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International Journal of Electrical and Computer Engineering
ISSN : 20888708     EISSN : 27222578     DOI : -
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.
Articles 6,301 Documents
Data analysis of financial burden index through KBO league FA pitcher's performance and contract amount size Semin Kim; Kangsoo You
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2555-2562

Abstract

Due to the recent pandemic of COVID-19, sports events around the world are being hit hard in 2020. However, with the KBO league having been opened even though it is late, it has attracted the attention of baseball fans around the world as it becomes an overseas broadcast. However, since the Republic of Korea is also affected by the global economic crisis, the financial burden of professional baseball teams that rely on their parent company has become a hot topic. Therefore, in this study, we collected records such as IP, ERA, and WAR of KBO league FA pitchers and news related to FA contracts. Then, we preprocessed the data and analyzed it. To this end, we analyzed the performance and financial burden indicators according to the pitcher's age and contract years. In addition, it was found that the performance of athletes was maintained until their mid 30’s, and through rational FA contracts, the team and athletes were able to suggest win-win strategies. And with this basic data analysis in the sports industry, the possibility of utilizing these strategies was confirmed.
Hybrid power control and contention window adaptation for channel congestion problem in internet of vehicles network Hayder M. Amer; Ethar Abduljabbar Hadi; Lamyaa Ghaleb Shihab; Hawraa H. Al Mohammed; Mohammed J. Khami
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp497-505

Abstract

Technology such as vehicular ad hoc networks can be used to enhance the convenience and safety of passenger and drivers. The vehicular ad hoc networks safety applications suffer from performance degradation due to channel congestion in high-density situations. In order to improve vehicular ad hoc networks reliability, performance, and safety, wireless channel congestion should be examined. Features of vehicular networks such as high transmission frequency, fast topology change, high mobility, high disconnection make the congestion control is a challenging task. In this paper, a new congestion control approach is proposed based on the concept of hybrid power control and contention window to ensure a reliable and safe communications architecture within the internet of vehicles network. The proposed approach performance is investigated using an urban scenario. Simulation results show that the network performance has been enhanced by using the hybrid developed strategy in terms of received messages, delay time, messages loss, data collision and congestion ratio.
Application of AHP algorithm on power distribution of load shedding in island microgrid An T. Nguyen; Nghia T. Le; Anh. H. Quyen; Binh T. T. Phan; Tan P. Trieu; Thien D. Hua
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1011-1021

Abstract

This paper proposes a method of load shedding in a microgrid system operated in an Island Mode, which is disconnected with the main power grid and balanced loss of the electrical power. This proposed method calculates the minimum value of the shed power with reference to renewable energy sources such as wind power generator, solar energy and the ability to control the frequency of the generator to restore the frequency to the allowable range and reduce the amount of load that needs to be shed. Computing the load importance factor (LIF) using AHP algorithm supports to determine the order of which load to be shed. The damaged outcome of load shedding, thus, will be noticeably reduced. The experimental results of this proposed method is demonstrated by simulating on IEEE 16-Bus microgrid system with six power sources.
Company bankruptcy prediction framework based on the most influential features using XGBoost and stacking ensemble learning Much Aziz Muslim; Yosza Dasril
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp5549-5557

Abstract

Company bankruptcy is often a very big problem for companies. The impact of bankruptcy can cause losses to elements of the company such as owners, investors, employees, and consumers. One way to prevent bankruptcy is to predict the possibility of bankruptcy based on the company's financial data. Therefore, this study aims to find the best predictive model or method to predict company bankruptcy using the dataset from Polish companies bankruptcy. The prediction analysis process uses the best feature selection and ensemble learning. The best feature selection is selected using feature importance to XGBoost with a weight value filter of 10. The ensemble learning method used is stacking. Stacking is composed of the base model and meta learner. The base model consists of K-nearest neighbor, decision tree, SVM, and random forest, while the meta learner used is LightGBM. The stacking model accuracy results can outperform the base model accuracy with an accuracy rate of 97%.
A study on the development of joint tracking-based exercise contents technology for improving the strength of older people Donggyu Choi; Minyoung Kim; Jongwook Jang
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2563-2568

Abstract

Currently, Korea's population is aging rapidly, and there is a lot of interest in the area of life in old age. Especially, as you get older, your ability to exercise gradually decreases, and you need to exercise continuously for your health. As a result, Korea older people exercise more than welfare powerhouse Japan. However, recently studies show that physical function lags even further. The results are based on a lack of diversity in motion. In Korea, people enjoy walking, hiking, and riding bicycles, but in Japan, there is a difference in the quality of exercise such as muscle-building exercise class, ball exercise, and underwater exercise. The old Korean ways of exercising are all high-intensity sports, but some do not know whether the purpose is for social club purposes. In this paper, real-time joint tracking using a single camera and deep learning was carried out to study the development direction of content that can measure or improve exercise ability to improve actual muscle strength.
Comparative study to realize an automatic speaker recognition system Fadwa Abakarim; Abdenbi Abenaou
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp376-382

