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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 14, No 2: April 2024" : 111 Documents clear
Transient response mitigation using type-2 fuzzy controller optimized by grey wolf optimizer in converter high voltage direct current Ginarsa, I Made; Nrartha, I Made Ari; Muljono, Agung Budi; Zebua, Osea
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1274-1286

Abstract

Long high voltage direct current (HVDC) transmission link is commonly used to transmit electrical energy via land or under-sea cable. The long HVDC avoids reactive power losses (RPL) and power stability problems (PSP). On the contrary, the RPL and PSP phenomena occur in long high voltage alternative current-link (HVAC) caused by the high reactive component in the HVAC-link. However, the HVDC produces a high and slow transient current response (TCR) on the high value of the up-ramp rate. Interval type-2 fuzzy (IT2F) control on converter-side HVDC is proposed to mitigate this TCR problem. The IT2F is optimized by grey wolf optimizer (GWO) to adjust input-output IT2F parameters optimally. The performance of IT2F-GWO is assessed by the minimum value of integral time squared error (ITSE), peak overshoot, and settling time of the TCR. The IT2FC-GWO performance is validated by the performance of IT2F control that is optimized by genetic algorithm (IT2F-GA) and proportional integral (PI) controller. Simulation results show that the IT2F-GWO performs better with small ITSE, low peak overshoot, and shorter settling times than competing controllers.
Image enhancement in palmprint recognition: a novel approach for improved biometric authentication Kusban, Muhammad; Budiman, Aris; Hari Purwoto, Bambang
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1299-1307

Abstract

Several researchers have used image enhancement methods to reduce detection errors and increase verification accuracy in palmprint identification. Divergent opinions exist among experts regarding the best method of image filtering to improve image palmprint recognition. Because of the unique characteristics of palmprints and the difficulties in preventing counterfeiting, image-filtering techniques are the subject of this current research. Researchers hope to create the best biometric system possible by utilizing various techniques. These techniques include image enhancement, Gabor orientation scales, dimension reduction techniques, and appropriate matching strategies. This study investigates how different filtering approaches might be combined to improve images. The palmprint identification system uses a 3W filter, which combines wavelet, Wiener, and weighted filters. Optimizing results entails coordinating the 3W filter with Gabor orientation scales, matching processes, and dimension reduction methods. The research shows that accuracy may be considerably increased using a 3W filter with a Gabor orientation scale of [8×7], the kernel principal component analysis (KPCA) dimension reduction methodology, and a cosine matching method. Specifically, a value of 99.722% can be achieved. These results highlight the importance of selecting appropriate settings and techniques for palmprint recognition systems.
An automated system for classifying types of cerebral hemorrhage based on image processing techniques Arabiat, Areen; Altayeb, Muneera
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1594-1603

Abstract

The brain is one of the most important vital organs in the human body. It is responsible for most of the body’s basic activities, such as breathing, heartbeat, thinking, remembering, speaking, and others. It also controls the central nervous system. Cerebral hemorrhage is considered one of the most dangerous diseases that a person may be exposed to during his life. Therefore, the correct and rapid diagnosis of the hemorrhage type is an important medical issue. The innovation in this work lies in extracting a huge number of effective features from computed tomography (CT) images of the brain using the Orange3 data mining technique, as the number of features extracted from each CT image reached (1,000). The proposed system then uses the extracted features in the classification process through logistic regression (LR), support vector machine (SVM), k-nearest neighbor algorithm (KNN), and convolutional neural networks (CNN), which classify cerebral hemorrhage into four main types: epidural hemorrhage, subdural hemorrhage, intraventricular hemorrhage, and intraparenchymal hemorrhage. A total of (1,156) CT images were tested to verify the validity of the proposed model, and the results showed that the accuracy reached the required success level with an average of (97.1%).
Bibliometric analysis and survey on electronic nose used in agriculture Farel Kiki, Manhougbé Probus Aymard; Martial Ahouandjinou, Sèmèvo Arnaud Roland; Assogba, Kokou Marc; Sutikno, Tole
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1369-1381

