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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 113 Documents
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Search results for , issue "Vol 12, No 6: December 2022" : 113 Documents clear
Recognition of compound characters in Kannada language Sridevi Tumkur Narasimhaiah; Lalitha Rangarajan
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6103-6113

Abstract

Recognition of degraded printed compound Kannada characters is a challenging research problem. It has been verified experimentally that noise removal is an essential preprocessing step. Proposed are two methods for degraded Kannada character recognition problem. Method 1 is conventionally used histogram of oriented gradients (HOG) feature extraction for character recognition problem. Extracted features are transformed and reduced using principal component analysis (PCA) and classification performed. Various classifiers are experimented with. Simple compound character classification is satisfactory (more than 98% accuracy) with this method. However, the method does not perform well on other two compound types. Method 2 is deep convolutional neural networks (CNN) model for classification. This outperforms HOG features and classification. The highest classification accuracy is found as 98.8% for simple compound character classification. The performance of deep CNN is far better for other two compound types. Deep CNN turns out to better for pooled character classes.
A review on predictive maintenance technique for nuclear reactor cooling system using machine learning and augmented reality Ahmad Azhari Mohamad Nor; Murizah Kassim; Mohd Sabri Minhat; Norsuzila Ya'acob
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6602-6613

Abstract

Reactor TRIGA PUSPATI (RTP) is the only research nuclear reactor in Malaysia. Maintenance of RTP is crucial which affects its safety and reliability. Currently, RTP maintenance strategies used corrective and preventative which involved many sensors and equipment conditions. The existing preventive maintenance method takes a longer time to complete the entire system’s maintenance inspection. This study has investigated new predictive maintenance techniques for developing RTP predictive maintenance for primary cooling systems using machine learning (ML) and augmented reality (AR). Fifty papers from recent referred publications in the nuclear areas were reviewed and compared. Detailed comparison of ML techniques, parameters involved in the coolant system and AR design techniques were done. Multiclass support vector machines (SVMs), artificial neural network (ANN), long short-term memory (LSTM), feed forward back propagation (FFBP), graph neural networks-feed forward back propagation (GNN-FFBP) and ANN were used for the machine learning techniques for the nuclear reactor. Temperature, water flow, and water pressure were crucial parameters used in monitoring a nuclear reactor. Image marker-based techniques were mainly used by smart glass view and handheld devices. A switch knob with handle switch, pipe valve and machine feature were used for object detection in AR markerless technique. This study is significant and found seven recent papers closely related to the development of predictive maintenance for a research nuclear reactor in Malaysia.
Smart internet of things kindergarten garbage observation system using Arduino uno Ali Abdulameer Aldujaili; Mohammed Dauwed; Ahmed Meri; Safa Sami Abduljabbar
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6820-6828

Abstract

Increase the in population and kindergarten number, especially in urban areas made it difficult to properly manage waste. Thus, this paper proposed a system dedicated to kindergartens to manage to dispose of waste, the system can be called smart garbage based on internet of things (SGI). To ensure a healthy environment and an intelligent waste in the kindergarten management system in an integrated manner and supported by the internet of things (IoT), we presented it in detail identification, the SGI system includes details like a display system, an automatic lid system, and a communication system. This system supplied capabilities to monitor the status of waste continuously and on IoT website can show the percentage of waste placed inside the bin. And by using a Wi-Fi communication system, between the system unit and the monitoring body, to collect waste when the trash is full. The smart system proposed in this paper is the most efficient system of traditional waste management systems because it reduces the use of manpower and significantly limits the spread of waste and fully controls it. Additionally, it can be linked via the IoT to the mobile, thus forming an integrated monitoring system.
International Journal of Electrical and Computer Engineering: a bibliometric analysis Yeison Alberto Garcés-Gómez; Vladimir Henao-Céspedes
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp5667-5673

Abstract

This study is focused on analyzing seven years of bibliometric data of the International Journal of Electrical and Computer Engineering (IJECE) from 2014 to 2020. The analysis of 2,928 papers exhibits multi-folded growth of 34.25%, rising from 109 articles in 2014 to 638 articles by 2020. In addition, the analysis of the structure of publications as well as the mapping of bibliographic data based on co-citation, bibliographic coupling, and co-occurrence showed the intellectual structure and connection between universities, countries, and contributing authors. As the journal’s first retrospective, this study not only educates and enriches IJECE’s global readership and aspiring contributors, but may also be useful to its editorial board, as it provides several inputs for navigating future research.
Comparison analysis of different classification methods of power quality disturbances Nur Adrinna Shafiqa Zakaria; Dalila Mat Said; Norzanah Rosmin; Nasarudin Ahmad; Mohamad Shazwan Shah Jamil; Sohrab Mirsaeidi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp5754-5764

Abstract

Good power quality delivery has always been in high demand in power system utilities where different types of power quality disturbances are the main obstacles. As these disturbances have distinct characteristics and even unique mitigation techniques, their detection and classification should be correct and effective. In this study, eight different types of power quality disturbances were synthetically generated, by using a mathematical approach. Then, continuous wavelet transform (CWT) and discrete wavelet transform with multi-resolution analysis (DWT-MRA) were applied, which eight features were then extracted from the synthesized signals. Three classifiers namely, decision tree (DT), support vector machine (SVM) and k-nearest neighbors (KNN) were trained to classify these disturbances. The accuracy of the classifiers was evaluated and analyzed. The best classifier was then integrated with the full model, which the performance of the proposed model was observed with 50 random signals, with and without noise. This study found that wavelet-transform was effective to localize the disturbances at the instant of their occurrence. On the other hand, the SVM classifier is superior to other classifiers with an overall accuracy of 94%. Still, the need for these classifiers to be further optimized is crucial in ensuring a more effective detection and classification system.
Estimation of water momentum and propeller velocity in bow thruster model of autonomous surface vehicle using modified Kalman filter Hendro Nurhadi; Mayga Kiki; Dieky Adzkiya; Teguh Herlambang
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp5988-5997

