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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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Feasibility analysis and modeling of a solar hybrid system for residential electric vehicle charging Thulasingam, Muthukumaran; Raj Periyanayagam, Ajay D. Vimal; Krishnamoorthy, Murugaperumal
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.pp1251-1262

Abstract

The process of transforming sunshine energy into electrical power is known as solar power generation. Photovoltaic (PV) technology has recently proved its cost-effectiveness and low environmental impact in generating power. The key goals of this study are to develop a solar PV system for charging electric vehicles (EVs) while utilizing the residential apartment's current domestic power supply. This study focuses on modeling grid-interactive solar PV systems for charging EVs inside a 40-unit residential apartment complex. The Solar Pro tool is used to do the techno-economic analysis of the modeled PV system. The research investigates the installation of a rooftop solar plant devoted to delivering electricity to EV charging devices on a real-time five-story residential building. The performance of the PV plant is tested under a variety of scenarios, including EV loading, shadow mapping, and local meteorological conditions. The PV plant's size is optimized at 150 kW, taking into consideration economic aspects as well as the actual proportions of the structure. In addition, the MiPower tool is used to do a load flow study of the modeled system, which includes both the grid and the PV system. This research evaluates line losses, line loading, and voltage levels at each bus at maximum loading circumstances.
Mechatronic system to classify plastic and metal bottles using capacitive and inductive sensors Melo, Napoly; Gonzales, Abigail Sanchez; Paiva-Peredo, Ernesto
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.pp4970-4976

Abstract

The problem addressed in this article focuses on the management of plastic waste, which has experienced a significant increase in recent years, posing challenges in its management and recycling. In addition, the concentration of microplastics in water and their impact on health and the food chain is highlighted. The proposed solution consists of developing a mechatronic system for sorting plastic and metal bottles using capacitive and inductive sensors, respectively. The system demonstrated efficiency in tests, achieving 100% sorting for plastic and metal bottles. The need for bottles to be properly positioned for optimal performance was identified. This work highlights the importance of automation in mechatronic systems and the effectiveness of capacitive and inductive sensors in sorting materials.
Average symbol error rate analysis of reconfigurable intelligent surfaces based free-space optical link over Weibull distribution channels Huu Ai, Duong; Tho Dang, Dai; Dat Vuong, Cong; Loi Nguyen, Van; Ty Luong, Khanh
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.pp443-450

Abstract

Optical wireless communication (OWC) enables wireless connectivity using ultraviolet bands, infrared or visible. With its advantages features as high bandwidth, low cost, and operation in an unregulated spectrum. Free-space optical (FSO) communication systems are near terrestrial as a communication link between transceivers, the link is line-of-sight and successfully transmitted optical signals. Nevertheless, the optical signals transmissions over the FSO channels bring challenges to the system. To overcome the challenges posed by the FSO channels, the most common technique is to use relay stations, the most recent is the reconfigurable intelligent surfaces (RISs) technique. This study introduces a Weibull distribution model for a free-space optical communication link with RISs assisted, the parameter used to evaluate the performance of the system is the average symbol error rate (ASER). The RISs effect is examined by considering the influence of the transmitter beam waist radius, shape parameter, aperture radius, scale parameter, and signal-to-noise ratio on the ASER.
Cardiovascular disease risk factors prediction using deep learning convolutional neural networks Almatari, Mohammad; Abuhaija, Belal; Alloubani, Aladeen; Haddad, Firas; M. Jaradat, Ghaith; Qawqzeh, Yousef; Alsmadi, Mutasem Khalil; Ali Alghamdi, Fahad; Saad Alqurni, Jehad; Alodat, Lena; Dong, Linyinxue
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.pp4471-4487

