European Journal of Electrical Engineering and Computer Science

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EISSN : 2506-9853
Total articles ≅ 175
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A. Paci, R. Bualoti, M. Çelo
European Journal of Electrical Engineering and Computer Science, Volume 5, pp 1-8; doi:10.24018/ejece.2021.5.3.264

Abstract:
The most fundamental problems in the distribution system are the quality, the continuity, and the power supply. Political and economic changes were accompanied by changes in the structure of the electric load in the distribution network. Lack of investment and aging of the distribution company assets was accompanied by a decrease in the reliability of the distribution system. Identification and classification of assets from the point of view of their maintenance and replacement was one of the problems that were posed to the engineers. Fuzzy logic can be successfully used to evaluate distribution system reliability indices. In this paper fuzzy logic is used to evaluate the distribution system reliability indices of lines and transformers using six input variables. These variables considered the most important are: Age, Operation, Maintenance, Electrical current loading, Exposure and Weather conditions (Wind or Temperature). The fuzzy inferences knowledge-based IF-THEN rule is developed using Matlab Fuzzy software. The detailed analysis of the fuzzy system surfaces shows that the factors taken in consideration are dynamically and accurately connected to each other. The constructed rules based in engineering experience accurately represent the Reliability Indices.
Mai Shawkat, Mahmoud Badawi, Ali I. Eldesouky
European Journal of Electrical Engineering and Computer Science, Volume 5, pp 5-12; doi:10.24018/ejece.2021.5.2.304

Abstract:
The global pandemic of new coronaviruses (COVID-19) has infected many people around the world and became a worldwide concern since this disease caused illness and deaths. The vaccine and drugs are not scientifically established, but patients are recovering with antibiotic drugs, antiviral medicine, chloroquine, and vitamin C. Now it is obvious to the world that a quicker and faster solution is needed for monitoring and combating the further spread of COVID-19 worldwide, using non-clinical techniques, for example, data mining tools, enhanced intelligence, and other artificial intelligence technologies. In this paper, association rule mining is developing for the frequent itemsets discovery in COVID-19 datasets, and the extraction of effective association relations between them. This is done by demonstrates the analysis of the Coronavirus dataset by using the Apriori_Association_Rules algorithm. It involves a scheme for classification and prediction by recognizing the associated rules relating to Coronavirus. The major contribution of this study employment determines the effectiveness of the Apriori_Association_Rules algorithm towards a classification of medical reports. The experimental results provide evidence of the Apriori_Association_Rules algorithm regarding the execution time, memory consumption, and several associated rules that reflect its potential applications to different contexts. Therefore, the Apriori_Association_Rules algorithm will be very useful in healthcare fields to demonstrate the latest developments in medical studies fighting COVID-19.
Prakash Kanade, Jai Prakash Prasad
European Journal of Electrical Engineering and Computer Science, Volume 5, pp 13-18; doi:10.24018/ejece.2021.5.2.303

Abstract:
MAR (Mobile Augmented Reality) is delegated an innovation that gives similar capacities as AR (Augmented Reality), yet without the actual limitations of the area of an exploration office or testing region. A Location-Based Service (LBS) is an application for portable figuring which gives clients administrations dependent on their topographical area. Area based administrations are getting progressively famous with the ascent of cell phones with an ever increasing number of highlights (particularly Apple's iPhone and Android-based gadgets). Increasingly more substance is improved with geo-information and can subsequently be seen in a virtual climate, however in real, portable conditions and in a setting delicate way fit to the requirements of the client. The definition and advantages of versatile increased reality and area based administrations and the mix of portable enlarged reality and area based administrations are broke down in this article. The issues are examined alongside the upsides and downsides.
Bisma Imtiaz, Imran Zafar, Cui Yuanhui
European Journal of Electrical Engineering and Computer Science, Volume 5, pp 18-25; doi:10.24018/ejece.2021.5.2.309

