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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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Detecting network attacks model based on a convolutional neural network Teba Ali Jasim Ali; Muna M. Taher Jawhar
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3072-3078

Abstract

Due to the increasing use of networks at present, Internet systems have raised many security problems, and statistics indicate that the rate of attacks or intrusions has increased excessively annually, and in the event of any malicious attack on network vulnerabilities or information systems, it may lead to serious disasters, violating policies on network security, i.e., “confidentiality, integrity, and availability” (CIA). Therefore, many detection systems, such as the intrusion detection system, appeared. In this paper, we built a system that detects network attacks using the latest machine learning algorithms and a convolutional neural network based on a dataset of the CSE-CIC-IDS2018. It is a recent dataset that contains a set of common and recent attacks. The detection rate is 99.7%, distinguishing between aggressive attacks and natural assertiveness.
Induction motors with copper rotor: a new opportunity for increasing motor efficiency Percy R. Viego Felipe; Vladimir Sousa Santos; Julio R. Gómez Sarduy; José P. Monteagudo Yanes; Enrique Ciro Quispe
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2409-2418

Abstract

The copper rotor induction motor (CURIM) was recently introduced because it has lower rotor fusion losses than the aluminum rotor (ALRIM). Furthermore, with CURIM, it is easier to reach IE4 and IE5 efficiency levels. The CURIM is advantageous for compact motors, escalators, and electric vehicle applications. However, CURIMs present slip, power factor, temperature increase, and torque decrease problems that must be analyzed. This study compared the economic feasibility of using CURIM with ALRIM by applying discount techniques. A case study was carried out in a sugar company with a cyclical operation, where 5.5 kW motors will be installed in the intermediate conductors of the mill's feeders. The facility works three shifts between 3 and 6 months. The cost increase (DCI) of CURIM over ALRIM was between 1.1 and 1.5 times. With 3,600 h/year and 4,000 h/year of operation, the DCI greater than 10%, it was found that the payback is more than four years, and the net present value (NPV) grows linearly.
Apply deep learning to improve the question analysis model in the Vietnamese question answering system Dang Thi Phuc; Dang Van Nghiem; Bui Binh Minh; Tran My Linh; Dau Sy Hieu
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3311-3321

Abstract

Question answering (QA) system nowadays is quite popular for automated answering purposes, the meaning analysis of the question plays an important role, directly affecting the accuracy of the system. In this article, we propose an improvement for question-answering models by adding more specific question analysis steps, including contextual characteristic analysis, pos-tag analysis, and question-type analysis built on deep learning network architecture. Weights of extracted words through question analysis steps are combined with the best matching 25 (BM25) algorithm to find the best relevant paragraph of text and incorporated into the QA model to find the best and least noisy answer. The dataset for the question analysis step consists of 19,339 labeled questions covering a variety of topics. Results of the question analysis model are combined to train the question-answering model on the data set related to the learning regulations of Industrial University of Ho Chi Minh City. It includes 17,405 pairs of questions and answers for the training set and 1,600 pairs for the test set, where the robustly optimized BERT pre-training approach (RoBERTa) model has an F1-score accuracy of 74%. The model has improved significantly. For long and complex questions, the mode has extracted weights and correctly provided answers based on the question’s contents.
Convolutional neural network based key generation for security of data through encryption with advanced encryption standard Ismail Negabi; Smail Ait El Asri; Samir El Adib; Naoufal Raissouni
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2589-2599

Abstract

Machine learning techniques, especially deep learning, are playing an increasingly important role in our lives. Deep learning uses different models to extract information from the data. They have already had a huge impact in areas such as health (i.e., cancer diagnosis), self-driving cars, speech recognition, and data encryption. Recently, deep learning models, including convolutional neural networks (CNN), have been proven to be more effective in the security field. Moreover, the National Institute of Standards and Technology (NIST) recommends the advanced encryption standard (AES) algorithm as the most often utilized encryption method in several security applications. In this paper, a crypt-intelligent system (CIS) capable of securing data is proposed. It is based on the combination of the performance of CNN with the AES, by substituting the key expansion unit of AES with a CNN architecture that performs the key generation. Our CIS is described using very high-speed integrated circuit (VHSIC) hardware description language (VHDL), simulated by ModelSim, synthesized, and implemented with Xilinx ISE 14.7. Finally, the Airtex-7 series XC7A100T device has achieved an encryption throughput of 965.88 Mbps. In addition, the CIS offers a high degree of flexibility and is supported by reconfigurability, based on the experimental results, if sufficient resources are available, the architecture can provide performance that can satisfy cryptographic applications.
Facial emotion recognition using deep learning detector and classifier Ng Chin Kit; Chee-Pun Ooi; Wooi Haw Tan; Yi-Fei Tan; Soon-Nyean Cheong
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3375-3383

Abstract

Numerous research works have been put forward over the years to advance the field of facial expression recognition which until today, is still considered a challenging task. The selection of image color space and the use of facial alignment as preprocessing steps may collectively pose a significant impact on the accuracy and computational cost of facial emotion recognition, which is crucial to optimize the speed-accuracy trade-off. This paper proposed a deep learning-based facial emotion recognition pipeline that can be used to predict the emotion of detected face regions in video sequences. Five well-known state-of-the-art convolutional neural network architectures are used for training the emotion classifier to identify the network architecture which gives the best speed-accuracy trade-off. Two distinct facial emotion training datasets are prepared to investigate the effect of image color space and facial alignment on the performance of facial emotion recognition. Experimental results show that training a facial expression recognition model with grayscale-aligned facial images is preferable as it offers better recognition rates with lower detection latency. The lightweight MobileNet_v1 is identified as the best-performing model with WM=0.75 and RM=160 as its hyper-parameters, achieving an overall accuracy of 86.42% on the testing video dataset.
Bio-inspired intelligence for minimizing losses in substrate integrated waveguide Souad Akkader; Hamid Bouyghf; Abdennaceur Baghdad
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2837-2846

