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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 6,301 Documents
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Development of a solar radiation sensor system with pyranometer Muchamad Rizky Nugraha; Andi Adriansyah
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.pp1385-1391

Abstract

Solar energy is a result of the nuclear fusion process in the form of a series of thermonuclear events that occur in the Sun's core. Solar radiation has a significant impact on the lives of all living things on earth. The uses, as mentioned earlier, are when the solar radiation received requires a certain amount and vice versa. As a result, a more accurate instrument of solar radiation is required. A specific instrument is typically used to measure solar radiation parameters. There are four solar radiation parameters: diffusion radiation, global radiation, direct radiation, and solar radiation duration. Thus, it needs to use many devices to measure radiation data. The paper designs to measure all four-radiation data by pyranometer with particular modification and shading device. This design results have a high correlation with a global standard with a value of R=0.73, diffusion with a value of R=0.60 and a sufficiently strong direct correlation with a value of R=0.56. It can be said that the system is much simpler, making it easier to monitor and log the various solar radiation parameters.
Identification study of solar cell/module using recent optimization techniques Mahmoud Abbas El-Dabah; Ragab Abdelaziz El-Sehiemy; Mohamed Ahmed Ebrahim; Zuhair Alaas; Mohamed Mostafa Ramadan
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.pp1189-1198

Abstract

This paper proposes the application of a novel metaphor-free population optimization based on the mathematics of the Runge Kutta method (RUN) for parameter extraction of a double-diode model of the unknown solar cell and photovoltaic (PV) module parameters. The RUN optimizer is employed to determine the seven unknown parameters of the two-diode model. Fitting the experimental data is the main objective of the extracted unknown parameters to develop a generic PV model. Consequently, the root means squared error (RMSE) between the measured and estimated data is considered as the primary objective function. The suggested objective function achieves the closeness degree between the estimated and experimental data. For getting the generic model, applications of the proposed RUN are carried out on two different commercial PV cells. To assess the proposed algorithm, a comprehensive comparison study is employed and compared with several well-matured optimization algorithms reported in the literature. Numerical simulations prove the high precision and fast response of the proposed RUN algorithm for solving multiple PV models. Added to that, the RUN can be considered as a good alternative optimization method for solving power systems optimization problems.
Inductanceless high order low frequency filters for medical applications Noor Thamer Almalah; Faris Hasan Aldabbagh
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.pp1299-1307

Abstract

In this paper, a designed circuit used for low-frequency filters is implemented and realized the filter is based on frequency-dependent negative resistance (FDNR) as an inductor simulator to substitute the traditional inductance, which is heavy and high cost due to the coil material manufacturing and size area. The simulator is based on an active operation amplifier or operation transconductance amplifier (OTA) that is easy to build in an integrated circuit with a minimum number of components. The third and higher-order Butterworth filter is simulated at low frequency for low pass filter to use in medical instruments and low-frequency applications. The designed circuit is compared with the traditional proportional integral controller enhanced (PIE) and T section ordinary filter. The results with magnitude and phase response were compared and an acceptable result is obtained. The filter can be used for general applications such as medical and other low-frequency filters needed.
Study and analysis of motion artifacts for ambulatory electroencephalography Asma Islam; Eshrat Jahan Esha; Sheikh Farhana Binte Ahmed; Md. Kafiul Islam
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.pp1520-1529

Abstract

Motion artifacts contribute complexity in acquiring clean electroencephalography (EEG) data. It is one of the major challenges for ambulatory EEG. The performance of mobile health monitoring, neurological disorders diagnosis and surgeries can be significantly improved by reducing the motion artifacts. Although different papers have proposed various novel approaches for removing motion artifacts, the datasets used to validate those algorithms are questionable. In this paper, a unique EEG dataset was presented where ten different activities were performed. No such previous EEG recordings using EMOTIV EEG headset are available in research history that explicitly mentioned and considered a number of daily activities that induced motion artifacts in EEG recordings. Quantitative study shows that in comparison to correlation coefficient, the coherence analysis depicted a better similarity measure between motion artifacts and motion sensor data. Motion artifacts were characterized with very low frequency which overlapped with the Delta rhythm of the EEG. Also, a general wavelet transform based approach was presented to remove motion artifacts. Further experiment and analysis with more similarity metrics and longer recording duration for each activity is required to finalize the characteristics of motion artifacts and henceforth reliably identify and subsequently remove the motion artifacts in the contaminated EEG recordings.
Input-output linearization of DC-DC converter with discrete sliding mode fuzzy control strategy Karthikeyan, Viji; Tiwari, Anil Kumar; Vedi, Agalya; Devaraju, Buvana
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.pp1223-1232

Abstract

The major thrust of the paper is on designing a fuzzy logic approach has been combined with a well-known robust technique discrete sliding mode control (DSMC) to develop a new strategy for discrete sliding mode fuzzy control (DSMFC) in direct current (DC-DC) converter. Proposed scheme requires human expertise in the design of the rule base and is inherently stable. It also overcomes the limitation of DSMC, which requires bounds of uncertainty to be known for development of a DSMC control law. The scheme is also applicable to higher order systems unlike model following fuzzy control, where formation of rule base becomes difficult with rise in number of error and error derivative inputs. In this paper the linearization of input-output performance is carried out by the DSMFC algorithm for boost converter. The DSMFC strategy minimizes the chattering problem faced by the DSMC. The simulated performance of a discrete sliding mode fuzzy controller is studied and the results are investigated.
Energy distribution and economic analysis of a residential house with the net-energy metering scheme in Malaysia Norshahidatul Shahida Mohamed Suhaime; Shaikh Zishan Suheel; Ahmad Afif Safwan; Hasila Jarimi; Mohd Faizal; Adnan Ibrahim; Sohif Mat; Ahmad Fazlizan
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2313-2322

