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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,174 Documents
 604   605   606   607   608 
Cellular automata model for emergent properties of pressure flow in single nephron compliance tubule Reddy Kesu, Siva Manohar; Ramasangu, Hariharan
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1227-1235

Abstract

Numerical analysis plays a vital role in the computational modeling of a nephron. The solutions from numerical methods exhibit regular, period doubling, and irregular oscillations as global behavior. A single nephron compliance tubular model with transport mechanism and autoregulatory mechanism has been developed using the cellular automata framework in this paper. Global emergent behavior of the biological system has been captured using cellular automata framework. An ultradiscretization technique is used to convert the governing partial differential equations of a single nephron compliance tubular model to cellular automata local rules. The global emergent behaviors from the local cellular automata rules have been compared with the reported experimental analysis. The cellular automata framework in biological modeling is an emerging perspective to model global functional behaviors.
Work function variations on electrostatic and RF performances of JLSDGM Device K K. E. Kaharudin; F. Salehuddin; A. S. M. Zain; Ameer F. Roslan; I Ahmad
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 1: July 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i1.pp150-161

Abstract

This paper offers a systematic analysis on the impact of work function (WF) variations on electrostatic and radio frequency (RF) performances of nchannel junctionless strained double gate (DG) (n-JLSDGM) metal oxide semiconductor field effect transistor (MOSFET). The study has been performed under othe constant level of design parameters that operates in saturation as a transconductance amplifier, considering the dependence of electrostatic and RF performance on the variation of WF. Furthermore, this paper aims to provide physical insight into the improved electrostatic and RF performances of the proposed n-JLSDGM device. The device layout and characteristics were designed and extracted respectively via a comprehensive 2-D simulation. Device performances such as on-state current (ION), off-state current (IOFF), on-off current ratio, subthreshold swing (SS), intrinsic capacitances, dynamic power dissipation (Pdyn), cut-off frequency (fT) and maximum oscillation frequency (fmax) are intensively investigated in conjunction with WF variations.
Segregation of oil palm fruit ripeness using color sensor Aiman Mustaffa; Faiz Arith; Nurin Izzati Fauzi Peong; Nurul Rafiqah Jaffar; Evelyn Larwy Linggie; Ahmad Nizamuddin Mustafa; Fara Ashikin Ali
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp130-137

Abstract

Oil palm is an important industry that has contributed to income and support to the economic sector especially for Malaysia and Indonesia. However, most of the equipment in the oil palm industry is still operated manually. This work developed a system to separate bunches of oil palm fruit using color sensors according to maturity level. Fruit color plays a decisive point in determining fruit maturity. Here, a specific threshold point of red green blue (RGB) was obtained for the determination of the maturity level of oil palm fruit. Point values of < 120, 120 < x < 150 and > 150 represent the maturity levels of unripe, under ripe and ripe, respectively. This paper is the first to report the RGB points for use in the development of automated oil palm segregation system in the oil palm plantation industry. Thus, this paper will pave the way in producing an accurate and reliable oil palm separation system, which in turn has a positive effect in reducing human error. In the future, a set of sensors is proposed to detect a bunch of the oil palm fruits. This further can speed up the segregation process and more suitable for adaptation to the industry.
Efficient intelligent system for diagnosis pneumonia (SARS-COVID19) in X-Ray images empowered with initial clustering Salam Saad Mohamed Ali; Ali Hakem Alsaeedi; Dhiah Al-Shammary; Hassan Hakem Alsaeedi; Hadeel Wajeeh Abid
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp241-251

Abstract

This paper proposes efficient models to help diagnose respiratory (SARS-COVID19) infections by developing new data descriptors for standard machine learning algorithms using X-Ray images. As COVID-19 is a significantly serious respiratory infection that might lead to losing life, artificial intelligence plays a main role through machine learning algorithms in developing new potential data classification. Data clustering by K-Means is applied in the proposed system advanced to the training process to cluster input records into two clusters with high harmony. Principle Component Analysis PCA, histogram of orientated gradients (HOG) and hybrid PCA and HOG are developed as potential data descriptors. The wrapper model is proposed for detecting the optimal features and applied on both clusters individually. This paper proposes new preprocessed X-Ray images for dataset featurization by PCA and HOG to effectively extract X-Ray image features. The proposed systems have potentially empowered machine learning algorithms to diagnose Pneumonia (SARS-COVID19) with accuracy up to %97.
A hybrid de-noising method for mammogram images Rashid Mehmood Gondal; Saima Anwar Lashari; Murtaja Ali Saare; Sari Ali Sari
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 3: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i3.pp1435-1443

Abstract

In general, mammogram images contaminated with noise which directly affect images quality. Several methods have been proposed to de-noise these images, however, there is always a risk of losing valuable information. In order to overcome the loss of information, the present study proposed a hybrid denoising method for mammogram images. The proposed hybrid method works in two steps: Firstly, preprocessing with mathematical morphology was applied for image enhancement. Secondly, global unsymmetrical trimmed median filter (GUTM) is applied to de-noise image. Experimental results prove that proposed method work well for mammogram images. Hence, the study provided an alternative method for denoising mammogram images.
An optimal proportional integral derivative tuning for a magnetic levitation system using metamodeling approach Abdalhadi, Abdualrhman; Wahid, Herman; Hanafi Burhanuddin, Dirman
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1356-1366

