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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
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Investigations on spectral efficiency of muticellnetworks using hybrid beamforming S. Deepa; J. Jeneetha Jebanazer; S. Rajakumar; J. Mercy Sheeba; J. Rryan
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp826-835

Abstract

Millimeter wave communication systems with antenna beamforming facilitates practical solutions to the capacity crunch issues in the upcoming 5G wireless networks. Multi-cell dense networks are prone to three major interferences-inter-cell, intra-cell and Inter layer interference-the most dominating being the inter-cell interference. This paper focuses to alleviate inter-cell interference using hybrid beamforming (HBF) approach, leveraging coordinated multipoint (CoMP) technique, thereby improving the SE of 5G networks. Simulation results show HBFpeforms in par with optimal weights, making it a suitable candidate for 5G networks. As the number of data streams is increased from Ns=1 to 4 for 0 dB signal to noise ratio (SNR) with Nt=64 and Nr=16, the SE increases from 9.5557 bits/s/Hz to 26.423 bits/s/Hz for optimal weights and from 9.1885 bits/s/Hz to 19.763 bits/s/Hz and hybrid weights, respectively. The second set of experiments are conducted to study the effect of number of transmit antennas on spectral efficiency (SE). The results show that as the number of transmit antennas is increased from Nt=16 to 64 for 0 dB SNR, with Nr=16 and Ns=4, the SE increases from 17.735 bits/s/Hz to 26.423 bits/s/Hz and 13.750 bits/s/Hz to 19.763 bits/s/Hz for optimal weights and hybrid weights, respectively.
Model based adaptive controller with grasshopper optimization algorithm for upper-limb rehabilitation robot Aliaa Adnan; Ekhlas H. Karam; Muaayed F. Al-Rawi
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp723-731

Abstract

Model based adaptive controllers (MBACs) are considered one of the most common adaptive controllers that are used with robotic systems due to their ensuring nonlinear robust scheme with global asymptotic stability for controlling nonlinear systems. However, this controller requires precise mathematical models of the controlled systems. In this paper, an optimal model-based adaptive controller (OMBAC) is suggested for controlling a two-link upper limb rehabilitation robot. This controller, in the presence of model uncertainties, can guarantee the robustness of the rehabilitation robot. Although the OMBAC is an adaptive and model-based controller, some of its parameters need to be determined precisely. In this paper, these parameters are determined by the grasshopper optimization algorithm (GOA). The Lyapunov method is used to analyze the stability assurance of controlled rehabilitation. The results of the simulation for two tested trajectories (linear and nonlinear trajectories) demonstrate the efficiency of the suggested OMBAC with fast settling time, minimum error steady state, and very small overshoot.
Modeling phasmophobia (fear of ghosts) using electroencephalogram Safie Sairul Izwan; Puteri Zarina M. Khalid; Mohd Aimullah
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp743-753

Abstract

Extreme fears towards ghosts and entities are defined as phasmaphobia. Those diagnosed with phasmophobia symptoms should control their own fears to avoid phasmaphobia attack. In this work, we present the development of phasmophobia detection electroencephalogram database (PDED).PDEDconsistsofanaverageof45minutes electroencephalography (EEG) recordings from eight electrodes situated on the frontal lobe of the brain area. A real-time fear assessment was conducted simultaneously with the EEG recording by the participant. Five different stimuli were used to induce fear in our experiment. 599 EEG epochs related to fear were extracted based on the timestamp recorded by each individual. Asymmetry relation ratio (ARR) techniques were used on these EEG to detect the presence of fear. The quality of long duration of EEG recording from PDED in recognizing fear was thoroughly presented based on ARR. In this study, 91.5% of fear emotion managed to be detected from these epochs. Using PDED, it is also proven that the changes of ARR reflected positive correlation towards the changes of the level of fear. Analysis using emotion recognition rate (ERR) curves indicated that, two electrodes, namely F7 and F8, were sufficient to recognized 88% of fear from the recordings.
Simulating the Covid-19 epidemic event and its prevention measures using python programming Mustofa Abi Hamid; Dimas Aditama; Endi Permata; Nur Kholifah; Muhammad Nurtanto; Nuur Wachid Abdul Majid
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp278-288

Abstract

A simulation is needed to observe and indicate how much preventive measures influence the pandemic flow, controlling and stopping it. This study succeeded in making a stochastic susceptible infected recovered deceased (SIRD) simulation using Python programming language to determine the effectiveness of prevention methods such as masks policy, social distancing, vaccination, quarantine, and lockdown. Every preventive measure is modeled based on an equivalent actual event and every essential aspect that affects the course of the pandemic. A person is represented as a circle moving freely in two-dimensional space, and disease spreads through person-to-person contact. This simulator then tested using parameters to simulate COVID-19 and found significant results between communities that implement preventive measures and those that do not. We found that within 106 days, 284 people were infected, but when five preventive methods are applied for a total of 33 days, only 31 people were infected. Adequate to simulate epidemic events and their prevention measures, this simulator can also be used as a learning tool with factors in epidemic events such as population density, mobility, infection rate, disease mortality, and every effect of each preventive measure. Users can change and influence the simulation course using interactive and straightforward software tools.
Real-time passenger social distance monitoring with video analytics using deep learning in railway station Iqbal Ahmad Dahlan; Muhammad Bryan Gutomo Putra; Suhono Harso Supangkat; Fadhil Hidayat; Fetty Fitriyanti Lubis; Faqih Hamami
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp773-784

