital transformation initiatives from the literature by scholars around the world and then applied these elements to organizations in Kuwait. 33 organizations (private and public) in Kuwait participated in a survey to investigate the level of readiness of organizations attempting to execute digital transformation plans and initiatives. Some insights were found: having a well-defined strategic vision in organizations, digital leadership, technical talent, and having digital expertise are significant elements in implementing digital transformation plans.
Power loss minimization with simultaneous location and sizing of distribution generation units using artificial algae algorithm Vineeta S. Chauhan; Jaydeep Chakravorty
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp28-36

Abstract

Power loss is one of the important pointers used to measure the performance of distributions networks. Many optimization algorithms have been proposed to solve various optimal power flow problems in Electrical Engineering. In this paper, a novel technique, artificial algae algorithm is developed to robustly detect the optimal location and size of distributed generation (DG) units for minimization of total power losses without violating the equality and inequality constraints. The main objective of optimal power flow (OPF) is to maximize or minimize the objective function using various constraint so that steady-state operation point is achieved. The concept of optimal power flow in power system helps to minimize real power loss. In the proposed approach, various control variables like generator bus, voltage magnitudes, and transformer tap settings are considered. The proposed algorithm is simulated in MATLAB and effectiveness is carried on IEEE 33 bus radial distribution system and satisfactory results are achieved when compared with other optimization techniques. A notable improvement in reduction of active power losses with 3 DG operating at different power factors are 65.5%, 42.4%, and 77.8% respectively, were achieved in comparison to the system without DGs and as compared with other research papers.

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