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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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Antennas Performance Comparison of Multi-Bands for Optimal Outdoor and Indoor Environments Wireless Coverage Karrar Shakir Muttair; Ali Z. Ghazi Zahid; Oras A. Shareef Al-Ani; Ahmed Mohammed Q. AL-Asadi; Mahmood F. Mosleh
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 4: December 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i4.3172

Abstract

This paper aims to implement a wireless Wi-Fi network (Indoor and Outdoor) in order to cover the environment of the Oxford Institute (to learn languages and computer skills) in the best methods and lowest cost in order to provide Wi-Fi service for faculty members and all members of the administrative board and students. The realistic three-floor indoor and outdoor environments of the Institute were designed with Wireless InSite Package (WIP). In addition, emphasis was focus on the use of two types of transmitting devices (Directional and Omni-Directional). The aim of using these two devices is to determine which device is better to cover the Institute's environment well. In this work, a different frequency bands scenario was used to determine which band is suitable for coverage and stability of the wireless network. These bands are S-Band (2.4GHz), C-Band (5GHz), C-Band (10GHz), Ku-Band (15GHz), Ka-Band (28GHz), and MmWave (39GHz). Moreover, the focus has been on the most important basic parameters to determine the performance level of the two devices (Directional and Omni-Directional) as well as to determine the performance level of the wireless network. The most important of these parameters are Path Losses (LPath), Path Gain (GPath), Received Signal Strength (RSS), Strongest Received Power, Coverage Ratio (CR), and Received Signal Quality Ratio (RSQR). According to the results that emerged, it was observed that Omni-Directional antennas are much better than Directional antennas, especially in NLOS (None-Line-of-Sight) regions. It was also noted that CR, LPath, and RSS at S-Band (2.4GHz) are much better than the rest of the bands, so that the CR and the RSQR at this band reach 83.2184% and 95.7383%, respectively. While at the MmWave-Band (39GHz), it reaches 31.0345% and 70.7937% respectively.
The review of heterogeneous design frameworks/Platforms for digital systems embedded in FPGAs and SoCs Abdelhakim Alali; Hasna Elmaaradi; Mohammed Khaldoun; Mohamed Sadik
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 4: December 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i4.3243

Abstract

Systems-on-a-chip integrate specialized modules to provide well-defined functionality. In order to guarantee its efficiency, designersare careful to choose high-level electronic components. In particular,FPGAs (field-programmable gate array) have demonstrated theirability to meet the requirements of emerging technology. However,traditional design methods cannot keep up with the speed andefficiency imposed by the embedded systems industry, so severalframeworks have been developed to simplify the design process of anelectronic system, from its modeling to its physical implementation.This paper illustrates some of them and presents a comparative studybetween them. Indeed, we have selected design methods of SoC(ESP4ML and HLS4ML, OpenESP, LiteX, RubyRTL, PyMTL,SysPy, PyRTL, DSSoC) and NoC networks on OCN chip (PyOCN)and in general on FPGA (PRGA, OpenFPGA, AnyHLS, PYNQ, andPyLog).The objective of this article is to analyze each tool at several levelsand to discuss the benefit of each in the scientific community. Wewill analyze several aspects constituting the architecture and thestructure of the platforms to make a comparative study of thehardware and software design flows of digital systems. 
Approach to Object Hardness Prediction by Rubber Ball Hardness Prediction Using Capsule Network Shota Shindo; Takaaki Goto; Kensei Tsuchida
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 4: December 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i4.3275

Abstract

A hardness is often used as an index to compare similar objects such as fruits or wood. To measure an object’s hardness, a hardness meter is required, and certain conditions must be met. The conditions are that the hardness meter is compatible with the object and must be close at hand. This research shows the possibility of measuring hardness without a hardness meter using a neural network. The method employs machine learning using a capsule network (CapsNet) of a neural network model. This research experimented using CapsNet with routing-by-agreement, CapsNet with expectation-maximization routing (EM routing) and the EM routing method with the addition of Tasks-Constrained Deep Convolutional Network (TCDCN). The four-layer CapsNet with EM routing implemented has achieved the state-of-the-art.  Multi-layered CapsNet with EM routing was a very effective method for regression analysis as well. And, CapsNet has higher discriminative power using EM-routing than routing-by-agreement.
Design and Evaluation of the Efficiency of Channel Coding LDPC Codes for 5G Information Technology Juliy Boiko; Ilya Pyatin; Oleksander Eromenko
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 4: December 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i4.3188