Abstract:
Due to the rapid increase in energy demand with depleting conventional sources, the world’s interest is moving towards renewable energy sources. Microgrid provides easy and reliable integration of distributed generation (DG) units based on renewable energy sources to the grid. The DG’s are usually integrated to microgrid through inverters. For a reliable operation of microgrid, it must have to operate in grid connected as well as isolated mode. Due to sudden mode change, performance of the DG inverter system will be compromised. Design and simulation of an optimized microgrid model in MATLAB/Simulink is presented in this work. The goal of the designed model is to integrate the inverter-interfaced DG’s to the microgrid in an efficient manner. The IEEE 13 bus test feeder has been converted to a microgrid by integration of DG’s including diesel engine generator, photovoltaic (PV) block and battery. The main feature of the designed MG model is its optimization in both operated modes to ensure the high reliability. For reliable interconnection of designed MG model to the power grid, a control scheme for DG inverter system based on PI controllers and DQ-PLL (phase-locked loop) has been designed. This designed scheme provides constant voltage in isolated mode and constant currents in grid connected mode. For power quality improvement, the regulation of harmonic current insertion has been performed using LCL filter. The performance of the designed MG model has been evaluated from the simulation results in MATLAB/ Simulink.
Thabat Thabet, John Woods
European Journal of Electrical Engineering and Computer Science, Volume 5, pp 6-12; doi:10.24018/ejece.2021.5.2.305

Abstract:
The technology of wireless power transfer using magnetic resonance coupling has become a subject of interest for researchers with the proliferation of mobile. The maximum efficiency is achieved at a specific gap between the resonators in the system. However, the resonance frequency splits as the gap declines or gets smaller. Different methods have been studied to improve this such as frequency tracking and impedance matching, including capacitive tuning. However, the system has to maintain the same working frequency to avoid moving out of the license exempt industrial, scientific, and medical (ISM) band; and the efficiency must be as large as possible. In this paper, a symmetric capacitance tuning method is presented to achieve these two conditions and solve the splitting problem. In the proposed method, the maximum efficiency at one of the splitting frequencies is moved to match the original resonance frequency. By comparison to other works, both simulation and experiment show considerable improvements for the proposed method over existing frequency tracking and impedance matching methods. The paper also presents a proposal to apply this method automatically which can achieve wireless charging for electronic applications with high efficiency and through variable distance.
Prakash Kanade, Fortune David, Sunay Kanade
European Journal of Electrical Engineering and Computer Science, Volume 5, pp 36-40; doi:10.24018/ejece.2021.5.2.314

Abstract:
To avoid the rising number of car crash deaths, which are mostly caused by drivers' inattentiveness, a paradigm shift is expected. The knowledge of a driver's look area may provide useful details about his or her point of attention. Cars with accurate and low-cost gaze classification systems can increase driver safety. When drivers shift their eyes without turning their heads to look at objects, the margin of error in gaze detection increases. For new consumer electronic applications such as driver tracking systems and novel user interfaces, accurate and effective eye gaze prediction is critical. Such systems must be able to run efficiently in difficult, unconstrained conditions while using reduced power and expense. A deep learning-based gaze estimation technique has been considered to solve this issue, with an emphasis on WSN based Convolutional Neural Networks (CNN) based system. The proposed study proposes the following architecture, which is focused on data science: The first is a novel neural network model that is programmed to manipulate any possible visual feature, such as the states of both eyes and head location, as well as many augmentations; the second is a data fusion approach that incorporates several gaze datasets. However, due to different factors such as environment light shifts, reflections on glasses surface, and motion and optical blurring of the captured eye signal, the accuracy of detecting and classifying the pupil centre and corneal reflection centre depends on a car environment. This work also includes pre-trained models, network structures, and datasets for designing and developing CNN-based deep learning models for Eye-Gaze Tracking and Classification.
Emmanuel Gbenga Dada, Hurcha Joseph Yakubu, David Opeoluwa Oyewola
European Journal of Electrical Engineering and Computer Science, Volume 5, pp 30-35; doi:10.24018/ejece.2021.5.2.313