Abstract

Work carried out at the beginning of this century on improvements in semiconductor materials, transducers, sensors and artificial intelligence algorithms led to the rapid expansion of research topics related to the electronic nose, with diverse applications. Agriculture is a recent application field that needs a review of the usage of electronic noses in this field and the future challenges. The paper provided a bibliometric analysis and review of electronic noses used in agriculture. A search of published works on the e-nose and its applications in agriculture was carried out in the Web of Science and Scopus databases, which provide comprehensive citation data for academic disciplines worldwide. In the end, 2,953 documents were identified, and the data collected was analyzed mainly using the bibliometric toolbox, and then a deep study was carried out. The study results show that in the agricultural field, some works were achieved on different varieties of plants to detect disease or plant damage with very good results using electronic noses. However, less research was carried out to directly identify animals in crops like pests or environmental monitoring using electronic noses in agriculture. Some recommendations for future research efforts are finally provided.
Plasmonic wave assessment via optomechatronics system for biosensor application Abdullah, Muhammad Rosli; Harun, Noor Hasmiza; Ibrahim, Siti Noorjannah; Abdul Wahab, Azimah; Jamilan, Mohd Azerulazree
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1382-1389

Abstract

Transduction biosensor (mass-based, optical and electrochemical) involves analysis, recognition and amplification in the acquired sample. In this work, the plasmonic-based biosensor was employed without using tags. It is crucial to determine angles of Brewster (Ɵb) and critical (Ɵc) for generating plasmonic resonance (Ɵr). The objective is to verify a cost-effective plasmonic biosensor through Fresnel simulation and experimentation of a developed optomechatronics system. The borosilicate glass, Au and Air layers were simulated with the Winspall 3.02 simulator. The optomechatronics system consists of: 1-optics (650 nm laser, slit, polarizer, photodiode), 2-mechanical (bipolar stepper motors, gears, stages) and 3-electronics (PIC18F4550, liquid crystal display (LCD) and drivers). Later, the software performs angular interrogation by reading the reflected beam from a rotating prism at 0.1125. Experimentation to simulation accuracy indicates that percentage differences for Ɵr and Ɵc are 1% and 0.2%, respectively. In conclusion, excellence verification was successfully achieved between experimentation and simulation. It proved that the low-cost optomechatronics system is capable and reliable to be deployed for the biosensor application.
Guava fruit disease identification based on improved convolutional neural network Mahamudul Hashan, Antor; Tariqur Rahman, Shaon Md; Avinash, Kumar; Ul Islam, Rizu Md Rakib; Dey, Subhankar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1544-1551

Abstract

Guava fruit cultivation is crucial for Asian economic development, with Indonesia producing 449,970 metric tons between 2022 and 2023. However, technology-based approaches can detect disease symptoms, enhancing production and mitigating economic losses by enhancing quality. In this paper, we introduce an accurate guava fruit disease detection (GFDI) system. It contains the generation of appropriate diseased images and the development of a novel improved convolutional neural network (improved-CNN) that is built depending on the principles of AlexNet. Also, several preprocessing techniques have been used, including data augmentation, contrast enhancement, image resizing, and dataset splitting. The proposed improved-CNN model is trained to identify three common guava fruit diseases using a dataset of 612 images. The experimental findings indicate that the proposed improved-CNN model achieve accuracy 98% for trains and 93% for tests using 0.001 learning rate, the model parameters are decreased by 50,106,831 compared with traditional AlexNet model. The findings of the investigation indicate that the deep learning model improves the accuracy and convergence rate for guava fruit disease prevention.
A review on machine learning based intrusion detection system for internet of things enabled environment Nisha, Nisha; Gill, Nasib Singh; Gulia, Preeti
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1890-1898

Abstract

Within an internet of things (IoT) environment, the fundamental purpose of various devices is to gather the abundant amount of data that is being generated and then transmit this data to the predetermined server over the internet. IoT connects billions of objects and the internet to communicate without human intervention. But network security and privacy issues are increasing very fast, in today's world. Because of the prevalence of technological advancement in regular activities, internet security has evolved into a necessary requirement. Because technology is integrated into every aspect of contemporary life, cyberattacks on the internet of things represent a bigger danger than attacks against traditional networks. Researchers have found that combining machine learning techniques into an intrusion detection system (IDS) is an efficient way to get beyond the limitations of conventional IDSs in an IoT context. This research presents a comprehensive literature assessment and develops an intrusion detection system that makes use of machine learning techniques to address security problems in an IoT environment. Along with a comprehensive look at the state of the art in terms of intrusion detection systems for IoT-enabled environments, this study also examines the attributes of approaches, common datasets, and existing methods utilized to construct such systems.
An on-chip soft-start pseudo-current hysteresis-controlled buck converter for automotive applications Boutaghlaline, Anas; El Khadiri, Karim; Tahiri, Ahmed
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1459-1472

