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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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iBeacon-based indoor positioning system: from theory to practical deployment Thai-Mai Dinh Thi; Ngoc-Son Duong
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5156-5164

Abstract

Developing an indoor positioning system became essential when global positioning system signals could not work well in indoor environments. Mobile positioning can be accomplished via many radio frequency technology such as Bluetooth low energy (BLE), wireless fidelity (Wi-Fi), ultra-wideband (UWB), and so on. With the pressing need for indoor positioning systems, we, in this work, present a deployment scheme for smartphone using Bluetooth iBeacons. Three main parts, hardware deployment, software deployment, and positioning accuracy assessment, are discussed carefully to find the optimal solution for a complete indoor positioning system. Our application and experimental results show that proposed solution is feasible and indoor positioning system is completely attainable.
Factors affecting students’ continuance intention to use teaching performance assessment application from technology continuance theory Nurdin Nurdin; Sagaf S Pettalongi; Muhammad Nur Ahsan; Vindy Febrianti Febrianti
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5342-5353

Abstract

This study aims to determine university students’ continuance intention in using an android-based teaching performance assessment (TPA) application. For the data gathering instrument, we employed an online structured questionnaire. Two hundred and forty students from four faculties were selected and assigned a five-scale survey. All completed questionnaires were analyzed using analysis of moment structure (AMOS). The findings show that the factors of productivity, performance, relevancy, quality and mobility of the android-based TPA have significantly influenced students’ continuance intention to use the application. The results highlighted that when an android-based system was developed based on the criteria, the long-term use of the android-based TPA application can be consistently maintained to improve universities’ teaching quality assessment. However, our study needs to improve in that the university students may evaluate teaching staffs who are not teaching a subject in their class because all teaching staff has appeared in the application database. In addition, further research needs to limit each lecture based on a specific course to be assessed by a particular student’s class.
Automotive Ethernet architecture and security: challenges and technologies Wael Toghuj; Nidal Turab
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5209-5221

Abstract

Vehicle infrastructure must address the challenges posed by today's advances toward connected and autonomous vehicles. To allow for more flexible architectures, high-bandwidth connections and scalability are needed to connect many sensors and electronic control units (ECUs). At the same time, deterministic and low latency is a critical and significant design requirement to support urgent real-time applications in autonomous vehicles. As a recent solution, the time-sensitive network (TSN) was introduced as Ethernet-based amendments in IEEE 802.1 TSN standards to meet those needs. However, it had hurdle to be overcome before it can be used effectively. This paper discusses the latest studies concerning the automotive Ethernet requirements, including transmission delay studies to improve worst-case end-to-end delay and end-to-end jitter. Also, the paper focuses on the securing Ethernet-based in-vehicle networks (IVNs) by reviewing new encryption and authentication methods and approaches.
Secured authentication of radio-frequency identification system using PRESENT block cipher Bharathi Ramachandra; Smitha Elsa Peter
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5462-5471

Abstract

The internet of things (IoT) is an emerging and robust technology to interconnect billions of objects or devices via the internet to communicate smartly. The radio frequency identification (RFID) system plays a significant role in IoT systems, providing most features like mutual establishment, key establishment, and data confidentiality. This manuscript designed secure authentication of IoT-based RFID systems using the light-weight PRESENT algorithm on the hardware platform. The PRESENT-256 block cipher is considered in this work, and it supports 64-bit data with a 256-key length. The PRESENT-80/128 cipher is also designed along with PRESENT-256 at electronic codebook (ECB) mode for Secured mutual authentication between RFID tag and reader for IoT applications. The secured authentication is established in two stages: Tag recognition from reader, mutual authentication between tag and reader using PRESENT-80/128/256 cipher modules. The complete secured authentication of IoT-based RFID system simulation results is verified using the chip-scope tool with field-programmable gate array (FPGA) results. The comparative results for PRESENT block cipher with existing PRESENT ciphers and other light-weight algorithms are analyzed with resource improvements. The proposed secured authentication work is compared with similar RFID-mutual authentication (MA) approaches with better chip area and frequency improvements.
A cost-effective and optimized maximum powerpoint tracking system for the photovoltaic model Yoganandini Arehalli Puttalingaiah; Anitha Gowda Shesadri
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp4942-4949

Abstract

Solar energy is naturally available from sun, and it can be extracted by using a photovoltaic (PV) cell. However, solar energy extraction entirely depends on the climatic conditions and angle of rays falling on PV cells. Hence, maximum powerpoint tracking (MPPT) is considered in most areas under variable climatic conditions, which acts as a controller unit for PV cells. MPPT can enhance the efficiency of PV cells. However, designing an MPPT model is challenging as different uncertainties in the climatic condition may lead to more fluctuations in voltage and current in PV cells. Under the shaded condition, the PV cell may have other MPPT points that lead to the PV cell’s low efficiency in analyzing maximum power. Hence, this paper introduces a cost-effective and optimized system for the PV model that can find optimal power and improve PV cells’ efficiency. The proposed system achieves better computational performance with ~35% and ~42% than existing MPPT techniques. The improved particle swarm optimization (PSO) is smoother due to the enhanced form of MPP tracking. Hence, improved PSO takes 0.038 sec while the existing PSO technique takes 0.045 sec to obtain the MPP tracking.
Control of 7-phase permanent magnet synchronous motor drive post three failures Kamel Saleh; Mark Sumner
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5006-5025