Abstract

This paper proposes a result of an investigation of a topical problem and the development of models for efficient coding in information networks based on codes with a low density of parity check. The main advantage of the technique is the presented recommendations for choosing a signal-code construction is carried out taking into account the code rate and the number of iterations decoding for envisaging the defined noise immunity indices. The noise immunity of signal-code constructions based on low-density codes has been increased by combining them with multi position digital modulation. This solution eventually allowed to develop a strategy for decoder designing of such codes and to optimize the code structure for a specific information network. To test the effectiveness of the proposed method, MATLAB simulations are carried out under for various Information channels binary symmetric channel (BSC), a channel with additive white Gaussian noise (AWGN), binary asymmetric channel (BAC), asymmetric channel Z type. In addition, different code rates were used during the experiment. The study of signal-code constructions with differential modulation is presented. The efficiency of different decoding algorithms is investigated. The advantage of the obtained results over the known ones consists in determining the maximum noise immunity for the proposed codes. The energy gain was on the order of 6 dB, and an increase in the number of decoding iterations from 3 to 10 leads to a gain in coding energy of 5 dB. Envisaged that the results obtained can be very useful in the development of practical coding schemes in 5G networks.
Analysis on performances of the optimization algorithms in CNN speech noise attenuator Haengwoo Lee
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 4: December 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i4.3245

Abstract

In this paper, we studied the effect of the optimization algorithm of weight coefficients on the performance of the CNN(Convolutional Neural Network) noise attenuator. This system improves the performance of the noise attenuation by a deep learning algorithm using the neural network adaptive predictive filter instead of using the existing adaptive filter. Speech is estimated from a single input speech signal containing noise using 64-neuron, 16-filter CNN filters and an error back propagation algorithm. This is to use the quasi-periodic nature of the voiced sound section of the voice signal. In this study, to verify the performance of the noise attenuator for the optimization, a test program using the Keras library was written and training was performed. As a result of simulation, this system showed the smallest MSE value when using the Adam algorithm among the Adam, RMSprop, and Adagrad optimization algorithms, and the largest MSE value in the Adagrad algorithm. This is because the Adam algorithm requires a lot of computation but it has an exellent ability to estimate the optimal value by using the advantages of RMSprop and Momentum SGD.
Evaluation on the Effect of EEG Pre-processing and Hyper parameters Tuning to the Performance of Convolutional Neural Network Motor Execution Classification Mohamed Ragab Mahmoud Farghaly; Lim Kim Chuan; Low Yin Fen; Feng Duan; Soo Yew Guan
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 4: December 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i4.3344

Abstract

Electroencephalogram (EEG) based classification has achieved a promising performance using deep learning models like Convolutional Neural Network. Various pre-processing strategies such as smoothing the EEG data or filtering are commonly used to pre-process the captured EEG signal before the subsequent feature extraction and classification while hyperparameters tuning might help to improve the classification performance. As well, the number of layers used in the CNN can affect the performance of the classification. In this paper, the number of layers needed for the CNN to classify the EEG data correctly, the effect of apply smoothing to pre-process the EEG signal for modern end-to-end CNN and the effect of enabling hyperparameters tuning during the training phase of CNN is investigated and analyzed. Two CNN models, namely Deep CNN with 5 layers and Shallow CNN with 1 layer, with convincing classification accuracy on motor execution classification as reported in the literature were chosen for this study. Both the CNN models are trained on EEG motor execution dataset with different training strategies and dataset pre-processing. Based on the obtained training and test classification accuracy, Shallow CNN trained with enabling hyper parameters tuning and without smoothing the EEG data achieved the best classification accuracy with average training accuracy of 99.9% and test accuracy of 96.87%. This indicates that CNN does not need to have many layers to correctly classify the motor execution data and the EEG data does not require smoothing.
Distribution network reconfiguration considering DGs using a hybrid CS-GWO algorithm for power loss minimization and voltage profile enhancement Pujari Harish Kumar; Mageshvaran Rudramoorthy
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 4: December 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i4.3432

