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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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An approach towards the development of an inclusive subject environment using additive manufacturing technologies Lotoshynska, Nataliia; Popova, Solomiya; Irianto, Irianto; Jamil Alsayaydeh, Jamil Abedalrahim
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4248-4260

Abstract

This research aims to identify the principles of designing the objects of the inclusive environment with the employment of additive manufacturing technologies, and to discover methods and techniques for creating an inclusive objective environment using the example of our own development. The results of the survey, which has been directed to investigate the topicality of the problem of inclusiveness in Ukraine and the means of its solution, are presented in the article. In the course of work, the principal peculiarities of three-dimensional (3D) modelling and printing technologies have been established, and promising areas of their application have been proposed. The principles of designing an inclusive objective environment have been detected with the use of photogrammetry and 3D printing, due to which the model can be constructed by considering a person’s individual physical characteristics. Moreover, due to the wide range of materials for 3D printing, various types of objects can be realized. It gives great potential for the employment of 3D printing when designing an inclusive environment and considerably simplifies the manufacturing process while taking the individual characteristics of every person into consideration.
Neural network optimizer of proportional-integral-differential controller parameters Siddikov, Isamiddin; Nashvandova, Gulruxsor; Alimova, Gulchekhra
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2533-2540

Abstract

Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
Performance enhancement of brushless direct current motor under different novel optimization techniques Ashok, Babu; Kumar, Mahesh
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6225-6236

Abstract

This research paper presents a novel attempt of speed control for brushless direct current (BLDC) motor in low power/servo motor applications. The performance is measured based on the swiftness for the recovery of desired speed amidst in disturbances, sensitive to supply/motor load fluctuations. The proportional integral (PI) controller is competent only for linear time invariant systems. The state of art technology is, PI controller is used with metaheuristic optimization algorithms viz. Honeybee mating optimization (HBO), artificial immune system (AIS), and frog leaping guided algorithm (FLG), for fine tuning of gain coefficients. Earlier literature survey shows power quality and time domain specifications for separate applications. An innovative approach for the assessment of performance indicators like maximum overshoot (M_p), settling time (t_s), power factor (PF) and total harmonic distortion (THD) simultaneously in the optimized PI controller is suggested. By avoiding local optima trapping, this method gives better dynamic performance for various test conditions. MATLAB/Simulink 2021a software is utilized in the examination of performance in various load and speed scenarios, subsequently validated with hardware where cost effective Arduino controller replaced programmable interface controllers (PIC) microcontroller.
Facial emotion recognition using enhanced multi-verse optimizer method Gummula, Ravi; Arumugam, Vinothkumar; Aranganathan, Abilasha
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.pp1519-1529

Abstract

In recent years, facial emotion recognition has gained significant improvement and attention. This technology utilizes advanced algorithms to analyze facial expressions, enabling computers to detect and interpret human emotions accurately. Its applications span over a wide range of fields, from improving customer service through sentiment analysis, to enhancing mental health support by monitoring emotional states. However, there are several challenges in facial emotion recognition, including variability in individual expressions, cultural differences in emotion display, and privacy concerns related to data collection and usage. Lighting conditions, occlusions, and the need for diverse datasets also impacts accuracy. To solve these issues, an enhanced multi-verse optimizer (EMVO) technique is proposed to improve the efficiency of recognizing emotions. Moreover, EMVO is used to improve the convergence speed, exploration-exploitation balance, solution quality, and the applicability in different types of optimization problems. Two datasets were used to collect the data, namely YouTube and surrey audio-visual expressed emotion (SAVEE) datasets. Then, the classification is done using the convolutional neural networks (CNN) to improve the performance of emotion recognition. When compared to the existing methods shuffled frog leaping algorithm-incremental wrapper-based subset selection (SFLA-IWSS), hierarchical deep neural network (H-DNN) and unique preference learning (UPL), the proposed method achieved better accuracies, measured at 98.65% and 98.76% on the YouTube and SAVEE datasets, respectively.
Predicting television programs success using machine learning techniques Fayq, Khalid El; Tkatek, Said; Idouglid, Lahcen
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5502-5512

