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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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Analysis of interference methods on transformers based on the results of dissolved gas analysis tests Yulianta Siregar; Timothy Juan Hartanto Lumbanraja
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp3672-3685

Abstract

In the operation of the power transformer, several maintenance efforts must be made to ensure the condition of the transformer is in good condition. The problems that usually arise are a thermal failure and electrical failure. The use of insulating media such as transformer oil and transformer insulation paper can be disrupted by this failure. Dissolved gas analysis, which identifies the types and concentrations of dissolved gas in transformer oil, can reveal details on fault indicators in power transformers (DGA). In this study, we used the interpretation of the IEEE std 2008-C57.104 (total dissolved combustible gas (TDCG), key gas, Rogers ratio method), the interpretation of IEC 2015-60599 (Duval triangle and basic gas ratio method), and the IEEE Std 2019-C57.104 interpretation (Duval pentagon method). The outcome of the DGA test is used to determine the conditions and indications of disturbances in the transformer for power. Using various gas analysis techniques also impacts the outcome of the fault indication. This variation has affected the types of gas used in the computation and the gas concentration limit value estimation. After the gas analysis, it was found that the oil purification process was also proven to reduce the concentration of combustible gases.
Towards a new intelligent traffic system based on deep learning and data integration Nadia Slimani; Siham Yousfi; Mustapha Amghar; Nawal Sbiti
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4649-4660

Abstract

Time series forecasting is an important technique to study the behavior of temporal data in order to forecast the future values, which is widely applied in intelligent traffic systems (ITS). In this paper, several deep learning models were designed to deal with the multivariate time series forecasting problem for the purpose of long-term predicting traffic volume. Simulation results showed that the best forecasts are obtained with the use of two hidden long short-term memory (LSTM) layers: the first with 64 neurons and the second with 32 neurons. Over 93% of the forecasts were made with less than ±2.0% error. The analysis of variances is mainly due to peaks in some extreme conditions. For this purpose, the data was then merged between two different sources: electromagnetic loops and cameras. Data fusion is based on a calibration of the reliability of the sources according to the visibility conditions and time of the day. The integration results were then compared with the real data to prove the improvement of the prediction results in peak periods after the data fusion step. 
Reinforcing optimization enabled interactive approach for liver tumor extraction in computed tomography images Jayanthi Muthuswamy; Chanda Verabadra Reddy; Rekha Narayanaswamy
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4076-4086

Abstract

Detecting liver abnormalities is a difficult task in radiation planning and treatment. The modern development integrates medical imaging into computer techniques. This advancement has monumental effect on how medical images are interpreted and analyzed. In many circumstances, manual segmentation of liver from computerized tomography (CT) imaging is imperative, and cannot provide satisfactory results. However, there are some difficulties in segmenting the liver due to its uneven shape, fuzzy boundary and complicated structure. This leads to necessity of enabling optimization in interactive segmentation approach. The main objective of reinforcing optimization is to search the optimal threshold and reduce the chance of falling into local optimum with survival of the fittest (SOF) technique. The proposed methodology makes use of pre-processing stage and reinforcing meta heuristics optimization based fuzzy c-means (FCM) for obtaining detailed information about the image. This information gives the optimal threshold value that is used for segmenting the region of interest with minimum user input. Suspicious areas are recognized from the segmented output. Both public and simulated dataset have been taken for experimental purposes. To validate the effectiveness of the proposed strategy, performance criteria such as dice coefficient, mode and user interaction level are taken and compared with state-of-the-art algorithms.
Classification of electroencephalography using cooperative learning based on participating client balancing Maytham N. Meqdad; Saif O. Husain; Alyaa Mohammed Jawad; Seifedine Kadry; Ahlam R. Khekan
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4692-4699

Abstract

Modern technologies are widely used today to diagnose epilepsy, neurological disorders, and brain tumors. Meanwhile, it is not cost-effective in terms of time and money to use a large amount of electroencephalography (EEG) data from different centers and collect them in a central server for processing and analysis. Collecting this data correctly is challenging, and organizations avoid sharing their and client information with others due to data privacy protection. It is difficult to collect these data correctly and it is challenging to transfer them to research centers due to the privacy of the data. In this regard, collaborative learning as an extraordinary approach in this field paves the way for the use of information repositories in research matters without transferring the original data to the centers. This study focuses on the use of a heterogeneous client balancing technique with an interval selection approach and classification of EEG signals with ResNet50 deep architecture. The test results achieved an accuracy of 99.14 compared to similar methods.
Cloud security: literature survey Amruta Gadad; Devi Anbusezhiyan
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4734-4742

Abstract

Today, the growth of digitalization has made the ease for livelihood for all the organizations. Cloud computing the storage provider for all the computer resources has made it easy for accessing the data from anywhere anytime. But at the same time the security for cloud data storage is the major drawback which is provided by various cryptographic algorithms. These algorithms convert the data into unreadable format, known as cipher text, Rivest, Shamir and Adleman (RSA) one of the most popularly used asymmetric algorithm. This paper gives detailed review about such different cryptographic algorithms used for the cloud data security. The comparison study is also made for the size of data and to analyze the encryption time and decryption time, which concludes that to enhance the cloud data security some addon techniques are to be used along with these cryptographic algorithms. To increase the security level and to increase the transmission speed of plaintext, integrated method will be proposed by encoding the plaintext to intermediate plaintext and then intermediate plaintext will be compressed using any one of the compression techniques to increase the compression ratio, lastly the compressed file is encrypted to further enhance the security level.
Reconfigurable negative bit line collapsed supply write-assist for 9T-ST static random access memory cell Chokkakula Ganesh; Fazal Noorbasha; Korlapati Satyanarayana Murthy
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp3747-3755

