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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 22 Documents
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Search results for , issue "Vol 10, No 4: December 2022" : 22 Documents clear
Effect of Reactive Power Capability of the PV Inverter on the Power System Quality Raghad Adeeb Othman; Omar Sharaf Al-Deen Al-Yozbaky
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.3913

Abstract

Distributed generation (DG) based on a photovoltaic system (PV) connected to a power system is a very promising solution to meet the present demand for energy and to reap the advantages of using clean energy. With the exponential increase in the deployment of distributed energy sources based on renewable energy, the reactive power drawn from the grid has increased dramatically compared to the active power. This affects the quality of the power from the network. Reactive power is usually required to regulate the power factor and the grid voltage so as to improve the ability of the system to handle power. In this paper, the reactive power capacity of a PV inverter connected to the grid was determined using the MATLAB/Simulink program. The power (active and reactive) injected into the network were independently controlled by their reference values. A study was conducted on the effects of the injection/absorption of reactive power on the quality of power under different operating conditions.
Optimal Design of Damping Control of Oscillations in Power System Using Power System Stabilizers with Novel Improved BBO Algorithm Gowrishankar Kasilingam; Jagadeesh Pasupuleti; Shantha Kumari Kasirajan; Arunachalam Nagarathinam; Deepa Natesan
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.4269

Abstract

Studies on power system stability are necessary for power network development & operation. Due to the great dimensionality and complexity of contemporary power systems, its significance has increased. The stability of an interconnected power system is seriously threatened by power system oscillation. Numerous strategies based on contemporary control theory, intelligent control, and optimization methods have been applied to the Power system stabilizers (PSSs) design problem recently. Each categorization contains a number of design techniques that increase the PSS's effectiveness and sturdiness in damping off low frequency vibrations. This work presents a new Modified and Improved Biogeography-Based Optimization (MIBBO) method to increase the optimization effectiveness of the usual Biogeography-Based Optimization (BBO) technique applied for the optimization of the parameters of the PSSs & Proportional Integral Derivative (PID) controller under the non-linear loading (NLL) conditions. The performance parameters which are obtained by the MIBBO based controller are compared with the results of normal BBO Method, Particle Swarm Optimization method (PSO) and Adaptation Law (AL) method. To justify the success and correctness of the proposed control approach, Matlab simulation results-based study of all the above-mentioned techniques is made and reported.
On Reducing ShuffleNets’ Block for Mobile-based Breast Cancer Detection Using Thermogram: Performance Evaluation Rizka Ramadhana; Khairun Saddami; Khairul Munadi; Fitri Arnia
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.4062

Abstract

In this paper, we proposed a reduced-block-Shufflenet (RB-ShuffleNet) for thermal breast cancer detection. RB-ShuffleNet is a modification of Shufflenet obtained by reducing blocks from the original architecture. The images for training and testing were obtained from Database for Mastology Research (DMR). First, we detected and cropped the image based on the region of interest (ROI), in which the ROI is determined by using the red intensity profile. Then, the ROI images were trained using RB-ShuffleNets. In the experiments, we built eight architectures, based on ShuffleNet, each with a different number of reduced blocks. The result showed that RB-Shufflenet with four reduced blocks had fewer than 50% of the learning parameters of the original Shufflenet, without compromising its performance. The RB-ShuffleNet with up to four reduced blocks could achieve 100% testing accuracy. Furthermore, The RB-ShuffleNets performed better than MobileNetV2 and resulted in higher accuracy when fed with ROI images. Due to its light structure and good performance, we recommend RB-ShuffleNet as mobile-based CNN model which is preferable to implement in breast cancer detection.
Non-destructive Inspection System Development for Secondary Battery Welding Part Kyunghan Chun
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.4177

