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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 66 Documents
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Search results for , issue "Vol 24, No 3: December 2021" : 66 Documents clear
Design a control system for observing vibration and temperature of turbines Omar Farhan Al-Hardanee; İlyas Çankaya; Abdulmuttalib A. Muhsen; Huseyin Canbolat
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1437-1444

Abstract

The core of a typical hydroelectric power plant is the turbine. Vibration and overheating in a turbine occur when water flows through it, and with increased vibration and high temperature, it will cause the turbine blade to break. In this study, the control and monitoring system is designed to predict and avoid any error before it occurs. This process is achieved by measuring vibration and temperature using sensors and sending signals through the Arduino to the graphical user interfaces (GUI), the system compares the signals taken from the sensors with the permissible limits, and when the permissible limits are exceeded, the processor takes appropriate measures to open and close the turbine gates, where the data is displayed in matrix laboratory graphical user interfaces (MATLAB’s GUI) screen. In this way, monitoring is done, and the appropriate action are taken to avoid mistakes.
Impact of micro hydro power plants on transient stability for the micro grid 20 kV system Syarifuddin Nojeng; Syamsir Syamsir; Reny Murniati
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1278-1287

Abstract

Transient stability analysis is conducted to determine the ability of the electric power system in maintaining the operating stability after a major disturbance. The disturbance can be trigger an impact on the stability of the rotor angle, voltage, and system frequency which can cause loss of synchronization. In this paper, the impact of the interconnection of the Tombolo-Pao mini hydro power plant (MHPP) on the stability of the system was analyzed by several scenarios to determine the behavior of system parameters in a 20 kV system interconnection network. This research is an implementation of regulatory provisions relating to the study of the connection to the PLN distribution network through by regulator. Based on the result of simulation study, transient stability of generators at TomboloPao power plant about 0.1 second, will not occur with network configuration according to modeling activation of anti-islanding protection of Tombolo Pao Power Plant which is set by 2 second. The simulation results show that the location of the disturbance in the electric power system has been influenced by the behavior of the power plant (synchronous generator) which can lead to the instability of the micro-hydro connected to the micro-grid system 20 kV.
Applications of artificial intelligence with cloud computing in promoting social distancing to combat COVID-19 Mohammed Ghadhban Al-Hamiri; Hayder Fadhil Abdulsada; Laith A. Abdul-Rahaim
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1550-1556

Abstract

The emergence of Coronavirus disease 2019 (COVID-19) disease and its rapid spread around the world has serious impacts on people's lives in addition to its effects on many aspects, including the economic and educational sectors. Researches have proved that social distance is effective in combating COVID-19. Maintaining social distance is hard to be handled by humans especially in crowded areas such as airports and campuses. So, there is a need to apply a robust and proactive design to manage this process automatically and smartly. This paper presents a design system to fight COVID-19 by maintaining the social distance with effective monitoring for suspected cases. This has been done using cloud computing and a framework including Arduino (node microcontroller unit (NodeMCU)) with several sensors. The operational aspects of this design system using cloud computing have been discussed. Generally, NodeMCU has been involved in checking the conditions, comparison processing, and communication with the webserver. Moreover, the webserver has been used for determining the maximum number of persons allowed to enter. The results state that this design system is effective in combating COVID-19 through maintaining the social distance and collecting information about suspected cases. This system is valuable, dependable, and stable since the whole process is contactless.
Pneumonia detection based on transfer learning and a combination of VGG19 and a CNN Built from scratch Oussama Dahmane; Mustapha Khelifi; Mohammed Beladgham; Ibrahim Kadri
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1469-1480

Abstract

In this paper, to categorize and detect pneumonia from a collection of chest X-ray picture samples, we propose a deep learning technique based on object detection, convolutional neural networks, and transfer learning. The proposed model is a combination of the pre-trained model (VGG19) and our designed architecture. The Guangzhou Women and Children's Medical Center in Guangzhou, China provided the chest X-ray dataset used in this study. There are 5,000 samples in the data set, with 1,583 healthy samples and 4,273 pneumonia samples. Preprocessing techniques such as contrast limited adaptive histogram equalization (CLAHE) and brightness preserving bi-histogram equalization was also used (BBHE) to improve accuracy. Due to the imbalance of the data set, we adopted some training techniques to improve the learning process of the samples. This network achieved over 99% accuracy due to the proposed architecture that is based on a combination of two models. The pre-trained VGG19 as feature extractor and our designed convolutional neural network (CNN).
New scaled algorithm for non-linear conjugate gradients in unconstrained optimization Ghada M. Al-Naemi; Ahmed H. Sheekoo
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1589-1595

Abstract

A new scaled conjugate gradient (SCG) method is proposed throughout this paper, the SCG technique may be a special important generalization conjugate gradient (CG) method, and it is an efficient numerical method for solving nonlinear large scale unconstrained optimization. As a result, we proposed the new SCG method with a strong Wolfe condition (SWC) line search is proposed. The proposed technique's descent property, as well as its global convergence property, are satisfied without the use of any line searches under some suitable assumptions. The proposed technique's efficiency and feasibility are backed up by numerical experiments comparing them to traditional CG techniques.
Multicriteria Cuckoo search optimized latent Dirichlet allocation based Ruzchika indexive regression for software quality management R. Chennappan; Vidyaa Thulasiraman
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1804-1813

