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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 112 Documents
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Search results for , issue "Vol 12, No 5: October 2022" : 112 Documents clear
Energy efficient and privacy protection window system for smart home using polymer-dispersed liquid crystals glass Adam Reda Hasan Alhawari; Abdulkarem Hussien Almawgani; Hisham Alghamidi; Ayman Taher Hindi; Azzan Alyami; Abdulrahman Alyami; Yahya Aldaweis; Mahdi Algannas; Abdultawab Qahtan
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5600-5608

Abstract

Hot climates smart home system is steadily advancing recently. The main concern is energy efficiency particularly in the Arabian Gulf region. Privacy is another prioritized concern culturally. This project presents a solution for both demanded priorities implementing a polymer-dispersed liquid crystals (PDLC) glass system. The proposed design was validated via a developed prototype that was measured experimentally. Its experimental results show about 39% improvement in energy saving compared to conventional systems without depriving the indoor-outdoor connections and the privacy of the smart home inhabitants. Furthermore, it also achieves several other additional goals, for instance, decreasing cost as well as wasted energy by automatically off the lighting and air conditioning systems whenever they are not in used. Moreover, it is also capable to significantly reduce the risk of harmful exposure to ultraviolet A/B (UVA/B) of solar radiations, which conventional curtains are not capable to protect.
Sentiment analysis on Bangla conversation using machine learning approach Mahmudul Hassan; Shahriar Shakil; Nazmun Nessa Moon; Mohammad Monirul Islam; Refath Ara Hossain; Asma Mariam; Fernaz Narin Nur
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5562-5572

Abstract

Nowadays, online communication is more convenient and popular than face-to-face conversation. Therefore, people prefer online communication over face-to-face meetings. Enormous people use online chatting systems to speak with their loved ones at any given time throughout the world. People create massive quantities of conversation every second because of their online engagement. People's feelings during the conversation period can be gleaned as useful information from these conversations. Text analysis and conclusion of any material as summarization can be done using sentiment analysis by natural language processing. The use of communication for customer service portals in various e-commerce platforms and crime investigations based on digital evidence is increasing the need for sentiment analysis of a conversation. Other languages, such as English, have well-developed libraries and resources for natural language processing, yet there are few studies conducted on Bangla. It is more challenging to extract sentiments from Bangla conversational data due to the language's grammatical complexity. As a result, it opens vast study opportunities. So, support vector machine, multinomial naïve Bayes, k-nearest neighbors, logistic regression, decision tree, and random forest was used. From the dataset, extracted information was labeled as positive and negative.
Optimized textural features for mass classification in digital mammography using a weighted average gravitational search algorithm Oludare Yinka Ogundepo; Isaac Ozovehe Avazi Omeiza; Jonathan Ponmile Oguntoye
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5001-5013

Abstract

Early detection of breast cancer cells can be predicted through a precise feature extraction technique that can produce efficient features. The application of Gabor filters, gray level co-occurrence matrices (GLCM) and other textural feature extraction techniques have proven to achieve promising results but were often characterized by a high false-positive rate (FPR) and false-negative rate (FNR) with high computational complexities. This study optimized textural features for mass classification in digital mammography using the weighted average gravitational search algorithm (WA-GSA). The Gabor and GLCM features were fused and optimized using WA-GSA to overcome the weakness of the textural feature techniques. With support vector machine (SVM) used as the classifier, the proposed algorithm was compared with commonly applied techniques. Experimental results show that the SVM with WA-GSA features achieved FPR, FNR and accuracy of 1.60%, 9.68% and 95.71% at 271.83 s, respectively. Meanwhile, SVM with Gabor features achieved FPR, FNR and accuracy of 3.21%, 12.90% and 93.57% at 2351.29 s, respectively, while SVM with GLCM features achieved FPR, FNR and accuracy of 4.28%, 18.28% and 91.07% at 384.54 s, respectively. The obtained results show the prevalence of the proposed algorithm, WA-GSA, in the classification of breast cancer tumor detection.
Electromagnetic interference measurement for axle counters light rapid transit railway in Indonesia Yudhistira Yudhistira; Yoppy Yoppy; Elvina Trivida; Tyas Ari Wahyu Wijanarko; Hutomo Wahyu Nugroho; Dwi Mandaris
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4632-4639