Abstract

The article is introducing a new control technique for the 7-phase permanent magnet synchronous motor (PMSM) drive to enhance its robustness against the failure of phases ‘a’ and ‘c’ in addition to the failure of the encoder occurring simultaneously. The article is firstly developing a new multi-dimension space vector pulse width modulation (SVPWM) technique as a part of the fault-tolerant control technique (FTC) to control the magnitudes and angles of the motor’s current after the failures of phases ‘a’ and ‘c’. Moreover, the paper is developing another FTC to obtain a sensorless operation of the 7-phase motor after the failure in the encoder while the phase ‘a’ and ‘c’ are faulted based on the tracking of the saturation saliency. Simulation results prove that the ripple in the speed post the three failures was maintained to be less than 10 rpm compared to 2 rpm when the 7-phase drive is running without faults. In addition to that, the results demonstrated that the motor responded to instant changes in speeds and loads with a dynamic response very close to that obtained when the 7-phase motor ran under healthy operating conditions.
Characterization of electricity demand based on energy consumption data from Colombia Santiago Toledo-Cortés; Juan Sebastián Lara; Alvaro Zambrano; Fabio Augusto González Osorio; Javier Rosero Garcia
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp4798-4809

Abstract

The development of dynamic energy distribution grids to optimize energy resources has become very important at the international level in recent years. A very important step in this development is to be able to characterize the population based on their consumption behaviour. However, traditional consumption meters that report information at a monthly rate provide little information for in-depth analysis. In Colombia, this has changed in recent years due to the implementation and integration of advanced metering infrastructure (AMI). This infrastructure allows to record consumption values in small time intervals, and the available data then allows for the execution of many analysis mechanisms. In this paper we present an analysis of the electricity demand profile from a new dataset of energy consumption in Colombia. A characterization of the users demand profiles is presented using a k-means clustering procedure. Whit this customer segmentation technique we show that is possible identify customer consumption patterns and to identify anomalies in the system. In addition, this type of analysis also allows to assess changes in the consumption pattern of users due to social measures such as those resulting from the coronavirus disease (COVID-19) pandemic.
Multi-label text classification of Indonesian customer reviews using bidirectional encoder representations from transformers language model Nuzulul Khairu Nissa; Evi Yulianti
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5641-5652

Abstract

Customer review is a critical resource to support the decision-making process in various industries. To understand how customers perceived each aspect of the product, we can first identify all aspects discussed in the customer reviews by performing multi-label text classification. In this work, we want to know the effectiveness of our two proposed strategies using bidirectional encoder representations from transformers (BERT) language model that was pre-trained on the Indonesian language, referred to as IndoBERT, to perform multi-label text classification. First, IndoBERT is used as feature representation to be combined with convolutional neural network-extreme gradient boosting (CNN-XGBoost). Second, IndoBERT is used both as the feature representation as well as the classifier to directly solve the classification task. Additional analysis is performed to compare our results with those using multilingual BERT model. According to our experimental results, our first model using IndoBERT as feature representation shows significant performance over some baselines. Our second model using IndoBERT as both feature representation and classifier can significantly enhance the effectiveness of our first model. In summary, our proposed models can improve the effectiveness of the baseline using Word2Vec-CNN-XGBoost by 19.19% and 6.17%, in terms of accuracy and F-1 score, respectively.
Channel and spatial attention mechanism for fashion image captioning Bao T. Nguyen; Son T. Nguyen; Anh H. Vo
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5833-5842

Abstract

Image captioning aims to automatically generate one or more description sentences for a given input image. Most of the existing captioning methods use encoder-decoder model which mainly focus on recognizing and capturing the relationship between objects appearing in the input image. However, when generating captions for fashion images, it is important to not only describe the items and their relationships, but also mention attribute features of clothes (shape, texture, style, fabric, and more). In this study, one novel model is proposed for fashion image captioning task which can capture not only the items and their relationship, but also their attribute features. Two different attention mechanisms (spatial-attention and channel-wise attention) is incorporated to the traditional encoder-decoder model, which dynamically interprets the caption sentence in multi-layer feature map in addition to the depth dimension of the feature map. We evaluate our proposed architecture on Fashion-Gen using three different metrics (CIDEr, ROUGE-L, and BLEU-1), and achieve the scores of 89.7, 50.6 and 45.6, respectively. Based on experiments, our proposed method shows significant performance improvement for the task of fashion-image captioning, and outperforms other state-of-the-art image captioning methods.
Fine-tuning U-net for medical image segmentation based on activation function, optimizer and pooling layer Remah Younisse; Rawan Ghnemat; Jaafer Al Saraireh
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5406-5417

Abstract

U-net convolutional neural network (CNN) is a famous architecture developed to deal with medical images. Fine-tuning CNNs is a common technique used to enhance their performance by selecting the building blocks which can provide the ultimate results. This paper introduces a method for tuning U-net architecture to improve its performance in medical image segmentation. The experiment is conducted using an x-ray image segmentation approach. The performance of U-net CNN in lung x-ray image segmentation is studied with different activation functions, optimizers, and pooling-bottleneck-layers. The analysis focuses on creating a method that can be applied for tuning U-net, like CNNs. It also provides the best activation function, optimizer, and pooling layer to enhance U-net CNN’s performance on x-ray image segmentation. The findings of this research showed that a U-net architecture worked supremely when we used the LeakyReLU activation function and average pooling layer as well as RMSProb optimizer. The U-net model accuracy is raised from 89.59 to 93.81% when trained and tested with lung x-ray images and uses the LeakyReLU activation function, average pooling layer, and RMSProb optimizer. The fine-tuned model also enhanced accuracy results with three other datasets.

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