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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 111 Documents
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Search results for , issue "Vol 12, No 2: April 2022" : 111 Documents clear
Integrated energy management converter based on maximum power point tracking for photovoltaic solar system Mounir Ouremchi; Said El Mouzouade; Karim El Khadiri; Ahmed Tahiri; Hassan Qjidaa
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1211-1222

Abstract

This paper presents an integrated power control system for photovoltaic systems based on maximum power point tracking (MPPT). The architecture presented in this paper is designed to extract more power from photovoltaic panels under different partial obscuring conditions. To control the MPPT block, the integrated system used the ripple correlation control algorithm (RCC), as well as a high-efficiency synchronous direct current (DC-DC) boost power converter. Using 180 nm complementary metal-oxide-semiconductor (CMOS) technology, the proposed MPPT was designed, simulated, and layout in virtuoso cadence. The system is attached to a two-cell in series that generates a 5.2 V average output voltage, 656.6 mA average output current, and power efficiency of 95%. The final design occupies only 1.68 mm2.
Crop leaf disease detection and classification using machine learning and deep learning algorithms by visual symptoms: a review Pallepati Vasavi; Arumugam Punitha; T. Venkat Narayana Rao
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp2079-2086

Abstract

A Quick and precise crop leaf disease detection is important to increasing agricultural yield in a sustainable manner. We present a comprehensive overview of recent research in the field of crop leaf disease prediction using image processing (IP), machine learning (ML) and deep learning (DL) techniques in this paper. Using these techniques, crop leaf disease prediction made it possible to get notable accuracies. This article presents a survey of research papers that presented the various methodologies, analyzes them in terms of the dataset, number of images, number of classes, algorithms used, convolutional neural networks (CNN) models employed, and overall performance achieved. Then, suggestions are prepared on the most appropriate algorithms to deploy in standard, mobile/embedded systems, Drones, Robots and unmanned aerial vehicles (UAV). We discussed the performance measures used and listed some of the limitations and future works that requires to be focus on, to extend real time automated crop leaf disease detection system.
Poincaré plots to analyze photoplethysmography signal between non-smokers and smokers Bagus Haryadi; Po-Hao Chang; Akrom Akrom; Arifan Q. Raharjo; Galih Prakoso
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1565-1570

Abstract

An analysis of blood circulation was used to identify variations of heart rate and to create an early warning system of autonomic dysfunction. The Poincaré plot analyzed blood circulation using photoplethysmography (PPG) signals between non-smokers and smokers in three different indices: SD1, SD2, and SD1 SD2 ratio (SSR). There were twenty subjects separated into non-smoker and smoker groups with sample sizes of 10, respectively. An independent sample t-test to compare the continuous variables. Whereas, the comparison between two groups employed Fisher’s exact test for categorical variables. The result showed that SD1 was found to be considerably lower in the group of smokers (0.03±0.01) than that of the non-smokers (0.06±0.03). Similarly, SSR was recorded at 0.0012±0.0005 and 0.0023±0.0012 for smoking and non-smoking subjects, respectively. As a comparison, SD2 for non-smokers (25.7±0.5) was lower than smokers (27.3±0.4). In conclusion, we revealed that the parameters of Poincaré plots (SD1, SD2, and SSR) exert good performances to significantly differentiate the PPG signals of the group of non-smokers from those of smokers. We also supposed that the method promises to be a suitable method to distinguish the cardiovascular disease group. Therefore, this method can be applied as a part of early detection system of cardiovascular diseases.
Frequency response analysis under faults in weak power systems Marino Godoy Arcia; Zaid Garcia Sanchez; Hernan Hernandez Herrera; José Antonio Gonzalez Cueto Cruz; Jorge Iván Silva Ortega; Gustavo Crespo Sánchez
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1077-1088

Abstract

The renewable energy sources (RESs) projects are solutions with environmental benefits that are changing the traditional power system operation and concept. Transient stability analysis has opened new research trends to guarantee a secure operation high penetration. Problems such as frequency fluctuations, decoupling between generator angular speed, network frequency fluctuation and kinetic energy storing absence are the main non-conventional RESs penetration in power systems. This paper analyzes short-circuit influence on frequency response, focusing on weak distribution networks and isolated, to demonstrate relevance in frequency stability. A study case considered a generation outage and a load input to analyze frequency response. The paper compares frequency response during a generation outage with a short-circuit occurrence. In addition, modular value and angle generator terminal voltage affectation by electric arc and network ratio R⁄X, failure type influence in power delivered behavior, considering fault location, arc resistance and load. The arc resistance is defined as an added resistance that appears during failure and influences voltage modulus and angle value results showing that intermittent non-conventional RES participation can lead to frequency fluctuations. Results showed that arc resistance, type of failure, location and loadability determine the influence of frequency response factors in weak power systems.
Method of optimization of the fundamental matrix by technique speeded up robust features application of different stress images Ahmed Chater; Hicham Benradi; Abdelali Lasfar
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1429-1436

Abstract

The purpose of determining the fundamental matrix (F) is to define the epipolar geometry and to relate two 2D images of the same scene or video series to find the 3D scenes. The problem we address in this work is the estimation of the localization error and the processing time. We start by comparing the following feature extraction techniques: Harris, features from accelerated segment test (FAST), scale invariant feature transform (SIFT) and speed-up robust features (SURF) with respect to the number of detected points and correct matches by different changes in images. Then, we merged the best chosen by the objective function, which groups the descriptors by different regions in order to calculate ‘F’. Then, we applied the standardized eight-point algorithm which also automatically eliminates the outliers to find the optimal solution ‘F’. The test of our optimization approach is applied on the real images with different scene variations. Our simulation results provided good results in terms of accuracy and the computation time of ‘F’ does not exceed 900 ms, as well as the projection error of maximum 1 pixel, regardless of the modification.
Optimization of system’s parameters for wavelength conversion of E-band signals Yazan Alkhlefat; Sevia Mahdaliza Idrus Sutan Nameh; Farabi M. Iqbal
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1659-1666