Abstract

Autonomous surface vehicle (ASV) is a vehicle in the form of an unmanned on-water surface vessel that can move automatically. As such, an automatic control system is essentially required. The bow thruster system functions as a propulsion control device in its operations. In this research, the water momentum and propeller velocity were estimated based on the dynamic bow thruster model. The estimation methods used is the Kalman filter (KF) and ensemble Kalman filter (EnKF). There are two scenarios: tunnel thruster condition and open-bladed thruster condition. The estimation results in the tunnel thruster condition showed that the root mean square error (RMSE) by the EnKF method was relatively smaller, that is, 0.7920 and 0.1352, while the estimation results in the open-bladed thruster condition showed that the RMSE by the KF method was relatively smaller, that is, 1.9957 and 2.0609.
3D printing part orientation optimization: discrete approximation of support volume Juan C. Guacheta Alba; Sebastian Gonzalez Garzon; Diego A. Nunez; Mauricio Mauledoux; Oscar F. Aviles
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp5958-5966

Abstract

In three-dimensional (3D) printing, due to the geometry of most parts, it is necessary to use extra material to support the manufacturing process. This material must be discarded after printing, so its reduction is essential to minimize manufacturing time and cost. An important parameter that must be defined before starting the printing process is the part orientation, which has repercussions on the quality, deposition path, and post-processing among others. Usually, the user sets up this parameter arbitrarily, so this paper takes advantage of it on optimization techniques and proposes an approximation of the volume be covered by the support material, which depends directly on the angle of the part to be printed and its geometry. Among mono-objectives optimization strategies, this work focuses on five of them. Their performance is compared by two metrics: support volume and execution time. Then, the best result is compared with commercial software.
Precise identification of objects in a hyperspectral image by characterizing the distribution of pure signatures Soumyashree M Panchal; Shivaputra Shivaputra
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6068-6078

Abstract

Hyperspectral image (HSI) has been widely adopted in many real-world applications due to its potential to provide detailed information from spectral and spatial data in each pixel. However, precise classification of an object from HSI is challenging due to complex and highly correlated features that exhibit a nonlinear relationship between the acquired spectral unique to the HSI object. In literature, many research works have been conducted to address this problem. However, the problem of processing high-dimensional data and achieving the best resolution factor for any set of regions remains to be evolved with a suitable strategy. Therefore, the proposed study introduces simplified modeling of the hyperspectral image in which precise detection of regions is carried out based on the characterization of pure signatures based on the estimation of the maximum pixel mixing ratio. Moreover, the proposed system emphasizes the pixel unmixing problem, where input data is processed concerning wavelength computation, feature extraction, and hypercube construction. Further, a non-iterative matrix-based operation with a linear square method is performed to classify the region from the input hyperspectral image. The simulation outcome exhibits efficient and precise object classification is achieved by the proposed system in terms classified HSI object and processing time.
Grouping based radio frequency identification anti-collision protocols for dense internet of things application Nnamdi H. Umelo; Nor K. Noordin; Mohd Fadlee A. Rasid; Tan K. Geok; Fazirulhisyam Hashim
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp5848-5860

Abstract

Radio frequency identification (RFID) is an important internet of things (IoT) enabling technology. In RFIDs collision occur among tags because tags share communication channel. This is called tag collision problem. The problem becomes catastrophic when dense population of tags are deployed like in IoT. Hence, the need to enhance existing dynamic frame slotted ALOHA (DFSA) based electronic product code (EPC) C1G2 media access control (MAC) protocol. Firstly, this paper validates through simulation the DFSA theory that efficiency of the RFID system is maximum when the number tags is approximately equal to the frame size. Furthermore, literature review shows tag grouping is becoming popular to improving the efficiency of the RFID system. This paper analyzes selected grouping-based algorithms. Their underlining principles are discussed including their tag estimation methods. The algorithms were implemented in MATLAB while extensive Monte Carlo simulation was performed to evaluate their strengths and weaknesses. Results show that with higher tag density, fuzzy C-means based algorithm (FCMBG) outperformed traditional DFSA by over 40% in terms of throughput rate. The results also demonstrate FCMBG bettered other grouping-based algorithms (GB-DFSA and GBSA) whose tag estimation method are based on collision slots in terms slot efficiency by over 10% and also in terms of identification time.
Recommender systems: a novel approach based on singular value decomposition Francesco Colace; Dajana Conte; Massimo De Santo; Marco Lombardi; Beatrice Paternoster; Domenico Santaniello; Carmine Valentino
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6513-6521

Abstract

Due to modern information and communication technologies (ICT), it is increasingly easier to exchange data and have new services available through the internet. However, the amount of data and services available increases the difficulty of finding what one needs. In this context, recommender systems represent the most promising solutions to overcome the problem of the so-called information overload, analyzing users' needs and preferences. Recommender systems (RS) are applied in different sectors with the same goal: to help people make choices based on an analysis of their behavior or users' similar characteristics or interests. This work presents a different approach for predicting ratings within the model-based collaborative filtering, which exploits singular value factorization. In particular, rating forecasts were generated through the characteristics related to users and items without the support of available ratings. The proposed method is evaluated through the MovieLens100K dataset performing an accuracy of 0.766 and 0.951 in terms of mean absolute error and root-mean-square error.

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