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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 20 Documents
 1   2 
Search results for , issue "Vol 10, No 3: September 2022" : 20 Documents clear
Performance of Anti-Lock Braking Systems Based on Adaptive and Intelligent Control Methodologies Ahmed J. Abougarair; Nasar Aldian A. Shashoa; Mohamed K. Aburakhis
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 3: September 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i3.3794

Abstract

Automobiles of today must constantly change their speeds in reaction to changing road and traffic circumstances as the pace and density of road traffic increases. In sophisticated automobiles, the Anti-lock Braking System (ABS) is a vehicle safety system that enhances the vehicle's stability and steering capabilities by varying the torque to maintain the slip ratio at a safe level. This paper analyzes the performance of classical control, model reference adaptive control (MRAC), and intelligent control for controlling the (ABS). The ABS controller's goal is to keep the wheel slip ratio, which includes nonlinearities, parametric uncertainties, and disturbances as close to an optimal slip value as possible. This will decrease the stopping distance and guarantee safe vehicle operation during braking. A Bang-bang controller, PID, PID based Model Reference Adaptive Control (PID-MRAD), Fuzzy Logic Control (FLC), and Adaptive Neuro-Fuzzy Inference System (ANFIS) controller are used to control the vehicle model. The car was tested on a dry asphalt and ice road with only straight-line braking. Based on slip ratio, vehicle speed, angular velocity, and stopping time, comparisons are performed between all control strategies. To analyze braking characteristics, the simulation changes the road surface condition, vehicle weight, and control methods. The simulation results revealed that our objectives were met. The simulation results clearly show that the ANFIS provides more flexibility and improves system-tracking precision in control action compared to the Bang-bang, PID, PID-MRAC, and FLC.
Traffic Occupancy Prediction Using a Nonlinear Autoregressive Exogenous Neural Network Nazhon Ismael Khaleel; Anuraj Uthayasooriyan; Joanna Hartley
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 3: September 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i3.3833

Abstract

The main aim of the intelligent transportation systems is the ability to accurately predict  traffic characteristics like traffic occupancy, speed, flow and accident based on historic and real time data collected by these systems in transportation networks. The main challenge of  a huge quantity of traffic data collected automatically, stored and processed by these systems is the way of handling and extracting the required traffic data to formulate the prediction traffic characteristic model. In this research, the required traffic data of a specified road link in UK are extracted from the big raw data of the SCOOT system by designing C++ extractor program. In addition, short term traffic prediction models are created by using deep learning technique NARX neural network to find accurate and exact traffic occupancy. Three scenarios of time interval which are 10 minutes, 20 minutes and 30 minutes are considered for analyzing the prediction accuracy. The results showed that the prediction models for the 30 minutes interval scenario have very good accuracy in estimating the future traffic occupancy compared to another scenarios of time intervals. In addition, the testing and validation study showed that the prediction models for 30 minutes intervals for particular road link yield better accuracy than 10 minutes and 20 minutes intervals.
Performance Evaluation of Different GNSS Positioning Modes Brahim Seddiki; Abd El Mounaim Moulay Lakhdar; Bilal Beldjilali
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 3: September 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i3.3719

Abstract

This paper gives a comparison of different GPS positioning modes using RTKLIB which is free and open-source software. The modes tested in this work are Single point positioning (SPP), precise point positioning (PPP), Satellite-based augmentation system (SBAS), Differential GPS (DGPS), and Real-Time Kinematic (RTK). The data for tests were obtained from NetR9 receivers, these types of receivers are multi-frequencies and multi-constellation receivers that provide carrier and phase measurements. The SPP mode is the very simplest mode, it can be used for applications where accuracy is not less than 5m, and it can be improved to achieve 1m by using SBAS corrections but only in the coverage area of the system. The DGPS can also provide 1m accuracy using a second receiver as a base station which can increase the cost of the operation. For applications that need very high accuracy, RTK and PPP can be used to reach centimeter-level accuracy. RTK needs a base station in addition to the rover receiver used for the positioning; PPP uses precise orbital and clock solutions which are not available in real time for all users.
Bone fracture detection through X-ray using Edge detection Algorithms Tatwamashi Panda; H.S. Pranavi Peddada; Annika Gupta; G Kanimozhi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 3: September 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i3.3776

