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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 113 Documents
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Search results for , issue "Vol 12, No 6: December 2022" : 113 Documents clear
A novel application of artificial neural network for classifying agarwood essential oil quality Noratikah Zawani Mahabob; Zakiah Mohd Yusoff; Aqib Fawwaz Mohd Amidon; Nurlaila Ismail; Mohd Nasir Taib
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6645-6652

Abstract

This work studies the agarwood oil classification into high and low quality by using two different techniques. Initially, the Forest Research Institute Malaysia (FRIM) and Universiti Malaysia Pahang (UMP) are where the sample preparation and compound extraction of agarwood oil is collected. The data collections were done from the previous researcher consists of 96 samples from seven significant agarwood oil compounds. The artificial neural network (ANN) and the proposed stepwise regression technique were used in this study. The stepwise regression was done the feature selection and successfully reduced agarwood oil compounds from seven to four. Then, the ANN technique was used to classify agarwood oil into high and low using input from seven and four compounds separately. The performance of ANN with different inputs is compared (ANN with seven inputs compared with ANN with four inputs). All the experimental work was performed using the MATLAB R2017b using the “patternet” implemented Levenberg Marquardt algorithm and ten hidden neurons. It was found that the ANN technique using seven compounds obtained the best performance according to high accuracy and lower mean square error (MSE) value. Finally, 1 hidden neuron in ANN with seven inputs selected as the best neuron for grading the agarwood oil compounds.
Smoothing-aided long-short term memory neural network-based LTE network traffic forecasting Mohamed Khalafalla Hassan; Sharifah Hafizah Sayed Ariffin; Sharifah Kamilah Syed-Yusof; Nurzal Effiyana Ghazali; Mohammed Eltayeb Ahmed Kanona; Mohamed Rava
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6859-6868

Abstract

There is substantial demand for high network traffic due to the emergence of new highly demanding services and applications such as the internet of things (IoT), big data, blockchains, and next-generation networks like 5G and beyond. Therefore, network resource planning and forecasting play a vital role in better resource optimization. Accordingly, forecasting accuracy has become essential for network operation and planning to maintain the minimum quality of service (QoS) for real-time applications. In this paper, a hybrid network- bandwidth slice forecasting model that combines long-short term memory (LSTM) neural network and various local smoothing techniques to enhance the network forecasting model's accuracy was proposed and analyzed. The results show that the proposed hybrid forecasting model can effectively improve the forecasting accuracy with minimal data loss.
Automatic video censoring system using deep learning Yash Verma; Madhulika Bhatia; Poonam Tanwar; Shaveta Bhatia; Mridula Batra
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6744-6755

Abstract

Due to the extensive use of video-sharing platforms and services, the amount of such all kinds of content on the web has become massive. This abundance of information is a problem controlling the kind of content that may be present in such a video. More than telling if the content is suitable for children and sensitive people or not, figuring it out is also important what parts of it contains such content, for preserving parts that would be discarded in a simple broad analysis. To tackle this problem, a comparison was done for popular image deep learning models: MobileNetV2, Xception model, InceptionV3, VGG16, VGG19, ResNet101 and ResNet50 to seek the one that is most suitable for the required application. Also, a system is developed that would automatically censor inappropriate content such as violent scenes with the help of deep learning. The system uses a transfer learning mechanism using the VGG16 model. The experiments suggested that the model showed excellent performance for the automatic censoring application that could also be used in other similar applications.
Development of a web-based single-phase load monitoring and auditing system Oluwaseun Ibrahim Adebisi; Isaiah Adediji Adejumobi; Simeon Matthew; Azeez Aderibigbe Abdulsalam
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6785-6795

Abstract

In a developing nation like Nigeria, the conventional load monitoring and billing system has proved to be tedious, time-consuming, expensive, and prone to human error over the years. Therefore, this creates the need for an efficient system that can assist the Utility to monitor the energy consumption trend of the customers remotely. This work developed a web-based single-phase load monitoring and auditing system using NodeMCU (ESP8266) microcontroller, PZEM-004T sensor, and liquid crystal display (LCD) module for the hardware unit and Blynk internet of things (IoT) platform for the software unit. The system design was implemented around the ESP8266 microcontroller with relevant design models, and standard power and energy equations programmed into the microcontroller in the Arduino integrated development environment. The developed system was load tested to examine its performance and determine its reading error. The hardware and software units of the system operated satisfactorily when tested. The reading accuracy for current and voltage measured by the device were ±0.2% and ±0.4%, respectively, giving a reading error of ±0.8% for power measurement. The developed system is suitable for residential, commercial, and similar applications where the energy usage trend of some small loads is required for management purposes.
Data augmentation by combining feature selection and color features for image classification Kittikhun Meethongjan; Vinh Truong Hoang; Thongchai Surinwarangkoon
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6172-6177

Abstract

Image classification is an essential task in computer vision with various applications such as bio-medicine, industrial inspection. In some specific cases, a huge training data is required to have a better model. However, it is true that full label data is costly to obtain. Many basic pre-processing methods are applied for generating new images by translation, rotation, flipping, cropping, and adding noise. This could lead to degrade the performance. In this paper, we propose a method for data augmentation based on color features information combining with feature selection. This combination allows improving the classification accuracy. The proposed approach is evaluated on several texture datasets by using local binary patterns features.
Auxetic material in biomedical applications: a systematic review Andrés Diaz Melgarejo; Jose Luis Ramírez; Astrid Rubiano
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp5880-5889

