cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Tole Sutikno
Contact Email
ijece@iaesjournal.com
Phone
-
Journal Mail Official
ijece@iaesjournal.com
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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 98 Documents
 6   7   8   9   10 
Search results for , issue "Vol 11, No 1: February 2021" : 98 Documents clear
Face recognition using fractional coefficients and discrete cosine transform tool Moussa, Mourad; Hmila, Maha; Douik, Ali
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i1.pp892-899

Abstract

Face recognition is a computer vision application based on biometric information for automatic person identification or verification from image sequence or a video frame. In this context DCT is the easy technique to determine significant parameters. Until now the main object is selection of the coefficients to obtain the best recognition. Many techniques rely on premasking windows to discard the high and low coefficients to enhance performance. However, the problem resides in the shape and size of premask. To improve discriminator ability in discrete cosine transform domain, we used fractional coefficients of the transformed images with discrete cosine transform to limit the coefficients area for a better performance system. Then from the selected bands, we use the discrimination power analysis to search for the coefficients having the highest power to discriminate different classes from each other. Feature selection algorithm is a key issue in all pattern recognition system, in fact this algorithm is utilized to define features vector among several ones, where these features are selected according a specified discrimination criterion. Many classifiers are used to evaluate our approach like, support vector machine and random forests. The proposed approach is validated with Yale and ORL Face databases. Experimental results prove the sufficiency of this method in face and facial expression recognition field.
Design of spark ignition engine speed control using bat algorithm Setiadi, Herlambang; Jones, Karl O.; Nugroho, Teguh Aryo; Abdillah, Muhammad; Trilaksana, Herri; Amrillah, Tahta
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i1.pp794-801

Abstract

The most common problem in spark ignition engine is how to increase the speed performance. Commonly researchers used traditional mathematical approaches for designing speed controller of spark ignition engine. However, this solution may not be sufficient. Hence, it is important to design the speed controller using smart methods. This paper proposes a method for designing speed controller of a spark ignition engine using the bat algorithm (BA). The simulation is carried out using the MATLAB/SIMULINK environment. Time domain simulation is carried out to investigate the efficacy of the proposed method. From the simulation results, it is found that by designing speed controller of spark ignition engine using PI based bat algorithm, the speed performance of spark ignition engine can be enhanced both in no load condition and load condition compared to conventional PI controler.
A deep learning framework to detect Covid-19 disease via chest X-ray and CT scan images Kamil, Mohammed Y.
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i1.pp844-850

Abstract

COVID-19 disease has rapidly spread all over the world at the beginning of this year. The hospitals' reports have told that low sensitivity of RT-PCR tests in the infection early stage. At which point, a rapid and accurate diagnostic technique, is needed to detect the Covid-19. CT has been demonstrated to be a successful tool in the diagnosis of disease. A deep learning framework can be developed to aid in evaluating CT exams to provide diagnosis, thus saving time for disease control. In this work, a deep learning model was modified to Covid-19 detection via features extraction from chest X-ray and CT images. Initially, many transfer-learning models have applied and comparison it, then a VGG-19 model was tuned to get the best results that can be adopted in the disease diagnosis. Diagnostic performance was assessed for all models used via the dataset that included 1000 images. The VGG-19 model achieved the highest accuracy of 99%, sensitivity of 97.4%, and specificity of 99.4%. The deep learning and image processing demonstrated high performance in early Covid-19 detection. It shows to be an auxiliary detection way for clinical doctors and thus contribute to the control of the pandemic.
Particle swarm optimization-based stator resistance observer for speed sensorless induction motor drive Ho, Sang Dang; Palacky, Petr; Kuchar, Martin; Brandstetter, Pavel; Tran, Cuong Dinh
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i1.pp815-826

Abstract

This paper presents a different technique for the online stator resistance estimation using a particle swarm optimization (PSO) based algorithm for rotor flux oriented control schemes of induction motor drives without a rotor speed sensor. First, a conventional proportional-integral controller-based stator resistance estimation technique is used for a speed sensorless control scheme with two different model reference adaptive system (MRAS) concepts. Finally, a novel method for the stator resistance estimation based on the PSO algorithm is presented for the two MRAS-type observers. Simulation results in the Matlab/Simulink environment show good adaptability of the proposed estimation model while the stator resistance is varied to 200% of the nominal value. The results also confirm more accurate stator resistance and rotor speed estimation in comparison with the conventional technique.
Parameters estimation of solar photovoltaic module using camel behavior search algorithm Hassan, Kadhim H.; Rashid, Abdulmuttalib T.; Jasim, Basil H.
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i1.pp788-793

Abstract

Finding accurate mathematical model of electrical equivalent circuit of solar photovoltaic (PV) cell is crucial to achieve and improve maximum power point, simulation design and efficiency computations for solar energy system. Due to the nonlinearity of the characteristic of solar PV cell, optimization methods are the best for estimating the electrical model parameters which lead to accurate estimating I-V curve. In this paper, camel behavior search algorithm is proposed as a new method for estimating the five different parameters for single diode model of PV solar module. This is tested on multicrystalline KC 200GT PV module. A measurement data of the module is used to verify and test the consistency of accurately estimating the set of parameters that govern the characteristics I-V relationship of solar cell. The simulation results show that the current-voltage characteristic and power-voltage curve obtained are matching to the measured experimental data set. For performance evaluation, the proposed method is simple, fast in convergence response and has an acceptable accuracy in obtaining the five estimated parameters.
A short-term hybrid forecasting model for time series electrical-load data using random forest and bidirectional long short-term memory Ferdoush, Zannatul; Mahmud, Booshra Nazifa; Chakrabarty, Amitabha; Uddin, Jia
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i1.pp763-771