Abstract

Heart disease remains a leading cause of mortality worldwide, prompting healthcare researchers to leverage analytical tools for comprehensive data analysis. This study focuses on exploring crucial parameters and employing deep learning (DL) techniques to enhance understanding and prediction of cardiovascular disease (CVD) risk factors. Utilizing SPSS and Weka tools, a cross-sectional and correlational design was employed to analyze extensive medical datasets. Binomial regression analysis revealed significant associations between age (???? = 0.004) and body mass index (???? = 0.002) with CVD development, highlighting their importance as risk factors. Leveraging Weka's DL algorithms, a predictive model was constructed to classify CVD causes. Particularly, convolutional neural networks (CNN) showcased remarkable accuracy, reaching 98.64%. The findings underscore the elevated risk of CVD among university students and employees in Saudi Arabia, emphasizing the need for heightened awareness and preventive measures, including dietary improvements and increased physical activity. This study underscores the importance of further research to enhance CVD risk perception among students and individuals in similar settings.
Fine-grained hate speech detection in Arabic using transformer-based models Bensoltane, Rajae; Zaki, Taher
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.pp2927-2936

Abstract

With the proliferation of social media platforms, characterized by features such as anonymity, user-friendly access, and the facilitation of online community building and discourse, the matter of detecting and monitoring hate speech has emerged as an increasingly formidable challenge for society, individuals, and researchers. Despite the crucial importance of hate speech detection task, the majority of work in this field has been conducted in English, with insufficient focus on other languages, particularly Arabic. Furthermore, most existing studies on Arabic hate speech detection have addressed this task as a binary classification problem, which is unreliable. Therefore, the aim of this study is to provide an enhanced model for detecting fine-grained hate speech in Arabic. To this end, three transformer-based models were evaluated to generate contextualized word embeddings from input sequence. Additionally, these models were combined with a bidirectional gated recurrent unit (BiGRU) layer to further improve the extracted semantic and context features. The experiments were conducted on an Arabic reference dataset provided by the open-source Arabic corpora and processing tools (OSACT-5) shared task. A comparative analysis indicates the efficiency of the proposed model over the baseline and related work models by achieving a macro F1-score of 61.68%.
Framework for detecting and resisting cyberattacks on cyber-physical systems in internet of things Metan, Jyoti; Mathapati, Mahantesh; Yogegowda, Prasad Adaguru; Ananda Kumar, Kurilinga Sannalingappa
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.pp7169-7177

Abstract

Cyber-physical system (CPS) is an integral part of an internet of things (IoT) with established wide spread applications. An increasing concern towards being highly vulnerable to various forms of dynamic cyber-attacks has been increasingly evolving. A review of existing research methodology showcases complex solutions that can offer sub-optimal security strength when exposed to dynamic cyber-attack forms while increasing the computational burden. Therefore, this manuscript presents a novel yet simplified computational framework capable of determining and resisting critical anomalies within internet-of-cyber physical systems (IoCPS). The presented scheme contributes towards preprocessing following a distinct oversampling method targeting balancing the data. An ensemble machine learning model using a discrete variant of AdaBoost and neural decision tree (NDT) has been implemented to optimize the learning process and improve the threat detection efficiency. The comparative outcome of the proposed study showcases that it offers approximately 7.2% increased threat detection accuracy and approximately 68% reduced response time compared to frequently adopted learning mechanisms towards threat detection over an IoT environment.
Energy management strategy for photovoltaic powered hybrid energy storage systems in electric vehicles Ramanjaneyulu Korada, Seetha; Brinda, Rajamony; Dhanusu Soubache, Irissappane
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.pp1228-1239