Abstract

This paper introduces a novel direct current to direct current (DC-DC) buck converter that uses a pseudo-current hysteresis controller and an on-chip soft start circuit for improved transient performance in automotive applications. The proposed converter, implemented with Taiwan semiconductor manufacturing company (TSMC) 0.18 µm complementary metal oxide semiconductor (CMOS) one-poly-six-metal (1P6M) technology, includes a rail-to-rail current detection circuit and an on-chip soft start circuit to handle transient responses and improve efficiency. Transient response analysis shows fast settling times of 28 µs for both load current changes from 100 mA to 1 A and reversals with consistent transient voltages of approximately 190 mV and peak power efficiency of 99.32% at 5 V output voltage and 100 mA load current. Additionally, the converter maintains a constant output voltage of approximately 5 V across the entire load current range with an average accuracy of 90.41%. A comparative analysis with previous work shows superior performance in terms of figure of merit (FOM). Overall, the proposed pseudo-current hysteresis controlled buck converter exhibits remarkable transient response, load regulation and power efficiency, positioning it as a promising solution for demanding applications, particularly in automotive systems where precise voltage regulation is crucial.
Design and analysis of 7-stage MOS current mode logic power gated MOSFETs in current starved voltage-controlled oscillator for the phase locked loop application Madheswaran, Sivasakthi; Panneerselvam, Radhika
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1398-1405

Abstract

This paper presents a new process, voltage and temperature (PVT) tolerant 7-stage ring type current starved voltage-controlled oscillator (CS-VCO). In this, a 7-stage ring VCO is proposed using power gated technique for phase locked loop (PLL) application. PLL plays a major role in clock and data recovery, Global Positioning System (GPS) system and satellite communications. For the high-speed application of PLL it is designed using 7-stage inverter delay cell with MOS current mode logic (MCML) technique. The circuit undergoes process, voltage and temperature variations with different parameters such as average power, oscillation frequency, phase noise, tuning range and output noise. The Monte-Carlo analysis justifies the proposed design provides better results. The circuit is simulated under 45 nm CMOS technology using cadence virtuoso. The average power consumption of the proposed circuit is 29.368 µW with the oscillation frequency of 3.06 GHz. The output noise and the phase noise of the proposed VCO are -161.55 dB and -125.92 dBc/Hz respectively. It achieves the frequency tuning range (FTR) of 95.09%. The obtained simulation results are highly robust with PVT making the circuit suitable for PLL application.
A novel approach to evaluate dynamic performance for photovoltaic system using software platform Yu, Byunggyu; Jung, Youngseok
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1185-1193

Abstract

With the growing demand for renewable energy, solar photovoltaic (PV) systems have gained popularity as a reliable source of clean electricity. However, the performance of these systems can be limited by factors such as suboptimal maximum power point tracking (MPPT) algorithms. In order to improve the power generation efficiency of PV systems, it is important to evaluate the performance of dynamic MPPT algorithms that can adapt to varying operating conditions. Traditionally, such evaluations have been time consuming and expensive, often requiring extensive testing and measurement equipment. In this paper, we propose a novel approach to evaluate dynamic MPPT performance very quickly and simply using PSIM software. This approach enables accurate and efficient evaluation of MPPT performance under a wide range of operating conditions, while minimizing the cost and time involved in traditional testing methods. When applying the proposed method to a 3.7 kW inverter using the traditional perturbation and observation (P and O) method, we found that the highest average efficiency was 98.92% at an MPPT control period of 0.1s and a voltage perturbation of 1 V. This evaluation technique provides valuable insights into the design and optimization of more efficient MPPT control algorithms, leading to improved power generation efficiency and increased adoption of solar PV systems.

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