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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 70 Documents
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Search results for , issue "Vol 20, No 3: December 2020" : 70 Documents clear
Thermal exchange optimization based control of a doubly fed induction generator in wind energy conversion systems Mohammed Mazen Alhato; Soufiene Bouallègue
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 3: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i3.pp1252-1260

Abstract

This paper presents a novel design method to attain the optimal parameters of proportional integral (PI) controllers for a doubly fed induction generator (DFIG) in wind energy based on a thermal exchange optimization (TEO) algorithm. Since the gains of PI controllers are usually selected by classical and tedious trials-errors based procedures, their tuning for such a wind energy converter is formulated as a constrained nonlinear optimization problem. Inspired by Newton’s law of cooling, the TEO algorithm is successfully applied to solve such a control problem under time-domain performances and operational constraints in order to extract the maximum available power. In order to assess the performances of the proposed TEO algorithm, a comparative study between the TEO algorithm and homologues ones is performed. Moreover, a statistical analysis using Friedman and Bonferroni-Dunn’s tests indicates that the TEO lgorithm gives very competitive results in comparison to the other reported metaheuristic algorithms.
Design of experiments approach for modeling the electrical response of a photovoltaic module Fatma Zohra Kessaissia; Abdallah Zegaoui; Rachid Taleb; Chahinez Fares; Michel Aillerie
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 3: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i3.pp1140-1147

Abstract

In the current paper, modeling and evaluation of the significant effect of independent variables on the behavior of the electrical response of a multi-crystalline photovoltaic (PV) module using design of experiments (DoE) approach is simulated. The main purpose of this contribution is to evaluate the maximum power response dependence within the indoor conditions of both variations of solar irradiation and surface temperature and checking the pertinent one on the defined response. the DoE approach is used for estimating both main and combined effect of the two independents considered variables. Multiple linear regression was been introduced to justify the relationship between the independent input variables and dependent output variable, also to determine which input factor is the most significant on the output variable. The DoE model can be used for predicting the response variable at different operating condition in a considered domain study. In addition, DoE approach based on statistical tool for analyzing the accuracy of the predictive model, then the significance of coefficients in the predictive model using statistical and graphical analysis. Therefore, an ANOVA Table can summarize the results, detect the parameters influences on responses variations and determine the best predictive model then reproduce the most possible the experimental data.
Detection and classification of various pest attacks and infection on plants using RBPN with GA based PSO algorithm Kapilya Gangadharan; G. Rosline Nesa Kumari; D. Dhanasekaran; K. Malathi
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 3: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i3.pp1278-1288

Abstract

Machine learning methodologies are commonly used in the field of precession farming. It prospects greatly in the plant safety measure like disease detection and classification of pest attacks. It highly influences the crop production and management. The venture of our system is to produce healthy plantation. The proposed system involves enhanced feature fractal texture analysis, Statistical feature selection and machine learning methodology for classification. Hence more than ever there is a need for such a tool that combines image processing methodologies and the neural network concepts and that is supported by huge cloud of structured data which makes the diagnosis and classification part much easier and convenient. The proposed system recognizes and classifies the plant taxonomy and the infection based on the selected statistical features. The neural network concept followed in our proposed system is focused on artificial neural network which uses recursive back propagation neural network to speed up the training process as well as reduce multiclass problem in the network and optimize the weights on hidden layers of the Network using Genetic algorithm based particle swarm optimization technique. We have used MATLAB to implement the concept and obtained better accuracy in disease/pest detection and proved to be an efficient method.
A new approach for improving clustering algorithms performance Anfal F. N. Alrammahi; Kadhim B. S. Aljanabi
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 3: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i3.pp1569-1575

Abstract

Clustering represents one of the most popular and used Data Mining techniques due to its usefulness and the wide variations of the applications in real world. Defining the number of the clusters required is an application oriented context, this means that the number of clusters k is an input to the whole clustering process. The proposed approach represents a solution for estimating the optimum number of clusters. It is based on the use of iterative K-means clustering under three different criteria; centroids convergence, total distance between the objects and the cluster centroid and the number of migrated objects which can be used effectively to ensure better clustering accuracy and performance. A total of 20000 records available on the internet were used in the proposed approach to test the approach. The results obtained from the approach showed good improvement on clustering accuracy and algorithm performance over the other techniques where centroids convergence represents a major clustering criteria. C# and Microsoft Excel were the software used in the approach.
Naïve Bayes and linear discriminate analysis based diagnostic analytic of harmonic source identification M. H Jopri; MR Ab Ghani; A.R Abdullah; Tole Sutikno; M Manap; J. Too
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 3: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i3.pp1626-1633

Abstract

The diagnostic analytic type of harmonic source is a vital research due to diagnose and identify type of harmonic source that exist in the power system. This paper presents a comparison of machine learning (ML) algorithm namely as the Naïve Bayes (NB) and linear discriminate analysis (LDA) in identifying and diagnosing the harmonic sources.  The MLs inputs are the voltage and current feature sets that estimated from the time-frequency representation (TFR) of S-transform analysis. Four specific cases of harmonic source location are considered in this research, whereas harmonic voltage (HV) and harmonic current (HC) source type-load are used in the diagnosing process. The sufficiency of the proposed methodology is tested and verified on the IEEE 4-bust test feeder, and to prevent overfitting, the K-fold cross-validation technique is implemented for performance evaluation. To identify the best ML, the performance measurement consist of the accuracy, precision, geometric mean, F-measure, sensitivity, and specificity are conducted.
Total energy consumption analysis in wireless Mobile ad hoc network with varying mobile nodes Mohammed Ayad Saad; Sameer A. S. Lafta; Raed Khalid Al-Azzawi; Adnan Hussein Ali; Sameer Alani; M. M. Hashim; Bassam Hasan
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 3: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i3.pp1397-1405