Abstract

Human beings are highly prone to bone fractures, to a great extent as an outcome of accidents or other factors such as bone cancer. Manual fracture detection takes a lengthy time and comes with a considerable chance of error. As a result, establishing a computer-based method to reduce fracture bone diagnosis time and risk of error is critical. The most common method for segmenting images based on sharp changes in intensity is edge detection. Sobel, Robert, Canny, Prewitt, and LoG (Laplacian of Gaussian) are some of the edge detection approaches that are examined for the study of bone fracture detection. The focal point of this paper is an endeavor to study, analyze and compare the Sobel, Canny, and Prewitt Techniques for detecting edges and identifying the fracture.
Detection of Bundle Branch Blocks using Machine Learning Techniques Praveena S Kammath; Vrinda V Gopal; Jerry Kuriakose
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 3: September 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i3.3852

Abstract

The most effective method used for the diagnosis of heart diseases is the Electrocardiogram (ECG). The shape of the ECG signal and the time interval between its various components gives useful details about any underlying heart disease. Any dysfunction of the heart is called as cardiac arrhythmia. The electrical impulses of the heart are blocked due to the cardiac arrhythmia called Bundle Branch Block (BBB) which can be observed as an irregular ECG wave. The BBB beats can indicate serious heart disease. The precise and quick detection of cardiac arrhythmias from the ECG signal can save lives and can also reduce the diagnostics cost. This study presents a machine learning technique for the automatic detection of BBB. In this method both morphological and statistical features were calculated from the ECG signals available in the standard MIT BIH database to classify them as normal, Left Bundle Branch Block (LBBB) and Right Bundle Branch Block (RBBB). ECG records in the MIT- BIH arrhythmia database containing Normal sinus rhythm, RBBB, and LBBB were used in the study. The suitability of the features extracted was evaluated using three classifiers, support vector machine, k-nearest neighbours and linear discriminant analysis. The accuracy of the technique is highly promising for all the three classifiers with k-nearest neighbours giving the highest accuracy of 98.2%. Since the ECG waveforms of patients with the same cardiac disorder is similar in shape, the proposed method is subject independent. The proposed technique is thus a reliable and simple method involving less computational complexity for the automatic detection of bundle branch block. This system can reduce the effort of cardiologists thereby enabling them to concentrate more on treatment of the patients.
Swarm Intelligence Autotune For Differential Drive Wheeled Mobile Robot Muhammad Auzan; Roghib Muhammad Hujja; M Ridho Fuadin; Danang Lelono
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 3: September 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i3.3528

Abstract

Differential Drive Wheeled Mobile Robot (DDWMR) is a nonholonomic robot with constrained movement. Such constraint makes robot position control more difficult. A closed-loop control system such as PID can control robot position. However, DDWMR is a Multiple-Input-Multiple-Output system. There will be many feedback gains to be tuned, and the wrong value will make the system unstable. Therefore this research proposes an offline autotune method to choose optimal feedback gain that minimizes a fitness function. The fitness function uses Integral Absolute Error (IAE) and Integral Time Absolute Error (ITAE). These works propose to autotune feedback gain for DDWMR Jetbot, which implements a PI control system with six feedback gains. The methods used to tune the feedback gain are Particle Swarm Optimization (PSO) and Bird Swarm Algorithm (BSA). There are four different scenarios to do the autotune. The autotune result performance shows that those two methods can find an optimal gain to make the robot follow four different continuous trajectories without much trajectory deformation. PSO and BSA can do an autotune PI gain with six variables to minimize the Integral Absolute Error (IAE) and Integral Time Absolute Error (ITAE).
Spectral Efficiency Enhancement using Hybrid Pre-Coder Based Spectrum Handover Mechanism Annarao V Patil; Rohitha Ujjinimated
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 3: September 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i3.3690