Abstract

In this research, we present an automatic speaker recognition system based on adaptive orthogonal transformations. To obtain the informative features with a minimum dimension from the input signals, we created an adaptive operator, which helped to identify the speaker’s voice in a fast and efficient manner. We test the efficiency and the performance of our method by comparing it with another approach, mel-frequency cepstral coefficients (MFCCs), which is widely used by researchers as their feature extraction method. The experimental results show the importance of creating the adaptive operator, which gives added value to the proposed approach. The performance of the system achieved 96.8% accuracy using Fourier transform as a compression method and 98.1% using Correlation as a compression method.
Wideband characteristics of density tapered array antennas Nguyen Thanh Binh; Nguyen Quoc Dinh; Yoshihide Yamada
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1356-1366

Abstract

In this paper, wideband characteristics of density tapered arrays are clarified by comparing directly the array factors and radiation patterns of 3 tapered arrays structures with array factors and radiation patterns of equally spaced arrays. Calculated results for a density tapered distribution array consisting of 30 elements claims that the array can perform within a bandwidth of 2.5:1 with grating lobe levels lower than -7.8 dB. Additionally, this paper shows a method of determining the effectiveness of unequal spacing arrays in the design of actual antennas. The method is based on calculation and analysis of input impedance of array elements caused by mutual coupling effects among array elements.
The study of convex-dual-layer remote phosphor geometry in upgrading WLEDs color rendering index Huu Phuc Dang; Nguyen Thi Phuong Loan; Nguyen Thi Kim Chung; Nguyen Doan Quoc Anh
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp3890-3896

Abstract

The white-light light-emitting diode (LED) is a semiconductor light source that usually has one chip and one phosphor layer. Because of that simple structure, the color rendering index (CRI) is really poor. Therefore, structure with double layer of phosphor and multiple chips has been studied with the phosphorus proportions and densities in the silicone are constantly changed to find the best option to improve optical properties. In research, we use red phosphor Ca5B2SiO10:Eu3+ layer to place above the yellow phosphor one, and both of them have a convex design. Then, the experiments and measurements are carried out to figure out the effects of this red phosphor as well as the convex-double-layer remote phosphor design on the LED’s performances. The measured results reveal that the light output is enhanced significantly when using convex-dual-layer structure instead of the single-layer design. Additionally, the Ca5B2SiO10:Eu3+ concentration benefits CRI and CQS at around 6600 K and 7700 K correlated color temperature (CCT). Yet, the lumen output shows a slight decline as this red phosphor concentration surpass 26% wt. Through the experiments, it is found that a double layer of chip and double phosphorus is the best structure which could support the quality of CRI and luminous flux.
Comparative analysis of short-term demand predicting models using ARIMA and deep learning Halima Bousqaoui; Ilham Slimani; Said Achchab
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3319-3328

Abstract

The forecasting consists of taking historical data as inputs then using them to predict future observations, thus determining future trends. Demand prediction is a crucial component in the supply chain’s process that allows each member to enhance its performance and its profit. Nevertheless, because of demand uncertainty supply chains usually suffer from many problems such as the bullwhip effect. As a solution to those logistics issues, this paper presents a comparative analysis of four time series demand forecasting models; namely, the autoregressive integrated moving Average (ARIMA) a statistical model, the multi-layer perceptron (MLP) a feedforward neural network, the long short-term memory model (LSTM) a recurrent neural network and the convolutional neural network (CNN or ConvNet) a deep learning model. The experimentations are carried out using a real-life dataset provided by a supermarket in Morocco. The results clearly show that the convolutional neural network gives slightly better forecasting results than the Long short-term memory network.
Partial discharges location in power transformers using piezoceramic sensors B. Danouj; S. A. Tahan; E. David; M. Lotfi
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp1942-1950

Abstract

The detection and the spatial localization of partial discharges in high-voltage electrical machines are considered as an effective method in predictive maintenance that can provide valuable information on the health of the insulation system and allow to determine accurately the location of the risky insulation elements, which in turn will avoid any premature equipment’s deterioration by scheduling preventive maintenance action. After confirming in a previous published paper the efficiency of a new generation of piezoceramics sensors (high temperature ultrasonic transducers) to detect and characterize partial discharges, we are going to investigate, in this work, a second potential of this technology to locate the partial discharge sources by relying on its ability to detect acoustic signals emitted by partial discharge sources. We will present experimental results, demonstrating the effectiveness of these sensors to locate partial discharges sources and, we will also present an algorithm for calculating the partial discharge foci, based on the acoustic wave flight time.

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