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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
 648   649   650   651   652 
Solar-wind power generation system for street lighting using internet of things Jahangir Hossain; Nasir A. Algeelani; Ahmed Hasan Hamood Al-Masoodi; Aida Fazliana Abdul Kadir
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp639-647

Abstract

Every country is subsidising millions of dollars for street lighting as those are connected to the grid. Besides, the generation of electricity comes from fossil fuels with emissions of carbon dioxide (CO2). Therefore, alternative generation of electricity can be done by using a hybrid system. Solar energy starts as the day begins, and the wind is accessible on the streets with a to-and-fro motion of the car. It does not rely on any factor. This hybrid system generates 12V DC, whereas no AC converters are used, resulting in a reduction the system's cost. The control system was constructed based on IoT and included the most sophisticated battery charging system to improve the battery's cells' life cycle. The hardware system has been simulated using EasyEDA and incorporated with the PCB design. The prototype is constructed alongside collected data to compare with the theoretical basis towards net-zero energy street lighting (nZESL). The prototype was able to lead to nZESL and backup stability of the system is 10 hours per day, along with the validation of theoretical analyses and effectiveness of the system. The system has the potential to make a significant contribution to lowering CO2 emissions and government subsidies for street lighting.
Adaptive backstepping control of linear induction motors using artificial neural network for load estimation Omar Mahmoudi; Abdelkrim Boucheta
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.pp202-210

Abstract

Linear induction motors (LIMs) make performing a direct linear motion possible without any mechanical rotary to linear motion transforming parts. Obtaining a precise mathematical model of such type of motors presents a difficulty due to time varying parameters and external load disturbance. This paper proposes an adaptive backstepping controller structure based on lyapunov stability for controlling a LIM position. Which can guarantee the annulment of position tracking error, despite of parameter uncertainties. Parameter update laws are extracted to estimate mover mass, friction coefficient and load force disturbance, which are assumed to be constant parameters; as a result, compensating their undesirable effect on control design. Then, load disturbance estimate is replaced with an artificial neural network (ANN) to reduce the estimation error. The numerical validation has shown better performance compared to the conventional backstepping controller, and proved the robustness of the proposed adaptive controller design against parameter changes.
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.
An analysis on micronutrient deficiency in plant leaf and soil using digital image processing Swetha Reddy Anthay; Arun Chokkalingam; Komathi B. Jeyashanker; Bharathiraja Natarajan
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.pp568-575

Abstract

The plant requires thirteen different nutrients. The two main types of nutrients are micronutrients and macronutrients. Diseases develop due to deficiency of vital nutrients, resulting in colored spots on the leaves. Plant development is affected by toxicity or lack of one or more of these nutrients, resulting in plant death. As a result, a continuous monitoring system is necessary to know the nutritional status of the plants to enhance production efficiency and output. Optical image recognition-based medical technology can identify indicators of inaccuracy faster than the human eye. Consequently, farmers are prepared to take prompt and effective remedial action. This article investigates the nutrient deficits in plants using image processing techniques.
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.
A heuristic approach to minimize three criteria using efficient solutions Dara Ali Hassan; Nezam Mehdavi Amiri; Ayad Mohammed Ramadan
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.pp334-341

Abstract

In optimization, scheduling problems is concerning allocations of some resources which are usually limited. These allocations are done in order to fulfil some criterion by performing some tasks or jobs to optimize one or more objective functions. Simultaneous multi-criteria scheduling problem is known as np-hard optimization problem. Here, we consider three criteria for scheduling a number of jobs on a single machine. The problem is to minimize the sum of total completion time, maximum earliness and maximum tardiness. Every job is to be processed without interruption and becomes available for processing at time zero. The aim is to find a processing order of the jobs to minimize three-objective functions simultaneously. We present a new heuristic approach to find a best overall solution (accepted) of the problem using efficient solutions of one of the other related criteria. We establish a result to restrict the range of the optimal solution, and the lower bound depends on the decomposition of the problem into three subproblems. The approach is tested on a set of problems of different number of jobs. Computational results demonstrate the efficiency of the proposed approach.
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.
Multilayer perceptron artificial neural networks-based model for credit card fraud detection Bassam Kasasbeh; Balqees Aldabaybah; Hadeel Ahmad
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.pp362-373

Abstract

Nowadays, credit card fraud has emerged as a major problem. People are becoming increasingly using credit cards to pay for their transactions, it has become more popular and essential in our lives. Fraudsters are developing new strategies and techniques over time, and it is not easy for humans to manually check out all transactions. The cost of fraudulent transactions is significant and without prevention mechanisms it is rising. Finding the best methodology to detect fraudulent transactions is a crucial asset to the industry to reduce the fraud financial loss. Artificial neural networks (ANN) technique is considered as one of the effective techniques that has proved its efficiency in detecting credit card fraud transactions with high precision and minimum cost. In this paper, we propose a multilayer perceptron (MLP) ANN-based model solution to improve the accuracy of the detection process. The performance of the methodology is measured based on the precision, sensitivity, specificity, accuracy, F-measure, area under curve (AUC) and root mean square error (RMSE). Moreover, we illustrate the performance results of these measures with a descriptive analysis. Experimental results have shown that the proposed ANN-based model is efficient and does improve the accuracy of the detection of fraudulent transactions.
Grey wolf optimization-recurrent neural network based maximum power point tracking for photovoltaic application Arumbu Venkadasamy Prathaban; Dhandapani Karthikeyan
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp629-638

Abstract

To increase the photovoltaic (PV) power-generation conversion, MPPT is the primary concern. This works explains about the grey wolf optimization (GWO - RNN)-based hybrid maximum power point tracking (MPPT) method to get quick and maximum photovoltaic (PV) power with zero oscillation tracking. The GWO – RNN based MPPT method doesn’t need additional sensor for measuring irradiance and temperature variables. The NLT is used for the multi-level inverter (MLI) control strategy to achieve less harmonics distraction and less switching losses with better voltage and current profile. This employed methodology brings remarkable aspects in the PV boosting potential extraction. A GWO – RNN controlled LUO converter is a zero output harmonic agreement impedance matching interface that is MPPT is performed by placing the PV modules between the load regulator power circuit and the load regulator power circuit. To actualize the proposed hybrid GWO – RNN model for the PV system, perturb and observe, RNN, ant colony optimization, and artificial bee colony MPPT techniques are employed. The MATLAB interfaced dSPACE interface is used to finish the hands-on validation of the intended grid-integrated PV system. The obtained results eloquently support the appropriate design of higher-performance control algorithms.
Performance analysis of different intonation models in Kannada speech synthesis Sadashiva Veerappa Chakrasali; Krishnappa Indira; Sunitha Yariyur Narasimhaiah; Shadaksharaiah Chandraiah
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.pp243-252

Abstract

Text to speech (TTS) is a system that generates artificial speech from text input. The prosodic models used improve the quality of the synthesized speech especially naturalness and intelligibility. The prosody involves intonation, intonation refers to the variations in the pitch frequency (F0) with respect to time in an utterance. This work mainly concentrates on building feedback neural network model to predict F0 contour in the utterances using Fujisaki intonation model parameters as the input features to the network since the Fujisaki intonation model is data driven and not a rule based one. In this work we have built 4-layer feedback neural network in the festival framework. Finally, the synthetically generated Kannada speech using the neural network model, is compared for its performance with the classification and regression tree (CART) model and Tilt model. Database of simple declarative Kannada sentences created by Carnegie Mellon University have been deployed in this work. From the study it is very clear that F0 contours can be accurately predicted using CART and neural network models, whereas naturalness and intelligibility is high in CART model rather than neural network model.

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