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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 62 Documents
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Search results for , issue "Vol 34, No 1: April 2024" : 62 Documents clear
Fuzzy controlled modified reduced switch converter for switched reluctance motor under dynamic loading Ritika Asati; Deepak S. Bankar; Aishwarya Apte; Amit L. Nehete; Yogesh Mandake
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp50-58

Abstract

In this paper, modified reduced switch converter topology is used to drive 8/6 pole, 7500 W switched reluctance motor (SRM) with an electric vehicle (EV) load. Fuzzy logic control (FLC) is developed for the modified converter topology and its performance is compared with the proportional integral (PI) controller. Analytical equations, switching pulses and different mode of operation are presented for modified reduced switch converter using double phase magnetization scheme. The converter topology adopts a modified switching sequence i.e., magnetization then freewheeling before demagnetization. It offers lesser torque ripples, reduced phase current and need only four switches for a 4-phase SRM drive. Modified reduced switch converter is simulated in MATLAB-simulation to investigate and compare the steady state waveforms and transient speed response of the PI and FLC. Torque ripple in modified converter is 50% less than the classical converter. Peak overshoot and settling time performance of FLC is superior as compared to PI, when applied to modified converter with EV loading.
Adaptive fuzzy weighted median filter for microcalcifications detection in digital breast tomosynthesis images Syafiqah Aqilah Saifudin; Siti Noraini Sulaiman; Muhammad Khusairi Osman; Iza Sazanita Isa; Noor Khairiah A Karim; Nur Athiqah Harron
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp197-209

Abstract

Breast cancer is a global leading cause of female mortality. Digital breast tomosynthesis (DBT) is pivotal for early breast cancer detection, with microcalcifications serving as crucial indicators. However, the movement of the DBT machine introduces blurry artefacts, potentially impacting accurate diagnosis. This study addresses this challenge by proposing an adaptive fuzzy weighted median filter (AFWMF) to enhance DBT images and aid microcalcification diagnosis. AFWMF automatically determines optimal parameters based on input images, outperforming conventional methods with a threshold range (C) from peak to end of switching. Quantitative assessment reveals peak signal to noise ratio (PSNR), and mean absolute error (MAE) values of 96.2267 and 0.0000636, respectively, demonstrating a significant improvement in microcalcification detection. This study contributes an effective and adaptive enhancement technique for DBT images, promising better breast cancer diagnosis, particularly in microcalcification scenarios.
Modelling and estimating trajectory points from RTK-GNSS based on an integrated modelling approach Ravenny Sandin Nahar; Kok Mun Ng; Fadhlan Hafizhelmi Kamaruzaman; Noorfadzli Abdul Razak; Juliana Johari
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp162-172

Abstract

The sparse Gaussian process regression (GPR) has been used to model trajectory data from Real time kinematics-global navigation satellite system (RTK-GNSS). However, upon scrutinizing the model residuals; the sparse GPR model poorly fits the data and exhibits presence of correlated noise. This work attempts to address these issues by proposing an integrated modeling approach called GPR-LR-ARIMA where the sparse GPR was integrated with the linear regression with autoregressive integrated moving average errors (LR-ARIMA) to further enhance the description of the trajectory data. In this integrated approach, the predicted trajectory points from the GPR were further described by the LR-ARIMA. Simulation of the GPR-LR-ARIMA on three sets of trajectory data indicated better model fit, revealed in the normally distributed model residuals and symmetrically distributed scatter plots. Correlated noise was also successfully eliminated by the model. The GPR-LR-ARIMA outperformed both the GPR and LRARIMA by its ability to improve mean-absolute-error in 2-dimension positioning by up to 86%. The GPR-LR-ARIMA contributes to enhancement of positioning accuracy of dynamic GNSS measurements in localization and navigation system with good model fit.
Implementation of an Arabic spell checker Rafik Kassmi; Samir Mbarki; Abdelaziz Mouloudi
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp322-332

Abstract

This paper outlines the implementation of a spell checker for the Arabic language, leveraging the capabilities of NooJ and its functionality, specifically noojapply. In this paper, we shall proceed to provide clear definitions and comprehensive descriptions of several categories of spelling errors. Next, we will provide a comprehensive introduction to the NooJ platform and its command-line utility, noojapply. In the subsequent section, we shall outline the four main phases of our spell checker prototype. We intend to develop a local grammar in NooJ for the purpose of error detection. Afterwards, a morphological grammar and a local grammar will be created in NooJ with the aim of providing an exhaustive list of possible corrections. Following that, a revised algorithm will be employed to arrange these candidates in descending order of ranking. Subsequently, a web user interface will be developed to visually represent our research efforts. Finally, we will proceed to showcase a series of tests and evaluations conducted on our prototype, Al Mudaqiq.
Performance analysis of photovoltaic panel using machine learning method Ganesh S. Wahile; Srikant Londhe; Shivshankar Trikal; Chandrakant Kothare; Prateek D. Malwe; Nitin P. Sherje; Prasad D. Kulkarni; Uday Aswalekar; Chandrakant Sonawane; Mustak Maher Abdul Zahra; Abhinav Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp19-30

