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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 29, No 3: March 2023" : 64 Documents clear
Segmentation of data when analyzing the state of telecommunication systems Ilya Lebedev; Babyr Rzayev
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1466-1472

Abstract

The identification of abnormal situations in information and telecommunication systems is considered, based on analyze statistical information of network traffic packages. The method of identifying an anomalous situation based on segmentation of data sample is proposed. The method is aimed at using classifying algorithms that have the best quality indicators on individual data segments. The proposed method will be useful for monitoring information security systems. The method registers of factors that affect the change in the properties of targeted variables. Impact detection allows you to generate data samples, depending on current and expected situations. On the example of the NSL-KDD dataset, there was a division of many data into subset, taking into account the influence of the factors on the range of values. The processing of factors is shown using the change point detection function in the time series. With its use, a division of data sample by the final number of non-intersecting measurable subsets has been made. The results of Accuracy, Precision, F-Measure, Recall for various classifiers are shown. The proposed method allows to increase the quality indicators of classification in continuously changing operating conditions of telecommunication systems.
Complex networks analysis: centrality measures Ali Ali Saber; Noor Kaylan Hamid
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1642-1647

Abstract

The centrality of an edge in a graph is proposed to be the degree of sensitivity of a graph distance function to the weight of the edge under consideration. Many centrality metrics are available in network analysis and are effectively used in the investigation of social network properties. Node position is one of them. In this paper, we propose a novel importance of nodes showing how to locate the most essential nodes in a network and to construct a centrality measure for each node in the network, sort the nodes by centralities, and focus on the top ranked nodes, which are the most relevant in terms of this centrality measure. Our research aims to explain how to identify the most important nodes in networks. A centrality metric should be established for each node in the network, and then the nodes based on their centralities, focusing on the top-ranked nodes, which in light of this importance, might be regarded as the most pertinent measure.
Real-time implementation of SVPWM-sensorless vector control of induction motor using an extended Kalman filter Mustapha Bendjima; Abdeldjebar Hazzab; Mansour Bechar; Medjdoub Khessam; Miloud Rezkallah; Ambrish Chandra; Hussein Ibrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1402-1411

Abstract

In this research paper, space vector pulse width modulation (SVPWM)-sensorless vector control of an induction motor using an extended Kalman filter is presented. The aim of the proposed sensorless control method is to design, implement, and test a sensorless vector control scheme by simulation and experimental implementation. An extended Kalman filter (EKF) simultaneously estimates the rotor speed, the stator stationary axis components (iαs, iβs), and the rotor fluxes (jαs, jβs). The measured stator voltages and currents are employed as inputs for a recursive filter. Simulation results under various operating conditions validate the performances and effectiveness of the proposed observer. The experimental system consists of a host computer with two subsystems: console (SC) and master (SM). The SM subsystem converts to real-time C code, and this code is uploaded into OP5600 real time digital simulation (RTDS) for real-time execution. The obtained experimental results prove that the EKF speed observer can replace the speed or position sensor. This has the benefits of reducing the drive system’s size and overall cost as well as high system reliability.
Sentiment analysis of Malayalam tweets using bidirectional encoder representations from transformers: a study Syam Mohan Elankath; Sunitha Ramamirtham
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1817-1826

Abstract

Sentiment analysis on views and opinions expressed in Indian regional languages has become the current focus of research. But, compared to a globally accepted language like English, research on sentiment analysis in Indian regional languages like Malayalam are very low. One of the major hindrances is the lack of publicly available Malayalam datasets. This work focuses on building a Malayalam dataset for facilitating sentiment analysis on Malayalam texts and studying the efficiency of a pre-trained deep learning model in analyzing the sentiments latent in Malayalam texts. In this work, a Malayalam dataset has been created by extracting 2,000 tweets from Twitter. The bidirectional encoder representations from transformers (BERT) is a pretrained model that has been used for various natural language processing tasks. This work employs a transformer-based BERT model for Malayalam sentiment analysis. The efficacy of BERT in analyzing the sentiments latent in Malayalam texts has been studied by comparing the performance of BERT with various machine learning models as well as deep learning models. By analyzing the results, it is found that a substantial increase in accuracy of 5% for BERT when compared with that of Bi-GRU, which is the next bestperforming model.
Study and innovation of effective classification of XML documents using an advanced deep learning approach Sahunthala Sanmugam; Angelina Geetha; Latha Parthiban
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1551-1559

Abstract

In the digital world, classifying real sensed data in huge volumes derived from numerical problems is a challenging task due to the computational complexity of the metaheuristic searching process. The deep learning approach includes convolutional neural network (CNN), long short-term memory (LSTM), and Bidirectional (BI)-LSTM, suitable for an optimistic processing time of analyzing XML datasets (i.e., social media, trade center, and surveillance data exchanged in the internet world). However, it faces process deviation when datasets extend their range beyond the expected volume. This paper proposes a novel deep learning formwork referred to as archimed improved numerical optimization deep learning (AINODL) to improve the classification of XML datasets. The proposed AINODL framework first extracts feature from XML documents using the vector space model. Secondly, it classifies the XML data using the inbuilt function of the AINODL framework. The experiments demonstrate that the performance parameters accuracy (90%), sensitivity (93%), and specificity (94%) of the proposed AINODL framework are significantly enhanced compared with the existing approaches CNN, LSTM, and BI-LSTM.
An evaluation of automated measurement of slice sensitivity profile of computed tomography image: field of view variations Elvira Rizqi Widyanti; Choirul Anam; Eko Hidayanto; Ariij Naufal; Mohammad Haekal
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1430-1437