Abstract

In the presence of the deregulated electric industry, load forecasting is more demanded than ever to ensure the execution of applications such as energy generation, pricing decisions, resource procurement, and infrastructure development. This paper presents a hybrid machine learning model for short-term load forecasting (STLF) by applying random forest and bidirectional long short-term memory to acquire the benefits of both methods. In the experimental evaluation, we used a Bangladeshi electricity consumption dataset of 36 months. The paper provides a comparative study between the proposed hybrid model and state-of-art models using performance metrics, loss analysis, and prediction plotting. Empirical results demonstrate that the hybrid model shows better performance than the standard long short-term memory and the bidirectional long short-term memory models by exhibiting more accurate forecast results.
Performance comparison between variants PID controllers and unity feedback control system for the response of the angular position of the DC motor Mhawesh, Mustafa A.
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i1.pp802-814

Abstract

This paper proposed a detailed study for the response of angular position of the rotor of a DC motor. It is a comparative analysis to the behavior of the DC motor when the motor is alone and when it is with controllers. Furthermore, a comparison between the responses of controllers themselves. A conventional PID controller which was tuned by using Ziegler and Nichols method was proposed in this paper. Also, Modification PID controllers were proposed in this paper. The modification PID controllers are PI-D controller and I-PD controller. Mathematical calculations were done to get the transfer functions for the DC motor and the controllers. Time domain specifications and figures were obtained for each case by using Matlab program. The obtained results were summarized in tables. This paper determined which the controller is the best one depending on the obtained figures and the time domain specifications.
Compressed sensing with continuous parametric reconstruction Andras, Imrich; Michaeli, Linus; Saliga, Jan
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i1.pp851-862

Abstract

This work presents a novel unconventional method of signal reconstruction after compressive sensing. Instead of usual matrices, continuous models are used to describe both the sampling process and acquired signal. Reconstruction is performed by finding suitable values of model parameters in order to obtain the most probable fit. A continuous approach allows more precise modelling of physical sampling circuitry and signal reconstruction at arbitrary sampling rate. Application of this method is demonstrated using a wireless sensor network used for freshwater quality monitoring. Results show that the proposed method is more robust and offers stable performance when the samples are noisy or otherwise distorted.

Page 10 of 10 | Total Record : 98

 6   7   8   9   10 

Filter by Year

2021 2021


Reset
Filter By Issues
All Issue Vol 16, No 1: February 2026 Vol 15, No 6: December 2025 Vol 15, No 5: October 2025 Vol 15, No 4: August 2025 Vol 15, No 3: June 2025 Vol 15, No 2: April 2025 Vol 15, No 1: February 2025 Vol 14, No 6: December 2024 Vol 14, No 5: October 2024 Vol 14, No 4: August 2024 Vol 14, No 3: June 2024 Vol 14, No 2: April 2024 Vol 14, No 1: February 2024 Vol 13, No 6: December 2023 Vol 13, No 5: October 2023 Vol 13, No 4: August 2023 Vol 13, No 3: June 2023 Vol 13, No 2: April 2023 Vol 13, No 1: February 2023 Vol 12, No 6: December 2022 Vol 12, No 5: October 2022 Vol 12, No 4: August 2022 Vol 12, No 3: June 2022 Vol 12, No 2: April 2022 Vol 12, No 1: February 2022 Vol 11, No 6: December 2021 Vol 11, No 5: October 2021 Vol 11, No 4: August 2021 Vol 11, No 3: June 2021 Vol 11, No 2: April 2021 Vol 11, No 1: February 2021 Vol 10, No 6: December 2020 Vol 10, No 5: October 2020 Vol 10, No 4: August 2020 Vol 10, No 3: June 2020 Vol 10, No 2: April 2020 Vol 10, No 1: February 2020 Vol 9, No 6: December 2019 Vol 9, No 5: October 2019 Vol 9, No 4: August 2019 Vol 9, No 3: June 2019 Vol 9, No 2: April 2019 Vol 9, No 1: February 2019 Vol 8, No 6: December 2018 Vol 8, No 5: October 2018 Vol 8, No 4: August 2018 Vol 8, No 3: June 2018 Vol 8, No 2: April 2018 Vol 8, No 1: February 2018 Vol 7, No 6: December 2017 Vol 7, No 5: October 2017 Vol 7, No 4: August 2017 Vol 7, No 3: June 2017 Vol 7, No 2: April 2017 Vol 7, No 1: February 2017 Vol 6, No 6: December 2016 Vol 6, No 5: October 2016 Vol 6, No 4: August 2016 Vol 6, No 3: June 2016 Vol 6, No 2: April 2016 Vol 6, No 1: February 2016 Vol 5, No 6: December 2015 Vol 5, No 5: October 2015 Vol 5, No 4: August 2015 Vol 5, No 3: June 2015 Vol 5, No 2: April 2015 Vol 5, No 1: February 2015 Vol 4, No 6: December 2014 Vol 4, No 5: October 2014 Vol 4, No 4: August 2014 Vol 4, No 3: June 2014 Vol 4, No 2: April 2014 Vol 4, No 1: February 2014 Vol 3, No 6: December 2013 Vol 3, No 5: October 2013 Vol 3, No 4: August 2013 Vol 3, No 3: June 2013 Vol 3, No 2: April 2013 Vol 3, No 1: February 2013 Vol 2, No 6: December 2012 Vol 2, No 5: October 2012 Vol 2, No 4: August 2012 Vol 2, No 3: June 2012 Vol 2, No 2: April 2012 Vol 2, No 1: February 2012 Vol 1, No 2: December 2011 Vol 1, No 1: September 2011 More Issue