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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 17 Documents
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Search results for , issue "Vol 9, No 4: December 2021" : 17 Documents clear
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.
A Comprehensive Study of Capacitive Loaded Resonant Converter Topologies for Charging Applications Geethanjali Pandeswara; Naresh Pasula
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.v9i4.3521

Abstract

Resonant converters (RCs) are perceiving global interests of the research community for its eminent contribution in design of many industrial and commercial applications. Rich literature and well-established technology is available to define the role of RCs in such applications where the load is predominantly passive and resistive. However in applications like charging, the nature of load is often interpreted as capacitive and the knowledge on how a RC reciprocates to such variable, non linear load is limited. Motivated by this, the paper investigates about 25 capacitive loaded resonant structures and each of them is thoroughly analyzed to evaluate various key parameters like the output current, peak input current,  and current gain. A comparative study is done to categorize and organize these topologies in regard to each of the said parameters.  This provides a quick overview of various resonant converter topologies and helps designers to choose a structure that may fit their application. To this base knowledge, the study is further narrowed down to find suitable topology for charging application and accordingly proposed a novel fourth-order RC topology called LA7. A hardware prototype was built to compare and validate the simulated and measured performances.
Low Voltage Capability of Generator for Frequency Regulation of Wind Energy System Smrutiranjan Nayak; Sanjeeb kumar Kar; Subhransu Sekhar Dash
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.v9i4.3426

Abstract

For the extraction of wind energy through a doubly fed induction generator (DFIG), low voltage is major particular essential controlled by the transmission structure executive. Under a structure issue condition, DFIG should remain with respect to the lattice for a particular least period and deal open power support on a case-by-case basis by the Transmission framework administrator. A pleasant control plot involving gear course of action through a superconducting resistance type issue current limiter (R-SFCL) and programming plan based on the rotor reference current direction control system (RRCOCS) with transient voltage control (TVC), is proposed in this paper to address the Low voltage essential. The results got by the proposed procedure are differentiated and RRCOCS and RRCOCS-TVC.
Classification of EEG Signal by Using Optimized Quantum Neural Network Dalael Saad Abdul-Zahra; Ali Talib Jawad; Hassan Muwafaq Gheni; Ali Najim Abdullah
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.v9i4.3486

Abstract

In recent years the algorithms of machine learning was used for brain signals identifing which is a useful technique for diagnosing diseases like Alzheimer's and epilepsy. In this paper, the Electroencephalogram (EEG) signals are classified using an optimized Quantum neural network (QNN) after normalizing these signals, wavelet transform (WT) and the independent component analysis (ICA), were utilized for feature extraction.  These algorithms used to reduces the dimensions of the data, which is an input to the optimized QNN for the purpose of performing the classification process after the feature extraction process. This research uses an optimized QNN, a form of feedforward neural network (FFNN), to recognize (EEG) signals. The Particle swarm optimization (PSO) algorithm was used to optimize the quantum neural network, which improved the training process of the system's performance. The optimized (QNN) provided us with somewhat faster and more realistic results. According to simulation results, the total classification for (ICA) is 82.4 percent, while the total classification for (WT) is 78.43 percent; from these results, using the ICA for feature extraction is better than using WT.
Hybrid Deep Neural Network for Facial Expressions Recognition Wijdan Rashid Abdulhussien; Nidhal K. El Abbadi; Abdul M. Gaber
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.v9i4.3425

Abstract

Facial expressions are critical indicators of human emotions where recognizing facial expressions has captured the attention of many academics, and recognition of expressions in natural situations remains a challenge due to differences in head position, occlusion, and illumination. Several studies have focused on recognizing emotions from frontal images only, while in this paper wild images from the FER2013 dataset have been used to make a more generalizing model with the existence of its challenges, it is among the most difficult datasets that only got 65.5 % accuracy human-level. This paper proposed a model for recognizing facial expressions using pre-trained deep convolutional neural networks and the technique of transfer learning. this hybrid model used a combination of two pre-trained deep convolutional neural networks, training the model in multiple cases for more efficiency to categorize the facial expressions into seven classes. The results show that the best accuracy of the suggested models is 74.39%  for the hybrid model, and 73.33% for Fine-tuned the single EfficientNetB0 model, while the highest accuracy for previous methods was 73.28%. Thus, the hybrid and single models outperform other state of art classification methods without using any additional, the hybrid and single models ranked in the first and second position among these methods. Also, The hybrid model has even outperformed the second-highest in accuracy method which used extra data. The incorrectly labeled images in the dataset unfairly reduce accuracy but our best model recognized their actual classes correctly.

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