Abstract

The paper presents the software quality management is a highly significant one to ensure the quality and to review the reliability of software products. To improve the software quality by predicting software failures and enhancing the scalability, in this paper, we present a novel reinforced Cuckoo search optimized latent Dirichlet allocation based Ruzchika indexive regression (RCSOLDA-RIR) technique. At first, Multicriteria reinforced Cuckoo search optimization is used to perform the test case selection and find the most optimal solution while considering the multiple criteria and selecting the optimal test cases for testing the software quality. Next, the generative latent Dirichlet allocation model is applied to predict the software failure density with selected optimal test cases with minimum time. Finally, the Ruzchika indexive regression is applied for measuring the similarity between the preceding versions and the new version of software products. Based on the similarity estimation, the software failure density of the new version is also predicted. With this, software error prediction is made in a significant manner, therefore, improving the reliability of software code and service provisioning time between software versions in software systems is also minimized. An experimental assessment of the RCSOLDA-RIR technique achieves better reliability and scalability than the existing methods.
A functional framework based on big data analytics for smart farming Loubna Rabhi; Noureddine Falih; Lekbir Afraites; Belaid Bouikhalene
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1772-1779

Abstract

Big data in agriculture is defined as massive volumes of data with a wide variety of sources and types which can be captured using internet of things sensors (soil and crops sensors, drones, and meteorological stations), analyzed and used for decision-making. In the era of internet of things (IoT) tools, connected agriculture has appeared. Big data outputs can be exploited by the future connected agriculture in order to reduce cost and time production, improve yield, develop new products, offer optimization and smart decision-making. In this article, we propose a functional framework to model the decision-making process in digital and connected agriculture.
Integrated NIR-HE based SPOT-5 image enhancement method for features preservation and edge detection Farizuwana Akma Zulkifle; Rohayanti Hassan; Mohammad Nazir Ahmad; Shahreen Kasim; Tole Sutikno; Shahliza Abd Halim
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1499-1514

Abstract

Recently, many researchers have directed their attention to methods of predicting shorelines by the use of multispectral images. Thus, a simple and optimised method using image enhancements is proposed to improve the low contrast of the Satellite pour l'Observation de la Terre-5 (SPOT-5) images in the detection of shorelines. The near-infrared (NIR) channel is important in this study to ensure the contrast of the vegetated area and sea classification, due to the high reflectance of leaves in the near infrared wavelength region. This study used five scenes of interest to show the different results in shoreline detection. The results demonstrated that the proposed method performed in an enhanced manner as compared to current methods when dealing with the low contrast ratio of SPOT-5 images. As a result, by utilising the near-infrared histogram equalization (NIR-HE), the contrast of all datasets was efficiently restored, producing a higher efficiency in edge detection, and achieving higher overall accuracy. The improved filtering method showed significantly better shoreline detection results than the other filter methods. It was concluded that this method would be useful for detecting and monitoring the shoreline edge in Tanjung Piai.
An enhanced framework for solving cold start problem in movie recommendation systems Salma Adel Elzeheiry; N. E. Mekky; A. Atwan; Noha A. Hikal
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1628-1637

Abstract

Recommendation systems (RSs) are used to obtain advice regarding decision-making. RSs have the shortcoming that a system cannot draw inferences for users or items regarding which it has not yet gathered sufficient information. This issue is known as the cold start issue. Aiming to alleviate the user’s cold start issue, the proposed recommendation algorithm combined tag data and logistic regression classification to predict the probability of the movies for a new user. First using alternating least square to extract product feature, and then diminish the feature vector by combining principal component analysis with logistic regression to predict the probability of genres of the movies. Finally, combining the most relevant tags based on similarity score with probability and find top N movies with high scores to the user. The proposed model is assessed using the root mean square error (RMSE), the mean absolute error (MAE), recall@N and precision@N and it is applied to 1M, 10M and 20M MovieLens datasets, resulting in an accuracy of 0.8806, 0.8791 and 0.8739.
Smart actuator for IM speed control with F28335 DSP application Abidaoun H. Shallal; Assaad F. Nashee; Aws Ezzaldeen Abbas
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1421-1431

Abstract

In the industrial application, the induction motors (IMs) and the digital signal processing (ZQ28335) combination are very important in the scientific field. Two thirds of consumption of electricity is due to motor driven equipment. The direct torque control (DTC) is the standard of the industry and it has fast response control system applications. The drawback of DTC is the flux and torque ripples in the measurements. The scalar control can be considered as a solution to this drawback but with poor response. Torque and speed of IM are controlling individually, the variable speed drive (VSDs) is used. This occurs with variation of the voltage and frequency of IM supply. To decrease the levels of flux and torque ripples, 3-level inverters represent an attractive technique. The compromise of a huge flux and torque at the beginning level and low values at steady state of operation is crucial to ensure better stability with feedback linearization of the nonlinear behavior. In this paper, VSD with DTC IM with multilevel inverter with the newest version of ZQ28335 digital signal processor (DSP) is proposed. Emulation and the results of experiment through DSP ZQ28335 make certain correct dynamic response to the operations of torque and flux.

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