Abstract:
Rainfall prediction is an important meteorological problem that can greatly affect humanity in areas such as agriculture production, flooding, drought, and sustainable management of water resources. The dynamic and nonlinear nature of the climatic conditions have made it impossible for traditional techniques to yield satisfactory accuracy for rainfall prediction. As a result of the sophistication of climatic processes that produced rainfall, using quantitative techniques to predict rainfall is a very cumbersome task. The paper proposed four non-linear techniques such as Artificial Neural Networks (ANN) for rainfall prediction. ANN has the capacity to map different input and output patterns. The Feed Forward Neural Network (FFNN), Cascade Forward Neural Network (CFNN), Recurrent Neural Network (RNN), and Elman Neural Network (ENN) were used to predict rainfall. The dataset used for this work contains some meteorological variables such as temperature, wind speed, humidity, rainfall, visibility, and others for the year 2015-2019. Simulation results indicated that of all the proposed Neural Network (NN) models, the Elman NN model produced the best performance. We also found out that Elman NN has the best performance for the year 2018 (having the lowest RMSE, MSE, and MAE of 6.360, 40.45, and 0.54 respectively). The results indicated that NN algorithms are robust, dependable, and reliable algorithms that can be used for daily, monthly, or yearly rainfall prediction.
Adedotun O. Owojori, Jane O. O. Mebawondu, Jacob O. Mebawondu
European Journal of Electrical Engineering and Computer Science, Volume 5, pp 55-62; doi:10.24018/ejece.2021.5.2.318

Abstract:
Out of seven billion of the world’s population, two billion and two million that amounts to 31.43% have visual impairment or blindness according to the World Health Organization (WHO) statistics report. Hence, the need to develop a wearable device with reduced size, efficient power usage, and for more comfortability of the visually impaired or blind people. This work aims at designing an obstacle detection system using an ultrasonic sensor interfaced with an Arduino board to track location, alert patient, and send location messages of visually impaired patient to guardians as a feedback mechanism using a GPRS and GSM module. The C programming language was used as the instruction code to interface Arduino device to carry out given tasks. At the design level, the circuit was first tested on Proteus software for simulation purposes before its hardware implementation. The results obtained from the test show the variation of distance as the patient approaches the obstacle, and messages received when a fix was obtained. This design concept would help reduce danger across the way of those with sight defects and allow them to go to familiar places without any aid smoothly.
Seema P. Nehete, Satish R. Devane
European Journal of Electrical Engineering and Computer Science, Volume 5, pp 1-5; doi:10.24018/ejece.2021.5.2.284

Abstract:
Recommendation system (RS) help user for purchasing the right product of their interest within the affordable right price. Presently many RS make use of only filtering methods to recommend products to the user which is not taking care of the quality of products. Quality of products can be found from textual reviews available on various e-commerce websites and hence this RS performs Sentiment Analysis (SA)of extracted relevant textual reviews along with Collaborative Filtering (CF) to give accurate and good quality recommendations to the user. Reviews are analyzed using optimized Artificial Neural Network (ANN) which shows notified improvement than traditional ANN on real-time extracted data of reviews.CF performance is proved by using the standard dataset of movilense used in many research papers. Results show high recall and accuracy of CF for the recommendation of products to the target user.
Thenmalar Kaliannan, Johny Renoald Albert, D. Muhamadha Begam, P. Madhumathi
European Journal of Electrical Engineering and Computer Science, Volume 5, pp 19-27; doi:10.24018/ejece.2021.5.2.315

Abstract:
Pulse width modulation (PWM) is a powerful technique employed in analog circuit convert with a microprocessor based digital output. Besides, Pseudo Random Multi Carrier (PRMC) involves in two random PWM strategies to minimize the harmonic order for 9- level cascaded multilevel H-bridge (CHB) inverter and 9-level Modular Multilevel inverter are introduced. The design mainly focuses on the (Pulse Width Modulation) PWM method, in which two nearest voltage levels are approached in estimated output voltage prediction based on the Partial swarm optimization (PSO) algorithm, and it conveys a random variation in the pulse position of output by Pseudo Random Multi Carrier- Pulse Width Modulation (PRMC-PWM). The CHB and the Modular inverters generate low distortion output by using PMRC. The simulation and prototype circuit are developed for the nine level output using sixteen switches and ten with Resistive-Inductive (R-L) load variation condition. The power quality is improved in CHB and Modular inverter (MoI) with minimized harmonics in various modulation index (MI) as varied from 0.1 up to 0.8. The circuit is designed by using a Field Programmable Gate Array (FPGA), Implementing a PSO algorithm for both CHB, and MoI are proposed. The comparisons of results are verified with lower order harmonics and find the best switching angle across the MLI switches. Modular inverter furthermore investigates with PRMC, Random Nearest level (RNL) modulation scheme are presented, and the proposed circuit is along with the respective degree of the output voltage were synthesized in non-linear load by the development of reactive power across a motor load.
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