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INDONESIA
Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Published by IAES Indonesia Section and Partners
ISSN : 20893272     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is a peer reviewed International Journal in English published four issues per year (March, June, September and December). The aim of Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the engineering of Telecommunication and Information Technology, Applied Computing & Computer, Instrumentation & Control, Electrical (Power), Electronics, and Informatics.
Arjuna Subject : -
Articles 783 Documents
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High-Performance Design of a 4-Bit Carry Look-Ahead Adder in Static CMOS Logic Mehedi Hasan; Abdul Hasib Siddique; Abdal Haque Mondol; Mainul Hossain; Hasan U. Zaman; Sharnali Islam
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 8, No 4: December 2020
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v8i4.2582

Abstract

Design of a 4-bit Carry Look-Ahead (CLA) process in static CMOS logic has been presented. CLA architecture proposed in this work computes carry-out terms without using carry-propagate and carry-generate signals which are used in conventional static CMOS (C-CMOS) 4-bit CLA adder. Performance parameters of the proposed 4-bit CLA architecture have been simulated and validated by comparing with the conventional design using Cadence design toolset in 45 nm technology. The designs were compared in terms of average power consumption, propagation delay and power delay product (PDP). The proposed 4-bit CLA topology obtained 34.53 % improvement in speed, 4.84 % improvement in power consumption and 37.696 % improvement in PDP while the source voltage was 1.0 V. Hence, as per acquired simulation results, the proposed 4-bit CLA structure is proven to be an excellent alternative to the conventional design for data-path design in modern high-performance processors.Design of a 4-bit Carry Look-Ahead (CLA) process in static CMOS logic has been presented. CLA architecture proposed in this work computes carry-out terms without using carry-propagate and carry-generate signals which are used in conventional static CMOS (C-CMOS) 4-bit CLA adder. Performance parameters of the proposed 4-bit CLA architecture has been simulated and validated by comparing with the conventional design using Cadence design toolset in 45 nm technology. The designs were compared in terms of average power consumption, propagation delay and power delay product (PDP). The proposed 4-bit CLA topology obtained 26.67 % improvement in speed, 5.966 % improvement in power consumption and 31.06 % improvement in PDP while the source voltage was 1.0 V. Hence, as per acquired simulation results, the proposed 4-bit CLA structure is proven to be an excellent alternative to the conventional design for data-path design in modern high-performance processors.
Evaluation of Differential Evolution Algorithm with Various Mutation Strategies for Clustering Problems Pyae Pyae Win Cho; Thi Thi Soe Nyunt
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 8, No 4: December 2020
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v8i4.2157

Abstract

Evolutionary Algorithms (EAs) based pattern recognition has emerged as an alternative solution to data analysis problems to enhance the efficiency and accuracy of mining processes. Differential Evolution (DE) is one rival and powerful instance of EAs, and DE has been successfully used for cluster analysis in recent years. Mutation strategy, one of the main processes of DE, uses scaled differences of individuals that are chosen randomly from the population to generate a mutant (trial) vector. The achievement of the DE algorithm for solving optimization problems highly relies on an adopted mutation strategy. In this paper, an empirical study was presented to investigate the effectiveness of six frequently used mutation strategies for solving clustering problems. The experimental tests were conducted on the most widely used data set for EAs based clustering, and the quality of cluster solutions and convergence characteristics of DE variants were evaluated. The obtained results pointed out that the mutation strategies that use the guidance information from the best solution mange to find more stable results whereas the random mutation strategies are able to find high quality solutions with slower convergence rate. This study aims to provide some information and insights to develop better DE mutation schemes for clustering.
PAPR reduction in OFDM system using combined MCS and DHMT precoding Mohd Danial Rozaini; Azlina Idris; Darmawaty Mohd Ali; Ezmin Abdullah
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 8, No 4: December 2020
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v8i4.1767

Abstract

Orthogonal Frequency Division Multiplexing (OFDM) has become a preferable scheme for most high data rate wireless communication standards. However, the non-linear power amplifier effect experienced in the OFDM system has increases the peak-to-average power ratio (PAPR). This paper proposed a Median Codeword Shift (MCS) as a new solution to alleviate the effect of high PAPR. MCS takes advantage of the codeword structure and bit position changes through the manipulation of the codeword structure and permutation process to achieve a low PAPR value. Additionally, the enhanced version of MCS is also being proposed by merging MCS with the Discrete Hartley matrix transform (DHMT) precoding method to boost the PAPR reduction. Simulation results show that MCS is capable of minimizing PAPR of conventional OFDM with 24% improvement and at the same time outperform Selective Codeword Shift (SCS) with a 0.5 dB gap. A remarkable result was also achieved by MCS-DHMT with a 15.1% improvement without facing any bit error rate (BER) degradation.
Traffic characterization in a communications channel for monitoring and control in real-time systems Leonardo Serna-Guarin; Luis J. Morantes-Guzman; Edilson Delgado-Trejos; Miguel A. Becerra
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 8, No 4: December 2020
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v8i4.1695