Abstract

This paper presents a study of various types of losses in substrate-integrated waveguides (SIW) using a genetic algorithm. Three main types of losses are considered and examined separately: conductor loss, dielectric loss, and radiation loss. Furthermore, the current analysis allows for a physical understanding of the loss impacts as well as the creation of design guidelines to reduce losses at 10 GHz frequency while keeping the miniaturized size of the SIW. Validation results obtained using the software Ansys HFSS, verify that the attenuation constant of the SIW can be significantly reduced to  0.4 dB/m, the Insertion loss S21 to -0.2 dB and the return loss to -38 dB if the geometric parameters are chosen properly. This study enables us to identify the source of losses in a SIW and, as a result, eliminate any type of dispersion. That demonstrates the usability of SIW technologies in the design of microwave circuits used in Internet of things applications.
Design and analysis of asymmetrical low-k source side spacer halo doped nanowire metal oxide semiconductor field effect transistor Padakanti Kiran Kumar; Bukya Balaji; Karumuri Srinivasa Rao
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3519-3529

Abstract

In this paper, we propose a low-k source side asymmetrical spacer halo-doped nanowire metal oxide semiconductor field effect transistor (MOSFET) design and analysis. High-k spacer materials are now being researched extensively for improving electrostatic control and suppressing short-channel effects in nanoscaled electronics. However, the high-k spacers' excessive increase in fringe capacitance degrades the dynamic circuit performance. Surprisingly, this approach achieves a significant reduction in gate capacitance by maximizing the use of high-k spacer material. Three different structures, symmetrical dual-k spacer, low-k drain side asymmetrical spacer, low-k source side asymmetrical spacer halo doped nanowire MOSFET architectures are simulated and among them low-k source side asymmetrical spacer halo doped nanowire MOSFET architecture giving lower gate capacitance. After doing 3D simulations in Silvaco technology computer-aided design (TCAD) we observed that the gate capacitance and intrinsic delay are 1.23x10-17 farads and 1.11x10-12 seconds respectively for low-k source side asymmetrical spacer architecture and these are less as compared to high-k spacer architecture. So, the proposed structure is highly recommended for digital applications.
Experimental study of compressor electric current detection for a split-type air conditioner affects energy savings Banjerd Saengchandr; Viroch Sukontanakarn; Kriangkrai Waiyagan
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2660-2668

Abstract

The paper presents an experimental study that aims to measure the compressor electric current of a split-type air conditioner for analyzing the various abnormal condition of the R-32 refrigerant pressure, especially for detecting compressor electric current while occurring dirt in the evaporator coil and condenser coil. The method was to install sensor devices to measure the temperature and humidity of inlet air and outlet air, and the velocity of the air outlet of the evaporator unit. In condenser unit was to measure the electric current compressor and electric power input. All data from sensors send to the Arduino board and using Parallax Data Acquisition (PLX-DAQ) Excel Macro for the record. The results show physical behavior and the changing of compressor electric current according to the abnormal condition of the refrigerant system, blocking of condenser and evaporator coil.
Residual balanced attention network for real-time traffic scene semantic segmentation Amine Kherraki; Shahzaib Saqib Warraich; Muaz Maqbool; Rajae El Ouazzani
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3281-3289

Abstract

Intelligent transportation systems (ITS) are among the most focused research in this century. Actually, autonomous driving provides very advanced tasks in terms of road safety monitoring which include identifying dangers on the road and protecting pedestrians. In the last few years, deep learning (DL) approaches and especially convolutional neural networks (CNNs) have been extensively used to solve ITS problems such as traffic scene semantic segmentation and traffic signs classification. Semantic segmentation is an important task that has been addressed in computer vision (CV). Indeed, traffic scene semantic segmentation using CNNs requires high precision with few computational resources to perceive and segment the scene in real-time. However, we often find related work focusing only on one aspect, the precision, or the number of computational parameters. In this regard, we propose RBANet, a robust and lightweight CNN which uses a new proposed balanced attention module, and a new proposed residual module. Afterward, we have simulated our proposed RBANet using three loss functions to get the best combination using only 0.74M parameters. The RBANet has been evaluated on CamVid, the most used dataset in semantic segmentation, and it has performed well in terms of parameters’ requirements and precision compared to related work.
Semi-automatic model to colony forming units counting Jesus Emilio Pinto-Lopera; Diana Carolina Meneses-Cabezas; Yuliana Zapata-Serna; Yeison Alberto Garces-Gomez
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2761-2768

Abstract

Colony forming units counting is a conventional process carry out in bacteriological laboratories, and it is used to follow the behavior of bacteria in different conditions. Currently exist different systems, automatic or semi-automatic, to counting colony forming units exits, but, in general, many laboratories continue using manual counting, which consumes considerable time and effort from researchers and laboratory employees. This paper presents a mathematical model carry out to segment the colony forming units and, in this way, counting them from a digital image of the sample. The method uses the color space information of some points in the image and shows good behavior for images with many or few colony forming units in the sample, according to manual counting. The results show efficiencies close to 98% with MacConkey agar.

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