Abstract

In this paper, we develop a non-destructive inspection system for secondary battery welding. In a secondary battery, when the insulation or separator of each electrode is damaged by an impact from the outside depending on the degree of welding, an internal short circuit occurs during charging and fires even if it does not ignite at that time. To detect this, a non-destructive AOI (Automatic Optical Inspection) system is developed that compares the inspection target with the reference image to determine whether there is a proper indentation in the welding part. The system consists of a precision alignment stage on the lower part and imaging equipment that performs AOI, a non-destructive inspection on the upper part. And the appropriate exposure, i.e., the aperture setting of the used camera, was confirmed through the experiment according to the position of the pole.
Application of Three-Phase Power Flow Analysis to the Nigerian Distribution Networks Samson Oladayo Ayanlade; Abdulrasaq Jimoh; Funso Kehinde Ariyo; Adedire Ayodeji Babatunde; Abdulsamad Bolakale Jimoh; Fatina Mosunmola Aremu
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.3921

Abstract

Single-phase power flow analysis is used to study most distribution networks in Nigeria. The use of single-phase-power flow analysis assumes that the network is balanced and that the conductor phases act identically. However, Nigerian distribution networks are highly imbalanced because of untransposed lines, irregularly distributed loads in conductor phases, mismatched conductor sizes, and spacing. Consequently, single-phase modeling of the networks fails to reflect actual network behavior, resulting in an incorrect power flow solution. This research presents the three-phase modeling of radial distribution networks for a three-phase-power flow study of Nigerian distribution networks. Olusanya's 54-bus and Ajinde's 62-bus distribution networks in Nigeria were evaluated, both of which were very imbalanced. Without making any assumptions about the network components, these two distribution networks were properly modeled. Each network's three-phase power flow study was carried out in the MATLAB environment. The power flow solutions for each network demonstrated unevenness in the voltage profile for each network phase, as well as inequality in the real and reactive power losses in each phase, indicating that the deployed three-phase-power flow analysis properly mirrored the underlying network characteristics. Therefore, applying three-phase power flow analysis to distribution networks is critical for proper assessment of distribution network performance.
Fruits Disease Classification using Machine Learning Techniques Yassine Benlachmi; Aymane El Airej; Moulay Lahcen Hasnaoui
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.3907

Abstract

Due to increased population, there is a high demand for agricultural products these days and therefore, effective growth and increased fruit production have become critical. Consequently, for better fruit yield cultivators employ traditional methods for monitoring fruit yield from harvest till ripening of fruit. However, manual monitoring and visual inspection doesn’t always bring the actual identification of fruit disease due to variety of reasons, such as less knowledge about pathogens, requiring more time for disease analysis and that too with less accuracy and so on, consequently, leaving for the need of a professional assistance and expertise. Moreover, the task also becomes difficult as various fruits demonstrate their gesticulation by changing the colour of their skin which can come from nature and resulting in various black or dark brown spots on the fruit skin indicating various diseases. As a result, it is necessary to propose an efficient smart farming strategy that will aid in increased productivity while at the same time involving less human effort. The proposed research work attempts to classify the fruit disease at its early stage by using machine learning techniques. For this purpose, fruit’s texture, and skin colour have been utilized. The approach fundamentally employs three machine learning classifier algorithms - KNN, Decision Tree, and Random Forest. Whereas the features have been determined by using three prominent feature extractors - Haralick, Hu Moments and colour histogram. Finally, the system has been evaluated by utilizing the k-fold cross validation method. Specimen dataset was divided into two groups — the training subset and the test subset. As a rule, four-fold cross-validation, three-fourths of the images were used for training the models whereas, the remaining one-fourth were used for testing purposes. Assessment results for suggested methodology after conducting experimentation on publicly available dataset and drawn confusion matrix and learning cure shows that Random Forest classifiers achieves accuracy about 99% while for K-Means accuracy statistics stands at 98.67% and for Decision trees it is about 97.75% - for colour histogram features.
Cryptanalysis the SHA-256 Hash Function using Rainbow Tables Olga Manankova; Mubarak Yakubova; Alimjan Baikenov
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.4247

Abstract

The research of the strength of a hashed message is of great importance in modern authentication systems. The hashing process is inextricably linked with the password system, since passwords are usually stored in the system not in clear text, but as hashes. The SHA-256 hash function was chosen to model the attack with rainbow tables. An algorithm for constructing a rainbow table for the SHA-256 hash function in the Java language is proposed. The conditions under which the use of rainbow tables will be effective are determined. This article aims to practically show the process of generating a password and rainbow tables to organize an attack on the SHA-256 hash function. As research shows, rainbow tables can reveal a three-character password in 3 seconds. As the password bit increases, the decryption time increases in direct proportion.
An S-Band Microstrip Patch Antenna Design and Simulation for Wireless Communication Systems Md Sohel Rana; Sk Ikramul Islam; Sharif Al Mamun; Laltu Kumar Mondal; Md. Toukir Ahmed; Md. Mostafizur Rahman
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.4141