Abstract

Recently, at the end of December, the world faced a severe problem which is a pandemic that is caused by coronavirus disease. It also must be considered by the railway station's authorities that it must have the capability of reducing the covid transmission risk in the pandemic condition. Like a railway station, public transport plays a vital role in managing the COVID-19 spread because it is a center of public mass transportation that can be associated with the acquisition of infectious diseases. This paper implements social distance monitoring with a YOLOv4 object detection model for crowd monitoring using standard CCTV cameras to track visitors using the DeepSORT algorithm. This paper used CCTV surveillance with the actual implementation in Bandung Railway Station with the accuracy at 96.5 % result on people tracking with tested in real-time processing by using minicomputer Intel(R) Xeon(R) CPU E3-1231 v3 3.40GHz RAM 6 GB around at 18 FPS.
Electricity generation from renewable energy based on abandoned wind fan Arni Munira Markom; Muhammad Hakimi Aiman Hadri; Tuah Zayan Muhamad Yazid; Zakiah Mohd Yusof; Marni Azira Markom; Ahmad Razif Muhammad
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp1-8

Abstract

In the 21st century, our world is facing difficult conditions for serious environmental pollution and the problem of energy shortage. An innovative idea has emerged to recycle wind energy from air conditioning condenser fans in outdoor buildings. Therefore, the main goal of this research is to develop renewable wind energy from the condenser fan of an air conditioner using Arduino as a microcontroller. This research moves towards a portable, low cost, environmentally friendly mini device that harnesses renewable energies with endless resources for future alternative power generation and reduces the burden of consumers' electricity bills.
Electric vehicle charging using roof top photovoltaic controlled with new hybrid optimization technique Dhamodharan Selvaraj; Dhanalakshmi Rangasamy
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1227-1234

Abstract

In this decade, electric car technology has advanced at a breakneck pace. People are also using electric vehicles more since they are more inexpensive. Electric car charging is one of the issues that most sectors confront, as there are many cities in India where charging stations have yet to be established. In this paper, an innovative approach for charging a vehicle while on the move is presented, utilising the solar panels on the vehicle's roof. The panels collect energy from the sun and use it to charge the vehicle's battery. Even when the vehicle is driving down the road, this happens. Partial shading is a concern for solar panels when travelling on the road. In this paper, a new hybrid optimization technique combining grey wolf optimization and crow search algorithms (GWO-CSA) is employed to compare an electric car model to the traditional particle swarm optimization (PSO) approach. The MATLAB simulation results demonstrate the vehicle's performance and tracking efficiency.
Magnetic resonance coupling wireless power transfer for green technologies Reem Emad Nafiaa; Aws Zuheer Yonis
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp289-295

Abstract

Wireless power transfer (WPT) is a technology that is considered the focus of scientists' attention for its development and creation to be compatible with many devices that are used today and also consider one of the green technology apps which means any technology can reduce the effect of people on the environment which is today grow continuously. In this paper, a wireless power transfer for a mobile charger had been discussed to get a maximum power and efficiency power transfer. WPT is considered as a reliable technology, efficient, fast, not using wires, and can be used for short and long-range. There are three methods for WPT, electromagnetic induction, magnetic resonance coupling, and radio waves which are classified by the distance that sends the power. Magnetic resonance coupling is the method that has been focused on in this paper because of compatibility with short or medium distances as battery chargers which depend on the magnetic field to transfer power without wires that can protect devices from damages and heating. As result the effect of distance on efficiency has been discussed with reached to nearer distance can improve efficiency however by using magnetic resonance technique, acceptable efficiency can be obtained with appropriate distance.
Deep convolutional neural network model for bad code smells detection based on oversampling method Nasraldeen Alnor Adam Khleel; Károly Nehéz
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1725-1735

Abstract

Code smells refers to any symptoms or anomalies in the source code that shows violation of design principles or implementation. Early detection of bad code smells improves software quality. Nowadays several artificial neural network (ANN) models have been used for different topics in software engineering: software defect prediction, software vulnerability detection, and code clone detection. It is not necessary to know the source of the data when using ANN models but require large training sets. Data imbalance is the main challenge of artificial intelligence techniques in detecting the code smells. To overcome these challenges, the objective of this study is to presents deep convolutional neural network (D-CNN) model with synthetic minority over-sampling technique (SMOTE) to detect bad code smells based on a set of Java projects. We considered four code-smell datasets which are God class, data class, feature envy and long method and the results were compared based on different performance measures. Experimental results show that the proposed model with oversampling techniques can provide better performance for code smells detection and prediction results can be further improved when the model is trained with more datasets. Moreover, more epochs and hidden layers help increase the accuracy of the model.
COVID-19 detection based on combined domain features Omar Munthir Al Okashi; Ismail Taha Ahmed; Leith Hamid Abed
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp965-973

Abstract

The computed tomography (CT) scan delivers more detailed information and higher judgment accuracy than a chest X-ray, which has a wide range of uses in diagnosing and decision-making to aid medical professionals. This paper proposed a method to detect COVID-19 from CT scan images using the combination of spatial domain and transform domain features. Using the lung segmentation step, the CT image is first processed and segmented, and then various domain features are extracted. From these domain features, the highest combined domain features (CDF) are obtained. Finally, the detection task is completed using random forest (RF) and Naive Bayesian (NB) classifiers. The proposed method is tested using a dataset of CT scan images, and the results are compared to several current techniques. The results showed that our method based on CDF outperforms previous methods, with an overall accuracy of nearly 98%. As can be shown, CDF is the best domain feature to apply for detecting COVID-19.

Page 656 of 918 | Total Record : 9174

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