Abstract

Malaysia demonstrates its commitment to alleviating the negative climate and energy issues through various initiatives. One of the latest initiatives is the amendment of the net-energy metering (NEM) scheme that takes effect from 2019. This paper presents the distribution of energy to the residential house that has a grid-connected solar photovoltaic (PV) system installed. The study quantifies the percentage of energy consumed from the PV system and the grid as well as the percentage of PV generated energy that is exported to the grid. On average, 38% of generated energy was used for self-consumption that contributed to 28% of the total consumption. Economic evaluation over a 25-year lifecycle of the PV system is also conducted shows that the simple payback period for NEM 2019 and NEM 2016 is 8 years and 20 years, respectively. The latest version of NEM shows a superior advantage compared to the previous one which may attract more investments in PV generation.
Scalable decision tree based on fuzzy partitioning and an incremental approach Somayeh Lotfi; Mohammad Ghasemzadeh; Mehran Mohsenzadeh; Mitra Mirzarezaee
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4228-4234

Abstract

Classification as a data mining materiel is the process of assigning entities to an already defined class by examining the features. The most significant feature of a decision tree as a classification method is its ability to data recursive partitioning. To choose the best attributes for partition, the value range of each continuous attribute should be divided into two or more intervals. Fuzzy partitioning can be used to reduce noise sensitivity and increase the stability of trees. Also, decision trees constructed with existing approaches, tend to be complex, and consequently are difficult to use in practical applications. In this article, a fuzzy decision tree has been introduced that tackles the problem of tree complexity and memory limitation by incrementally inserting data sets into the tree. Membership functions are generated automatically. Then Fuzzy Information Gain is used as a fast-splitting attribute selection criterion and the expansion of a leaf is done attending only with the instances stored in it. The efficiency of this algorithm is examined in terms of accuracy and tree complexity. The results show that the proposed algorithm by reducing the complexity of the tree can overcome the memory limitation and make a balance between accuracy and complexity.
A review on modelling and analysis of 7 level multi level inverter with various circuit configurations Karri, Nanajee; Pandian, Alagappan
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp%p

Abstract

For the last decade, the multilevel inverters became popular and are extensively using for a high power and medium voltage applications. The multi level inverter (MLI) concept has been introduced with lot of topologies like diode clamped type, Flying capacitor type and cascaded H-Bridge (CHB) type multi level inverter. The major objective of the multilevel inverter is to get higher voltage levels with lower switching components. Out of available topologies, the cascaded H-Bridge type has been focused more. But, the cascaded H-Bridge type requires more and more number of switches. In this review paper, three topologies have been discussed and compared for seven level output voltage. The total harmonic distortion (THD) is also measured. The comparison table is also shown. The work is done by using MATLAB/SIMULINK software.
Fake accounts detection system based on bidirectional gated recurrent unit neural network Faouzia Benabbou; Hanane Boukhouima; Nawal Sael
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3129-3137

Abstract

Online social networks have become the most widely used medium to interact with friends and family, share news and important events or publish daily activities. However, this growing popularity has made social networks a target for suspicious exploitation such as the spreading of misleading or malicious information, making them less reliable and less trustworthy. In this paper, a fake account detection system based on the bidirectional gated recurrent unit (BiGRU) model is proposed. The focus has been on the content of users’ tweets to classify twitter user profile as legitimate or fake. Tweets are gathered in a single file and are transformed into a vector space using the GloVe word embedding technique in order to preserve the semantic and syntax context. Compared with the baseline models such as long short-term memory (LSTM) and convolutional neural networks (CNN), the results are promising and confirm that using GloVe with BiGRU classifier outperforms with 99.44% for accuracy and 99.25% for precision. To prove the efficiency of our approach the results obtained with GloVe were compared to Word2vec under the same conditions. Results confirm that GloVe with BiGRU classifier performs the best results for detection of fake Twitter accounts using only tweets content feature.
The effect of Gaussian filter and data preprocessing on the classification of Punakawan puppet images with the convolutional neural network algorithm Kusrini, Kusrini; Arif Yudianto, Muhammad Resa; Al Fatta, Hanif
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3752-3761

Abstract

Nowadays, many algorithms are introduced, and some researchers focused their research on the utilization of convolutional neural network (CNN). CNN algorithm is equipped with various learning architectures, enabling researchers to choose the most effective architecture for classification. However, this research suggested that to increase the accuracy of the classification, preprocessing mechanism is another significant factor to be considered too. This study utilized Gaussian filter for preprocessing mechanism and VGG16 for learning architecture. The Gaussian filter was combined with different preprocessing mechanism applied on the selected dataset, and the measurement of the accuracy as the result of the utilization of the VGG16 learning architecture was acquired. The study found that the utilization of using contrast limited adaptive histogram equalization (CLAHE) + red green blue (RGB) + Gaussian filter and thresholding images showed the highest accuracy, 98.75%. Furthermore, another significant finding is that the Gaussian filter was able to increase the accuracy on RGB images, however the accuracy decreased for green channel images. Finally, the use of CLAHE for dataset preprocessing increased the accuracy dealing with the green channel images.

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