Abstract

This study aims to evaluate the automated measurement of slice sensitivity profile (SSP) on the American Association of Physicists in Medicine (AAPM) computed tomography (CT) performance phantom for variations of slice thickness and field of view (FOV). The AAPM CT performance phantom was scanned using a Philips MRC 880 CT Scanner for variations of slice thickness and FOV. The slice thickness values were 1, 3, and 5 mm. The FOV values were 240, 300, 340, 400, and 440 mm. The automated SSPs and their fullwidth at half maximums (FWHMs) were automatically measured from the middle stair object of the phantom. To validate the automated measurement results, the FWHM values of SSPs obtained were compared to those from manual measurements. The differences between FWHMs from automated measurements and set slice thicknesses are less than 0.3 mm, while the differences between FWHMs from automated and manual measurements are less than 0.2 mm. The results from automated measurements are closer to the set slice thickness than those from manual measurements. This automated SSP measurement provides high accuracy and precision for both the slice thickness and the FOV variations.
Druken alcohol intelligent detection system IoT based Arduino controller Siti Aminah Nordin; Zakiah Mohd Yussof; Nurul Nadia Mohammad
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1310-1317

Abstract

An intelligent alcohol detection system has been created to recognize the location of drunken drivers to prevent such road incidents. The project is being enhanced to use the internet of things (IoT) to make it easier for users to track their location and receive messages through their smartphones. The alcohol breath analyzer sensor detects the amount of alcohol in the driver's breath and the liquid crystal display (LCD) will display "Alcohol Detected" and the blood alcohol concentration (BAC) value if the level exceeds the threshold. İn addition, the global system for mobile (GSM) will send short message service (SMS) or make phone calls and telegram will sends BAC value, while the GPS will broadcast the discovered alcohol's location. This project implicitly provides more benefits for current efforts in the development of accident prevention systems in the hopes of putting them into practice in the real world to improve road safety.
Implementation of 1D convolutional neural network for improvement remote photoplethysmography measurement Riza Agung Firmansyah; Yuliyanto Agung Prabowo; Titiek Suheta; Syahri Muharom
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1326-1335

Abstract

Remote photoplethysmography (rPPG) for non-contact heart rate measurement has been widely developed and shows good development. However, motion artifact due to changes in illumination and subject movement is still the main problem. Especially when measurements are taken in real conditions. In these conditions, it will be vulnerable to rPPG signal readings with poor signal quality. So, in this paper, it is proposed to classify the signal quality using one dimensional convolutional neural network (1D CNN). The classification is carried out based on the extraction of the temporal features of the rPPG signal that has been obtained from the plane orthogonal to skin algorithm and the magnitude of the subject's movement when measured. The classification results are entered into a compensated network if the signal obtained shows moderate quality. The compensated network will provide a more accurate estimate of hr value. The test was carried out using a dataset of 10 subjects, each measured with 3 different types of illumination. In the experiments conducted, the system's performance showed an improvement compared to the POS algorithm alone. The experiment found that the mean absolute error measurement was 2.78, and the mean error was relative at 3.67%.
An evolutionary- convolutional neural network for fake image detection Retaj Matroud Jasim; Tayseer Salman Atia
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1657-1667

Abstract

The fast development in deep learning techniques, besides the wide spread of social networks, facilitated fabricating and distributing images and videos without prior knowledge. This paper developed an evolutionary learning algorithm to automatically design a convolutional neural network (CNN) architecture for deepfake detection. Genetic algorithm (GA) based on residual network (ResNet) and densely connected convolutional network (DenseNet) as building block units for feature extraction versus multilayer perceptron (MLP), random forest (RF) and support vector machine (SVM) as classifiers generates different CNN structures. A local search mutation operation proposed to optimize three layers: (batch normlization, activation function, and regularizes). This method has the advantage of working on different datasets without preprocessing. Findings using two datasets evidence the efficiency of the suggested approach where the generated models outperform the state-of-art by increasing 1% in the accuracy; this confirms that intuitive design is the new direction for better generalization.
Automated drainage system for thermoelectric power plant Max Melgarejo-Jara; Omar Chamorro-Atalaya; Florcita Aldana-Trejo; Nestor Alvarado-Bravo; José Farfán-Aguilar; Erika Zevallos-Vera; Evelyn Anicama-Navarrete
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1393-1401

Abstract

The Chilca 2 thermoelectric power plant, located in the province of Lima, Peru, has an open cycle gas turbine and a combined cycle steam turbine, whose combined capacity is 112.8 MW (Mega Watts). This plant requires auxiliary equipment for its operation, which is why it consists of electrical systems, lubrication system, hydraulic ventilation, pumps, vacuum systems and drainage of condensate generated by the difference in temperature in the steam conductor. Said drainage system is inside a 5-meter-deep basement that, being exposed to the elements, is exposed to falling drops of water that are generated by the vapors that are released due to the difference in temperature, repeatedly flooding and exposing to hazards that affect the normal operation of the thermoelectric plant. The proposed solution is based on the philosophy of a feedback control system, which uses a programmable logic controller (PLC) Siemens 1214AC/DC/Relay programmable logic controller, which, through a frequency inverter, activates the drainage pumps; the frequency range at which the variator works is linked to a 4-position level sensor. The result shows that it was possible to activate the frequency variator in a controlled manner through frequencies of 10 Hz, 30 Hz and 60 Hz, in this way a sustained operation of the drainage system is guaranteed.

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