Abstract

The measurement and analysis of electromagnetic interference (EMI) from light rapid transit (LRT) axle counters against magnetic field interference in Indonesia has been carried out. The low-cost magnetic sensors were developed according to the British Standard (BS) EN 50592:2016. The measurement setup and magnetic field limit were based on the British Standard EN 50592:2016 and ERA/ERTMS/033281 standard. Two frequency range of the measurements, lower and higher frequencies with two different train running mode, acceleration mode and deceleration mode were applied in this research. The results in lower frequency range (10 to 100 kHz) were very close to the limit value in both acceleration and deceleration mode, especially at the 30 to 50 kHz for the y and z directions. Although there may possibly magnetic interference at low frequencies, most of the magnetic field emissions were still in acceptable range.
Short term residential load forecasting using long short-term memory recurrent neural network Amgad Muneer; Rao Faizan Ali; Ahmed Almaghthawi; Shakirah Mohd Taib; Amal Alghamdi; Ebrahim Abdulwasea Abdullah Ghaleb
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5589-5599

Abstract

Load forecasting plays an essential role in power system planning. The efficiency and reliability of the whole power system can be increased with proper planning and organization. Residential load forecasting is indispensable due to its increasing role in the smart grid environment. Nowadays, smart meters can be deployed at the residential level for collecting historical data consumption of residents. Although the employment of smart meters ensures large data availability, the inconsistency of load data makes it challenging and taxing to forecast accurately. Therefore, the traditional forecasting techniques may not suffice the purpose. However, a deep learning forecasting network-based long short-term memory (LSTM) is proposed in this paper. The powerful nonlinear mapping capabilities of RNN in time series make it effective along with the higher learning capabilities of long sequences of LSTM. The proposed method is tested and validated through available real-world data sets. A comparison of LSTM is then made with two traditionally available techniques, exponential smoothing and auto-regressive integrated moving average model (ARIMA). Real data from 12 houses over three months is used to evaluate and validate the performance of load forecasts performed using the three mentioned techniques. LSTM model has achieved the best results due to its higher capability of memorizing large data in time series-based predictions.
Fuzzy logic-based energy management strategy on dual-source hybridization for a pure electric vehicle Hatim Jbari; Rachid Askour; Badr Bououlid Idrissi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4903-4914

Abstract

This paper presents a fuzzy logic controller (FLC) based energy management strategy (EMS), combined with power filtering for a pure electric vehicle. The electrical power supply is provided by a hybrid energy storage system (HESS), including Li-Ion battery and supercapacitors (SCs), adopting a fully active parallel topology. The vehicle model was organized and constructed using the energetic macroscopic representation (EMR). The main objective of this work is to ensure an efficient power distribution in the proposed dual source, in order to reduce the battery degradation. To evaluate the impact of the developed design and the efficiency of the developed EMS, the proposed FLC strategy is compared to a classical EMS using SCs-filtering strategy and architecture based on battery storage model. To validate the proposed topology, simulation results are provided for the new European driving cycle (NEDC) using MATLAB/Simulink environment.
Hybrid winding function method for inductance analysis of a line start synchronous reluctance machine Redjem Rebbah; Mohamed Yazid Kaikaa; Loubna Boudjelida
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4841-4851

