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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 9,174 Documents
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Intelligent fault diagnosis for power distribution system-comparative studies Thi Thom Hoang; Thi Huong Le
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp601-609

Abstract

Short circuit is one of the most popular types of permanent fault in power distribution system. Thus, fast and accuracy diagnosis of short circuit failure is very important so that the power system works more effectively. In this paper, a newly enhanced support vector machine (SVM) classifier has been investigated to identify ten short-circuit fault types, including single line-to-ground faults (XG, YG, ZG), line-to-line faults (XY, XZ, YZ), double line-to-ground faults (XYG, XZG, YZG) and three-line faults (XYZ). The performance of this enhanced SVM model has been improved by using three different versions of particle swarm optimization (PSO), namely: classical PSO (C-PSO), time varying acceleration coefficients PSO (T-PSO) and constriction factor PSO (K-PSO). Further, utilizing pseudo-random binary sequence (PRBS)-based time domain reflectometry (TDR) method allows to obtain a reliable dataset for SVM classifier. The experimental results performed on a two-branch distribution line show the most optimal variant of PSO for short fault diagnosis.
Sarcasm detection of tweets without #sarcasm: data science approach Rupali Amit Bagate; R. Suguna
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp993-1001

Abstract

Identifying sarcasm present in the text could be a challenging work. In sarcasm, a negative word can flip the polarity of a positive sentence. Sentences can be classified as sarcastic or non-sarcastic. It is easier to identify sarcasm using facial expression or tonal weight rather detecting from plain text. Thus, sarcasm detection using natural language processing is major challenge without giving away any specific context or clue such as #sarcasm present in a tweet. Therefore, research tries to solve this classification problem using various optimized models. Proposed model, analyzes whether a given tweet, is sarcastic or not without the presnece of hashtag sarcasm or any kind of specific context present in text. To achieve better results, we used different machine learning classification methodology along with deep learning embedding techniques. Our optimized model uses a stacking technique which combines the result of logistic regression and long short-term memory (LSTM) recurrent neural net feed to light gradient boosting technique which generates better result as compare to existing machine learning and neural network algorithm. The key difference of our research work is sarcasm detection done without #sarcasm which has not been much explored earlier by any researcher. The metrics used for evolutionis F1-score and confusion matrix.
Design of an environmental management information system for the Universidad Distrital Edwin Arturo Quintero Torres; William Andrés León Beltrán; Juan Manuel Sánchez Céspedes
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp529-539

Abstract

This article presents the design, development and implementation of a software tool, serving as an alternative to the problems involving management, control and reporting of processes within the institutional plan for environmental management (known as plan institucional de gestión ambiental (PIGA) by its Spanish acronym) for the Universidad Distrital Francisco José de Caldas. The software is focused on carrying out such processes to the automation setting, based on the extreme programming (XP) Agile methodology that mainly centers on the continuous development of the customer requirements to offer a more assertive tool, in line with the plan institucional de gestión ambiental in Spanish (PIGA) processes. The result is a complete satisfaction of users and a highly usable, adaptable and efficient software, inherently optimizing and automating the environmental management processes of the PIGA program. This work delivers an applet that meets the design and implementation requirements of environmental management policies. The proposed tool manages to reduce process-related times by 97%, therefore, allowing to aim efforts in other missional functions and increase the overall value offer of the organization.
Automated brain tumor classification using various deep learning models: a comparative study Alaa Ahmed Abbood; Qahtan Makki Shallal; Mohammed Abdulraheem Fadhel
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp252-259

Abstract

The brain tumor, the most common and aggressive disease, leads to a very shorter lifespan. Thus, planning treatments is a crucial step in improving a patient's quality of life. In general, several image techniques such as CT, MRI, and ultrasound have been used for assessing tumors in the prostate, breast, lung, brain, etc. Primarily, MRI images are applied to detect tumors in the brain during this work. The enormous amount of data produced by the MRI scan thwarts tumor vs. non-tumor manual classification at a particular time. Unfortunately, with a small number of images, it has certain limitations (i.e., precise quantitative measurements). Therefore, an automated classification system is necessary to avoid human mortality. The automatic categorization of brain tumors in the surrounding tumor region is a challenging task concerning space and structural variability. Four deep learning models: AlexNet, VGG16, GoogleNet, and RestNet50, are used in this comparative study to classify brain tumors. Based on accuracy, the results showed that RestNet50 is the best model with an accuracy of 95.8%, while AlexNet has the fast performance with a processing time of 1.2 seconds. In addition, a hardware parallel processing unit (GPU) is employed for real-time purposes, where AlexNet (the fastest model) has a processing time of only 8.3 msec.
Investigate and analysis the efficiency of photovoltaic system with active cooling based on numerical method Nabeel Mohamed akram Samad; Khaleel Ali Khudhur; Ghanim Thiab Hasan
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.pp1249-1259