Abstract

Nowadays, electric vehicles (EVs) using additional energy sources frequently deliver a safe ride without concern about the distance. The energy sources including a battery, an ultra-capacitor (UC), and a photovoltaic (PV) are considered in this research for driving the EV. Vehicles that only use battery-oriented technologies experience problems with charging and quick battery discharge. EVs are used with an ultracapacitor to decrease the quick discharge effects and increase the lifetime of the battery. Furthermore, bidirectional DC-DC converters are a type of power electronics device used to verify the smooth transfer of generated power from energy sources to the motor throughout various stages of the driving cycle. Therefore, this study proposes a perturb and observe (P&O) energy management control technique based on tuna swarm optimization (TSO). The suggested TSO-P&O completely uses UC while regulating the battery because it lowers dynamic battery charging and discharging currents. Due to the aforementioned aspect, the suggested TSO-P&O increases battery life and demonstrates a very dependable, long range power source for an electric car. The TSO-P&O technique achieves the EVs by obtaining the maximum speed of 91.93 km/hr. with a quicker settling time of 4,930 ms when compared with the existing zero-fuel zero-emission (ZFZE) method.
Assessing power quality in individual circuits of industrial electrical system Angarita, Eliana Noriega; Santos, Vladimir Sousa; Donolo, Pablo Daniel; Quispe, Enrique Ciro
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.pp4888-4896

Abstract

This article evaluates energy quality in individual circuits within an industrial electrical system and its impact on common connection point parameters. The research is crucial due to rising challenges in power quality arising from increased nonlinear electrical loads in industrial processes. The study involves sequential steps, covering the industrial electrical system´s description, power quality parameters analysis, and issue identification. A comprehensive assessment was conducted on a 3,000 kVA, 13.8 kV/460 V point of common coupling (PCC) transformer, and 10 transformers (10 to 250 kVA) supplying individual circuits. Findings indicated load factors below 70% in all transformers and a power factor below 0.9 in eight. Issues like voltage variation, current imbalance, and harmonic distortion were identified in nine transformers supplying individual circuits, while the PCC exhibited no power quality problems. The research emphasizes the importance of including individual circuits in power quality assessments, as compliance with regulatory limits at the PCC may not guarantee the absence of power quality issues in individual circuits, affecting equipment lifespan and increasing energy losses.
A dilution-based defense method against poisoning attacks on deep learning systems Park, Hweerang; Cho, Youngho
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.pp645-652

Abstract

Poisoning attack in deep learning (DL) refers to a type of adversarial attack that injects maliciously manipulated data samples into a training dataset for the purpose of forcing a DL model trained based on the poisoned training dataset to misclassify inputs and thus significantly degrading its performance and reliability. Meanwhile, a traditional defense approach against poisoning attacks tries to detect poisoned data samples from the training dataset and then remove them. However, since new sophisticated attacks avoiding existing detection methods continue to emerge, a detection method alone cannot effectively counter poisoning attacks. For this reason, in this paper, we propose a novel dilution-based defense method that mitigates the effect of poisoned data by adding clean data to the training dataset. According to our experiments, our dilution-based defense technique can significantly decrease the success rate of poisoning attacks and improve classification accuracy by effectively reducing the contamination ratio of the manipulated data. Especially, our proposed method outperformed an existing defense method (Cutmix data augmentation) by 20.9%p at most in terms of classification accuracy.
Optimized automated testing: test case generation and maintenance using latent semantic analysis-based TextRank and particle swarm optimization algorithms Swathi, Baswaraju; Kolisetty, Soma Sekhar
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.pp4315-4324

Abstract

Software development would have to include automated testing to ensure the finished product and performs as intended. However, the process of Test Case Generation and Maintenance can be time-consuming and error-prone, especially when manual methods are used. This research proposes a new approach to improve the efficiency and accuracy of automated testing using latent semantic analysis (LSA)-based TextRank (TR) and particle swarm optimization (PSO) algorithms. The study aims to evaluate the effectiveness of these algorithms in generating and optimizing test cases based on requirements analysis. To retrieve key information from the criteria, methods including text classification (TC), named entity recognition (NER), and sentiment analysis (SA) are used to evaluate the text. Test cases are then generated using LSA-based TR for text summarization and PSO for optimization. The aim of this work is to identify any limitations that need to be addressed and to evaluate the overall efficiency and accuracy of automated testing (AT) using proposed algorithms. The results of this research are expected to have important implications for the software industry, helps to improve the overall efficiency and accuracy of AT. The findings could guide future research that led to the creation of more advanced and effective tools for AT.

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