Abstract

The response time for remote monitoring and control in real-time systems is a sensitive issue in device interconnection elements. Therefore, it is necessary to analyze the traffic of the communication system in pre-established time windows. In this paper, a methodology based on computational intelligence is proposed for identifying the availability of a data channel and the variables or characteristics that affect the performance and data transfer, which is made up of four stages: a) integration of a communication system with an acquisition module and a final control structure; b) communication channel characterization by means of traffic variables; and c) relevance analysis from the characterization space using SFFS (sequential forward oating selection); d) Channel congestion classification as Low or High using a classifier based on Naive Bayes algorithm. The experimental setup emulates a real process using an on/off remote control of a DC motor on an Ethernet network. The communication time between the client and server was integrated with the operation and control times, to study the whole response time. This proposed approach allows support decisions about channel availability, to establish predictions about the length of the time window when the availability conditions are unknown.
Qualified Two-Hybrid Techniques by DWT Output to Predict Fault Location Azriyenni Azhari Zakri; Syukri Darmawan; Sandy Ahmad; Mohd Wazir Mustafa; Jafaru Usman
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 8, No 4: December 2020
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v8i4.1908

Abstract

The power transmission system is essential for the power scheme to transfer the energy from generators to consumers. The short circuit problem repeatedly occurs in the transmission system, and the main problem is to separate the sources from users. This research has applied two hybrid techniques to predict fault location. The first hybrid technique has involved the Discrete Wavelet Transformation (DWT) and Adaptive Neuro-Fuzzy Inference System (ANFIS), while the second hybrid technique is for DWT grouping and Support Vector Machine (SVM). These hybrid techniques are intended to estimate the fault location of each fault category in a transmission system. The DWT was applied to both D8 and D9 level at the 50 kHz sample frequency. The root mean square (RMS) values of the D8 and D9 coefficients were used for training using ANFIS and SVM techniques. After that, ANFIS and SVM were utilised to detect faults in the phase and ground lines. Several types of fault have been simulated, i.e. fault location, fault resistance, and original point of view. The RMS results from the two hybrid techniques were compared to find the best results. The tests of error estimation were performed for the three bus systems. The comparison of error estimation of the two methods shows that both hybrid techniques can be applied to predict fault locations.
Student activities detection of SUST using YOLOv3 on Deep Learning Md. Yousuf Ali; Xuan-De Zhang; Md. Harun -Ar- Rashid
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 8, No 4: December 2020
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v8i4.2585

Abstract

This article describes the main phases of a new learning system in which YOLOv3 is used for deep learning to identify student activities. A student’s exploits in the SUST- (Shaanxi University of Science and Technology) should be perceived to circumvent any unwanted problems. In this project, we have investigated the problem of image-based student activity detection in the SUST. It involves making a prediction by analyzing student poses, behavior, and activities with objects from complex images instead of videos. Comparing with all approaches, we conclusively decided to use an algorithm YOLOv3 (You Only Look Once) which is the latest and more convenient. The algorithm utilizes anchor boxes, bounding boxes, and a variant of Darknet. We have created our own dataset collecting images from SUST and annotated the dataset manually. During the research with this project, we have considered student activities in the SUST into seven sections namely reading, phoning, using a laptop, taking books, smiling, looking, and sleeping. The proposed system provides not only multi-tasking knowledge with classification but also localization of students and the equivalent actions instantaneously. Our intention is to detect the student position automatically, efficiently, confidently, and strictly with the help of extracted image functions. Interestingly, the proposed approach achieved a mean average precision (mAP) 97%. In the future, a combination of real-time data analysis will improve value to this scheme.
Integral Backstepping Based Nonlinear Control for Maximum Power Point Tracking and Unity Power Factor of a Grid Connected Hybrid Wind-Photovoltaic System Mohammed El malah; Abdellfattah Ba-razzouk; Elhassane Abdelmounim; Mhamed Madark
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 8, No 4: December 2020
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v8i4.2327