Abstract

In the ever-evolving media landscape, television (TV) remains a coveted platform, compelling industry players to innovate amid intense competition. This study focuses on leveraging machine learning regression models to precisely predict TV program reach. Our objective is to assess the models' efficacy, revealing a standout performer with a mean absolute percent error of just under 8%. Significantly, we identify features exerting a substantial impact on predictions and explore the potential for model enhancement through expanded datasets. This research extends beyond statistical insights, offering actionable implications for TV channel managers. Empowered by these findings, managers can make informed decisions in program planning and scheduling, optimizing viewer engagement. The temporal analysis of evolving trends over time adds a nuanced layer to our study, aligning it with the dynamic nature of the media landscape. As television retains its dynamic force, our insights contribute not only to academic discourse but also provide practical guidance, enhancing the competitive edge of television channels.
An efficient unused integrated circuits detection algorithm for parallel scan architecture Sathyanarayana, Rekha; Kanathur Ramaswamy, Nataraj; Srikantaswamy, Mallikarjunaswamy; Kanathur Ramaswamy, Rekha
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp469-478

Abstract

In recent days, many integrated circuits (ICs) are operated parallelly to increase switching operations in on-chip static random access memory (SRAM) array, due to more complex tasks and parallel operations being executed in many digital systems. Hence, it is important to efficiently identify the long-duration unused ICs in the on-chip SRAM memory array layout and to effectively distribute the task to unused ICs in SRAM memory array. In the present globalization, semiconductor supply chain detection of unused SRAM in large memory arrays is a very difficult task. This also results in reduced lifetime and more power dissipation. To overcome the above-mentioned drawbacks, an efficient unused integrated circuits detection algorithm (ICDA) for parallel scan architecture is proposed to differentiate the ‘0’ and ‘1’ in a larger SRAM memory array. The proposed architecture avoids the unbalancing of ‘0’ and ‘1’ concentrations in the on-chip SRAM memory array and also optimizes the area required for the memory array. As per simulation results, the proposed method is more efficient in terms of reliability, the detection rate in both used and unused ICs and reduction of power dissipation in comparison to conventional methods such as backscattering side-channel analysis (BSCA) and network attached storage (NAS) algorithm.
An 8-bit successive-approximation register analog-to-digital converter operating at 125 kS/s with enhanced comparator in 180 nm CMOS technology Zghoul, Fadi Nessir; Al-Bakrawi, Yousra Hussein; Etier, Issa; Kannan, Nithiyananthan
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3830-3854

Abstract

Data converters are necessary for the conversion process of analog and digital signals. Successive approximation register (SAR) analog-to-digital converters (ADC) can achieve high levels of accuracy while consuming relatively low amounts of power and operating at relatively high speeds. This paper describes a design of 8-bit 125 kS/s SAR ADC with a proposed high-speed comparator design based on dynamic latch architecture. The proposed design of the comparator enhances the performance compared to a conventional dynamic comparator by adding two parallel clocked input complementary metal-oxide semiconductor (CMOS) transistors which reduce the parasitic resistance in the latch ground path and serve to minimize the latch delay time. The design of each sub-system for the ADC is explained thoroughly, which contains a sample and hold circuit, successive approximation register, charge redistribution types digital-to-analog converter, and the new proposed comparator. The proposed design is implemented using 180 nm CMOS technology with a power supply of 1.2 V. The average inaccuracy in differential non-linearity (DNL) is +0.6/−0.8 LSB (least significant bit), and integral non-linearity (INL) is +0.4/−0.7 LSB. The proposed design exhibits a delay time of 157 ps at 1 MHz clock frequency.
Face recognition with occluded face using improve intersection over union of region proposal network on Mask region convolutional neural network Budiarsa, Rahmat; Wardoyo, Retantyo; Musdholifah, Aina
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3256-3265