Abstract

This paper presents a reconfigurable negative bit line collapsed supply (RNBLCS) write driver circuit for the 9T Schmitt trigger-based static random-access memory (SRAM) cell (9T-ST), significantly improving write performance for real-time memory applications. In deep sub-micron technology, increasing device parameter deviations significantly reduce SRAM cells' write-ability. The proposed RNBLCS write-assist driver for 9T-ST SRAM cell has 0.84×, 0.48×, 0.27× optimized write access delay and 1.05×, 1.08×, 1.19× improvement in write static noise margin (WSNM), 1.05×, 1.13×, and 1.39× improvement in write margin (WM), 0.96×, 0.89× and 0.72× minimum write trip-point (WTP) from transient-negative bit line (Tran-NBL), capacitive charge sharing (CCS), and conventional write circuits respectively. The proposed RNBLCS is functionally verified using a synopsys custom compiler with a 16 nm BSIM4 model card for bulk complementary metal-oxide semiconductor (CMOS).
A review on detecting brain tumors using deep learning and magnetic resonance images Nawras Q. Al-Ani; Omran Al-Shamma
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4582-4593

Abstract

Early detection and treatment in the medical field offer a critical opportunity to survive people. However, the brain has a significant role in human life as it handles most human body activities. Accurate diagnosis of brain tumors dramatically helps speed up the patient's recovery and the cost of treatment. Magnetic resonance imaging (MRI) is a commonly used technique due to the massive progress of artificial intelligence in medicine, machine learning, and recently, deep learning has shown significant results in detecting brain tumors. This review paper is a comprehensive article suitable as a starting point for researchers to demonstrate essential aspects of using deep learning in diagnosing brain tumors. More specifically, it has been restricted to only detecting brain tumors (binary classification as normal or tumor) using MRI datasets in 2020 and 2021. In addition, the paper presents the frequently used datasets, convolutional neural network architectures (standard and designed), and transfer learning techniques. The crucial limitations of applying the deep learning approach, including a lack of datasets, overfitting, and vanishing gradient problems, are also discussed. Finally, alternative solutions for these limitations are obtained.
Online detection of interturn short-circuit fault in induction motor based on 5th harmonic current tracking using Vold-Kalman filter Manuel A. Mazzoletti; Francisco R. Gentile; Pablo D. Donolo; Guillermo R. Bossio
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp3593-3605

Abstract

In this paper we propose a strategy for real-time detection of interturn short-circuit faults (ISCF) on three-phase induction motor (IM) by using a Vold-Kalman filter (VKF) algorithm. ISCF produce a thermal stress into the stator winding due to large current that flows through the short-circuited turns. Therefore, incipient fault detection is required in order to avoid catastrophic failures such as phase to phase, or phase to ground faults. The strategy is based on an analytical IM model that includes a ISCF fault in any of the phase windings and considering the h-th harmonic in the voltage supply. Based on equivalent electrical circuits with harmonics in sequence components, we propose a strategy for detection of an ISCF on IM by tracking the 5th harmonic current component using a VKF algorithm. The proposed model is experimentally validated using a three-phase IM with modified stator windings to generate ISCF. Also, the IM is feeded by a programmable voltage source to synthesize distorted voltage supply with the 5th harmonic. The results demonstrated that the positive-sequence magnitude for the 5th harmonic current component is a good indicator of the fault severity once it exceeds a threshold limit value, even under load variations and unbalanced voltages.
Genetic algorithm to optimization mobility-based dengue mathematical model Tegar Arifin Prasetyo; Roberd Saragih; Dewi Handayani
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4535-4546

Abstract

Implementation of vaccines, mosquito repellents and several Wolbachia schemes have been proposed recently as strategies against dengue. Research showed that the use of vaccine and repellent is highly effective when implemented to individuals who are in area with high transmission rates, while the use of Wolbachia bacteria is strongly effective when implemented in area with low transmission rates. This research is to show a three-strategy combination to cope with the dengue using mathematical model. In dengue mathematical model construction, several parameters are not yet known, therefore a genetic algorithm method was used to estimate dengue model parameters. Numerical simulation results showed that the combination of three strategies were able to reduce the number of infected humans. The dynamic of the human population with the combination of three strategies on average was able to reduce the infected human population by 45.2% in immobility aspect. Furthermore, the mobility aspect in dengue model was presented by reviewing two areas; Yogyakarta and Semarang in Indonesia. The numerical solutions showed that the trend graph was almost similar between the two areas. With the maximum effort given, the combination control values decreased slowly until the 100th day.
Improved ciphertext-policy time using short elliptic curve Diffie–Hellman Pongpisit Wuttidittachotti; Parinya Natho
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4547-4556

Abstract

Ciphertext-policy attribute-based encryption (CP-ABE) is a suitable solution for the protection of data privacy and security in cloud storage services. In a CP-ABE scheme which provides an access structure with a set of attributes, users can decrypt messages only if they receive a key with the desired attributes. As the number of attributes increases, the security measures are strengthened proportionately, and they can be applied to longer messages as well. The decryption of these ciphertexts also requires a large decryption key which may increase the decryption time. In this paper, we proposed a new method for improving the access time to the CP using a new elliptic curve that enables a short key size to be distributed to the users that allows them to use the defined attributes for encryption and decryption. Each user has a specially created key which uses the defined attributes for encryption and decryption based on the Diffie-Hellman method. After the implement, the results show that this system saves nearly half of the execution time for encryption and decryption compared to previous methods. This proposed system provides guaranteed security by means of the elliptic curve discrete logarithmic problem.

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