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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
 397   398   399   400   401 
Centrifugal compressor anti-surge control system modelling Nurlan Batayev; Batyrbek Suleimenov; Sagira Batayeva
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1419-1428

Abstract

From the middle of XX century, natural gas is an important mineral, widely used in the energy sector. Transportation of natural gas is carried out via gas pipeline networks and compression stations. One of the key features which need to be implemented for any centrifugal gas compressor is a surge protection. This article describes the method and develops software application intended for simulation and study of surge protection system of a centrifugal compressor used in modern gas compression stations. Within the article research method, modelling environment’s block diagram, proposed algorithms and results are described. For surge cases control and prediction, Anti-surge control block implemented which based on practical experience and centrifugal compressor theory. To avoid complicated energy balancing differential equations the volumetric flow calculation algorithm proposed which is used in combination with Redlich-Kwong equation of state. Developed software’s adequacy test performed through modeling of one-stage gas compression scheme at rated speed with comparison of parameters with reference commercial software and verification of the anti-surge control system.
Kalman filter applied to Thevenin’s modeling of a lead-acid battery Jose Alfredo Palacio-Fernádez; Edwin García Quintero
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1350-1357

Abstract

This article determines the internal parameters of a battery analyzed from its circuit equivalent, reviewing important information that can help to identify the battery’s state of charge (SOC) and its state of health (SOH). Although models that allow the dynamics of different types of batteries to be identified have been developed, few have defined the lead-acid battery model from the analysis of a filtered signal by applying a Kalman filter, particularly taking into account the measurement of noise not just at signal output but also at its input (this is a novelty raised from the experimental). This study proposes a model for lead-acid batteries using tools such as MATLAB® and Simulink®. First, a method of filtering the input and output signal is presented, and then a method for identifying parameters from 29 charge states is used for a lead-acid battery. Different SOCs are related to different values of open circuit voltage (OCV). Ultimately, improvements in model estimation are shown using a filter that considers system and sensor noise since the modeled and filtered signal is closer to the original signal than the unfiltered modeled signal.
An efficient design of 45-nm CMOS low-noise charge sensitive amplifier for wireless receivers Islam T. Almalkawi; Ashraf H. Al-Bqerat; Awni Itradat; Jamal N. Al-Karaki
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1274-1285

Abstract

Amplifiers are widely used in signal receiving circuits, such as antennas, medical imaging, wireless devices and many other applications. However, one of the most challenging problems when building an amplifier circuit is the noise, since it affects the quality of the intended received signal in most wireless applications. Therefore, a preamplifier is usually placed close to the main sensor to reduce the effects of interferences and to amplify the received signal without degrading the signal-to-noise ratio. Although different designs have been optimized and tested in the literature, all of them are using larger than 100 nm technologies which have led to a modest performance in terms of equivalent noise charge (ENC), gain, power consumption, and response time. In contrast, we consider in this paper a new amplifier design technology trend and move towards sub 100 nm to enhance its performance. In this work, we use a pre-well-known design of a preamplifier circuit and rebuild it using 45 nm CMOS technology, which is made for the first time in such circuits. Performance evaluation shows that our proposed scaling technology, compared with other scaling technology, extremely reduces ENC of the circuit by more than 95%. The noise spectral density and time resolution are also reduced by 25% and 95% respectively. In addition, power consumption is decreased due to the reduced channel length by 90%. As a result, all of those enhancements make our proposed circuit more suitable for medical and wireless devices.
Computed tomography scans image processing for nasal symptoms severity prediction Amjad Nuseir; Hasan Albalas; Aya Nuseir; Maulla Alali; Firas Zoubi; Mahmoud Al-Ayyoub; Mohammed Mahdi; Ahmad Al Omari
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1488-1497

Abstract

This paper aims to use a new technique of computed tomography (CT) scan image processing to correlate the image analysis with sinonasal symptoms. A retrospective cross-sectional study is conducted by analyzing the digital records of 50 patients who attended the ear, nose and throat (ENT) clinics at King Abdullah University Hospital, Jordan. The coronal plane CT scans are analyzed using our developed software. The purposes of this software are to calculate the surface area of the nasal passage at three different levels visible on coronal plane CT scans: i) the head of the inferior turbinate, ii) the head of the middle turbinate, and iii) the tail of the inferior turbinate. We employ image processing techniques to correlate the narrowing of nasal surface area with sinonasal symptoms. As a consequence, obstruction in the first level is correlated significantly with the symptoms of nasal obstruction while the narrowing in the second level is related to frontal headache. No other significant correlations are found with nasal symptoms at the third level. In our study, we find that image processing techniques can be very useful to predict the severity of common nasal symptoms and they can be used to suggest treatment and to follow up on the case progression.
An enhanced control strategy based imaginary swapping instant for induction motor drives Iliass Rkik; Mohamed El Khayat; Abdelali Ed-Dahhak; Mohammed Guerbaoui; Abdeslam Lachhab
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1102-1112

Abstract

The main aim of this paper is to present a novel control approach of an induction machine (IM) using an improved space vector modulation based direct torque control (SVM-DTC) on the basis of imaginary swapping instant technique. The improved control strategy is presented to surmount the drawbacks of the classical direct torque control (DTC) and to enhance the dynamic performance of the induction motor. This method requires neither angle identification nor sector determination; the imaginary swapping instant vector is used to fix the effective period in which the power is transferred to the IM. Both the classical DTC method and the suggested adaptive DTC techniques have been carried out in MATLAB/SimulinkTM. Simulation results shows the effectiveness of the enhanced control strategy and demonstrate that torque and flux ripples are massively diminished compared to the conventional DTC (CDTC) which gives a better performance. Finally, the system will also be tested for its robustness against variations in the IM parameters.
Cosine similarity-based algorithm for social networking recommendation Shaha Al-Otaibi; Nourah Altwoijry; Alanoud Alqahtani; Latifah Aldheem; Mohrah Alqhatani; Nouf Alsuraiby; Sarah Alsaif; Shahla Albarrak
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1881-1892