Abstract

In recent years, there has been renewed interest in line-start synchronous reluctance machines (LSSRMs) due to their simple construction, magnet-free rotor, and low cost. To improve control performance, design optimization, and fault diagnostic analysis of these machines, it requires accurate estimation of their electromagnetic characteristics using detailed and time-consuming finite element analyses (FEAs). In this paper, inductances and electromagnetic torque of the LSSRM were calculated using the combination of winding function analysis and conformal mapping instead of FEA. The hybrid approach can be applied to the prediction of motor behavior, taking into account all space harmonics of the air-gap permeance without any restriction on the rotor saliencies and topologies. The influence of the core saturation, stator slots, and rotor bars were also considered. The results obtained by simulations were compared with FEA in terms of accuracy and computational time.
Strategy to reduce transient current of inverter-side on an average value model high voltage direct current using adaptive neuro-fuzzy inference system controller Ginarsa, I Made; Ari Nrartha, I Made; Budi Muljono, Agung; Sultan, Sultan; Nababan, Sabar
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4790-4800

Abstract

Growing-up of high voltage direct current (HVDC) penetration into modern power systems (PS) makes difficulty on the PS operation. The HVDC produces high and slow transient current (TC) at start-time, especially for higher up-ramp rate (Urr>20 pu/s). Its condition makes the HVDC cannot be linked and synchronized into the PS rapidly. A strategy to reduce the TC is proposed by an adaptive network based fuzzy inference system (ANFIS) control on inverter HVDC-link to cope up this problem. The ANFIS control is tuned with the help of conventional control in various train-data by using offline mode. Response of ANFIS scheme is improved by suppressing TC at the values of 3.75% for the both phases (A and C), and 3.95% for phase B, for the Urr=30 pu/s. While the conventional control achieved at 9.1% for the both phases (A and B), and 9.2% for the phase C. The ANFIS control gives shorter settling time (0.553 s) than the conventional control (0.584 s) for all phases. The proposed control is more effective than the conventional control at all the scenario.
A predictive sliding mode control for quadrotor’s trackingtrajectory subject to wind gusts and uncertainties Dounia Meradi; Zoubir Abdeslem Benselama; Ramdane Hedjar
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4861-4875

Abstract

In this paper, a predictive sliding mode control (PSMC) strategy for the quadrotors tracking trajectory problem is proposed. This strategy aims to combine the advantages of sliding mode control (SMC) and non-linear model predictive control (NMPC) to improve the tracking control performance for quadrotors in terms of optimality, inputs/states constraints satisfaction, and strong robustness against disturbances. A comparative study of three popular controllers: the SMC, NMPC, and the integral backstepping control (IBC) is performed with different criteria. Accordingly, IBC and SMC show less computational time and strong robustness, while NMPC has minimum control effort. The discrete Dryden turbulence model is used as a benchmark model to represent the wind effect on the trajectory tracking accuracy. The effectiveness of the proposed method PSMC has been proven and compared with discrete-time slidingmode control (DSMC) and NMPC in several scenarios. Simulation results show that under both wind turbulence and time-variant uncertainties, the PSMC outperforms the other controllers by providing simultaneously disturbance rejection and guarantee that the control inputs are within bounded constraints.
In-wheel, outer rotor, permanent magnet synchronous motor design with improved torque density for electric vehicle applications Mustafa Yaseen Bdewi; Mohammed Moanes Ezzaldean Ali; Ahmed Mahmood Mohammed
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4820-4831

Abstract

In electric vehicle applications, the torque density of electric motor plays an important role in improving the performance of the vehicle. The main objective of this paper is to investigate a possible method for improving the torque density of a permanent magnet synchronous motor used in electric vehicles. At the same time, other machine specifications were taken into account and kept within the acceptable level. This was achieved by incorporating performance enhancement strategies such as investigating ofhigh-efficient winding topology for the motor’s stator to give the highest winding factor and optimizing the machine dimensions to achieve the best performance. MagNet 7.4.1 software package with static and transient finite element method solver was used for implementing the proposed design. The results showed a significant improvement in the torque density with keeping the overall machine performance.

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