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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 3: June 2022" : 64 Documents clear
A new application for fast prediction and protection of electrical drive wheel speed using machine learning methodology Medjdoub Khessam; Abdelkader Lousdad; Abdeldjebar Hazzab; Miloud Rezkallah; Ambrish Chandra
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1290-1298

Abstract

This paper introduces a non-linear implementation of the speed control technique of permanent magnetic synchronous motors (PMSM) using electronic differential (ED) command. Artificial neural network (ANN) coupled with particles swarm optimization (ANN-PSO) are implemented to control wheel speed and steering angle. The main purpose of the PMSM system and its application is the command of electric vehicles (EV). In the controller design, three-phase currents and rotor speed shall be measurable and eligible for feedback. Our propulsion platform consists of two PMSM in the back. The study with implemented ANN-PSO is performed after collecting the data from the ED to manage the control of speed EV, Left and right of steering angle and steering ahead. Based on this strategy, a new application can be provided in the GPS application to give the information as input (curved path angle) to ANN-PSO. Next, the application of ANN-PSO can estimate the parameters of ED to avoid the slip, as well as improves better performance and dynamic stability of electric vehicle drive systems.
Predictive and probabilistic modelling using machine learning for building indoor climate control Shokhjakhon Abdufattokhov; Nurilla Mahamatov; Kamila Ibragimova; Dilfuza Gulyamova
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1306-1314

Abstract

For the last few decades, thermal comfort has been considered an aspect of sustainable building evaluation methods and tools. However, estimating the indoor air temperature of buildings is a complicated task due to the nonlinear behaviour of heating, ventilation and air conditioning systems combined with complex dynamics characterized by the time-varying environment with disturbances. This issue can be alleviated by modelling the building dynamics using Gaussian processes since it also measures the uncertainty bounds. The main focus of this paper is designing a predictive and probabilistic room temperature model of buildings using Gaussian processes and incorporating it into model predictive control to minimize energy consumption and provide thermal comfort satisfaction. We exploited the Gaussian processes’ full probabilistic capabilities as the mean prediction for the room temperature model and used the model uncertainty in the objective function not to lose the desired performance and to design a robust control scheme. We illustrated the potentials of the proposed method in a numerical example with simulation results.
Perspectives on adherence to ethics standards and behaviour in software development Senyeki Milton Marebane; Robert Toyo Hans; Jacqui Coosner
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1573-1580

Abstract

Considering the powerful position assumed in creating software that changes human lives and society; it is compelling that in addition to technical standards, ethical standards, especially those captured in codes of ethics and practice be adhered to by software engineers. Despite efforts by professional bodies on ethical standards awareness, it is alleged that software engineers are not able to uphold ethical standards in software development projects. This research study probed lecturers concerned with the teaching of software development courses to determine their perceived levels of ethical standards and behavior. The findings show that the importance of ethical standards is recognized. The respondents reported high levels of ethical standards of their own work, colleagues’ work and their students’ work. Although in general the respondents reported high ethical standards, elements of lesser perceived ethical standards on students indicate the need for improvements. The findings of the study are important to educators and industry to recognize the significance of levels of ethics standards and the role educators have in terms of inculcating such ethical standards early in the making of future software engineers.
Enhancement in privacy preservation in cloud computing using apriori algorithm Raniah Ali Mustafa; Haitham Salman Chyad; Jinan Redha Mutar
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1747-1757

Abstract

Cloud computing provides advantages, like flexibly of space, security, cost optimization, accessibility from any remote location. Because of this factor cloud computing is emerging as in primary data storage for individuals as well as organisations. At the same time, privacy preservation is an also a significant aspect of cloud computing. In regrades to privacy preservation, association rule mining was proposed by previous researches to protect the privacy of users. However, the algorithm involves creation of fake transaction and this algorithm also fails to maintain the privacy of data frequency. In this research an apriori algorithm is proposed to enhance the privacy of encrypted data. The proposed algorithm is integrated with elagmal cryptography and it does not require fake transactions. In this way, the proposed algorithm improves the data protection as well as query privacy and it hides data frequency. Result analysis shows that the proposed algorithm improves the privacy as compared to previously proposed association rule mining and the algorithm also shows 3% to 5% improvement in performance when compared to other existing algorithms. This performance analysis with varying number of the data and fake transactions shows that the proposed algorithm doesn’t require fake transactions, like data privacy association rule mining.
An approach for metadata extraction and transformation for various data sources using R programming language Forat Falih Hasan; Muhamad Shahbani Abu Bakar
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1520-1529

Abstract

The metadata system is the key system for sharing and transforming data between various information systems (ISs), and each database system has its own structure for storing and retrieving metadata information. Metadata information must be extracted for data transformation. Furthermore, these procedures were required to communicate with each type of database system and retrieve the stored metadata; these processes required much information and effort. To overcome the challenge of accessing and extracting metadata from any type of data source, a unifomed method must be developed and integrated into any organization's information systems. The semi-structured data extraction method (SeDEM) is a developed method that includes three main operations: logical structure operation, unique key operation, and relationships operation. Finally, the accurate information obtained using the SeDEM addressed data quality issues concerning the integrity and completeness of the data transformation.
Design of elderly-assistant mobile servant robot Minh Son Nguyen; The Tung Than; Tri Nhut Do; Hoai Nhan Nguyen
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1338-1350