Abstract

This paper proposes a novel integral backstepping-based nonlinear control strategy for a grid-connected wind-photovoltaic hybrid system. Firstly, detailed three-phase models of the hybrid system elements are presented, and then an overall state-space model is derived. Secondly, nonlinear control laws for the hybrid system’s converters are developed with the aim of ensuring maximum extraction of the available renewable energy, stabilizing the DC bus voltage and guaranteeing the operation of the hybrid system at unity power factor. The overall stability of the closed-loop system is demonstrated on the basis of Lyapunov’s stability theory. Comprehensive simulations, using the MATLAB/Simulink software environment, are carried out to assess the effectiveness of the proposed control methodology. The simulation results obtained confirm that the proposed control strategy offers high efficiency in various operating modes of the hybrid generation system.
Optimal solutions for fixed head short-term hydrothermal system scheduling problem Thanh Long Duong; Van-Duc Phan; Thuan Thanh Nguyen; Thang Trung Nguyen
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 8, No 4: December 2020
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v8i4.2269

Abstract

In this paper, optimal short-term hydrothermal operation (STHTO) problem is determined by a proposed high-performance particle swarm optimization (HPPSO). Control variables of the problem are regarded as an optimal solution including reservoir volumes of hydropower plants (HdPs) and power generation of thermal power plants (ThPs) with respect to scheduled time periods. This problem focuses on reduction of electric power generation cost (EPGC) of ThPs and exact satisfactory of all constraints of HdPs, ThPs and power system. The proposed method is compared to earlier methods and other implemented methods such as particle swarm optimization (PSO), constriction factor (CF) and inertia weight factor (IWF)-based PSO (FCIW-PSO), two time-varying acceleration coefficient (TTVACs)-based PSO (TVAC-PSO), salp swarm algorithm (SSA), and Harris hawk algorithm (HHA). By comparing EPGC from 100 trial runs, speed of search and simulation time, the suggested HPPSO method sees it is more robust than other ones. Thus, HPPSO is recommended for applying to the considered and other problems in power systems.
Circuit Modelling of Bandpass/Channel Filter with Microstrip Implementation Augustine Onyenwe Nwajana
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 8, No 4: December 2020
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v8i4.2466

Abstract

This paper presents a step-by-step approach to the design of bandpass/channel filters. A 3-pole Chebyshev bandpass filter (BPF) with centre frequency of 2.6 GHz, fractional bandwidth of 3%, passband ripple of 0.04321 dB and return loss of 20 dB has been designed, implemented, and simulated. The designed filter implementation is based on the Rogers RT/Duroid 6010LM substrate with a 10.7 dielectric constant and 1.27 mm thickness. The BPF was also fabricated using the same substrate material used for the design simulation. The circuit model and microstrip layout results of the BPF are presented and show good agreement. The microstrip layout simulation results show that a less than 1.8 dB minimum insertion loss and a greater than 25 dB in-band return loss were achieved. The overall device size of the BPF is 18.0 mm by 10.7 mm, which is equivalent to 0.16λg x 0.09λg, where λg is the guided wavelength of the 50 Ohm microstrip line at the filter centre frequency.
Deep Learning-aided Brain Tumor Detection: An Initial ‎Experience based Cloud Framework ‎ Safia Abbas; Abeer M Mahmoud
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 8, No 4: December 2020
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v8i4.2436

Abstract

Lately, the uncertainty of diagnosing diseases increased and spread due to the huge intertwined and ambiguity of symptoms, that leads to overwhelming and hindering the reliability of the diagnosis ‎process. Since tumor detection from ‎MRI scans depends mainly on the specialist experience, ‎misdetection will result an inaccurate curing that might cause ‎critical harm consequent results. In this paper, detection service for brain tumors is introduced as ‎an aiding function for both patients and specialist. The ‎paper focuses on automatic MRI brain tumor detection under a cloud based framework for multi-medical diagnosed services. The proposed CNN-aided deep architecture contains two phases: the features extraction phase followed by a detection phase. The contour ‎detection and binary segmentation were applied to extract the region ‎of interest and reduce the unnecessary information before injecting the data into the model for training. The brain tumor ‎data was obtained from Kaggle datasets, it contains 2062 cases, ‎‎1083 tumorous and 979 non-tumorous after preprocessing and ‎augmentation phases. The training and validation phases have been ‎done using different images’ sizes varied between (16, 16) to ‎‎ (128,128). The experimental results show 97.3% for detection ‎accuracy, 96.9% for Sensitivity, and 96.1% specificity. Moreover, ‎using small filters with such type of images ensures better and faster ‎performance with more deep learning.‎

Page 37 of 79 | Total Record : 783

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