Abstract

The energy protocols that have a mechanisms of shortest path routing considered predominant in the networking scenarios. The interesting matter in the routing protocols designing deal with mobile ad hoc network (MANET) must have an energy efficient network for better network performances. The Performances of such routing protocols that can be assessed will be focused on many metrics like delay, throughput, and packet delivery.  MANET is a distribution network, having no infrastructure and network decentralization. There routing protocols are utilized for detecting paths among mobile nodes to simplify network communication. The performance comparison of three protocols are Optimized Link State Routing (OLSR), the second is Ad hoc On-Demand Distance Vector (AODV), while the third is Dynamic Source Routing (DSR) routing protocols concerning to average energy consumption and mobile node numbers are described thoroughly by NS-3 simulator.  The nodes number is changing between 10 and 25 nodes, with various mobility models. The performance analysis shows that the suggested protocols are superior in relations to the energy consumption for networking data transmission and the performance of the wireless network can be improved greatly.
Enhancement in resource allocation system for cloud environment using modified grey wolf technique Soukaina Ouhame; Youssef Hadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 3: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i3.pp1530-1537

Abstract

Cloud computing is new trend of technology which provides services with the help of internet based on specific rules.VM is one of the main elements of cloud computing it work on virtualizations concept. Due to the growth of cloud computing user demands for better service are increasing and it make different kind of issues in cloud environment. Data allocation sysytem in VM is one of them for that reason in this paper a new technique used for improvment of data allocation system in VM for cloud computing. The improvement took place GWO algorithm two main section of this algorithm are modified which are local search section and fitness function value. The above proposed technique used to improve three main parameter of scheduling which are energy consumption, throughput and average network executation time in VM for cloud computing. The proposed technique result are compare with ABC algorithm and GWO algorithm based on those result the proposed algorithm improved the three main parameter of load balancing technique in cloud computing.
Biometric authenticator algorithm based on multiresolution analysis Kerrache Soumia; Beladgham Mohammed; Hamza Aymen; Kadri Ibrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 3: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i3.pp1332-1341

Abstract

In this paper, we propose a feature extraction method for two-dimensional imageauthentication algorithm using curvelet transform and principal component analysis(PCA). Since wavelet transform can not adequately describe facial curves features,Theproposed approach involves image denoising applying a 2D-Curvelet transform toachieve compact representations of curves singularities. To assess the performanceof the presented method, we have employed three classifification techniques: Neuralnetworks (NN), K-Nearest Neighbor (KNN) and Support Vector machines (SVM).Extensive experimental results and comparison with the existing methods show the effectiveness of the proposed recognition method in the ORL face database and CASIAiris database.
An approach for enhancing data confidentiality in Hadoop Shahab Wahhab Kareem; Raghad Zuhair Yousif; Shadan Mohammed Jihad Abdalwahid
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 3: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i3.pp1547-1555

Abstract

The amount of data processed and stored in the cloud is growing dramatically. The traditional storage devices at both hardware and software levels cannot meet the requirement of the cloud. This fact motivates the need for a platform which can handle this problem. Hadoop is a deployed platform proposed to overcome this big data problem which often uses MapReduce architecture to process vast amounts of data of the cloud system. Hadoop has no strategy to assure the safety and confidentiality of the files saved inside the Hadoop distributed File system(HDFS). In the cloud, the protection of sensitive data is a critical issue in which data encryption schemes plays avital rule. This research proposes a hybrid system between two well-known asymmetric key cryptosystems (RSA, and Paillier) to encrypt the files stored in HDFS. Thus before saving data in HDFS, the proposed cryptosystem is utilized for encrypting the data. Each user of the cloud might upload files in two ways, non-safe or secure. The hybrid system shows higher computational complexity and less latency in comparison to the RSA cryptosystem alone.
Biometric and RFID based authentication system for exam paper leakages detection using IoT technology J. Ann Roseela; T. Godhavari
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 3: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i3.pp1271-1277

Abstract

Education is the soul of a community; it goes from one generation to another. Exam is the prime responsibility of an educational framework. The reason for an examination is to select talented applicants for multiple positions. Exam is an important aspect of the education system to test students' abilities online and in oral papers. Every year we receive deferred/canceled exam messages due to paper leakage. Therefore, we have come up with a affordable and concise outcome, and we have decided to design and implement the "Exam Paper Leak Protection Framework", which will be a much more secure structure depending on the controller. Together with RFID Reader, Fingerprints sensor, Buzzer, LCD, and Wi-Fi module. First, the university will send a selection sheet to an educational institution "electronic sealed box" known the "electronic control box". Electronic control box is a prototype that can be proposed using controller, and the RTC to display the current date and time. If anybody tries to intrude the box the buzzer will beep and alert message will be send to the university.

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