Abstract

Recently, elderly population increasing worldwide has put higher pressure on health-care providers and their families. The advent of elderly care robots will reduce that pressure. In this paper, a design of mobile servant robot with integrated tracking algorithm in order to assist the elderly by companionship is proposed not only to help families take care of their elderly at home but also reduce the pressure on health-care providers. The proposed robot is based on humanoid structure and AI-embedded-GPU controller. The design allows the robot to follow the elderly and accompany them in real-time. In addition, the video streaming algorithm with the pipeline mechanism is integrated on robot controllers so that the owner interacts with the elderly through the internet. The robot controller is embedded into hardware of 128 graphics processing unit cores and 4 ARM Cortex-A9 cores in order to execute convolutional neural network (NCNN) algorithms for elderly recognition and body tracking. The processing speed at 14 fps of video stream in real-time. The proposed robot can move on uneven surfaces with a speed at 0.21 m/s and an accuracy over 90%. However, the video stream processing speed is able to be reduced at 15 fps and latency less than 415 ms when four users appear concurrently.
Optimum control for dynamic voltage restorer based on particle swarm optimization algorithm Saddam Subhi Salman; Abdulrahim Thiab Humod; Fadhil A. Hasan
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1351-1359

Abstract

This article addresses a variety of power quality concerns, including voltage sag and swell, surges, harmonics, and so on, utilizing a dynamic voltage restorer (DVR). The proposed controller for DVR is proportional plus integral (PI) controller. Two methods are used for tuning the parameters of PI controller, trial and error and intelligent optimal method. The utilized optimal method is particle swarm optimization (PSO) method. Results depicted that DVR using PI controller tuned by PSO has improved performance than PI controller tuned by trial and error in term of rise time, maximum overshoot and settling time, as well as total harmonic distortion (THD). These improvements are applicable for voltage sag and swell conditions.
Artificial neural network modeling for predicting the quality of water in the Sabak Bernam River Faqihah Affandi; Mohamad Faizal Abd Rahman; Adi Izhar Che Ani; Mohd Suhaimi Sulaiman
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1616-1623

Abstract

Water quality prediction is aided by environmental monitoring, ecological sustainability, and aquaculture. Traditional prediction approaches capture the nonlinearity and non-stationarity of water quality well. Due to their rapid progress, artificial neural networks (ANNs) have become a hotspot in water quality prediction in recent years. ANNs are utilised in this study to predict water quality using soft computing techniques. The feedforward network and the standard back-propagation method of Levenberg-Marquardt and scaled conjugate gradient learning algorithm were employed in this research. One hidden layer has been recommended for the modelling, with the number of hidden neurons set at 3, 24, and 49. For this analysis, six different testing percentages were used, and the output data can be categorised as '0' for clean water and '1' for polluted water. From the results, it can be shown that the most optimised model was from the model of trainlm with a testing percentage of 18% and with 3 number of neurons. This most optimised model obtains an accuracy of 91.7%, the best validation performance of 0.073346 with 24 epochs, and having a receiver operating characteristic (ROC) curve that is closer to the true positive rate compared to other samples.
Knowledge discovery in manufacturing datasets using data mining techniques to improve business performance Amani Gomaa Shaaban; Mohamed Helmy Khafagy; Mohamed Abbas Elmasry; Heba El-Beih; Mohamed Hasan Ibrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1736-1746

Abstract

Recently due to the explosion in the data field, there is a great interest in the data science areas such as big data, artificial intelligence, data mining, and machine learning. Knowledge gives control and power in numerous manufacturing areas. Companies, factories, and all organizations owners aim to benefit from their huge; recorded data that increases and expands very quickly to improve their business and improve the quality of their products. In this research paper, the knowledge discovery in databases (KDD) technique has been followed, “association rules” algorithms “Apriori algorithm”, and “chi-square automatic interaction detection (CHAID) analysis tree” have been applied on real datasets belonging to (Emisal factory). This factory annually loses tons of production due to the breakdowns that occur daily inside the factory, which leads to a loss of profit. After analyzing and understanding the factory product processes, we found some breakdowns occur a lot of days during the product lifecycle, these breakdowns affect badly on the production lifecycle which led to a decrease in sales. So, we have mined the data and used the mentioned methods above to build a predictive model that will predict the breakdown types and help the factory owner to manage the breakdowns risks by taking accurate actions before the breakdowns happen.
Word recognition and automated epenthesis removal for Indonesian sign system sentence gestures Erdefi Rakun; I Gusti Bagus Hadi Widhinugraha; Noer Fitria Putra Setyono
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1402-1414

Abstract

This research focuses on building a system to translate continuous Indonesian sign system (SIBI) gestures into text. In a continuous gesture, a signer will add an epenthesis (transitional) gesture, which is hand movement with no meaning but needed to connect the hand movement of one word with the next word in a continuous gesture. Reducing the number of irrelevant inputs to the model through automated epenthesis removal can improve the system's ability to recognize the words in continuous gestures. We implemented threshold conditional random fields (TCRF) to identify epenthesis gestures. The dataset consists of 2,255 videos representing 28 common sentences in SIBI. The translation system consists of MobileNetV2 as a feature extraction technique, removing epenthesis gestures found by the TCRF, and a long short-term memory (LSTM) for the classifier. With the MobileNetV2-TCRF-bidirectional LSTM model, the best word error rate (WER) and sentence accuracy (SAcc) were 33.4% and 16.2%, respectively. Intermediate-stage processing steps consisting of sandwiched majority voting of the TCRF and the removal of word labels whose number of frames is less than two frames, along with LSTM output grouping, were able to reduce WER from 33.4% to 3.4% and increase SAcc from 16.2% to 80.2%.

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