Abstract

Current and future wireless communication systems are designed to achieve the user’s demands such as high data rate and high speed with low latency and simultaneously to save bandwidth and spectrum. In 5G and 6G networks, a high speed of transmitting and switching is required for internet of things (IoT) applications with higher capacity. To achieve these requirements a semiconductor optical amplifier (SOA) is considered as a wavelength converter to transmit a signal with an orthogonal frequency division multiplexing with subcarrier power modulation (OFDM-SPM). It exploits the subcarrier’s power in conventional OFDM block in order to send additional bits beside the normally transmitted bits. In this paper, we optimized the SOA’s parameters to have efficient wavelength conversion process. These parameters are included the injection current (IC) of SOA, power of pump and probe signals. A 7 Gbps OFDM-SPM signal with a millimeter waves (MMW) carrier of 80 GHz is considered for signal switching. The simulation results investigated and analyzed the performance of the designed system in terms of error vector magnitude (EVM), bit error rate (BER) and optical signal-to-noise ratio (OSNR). The optimum value of IC is 0.6 A while probe power is 9.45 and 8.9 dBm for pump power. The simulation is executed by virtual photonic integrated (VPI) software.
Sliding-mode controller for a step up-down battery charger with a single current sensor Juan Pablo Villegas Ceballos; Carlos Andres Ramos-Paja; Elkin Edilberto Henao-Bravo
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1251-1264

Abstract

This paper proposes a battery charger solution based on the Zeta DC/DC converter to provide a general interface between batteries and microgrid direct current (DC) buses. This solution enables to interface batteries and DC buses with voltage conversion ratios lower, equal, and higher than one using the same components and without redesigning the control system, thus ensuring global stability. The converter controller is designed to require only the measurement of a single inductor current, instead of both inductors currents, without reducing the system flexibility and stability. The controller stability is demonstrated using the sliding-mode theory, and a design procedure for the parameters is developed to ensure a desired bus performance. Finally, simulations and experiments validate the performance of the proposed solution under realistic operation conditions.
Incentive mechanism design for citizen reporting application using Stackelberg game I Made Ariya Sanjaya; Suhono Harso Supangkat; Jaka Sembiring; Widya Liana Aji
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp2147-2157

Abstract

The growing utilization of smartphones equipped with various sensors to collect and analyze information around us highlights a paradigm called mobile crowdsensing. To motivate citizens’ participation in crowdsensing and compensate them for their resources, it is necessary to incentivize the participants for their sensing service. There are several studies that used the Stackelberg game to model the incentive mechanism, however, those studies did not include a budget constraint for limited budget case. Another challenge is to optimize crowdsourcer (government) profit in conducting crowdsensing under the limited budget then allocates the budget to several regional working units that are responsible for the specific city problems. We propose an incentive mechanism for mobile crowdsensing based on several identified incentive parameters using the Stackelberg game model and applied the MOOP (multi-objective optimization problem) to the incentive model in which the participant reputation is taken into account. The evaluation of the proposed incentive model is performed through simulations. The simulation indicated that the result appropriately corresponds to the theoretical properties of the model.
A hybrid approach to medical decision-making: diagnosis of heart disease with machine-learning model Tamilarasi Suresh; Tsehay Admassu Assegie; Subhashni Rajkumar; Napa Komal Kumar
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1831-1838

Abstract

Heart disease is one of the most widely spreading and deadliest diseases across the world. In this study, we have proposed hybrid model for heart disease prediction by employing random forest and support vector machine. With random forest, iterative feature elimination is carried out to select heart disease features that improves predictive outcome of support vector machine for heart disease prediction. Experiment is conducted on the proposed model using test set and the experimental result evidently appears to prove that the performance of the proposed hybrid model is better as compared to an individual random forest and support vector machine. Overall, we have developed more accurate and computationally efficient model for heart disease prediction with accuracy of 98.3%. Moreover, experiment is conducted to analyze the effect of regularization parameter (C) and gamma on the performance of support vector machine. The experimental result evidently reveals that support vector machine is very sensitive to C and gamma.
Super-linear speedup for real-time condition monitoring using image processing and drones Moath Alsafasfeh; Bradely Bazuin; Ikhlas Abdel-Qader
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1548-1557

Abstract

Real-time inspections for the large-scale solar system may take a long time to get the hazard situations for any failures that may take place in the solar panels normal operations, where prior hazards detection is important. Reducing the execution time and improving the system’s performance are the ultimate goals of multiprocessing or multicore systems. Real-time video processing and analysis from two camcorders, thermal and charge-coupling devices (CCD), mounted on a drone compose the embedded system being proposed for solar panels inspection. The inspection method needs more time for capturing and processing the frames and detecting the faulty panels. The system can determine the longitude and latitude of the defect position information in real-time. In this work, we investigate parallel processing for the image processing operations which reduces the processing time for the inspection systems. The results show a super-linear speedup for real-time condition monitoring in large-scale solar systems. Using the multiprocessing module in Python, we execute fault detection algorithms using streamed frames from both video cameras. The experimental results show a super-linear speedup for thermal and CCD video processing, the execution time is efficiently reduced with an average of 3.1 times and 6.3 times using 2 processes and 4 processes respectively.

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