Abstract

Face recognition entails detecting and identifying facial attributes. Mask region convolutional neural network (R-CNN) method is a prominent approach, while prior research predominantly delved into refining loss functions and perfecting object and face detection, recognizing, and identifying faces using imperfect data remained relatively unexplored. This study focuses on an occluded dataset comprising Indonesian faces, wherein 'occluded' denotes facial data that lacks complete visibility-encompassing instances where objects obscure faces or are partially cropped. This investigation involves a deliberate experiment that tailors the intersection over union (IoU) of the region proposal network (RPN) to suit the nuances of occluded Indonesian faces, thereby augmenting accuracy in recognition and segmentation tasks. The innovation IoU in the strategic utilization of Anchors, which involves the exclusion of anchors falling beyond the image borders to optimize computational efficiency. The outcomes of this research are striking; it showcases a remarkable 14.75%, 10.9%, and 12.97% surge based on mean average precision (mAP), mean average recall (mAR), and F1-Scores compared to the conventional Mask R-CNN approach. Notably, our proposed model elevates the average accuracy by 10% to 15% and decreases running time by 21%, a noteworthy enhancement compared to the preceding model. This progress is substantiated by validation utilizing 300 instances dataset, reinforcing the robustness of our approach.
Developing a mathematical model for predicting ultimate tensile strength to identify optimal machining parameters Thilagham, Kancheepuram Thirumal; Ladha, Lekshmy Premachandran; Tiwary, Anand Prakash; Haribhau, Munde Kashinath; Dudhajirao, Darade Pradipkumar; Kumar, Shailseh Ranjan
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7116-7125

Abstract

Identifying the ultimate tensile strength (UTS) for friction stir welded joints between AA6082-T6 and AA2014-T87 is crucial for ensuring material compatibility, optimizing welding parameters, and assessing mechanical performance. This information helps engineers design safer, more reliable structures and optimize the welding process, improving the utilization of these aluminum alloys in high-performance applications. Traditional methods for identifying UTS face challenges such as material variability, precise experimental setup, the influence of welding parameters, and are time-consuming and costly. This research aims to develop a mathematical model capable of identifying the UTS based on given inputs, specifically optimal tilt angle, travel speed, and rotational speed. The developed model is further utilized to identify the optimal machining parameters. Processing this manually or through trial and error is time-consuming and complex, highlighting the need to incorporate optimization techniques to determine the optimal parameters efficiently. This research involves several optimization techniques, among which the evolved wild horse optimization (EWHO) performs better, achieving a mean square error of 0.45. This is superior performance compared to other optimization techniques and employed prediction models. This approach saves time, reduces complexity, and enhances precision compared to manual or trial-and-error methods, ultimately improving the efficiency and reliability of material processing.
Swarm flip-crossover algorithm: a new swarm-based metaheuristic enriched with a crossover strategy Kusuma, Purba Daru; Hasibuan, Faisal Candrasyah
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.pp2145-2155

Abstract

A new swarm-based metaheuristic that is also enriched with the crossover technique called swarm flip-crossover algorithm (SFCA) is introduced in this work. SFCA uses swarm intelligence as its primary technique and the crossover as its secondary one. It consists of three searches in every iteration. The swarm member walks toward the best member as the first search. The central point of the swarm becomes the target in the second search. There are two walks in the second search. The first walk is getting closer to the target, while the second is avoiding the target. The better result between these two walks becomes the candidate for the replacement. In the third search, the swarm member performs balance arithmetic crossover with the central point of the space or jumps to the opposite location within the area (flipping). The assessment is taken by confronting SFCA with five new metaheuristics: slime mold algorithm (SMA), golden search optimization (GSO), osprey optimization algorithm (OOA), coati optimization algorithm (COA), and walrus optimization algorithm (WaOA) in handling the set of 23 functions. The result shows that SFCA performs consecutively better than SMA, GSO, OOA, COA, and WaOA in 20, 23, 17, 17, and 17 functions.

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