Abstract

In this paper, a 3.5 GHz microstrip patch antenna for the future of wireless communication is designed and studied. As a substrate, Rogers RT/Duroid5880 is utilized. This material has a thickness of 0.077mm and a dielectric loss of 2.2. The proposed antenna layout is simulated using the CST studio suite of software programs. This research aimed to achieve a lower return loss, higher gain, lower VSWR, directivity, and improved efficiency. The simulation revealed that the return loss, gain, VSWR, and directivity were correspondingly -13.772 dB, 7.55 dB, 1.5152, and 8.43dBi. The efficiency was 89.56%. This antenna has been developed and assessed for use in various wireless communication applications with a 3.5 GHz operating frequency, which is used as a reference antenna in communication satellites, weather radar, surface ship radar, wireless LAN-802.11b and 802.11g, multimedia applications in mobile TV and satellite radio, optical communications at 1460 to 1530 nm wavelength, and is utilized for other wireless fidelity applications.
The Impact of Telemetry Received Signal Strength of IMU/GNSS Data Transmission on Autonomous Vehicle Navigation Muhammad Khosyi'in; Sri Arttini Dwi Prasetyowati; Bhakti Yudho Suprapto; Zainuddin Nawawi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.3901

Abstract

This paper presents the effect of received signal strength on IMU/GNSS sensor data transmission for autonomous vehicle navigation. A pixhawk 2.1 flight controller is used to build the navigation system. Straight lines with back-and-forth routes were tested using two types of SiK telemetry: Holybro and RFD. The results of the tests show that when the RSSI value falls close to the receiver's sensitivity value, the readings of the gyro sensor data, accelerometer, magnetometer, and GNSS compass data are disturbed. When the RSSI signal collides with noise, the radio telemetry link is lost, affecting the accuracy of speed data and the orientation of autonomous vehicles. According to Cisco's conversion table, the highest RSSI on Holybro telemetry is -48 dBm, and the lowest is -103 dBm, with a receiver sensitivity of -117 and data reading at a distance of about 427 meters. While the highest RSSI value on RFD telemetry is -17 dBm and the lowest is -113 dBm, even the lowest value is above the receiver's sensitivity limit of -121 dBm with data readings at a distance of approximately 749.4 meters. RFD outperforms Holybro in terms of RSSI and sensitivity at low data rates. When reading distance data to reference distance data using Google Earth and ArcGIS, RFD telemetry has a higher accuracy, with an average accuracy of 98.8%.
Weightless Neural Networks Face Recognition Learning Process for Binary Facial Pattern Ahmad Zarkasi; Siti Nurmaini; Deris Stiawan; Bhakti Yudho Suprapto
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.3957

Abstract

The facial recognition process is normally used to verify and identify individuals, especially during the process of analyzing facial biometrics. The face detection algorithm automatically determines the presence or absence of a face. It is, however, theoretically difficult to analyze the face of a system with limited resources due to the complex pattern of a face. This implies an embedded platform scheme which is a combination of several learning methods supporting each other is required. Therefore, this research proposed the combination of the Haar Cascade method for the face detection process and the WNNs method for the learning process. The WNNs face recognition Algorithm (WNNs-FRA) uses facial data at the binary level and for binary recognition. Moreover, the sample face data in the binary were compared with the primary face data obtained from a particular camera or image. The parameters tested in this research include detection distance, detection coordinates, detection degree, memory requirement analysis, and the learning process. It is also important to note that the RAM node has 300 addresses divided into three face positions while the RAM discriminator has three addresses with codes (00), (10), and (10). Meanwhile, the largest amount of facial ROI data was found to be 900 pixels while the lowest is 400 pixels. The total RAM requirements were in the range of 32,768 bytes and 128 bytes and the execution time for each face position was predicted to be 33.3% which is an optimization because it is 66.67% faster than the entire learning process

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