Abstract

This paper presents an implementation of the hybrid Cuckoo search and Grey wolf (CS-GWO) optimization algorithm for solving the problem of distribution network reconfiguration (DNR) and optimal location and sizing of distributed generations (DGs) simultaneously in radial distribution systems (RDSs). This algorithm is being used significantly to minimize the system power loss, voltage deviation at load buses and improve the voltage profile. When solving the high-dimensional datasets optimization problem using the GWO algorithm, it simply falls into an optimum local region. To enhance and strengthen the GWO algorithm searchability, CS algorithm is integrated to update the best three candidate solutions. This hybrid CS-GWO algorithm has a more substantial search capability to simultaneously find optimal candidate solutions for problem. Furthermore, to validate the effectiveness and performances of the proposed hybrid CS-GWO algorithm is being tested and evaluated for standard IEEE 33-bus and 69-bus RDSs by considering different scenarios.
DALF: An AI Enabled Adversarial Framework for Classification of Hyperspectral Images Tatireddy Subba Reddy; Jonnadula Harikiran
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 4: December 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i4.3339

Abstract

Hyperspectral image classification is very complex and challenging process. However, with deep neural networks like Convolutional Neural Networks (CNN) with explicit dimensionality reduction, the capability of classifier is greatly increased. However, there is still problem with sufficient training samples. In this paper, we overcome this problem by proposing an Artificial Intelligence (AI) based framework named Deep Adversarial Learning Framework (DALF) that exploits deep autoencoder for dimensionality reduction, Generative Adversarial Network (GAN) for generating new Hyperspectral Imaging (HSI) samples that are to be verified by a discriminator in a non-cooperative game setting besides using aclassifier. Convolutional Neural Network (CNN) is used for both generator and discriminator while classifier role is played by Support Vector Machine (SVM) and Neural Network (NN). An algorithm named Generative Model based Hybrid Approach for HSI Classification (GMHA-HSIC) which drives the functionality of the proposed framework is proposed. The success of DALF in accurate classification is largely dependent on the synthesis and labelling of spectra on regular basis. The synthetic samples made with an iterative process and being verified by discriminator result in useful spectra. By training GAN with associated deep learning models, the framework leverages classification performance. Our experimental results revealed that the proposed framework has potential to improve the state of the art besides having an effective data augmentation strategy.
Optimum Switching Angle Of Switched Reluctance Motor Using Response Surface Methodology Agus Adhi Nugroho; Muhammad Khosyi'in; Bustanul Arifin; Bhakti Yudho Suprapto; Muhamad Haddin; Zainuddin Nawawi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 4: December 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i4.3321

Abstract

Switched Reluctance Motor has numerous advantages compared to another electric motor. Simple structure, low-cost production, robustness, and high fault tolerance have been remarkable milestones. Still, the problem of excitation angle at power converter becomes crucial, especially for traction use, requiring higher torque at low speed for starting and acceleration. Therefore, this research emphasized finding the optimum excitation angle at low speed using Response Surface Methodology, a practical application to achieve the highest torque, as indicated by the best speed in the constant torque region. As a result, using Matlab simulation, the adaptive combination of optimum angles reached 2691 rpm quicker than a single excitation angle with 2568 rpm, an increase of 4.79% higher speed using RSM optimization. According to the experimental data, the adaptive combination of optimum angle achieved 2475 rpm better than the single excitation angle reached 2340 rpm, an increase of 5.77% higher speed using the Response Surface Methodology.
Flexible Gripper, Design and Control for Soft Robotics Catalina Castillo-Rodriguez; Robinson Jimenez-Moreno
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 4: December 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i4.3325

Abstract

This paper presents the 3D design of a flexible gripper used for gripping polyform objects that require a certain degree of adaptation of the effector for its manipulation. For this case, the 3D printing of the gripper and its construction is exposed, where a fuzzy controller is implemented for its manipulation. The effector has a flexo resistance that provides information of the deflection of the gripper, this information and the desired grip force are part of the fuzzy controller that seeks to regulate the current of the servomotors that make up the structure of the gripper and are responsible for ensuring the grip. An efficient system is obtained for gripping polyform objects involving deflection of up to 5 mm with a current close to 112 mA.

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