Abstract

The aim of this research is to establish a simulation model to examine the performance and working efficiency of a solar cell system by using computational fluid dynamics (CFD). The proposed model consists of a water feed tube and an absorber plate, as well as a convection heat transfer system, the ansys fluent system simulation program was used. The electrical output of the panel and its efficiency have been investigated and the effect of changes in the intensity of solar radiation on the temperature of the liquid and the absorption plate on the performance of the system have been studied. A dynamic analysis of the thermal hybrid system was performed with a circulation pump. Calculations were performed using a detailed mathematical model. The analysis was performed in three cases, the first case when the system has no cooling, in the second case with constant flow, and in the third case when the pump was optimized. Finally, numerical results were compared with the practical reference results. Both results are in good agreement. The results obtained showed that the system with optimization case give a good improvement in efficiency with low reduction of the thermal efficiency compared to a constant flow.
Developing collaboration tool for virtual team using UML models Yasmin Makki Mohialden; Huda Abdulaali abdulbaqi; Narjis Mezaal Shati
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp38-44

Abstract

Computer aided software engineering (CASE) uses the unified modelling language UML to produce executable code to visualize software architectural blueprint systems. UML is a standard software modeling language for specifying, constructing, documenting and visualizing the artifacts of produced software-intensive systems. However, UML lacks virtual collaboration regardless of their geographical location. The present work developed a prototype of web-based CASE tool that enables UML modes to manage software projects as a virtual collaboration tool. The present methodology starts with the specification of a set of UML triplets; then, the tool generates the visual models automatically with high quality SVG graphs. The system eliminates the manual diagram requirements based on the UML possibility and supported by three diagrams that involves class and use cases. The independent scripting language was PHP7 and MYSQL 8.0 which was used to save the system data.
Improving signal detection accuracy at FC of a CRN using machine learning and fuzzy rules Md Abul Kalam Azad; Anup Majumder; Jugal Krishna Das; Md Imdadul Islam
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 2: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i2.pp1140-1150

Abstract

The performance of a cognitive radio network (CRN) mainly depends on the faithful signal detection at fusion center (FC). In this paper, the concept of weighted Fuzzy rule in Iris data classification, as well as, four machine learning techniques named fuzzy inference system (FIS), fuzzy c-means clustering (FCMC), support vector machine (SVM) and convolutional neural network (CNN) are applied in signal detection at FC taking signal-to-interference plus noise ratio of secondary users as parameter. The weighted Fuzzy rule gave the detection accuracy of 86.6%, which resembles the energy detection model of majority rule of FC; however, CNN gave an accuracy of 91.3% at the expense of more decision time. The FIS, FCMC and SVM gave some intermediate results; however, the combined method gave the best result compared to that of any individual technique.
Induction motor efficiency maximizing based on torque per power index Abbas, Najimaldin M.; Mustafa, Mohammed Obaid; Shakor, Ali M.
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1266-1274

Abstract

In this paper, efficiency maximization of induction motor variable frequency speed regulation system based on torque per power (TPP) index is proposed. The detail of the mathematical model of the induction motor considering the iron loss and the rotor field orientation, the relationship between the motor torque loss power ratio and the motor speed and slip frequency presented. The functional relationship between the torque loss power ratio and the motor speed and slip is derived, and the derivative is obtained to find the optimal slip frequency corresponding to the maximum value. The simulation model and experimental platform of the control system were built in Matlab/Simulink to verify the effectiveness of the method. The result approved the torque loss power ratio takes the maximum value, the high energy efficiency operation with the minimum power loss of the motor control system is realized.
Experimental design for an enhanced parametric modeling of supercapacitor equivalent circuit model Ali Mohsen Alsabari; M. K Hassan; Azura CS; Ribhan Zafira
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 1: July 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i1.pp63-74

Abstract

The modelling of the supercapacitor (SC) plays an important role for the industrial application with many model representations such as electrical, chemical, and electrochemical models. Among one of those models are the equivalent circuit model which has been used to describe the real-time (charging/discharging) operation characteristics of the SC. Apart of its mathematical complexity, the time-consuming experimentally is also a real challenge for obtaining the internal parameters values for the SC. Choices of test equipment with a structure design of experiment also play important criteria affect the accuracy of the model. This research emphasis on a structured of experimental design for SC modelling by using Neware battery tester. The experimental exercise to attain internal parameters of the SC are described and discussed in the paper. The findings were benchmarked with an empirical model of previous researchers. The terminal voltage of SC was validated via experiment with maximum relative error of 0.045%. The model successfully reproduce the SC dynamic behavior during the charge/discharge phase which indicates the proposed method and model accuracy.
Intelligent aquaculture system for pisciculture simulation using deep learning algorithm Sherwin B. Sapin; Bryan A. Alibudbud; Paulo B. Molleno; Maureen B. Veluz; Jonardo R. Asor
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp561-568

Abstract

The project aims to develop an intelligent system for simulating pisciculture in Taal Lake in the Philippines through geographical information system and deep learning algorithm. Records of 2018-2020 from the database of Bureau of fisheries and aquatic resources IV-A-protected area management board (BFAR IVA-PAMB) was collected for model development. Deep learning algorithm model was developed and integrated to the system for time series analysis and simulation. Different technologies including tensorflow.js were used to successfully developed the intelligent system. It is found on this paper that recurrent neural network (RNN) is a good deep learning algorithm for predicting pisciculture in Taal lake. Further, it is also shown in the initial visualization of the system that barangay Sampaloc in Taal has highest rate of fish production in Taal while Tilapia nilotica sp. is the major product of the latter.

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