Abstract

Social media have become a discussion platform for individuals and groups. Hence, users belonging to different groups can communicate together. Positive and negative messages as well as media are circulated between those users. Users can form special groups with people who they already know in real life or meet through social networking after being suggested by the system. In this article, we propose a framework for recommending communities to users based on their preferences; for example, a community for people who are interested in certain sports, art, hobbies, diseases, age, case, and so on. The framework is based on a feature extraction algorithm that utilizes user profiling and combines the cosine similarity measure with term frequency to recommend groups or communities. Once the data is received from the user, the system tracks their behavior, the relationships are identified, and then the system recommends one or more communities based on their preferences. Finally, experimental studies are conducted using a prototype developed to test the proposed framework, and results show the importance of our framework in recommending people to communities.
Gender recognition from unconstrained selfie images: a convolutional neural network approach Saddam Bekhet; Abdullah M. Alghamdi; Islam F. Taj-Eddin
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp2066-2078

Abstract

Human gender recognition is an essential demographic tool. This is reflected in forensic science, surveillance systems and targeted marketing applications. This research was always driven using standard face images and hand-crafted features. Such way has achieved good results, however, the reliability of the facial images had a great effect on the robustness of extracted features, where any small change in the query facial image could change the results. Nevertheless, the performance of current techniques in unconstrained environments is still inefficient, especially when contrasted against recent breakthroughs in different computer vision research. This paper introduces a novel technique for human gender recognition from non-standard selfie images using deep learning approaches. Selfie photos are uncontrolled partial or full-frontal body images that are usually taken by people themselves in real-life environment. As far as we know this is the first paper of its kind to identify gender from selfie photos, using deep learning approach. The experimental results on the selfie dataset emphasizes the proposed technique effectiveness in recognizing gender from such images with 89% accuracy. The performance is further consolidated by testing on numerous benchmark datasets that are widely used in the field, namely: Adience, LFW, FERET, NIVE, Caltech WebFaces andCAS-PEAL-R1.
Ontology specific visual canvas generation to facilitate sense-making-an algorithmic approach Kaneeka Vidanage; Noor Maizura Mohamad Noor; Rosmayati Mohemad; Zuriana Abu Bakar
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1818-1830

Abstract

Ontologies are domain-specific conceptualizations that are both human and machine-readable. Due to this remarkable attribute of ontologies, its applications are not limited to computing domains. Banking, medicine, agriculture, and law are a few of the non-computing domains, where ontologies are being used very effectively. When creating ontologies for non-computing domains, involvement of the non-computing domain specialists like bankers, lawyers, farmers become very vital. Hence, they are not semantic specialists, particularly designed visualization assistance is required for the ontology schema verifications and sense-making. Existing visualization methods are not fine-tuned for non-technical domain specialists and there are lots of complexities. In this research, a novel algorithm capable of generating domain specialists’ friendlier visualization canvas has been explored. This proposed algorithm and the visualization canvas has been tested for three different domains and overall success of 85% has been yielded.
Using deep learning to detecting abnormal behavior in internet of things Mohammed Al-Shabi; Anmar Abuhamdah
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp2108-2120

Abstract

The development of the internet of things (IoT) has increased exponentially, creating a rapid pace of changes and enabling it to become more and more embedded in daily life. This is often achieved through integration: IoT is being integrated into billions of intelligent objects, commonly labeled “things,” from which the service collects various forms of data regarding both these “things” themselves as well as their environment. While IoT and IoT-powered decices can provide invaluable services in various fields, unauthorized access and inadvertent modification are potential issues of tremendous concern. In this paper, we present a process for resolving such IoT issues using adapted long short-term memory (LSTM) recurrent neural networks (RNN). With this method, we utilize specialized deep learning (DL) methods to detect abnormal and/or suspect behavior in IoT systems. LSTM RNNs are adopted in order to construct a high-accuracy model capable of detecting suspicious behavior based on a dataset of IoT sensors readings. The model is evaluated using the Intel Labs dataset as a test domain, performing four different tests, and using three criteria: F1, Accuracy, and time. The results obtained here demonstrate that the LSTM RNN model we create is capable of detecting abnormal behavior in IoT systems with high accuracy.
A face recognition system using convolutional feature extraction with linear collaborative discriminant regression classification Sangamesh Hosgurmath; Viswanatha Vanjre Mallappa; Nagaraj B. Patil; Vishwanath Petli
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1468-1476

Abstract

Face recognition is one of the important biometric authentication research areas for security purposes in many fields such as pattern recognition and image processing. However, the human face recognitions have the major problem in machine learning and deep learning techniques, since input images vary with poses of people, different lighting conditions, various expressions, ages as well as illumination conditions and it makes the face recognition process poor in accuracy. In the present research, the resolution of the image patches is reduced by the max pooling layer in convolutional neural network (CNN) and also used to make the model robust than other traditional feature extraction technique called local multiple pattern (LMP). The extracted features are fed into the linear collaborative discriminant regression classification (LCDRC) for final face recognition. Due to optimization using CNN in LCDRC, the distance ratio between the classes has maximized and the distance of the features inside the class reduces. The results stated that the CNN-LCDRC achieved 93.10% and 87.60% of mean recognition accuracy, where traditional LCDRC achieved 83.35% and 77.70% of mean recognition accuracy on ORL and YALE databases respectively for the training number 8 (i.e. 80% of training and 20% of testing data).

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