Abstract

This study reviews and analyzes the different auxetic materials that have been developed in recent years. The search for research articles was carried out through one of the largest databases such as ScienceDirect, where 845 articles were collected, of which several filters were carried out to have a base of 386 articles. There are a variety of materials depending on their structure, composition, and industrial application, highlighting biomedical applications from tissue engineering, cell proliferation, skeletal muscle regeneration, transportation, bio-prosthesis to biomaterial. The present paper provides an overview of auxetic materials and its applications, providing a guide for designers and manufacturers of devices and accessories in any industry.
Optimal trajectory tracking control for a wheeled mobile robot using backstepping technique Said Fadlo; Abdelhafid Ait Elmahjoub; Nabila Rabbah
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp5979-5987

Abstract

This work studies an optimal trajectory tracking of a wheeled mobile robot with the objective of minimizing energy consumption. First, the mathematical model, which takes into account the kinematic model of the mobile robot and the dynamic model of the actuators is presented. Then, a backstepping controller is designed and its parameters are tuned to satisfy several strict criteria such as rapid convergence, matching desired trajectory, and minimizing energy. For that, two cost functions were investigated and the best one has been selected. The significant reduction in energy losses achieved for all the proposed motion scenarios proves the effectiveness of our approach.
Control of robot-assisted gait trainer using hybrid proportional integral derivative and iterative learning control Elang Parikesit; Dechrit Maneetham; Petrus Sutyasadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp5967-5978

Abstract

An inexpensive exoskeleton of the lower limb was designed and developed in this study. It can be used as a gait trainer for persons with lower limb problems. It plays an essential role in lower limb rehabilitation and aid for patients, and it can help them improve their physical condition. This paper proposes a hybrid controller for regulating the lower limb exoskeleton of a robot-assisted gait trainer that uses a proportional integral and derivative (PID) controller combined with an iterative learning controller (ILC). The direct current motors at the hip and knee joints are controlled by a microcontroller that uses a preset pattern for the trajectories. It can learn how to monitor a trajectory. If the trajectory or load is changed, it will be able to follow the change. The experiment showed that the PID controller had the smallest overshoot, and settling time, and was responsible for system stability. Even if there are occasional interruptions, the tracking performance improves with the ILC.
Analytical transformations software for stationary modes of induction motors and electric drives Mohamed Zaidan Qawaqzeh; Oleksandr Miroshnyk; Aleksandr Osichev; Andrii Tkachenko; Dmytro Danylchenko
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp5738-5753

Abstract

A program was developed in the package of symbolic transformations Maple. It provides automatic analytical transformation and derivation of formulas and plotting of the main characteristics of induction motors (IM) in a convenient form for an electrical engineer and student: torque=f(slip) T=f(s), angular speed=f(Torque) ω=f(T), angular speed=f(Current) ω=f(I), current=f(slip) I=f(s); cos(φ) and phase angle (phi) φ for stator currents and rotor currents, and magnetizing circuit, machine efficiency η=f(s) and a number of other characteristics. The calculation is based on the equivalent circuit of IM motors in its different variants: with one cage in the rotor, with two or more cages in the rotor, taking into account the skin effect in the rotor rods and without it. The user can build up the equivalent circuit to the desired configuration. The algorithm of further transformations is based on analytical obtaining of amplitude/frequency and phase/frequency characteristics in the nodes of the equivalent circuits with further calculation by power and slip. Online animation of the graphs with alternate variations of all resistances R and inductances L values of the model is provided. The article contains screenshots of important parts of the programs and illustrates the complete set of graphs.
A two-element planar multiple input multiple output array for ultra-wideband applications Abdul Kayum Mohammad Zakir Hossain; Muhammad Ibn Ibrahimy; Tole Sutikno; Mohd Hatta Jopri; Jamil Abedalrahim Jamil Alsayaydeh; Mustafa Manap
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6847-6858

Abstract

In this article, a planar monopole two-element multiple input multiple output (MIMO) array has been designed and characterized with the intention of ultra-wideband (UWB) applications. The array has a voltage standing wave ratio (VSWR) working bandwidth (BW) of 13.258 GHz between 3.394-16.652 GHz, with a fractional BW (FBW) of 132.28% with respect to a center frequency of 10.023 GHz. The two elements of the MIMO array are 900 polarizations mismatched for better isolation. Consequently, less than 20 dB of isolation has been achieved throughout the entire BW. Also observed was a good combined realized peak gain of up to 5.85 dBi and total efficiency of greater than 85%. For MIMO performance key parameters, the array exhibits the envelope correlation coefficient (ECC) <0.0033, diversity gain (DG) >9.983, total active reflection coefficient (TARC) <0.445, mean effective gain difference (MEG12) ≈0 dB, and the channel capacity loss (CCL) <0.4 bps/Hz. This design would encourage designers to create high-performance MIMO antennas for UWB frequency-related applications.

Page 11 of 12 | Total Record : 113

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