Abstract

The use of Millimeter-wave (mm-Wave) has recently immensely enhanced in various communication applications due to massive technological developments in wireless communications. Furthermore, mm-Wave consists of a high bandwidth spectrum that can handle large demands of data transmission and internet services. However, high interference is observed in previous research at the time of spectrum handover from secondary (unlicensed) users to primary (licensed) users. Thus, interference reduction by achieving high spectral efficiency and an easy spectrum handoff process with minimum delay is an important research area. Therefore, a Hybrid Pre-coder Design based Spectrum Handoff (HPDSH) Algorithm is proposed in this article to increase spectrum efficiency in Cognitive Radio Networks(CRNs) and to access the large bandwidth spectrum of mm-Wave systems to meet the high data rate demands of current cellular networks. Moreover, a HPDSH Algorithm is presented to enhance spectral efficiency and this algorithm is utilized to take handover decisions and select backup channels. Here, different scenarios and parameters are considered to evaluate the performance efficiency of the proposed HPDSH Algorithm in terms of spectral efficiency and Signal to Noise (SNR) ratio. The proposed HPDSH Algorithm is compared against precoding methods like the OMP algorithm and SIC based methods.
Multi-objective Predictive Control of 3L-NPC Inverter Fed Sensorless PMSM Drives for Electrical Car Applications Paul Michael Dongmo Zemgue; Roland Christian Gamom Ngounou Ewo; Alexandre Teplaira Boum
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 3: September 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i3.3751

Abstract

This paper proposes a multi-objective FS-MPC approach based on three-step optimization for a surface-mounted PMSM fed by a 3L-NPC inverter. It helps to significantly reduce torque ripples, current harmonics while controlling the inverter's neutral point voltage. To overcome the drawbacks of using mechanical sensors, a sliding mode observer is used to estimate the machine speed and rotor angular position. Compared to existing works, the proposed control method is implemented using the proportionality between the electromagnetic torque and the current component on the q-axis to eliminate the computational redundancy related to the current and torque control. To further reduce torque ripples and current harmonics, a 3L-NPC inverter is used. Compared to other types of three-level inverters, it uses less power semiconductors and attenuates the problem of voltage fluctuation at the neutral point and current harmonics. Matlab/Simulink simulations of the proposed approach yield a current THD of 1.69 %.
Proposal of a Sizing Algorithm for an Optimal Design of DC/DC Converters Used in Photovoltaic Conversion Amrouayache Mohamed
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 3: September 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i3.3890

Abstract

The solar energy is converted to electrical energy by means of semiconductor materials called solar panels. However, the conversion efficiency is low, and hence the need to harvest the maximum power to optimize the photovoltaic conversion, for that the MPPT (Maximum Power Point Tracker) technique is used to maximize the power delivered by the solar panel (PV); this power is very fluctuating because it depends on the lightning and the temperature, the maximum power point is acquired by a DC / DC converter connected to the closed loop MPPT algorithm. The design of the circuit (the closed loop) must be robust in the face of changes in operating points caused by variations in meteorological conditions (temperature and lighting) and must always maintain certain performances such as stability, a fast and well-damped transient system, precision. In this paper, we presented a study of closed loop, for that, we established the average small signal model of the different topologies of the converters (boost; buck, buck-boost) to have a linear model. A comparative study between the three topologies has been established, to make an optimal choice of the circuit parameters.
Class-D Audio Amplifier using Sigma-Delta (ΣΔ) Modulator Nour El Imane Bellili; Khaled Bekhouche
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 3: September 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i3.3872

Abstract

Pulse width modulation and pulse density modulation are deemed to be main modulation techniques, even PDM could not emulate PWM, in terms of, basically, simplicity. PDM bitstream is encoded through sigma-delta modulation. Since sigma-delta modulation, compared to PWM, needs very high switching frequency and more complicated materials to compose circuits, it’s more difficult to design one. In this article we design a low-power class-D audio amplifier circuit where the analog signal is encoded into pulse density modulation (PDM) using a first-order sigma-delta (ΣΔ) modulator. The designed circuit is built using Orcad-PSpice and results are analyzed with Matlab. A second-order integrator, a voltage divider as a feedback loop are used to mitigate basically, THD and get high efficiency. The audio signal is passed to the EM speaker through a Butterworth low-pass filter. A low THD of less than 0.2 % is obtained comparing to similar circuits in the literature and a high efficiency of 92 % is achieved. 

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