Abstract

A magnetic levitation system (MLS) is a complex nonlinear system that requires an electromagnetic force to levitate an object in the air. The electromagnetic field is extremely sensitive to noise which can cause the acceleration on the spherical object, leading it to move into the unbalanced region. This paper presents a comparative assessment of controllers for the magnetic levitation system using proportional integral derivative (PID) controller based optimal tuning. The analysis was started by deriving the mathematical model followed by the implementation of radial basis function neural network (RBFNN) based metamodel. The optimal tuning of the PID controller has offered better transient responses with the improvement of overshoot and the rise time as compared to the standard optimization methods. It is more robust and tolerant as compared to gradient descent method. The simulation output using the radial basis based metamodel approach showed an overshoot of 9.34% and rise time of 9.84 ms, which are better than the gradient descent (GD) and conventional PID methods. For the verification purpose, a Simscape model has been developed which mimic the real model. It was found that the model has produced about similar performance as what has been obtained from the MATLAB simulation.
A self adaptive new crossover operator to improve the efficiency of the genetic algorithm to find the shortest path Mrinmoyee Chattoraj; Udaya Rani Vinayakamurthy
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp1011-1017

Abstract

Route planning is an important part of road network. To select an optimized route several factors such as flow of traffic, speed limits of road. are concerned. Total cost of such a network depends on the number of junctions between the source and the destination. Due to the growth of the nodes in the network it becomes a tough job to determine the exact path using deterministic algorithms so in such cases genetic algorithms (GA) plays a vital role to find the optimized route. Crossover is an important operator ingenetic algorithm. The efficiency of thegenetic algorithmis directlyinfluenced by the time of a crossover operation. In this paper a new crossoveroperator closest-node pairing crossover (CNPC) is recommended which is explicitly designed to improve the performance of the genetic algorithm compared to other well-known crossover operators such as point based crossover and order crossover. The distance aspect of the network problem has been exploited in this crossover operator. This proposed technique gives a better result compared to the other crossover operator with the fitness value of 0.0048. The CNPC operator gives better rate of convergence compared to the other crossover operators.
Hunting strategy for multi-robot based on wolf swarm algorithm and artificial potential field Oussama Hamed; Mohamed Hamlich; Mohamed Ennaji
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp159-171

Abstract

The cooperation and coordination in multi-robot systems is a popular topic in the field of robotics and artificial intelligence, thanks to its important role in solving problems that are better solved by several robots compared to a single robot. Cooperative hunting is one of the important problems that exist in many areas such as military and industry, requiring cooperation between robots in order to accomplish the hunting process effectively. This paper proposed a cooperative hunting strategy for a multi-robot system based on wolf swarm algorithm (WSA) and artificial potential field (APF) in order to hunt by several robots a dynamic target whose behavior is unexpected. The formation of the robots within the multi-robot system contains three types of roles: the leader, the follower, and the antagonist. Each role is characterized by a different cognitive behavior. The robots arrive at the hunting point accurately and rapidly while avoiding static and dynamic obstacles through the artificial potential field algorithm to hunt the moving target. Simulation results are given in this paper to demonstrate the validity and the effectiveness of the proposed strategy.
An accurate signature verification system based on proposed HSC approach and ANN architecture Mustafa S. Kadhm; Mamoun Jassim Mohammed; Hayder Ayad
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 1: January 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i1.pp215-223

Abstract

With the rapid development of technology in all life fields, and due to the huge daily needs for banking systems process, documents processing and other similar systems. The authentication became more required key for these systems. One of the successful system to verify the any person is the signature verification system. However, a reliable and accurate system is still needed. For this reason, the security challenge is take place via authentic signatures. However, a reliable and accurate system is still needed. For this reason, the security challenge is take place via authentic signatures. Therefore, this paper present a reliable signature verification system using proposed histogram of sparse codes (HSC) feature extraction approach and artificial neural networks (ANN) architecture for classification. The system achieved fast computing 0.09 ms and accurate verification results that is 99.7% using three different signature images datasets CEDAR, UTSig, and ICDAR.
Bangla numerical sign language recognition using convolutional neural networks (CNNs) F. M. Javed Mehedi Shamrat; Sovon Chakraborty; Md. Masum Billah; Moumita Kabir; Nazmus Shakib Shadin; Silvia Sanjana
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 1: July 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i1.pp405-413

Abstract

The amount of deaf and mute individuals on the earth is rising at an alarmingrate. Bangladesh has about 2.6 million people who are unable to interact with the community using language. Hearing-impaired citizens in Bangladesh use Bangladeshi sign language (BSL) as a means of communication. In this article,we propose a new method for Bengali sign language recognition based on deep convolutional neural networks. Our framework employs convolutional neural networks (CNN) to learn from the images in our dataset and interpret hand signs from input images. Checking their collections of ten indications (we usedten sets of images with 31 distinct signs) for a total of 310 images. The proposed system takes snap shots from a video by using a webcam with applying a computer vision-based approach. After that, it compares those photos to a previously trained dataset generated with CNN and displays the Bengali numbers (০-৯). After estimating the model on our dataset, weobtained an overall accuracy of 99.8%. We want to streng then things as far aswe can to make silent contact with the majority of society as simple asprobable.

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