Abstract

Demand for energy is increasing as the world’s population grows, fossil fuels deplete on a daily basis, and climate conditions change. Renewable energy is more important than ever. Solar energy is the most accessible and cost-effective renewable energy source available today. Photovoltaic (PV) cells are the most promising way to convert solar energy into electricity. Wind speed, ambient temperature, incident radiation rate, and dust deposition are some of the internal and external variables that affect photovoltaic panel performance. Unwanted heat from the sun’s rays raises panel temperatures, reduces the amount of energy that solar cells can produce, and lowers conversion efficiency. Solar panels must be adequately cooled. The current research is focused on improving photovoltaic panel performance. The experimental system includes a fully automated photovoltaic panel, a microcontroller (NodeMCU8266), a DC pump, voltage and temperature sensors. The experiment was carried out with and without cooling of the PV panel. The findings suggest that keeping PV panel temperatures close to ambient temperatures improves performance. The Wi-Fi module collects real-time data on PV panel temperature, irradiation, ambient temperature, water temperature, and PV panel power output. The collected data was analyzed using machine learning. The PV panel’s performance was analyzed using the linear regression method.
Education and awareness: keys to solid waste reduction Laberiano Andrade-Arenas; Elizabeth Liñan Espinoza
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp520-533

Abstract

In the research, education, and awareness were focused on as essential pillars to successfully address the problem of reducing solid waste. The objective is to implement educational and awareness solutions within the community to encourage a substantial change in behavior toward the reduction of solid waste. The design thinking methodology was applied to develop effective solutions. To measure the level of public awareness, we conducted interviews using the Atlas ti22 which allowed us to triangulate with the surveys that revealed that 60% of those surveyed agreed with the policies regarding environmental impact, 55% agreed that the authorities take preventive measures regarding public health and 58% stated that the participation of the citizens in recycling programs. Then, innovative prototypes were developed that satisfied the real needs of users and experts in their evaluation, thus laying a solid foundation. It was concluded that citizens as well as authorities must be aware that by working collaboratively, we can contribute to society.
Mutual coupling reduction between antennas array for 5G mobile applications Noha Chahboun; Abderrahim Bellekhiri; Jamal Zbitou; Yassin Laaziz
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp362-369

Abstract

This paper introduces the design of a multiple-input-multiple-output (MIMO) antenna optimized for low-profile applications supporting sub-6 GHz fifth-generation (5G) wireless applications. We have started the design from a single antenna with a square patch shape, each antenna array is composed from 4-element radiators fed by using power dividers and quarter microstrip lines. Mounted on a single Rogers RT5880 substrate, the MIMO antenna functions at 3.5 GHz. In order to miniature and to decrease the mutual coupling between the both antennas array we have optimised a magnetic wall based on periodic structures permitting to decrease the mutual coupling between the both antenna array. The unit element from the wall was optimised, studied and validated in order to absorb the surface current and to enhance the isolation between the different radiating elements. The dimensions of the proposed MIMO antenna are 154×220×0.578 mm³. The MIMO antenna final circuit achieves a peak gain of 9 dBi and an isolation around -30 dB. The introduction of the magnetic wall permits to enhance the isolation between the antenna array from -20 dB to -30 dB at 3.5 GHz band. This advancement contributes to the overall performance improvement of the MIMO antenna system.
An intelligent time aware food recommender system using support vector machine Minakshi Panwar; Ashish Sharma; Om Prakash Mahela; Baseem Khan; Ahmed Ali
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp620-629

Abstract

This paper formulated a support vector machine powered time-aware food recommender system (SVMTAFRS) to recommend healthy food for the customers. The rated food item incorporates the user preference (UP) in terms of calories, nutrition factor, and all food contents required for a healthy diet. This also takes into account the user age, time of day and week day while predicting the food rating. The SVMTAFRS involves two steps for computation of user identity document (UID) and predicted food rating (PFR). UID is computed considering the customer age (CA), UP in terms of calories and suitable weight factors. PFR is computed considering the UID and time of day (TOD). PFR for week end day is computed by multiplying the PFR by week end multiplying factor (WEMF). Support vector machine (SVM) is used for recommending the suitable healthy food for customer in terms of correct values of PFR. Efficacy of PFR is tested in terms of mean absolute error (MAE) and root mean squared error (RMSE). This is established that performance of the SVMTAFRS is superior compared to the rule-based food recommender system (RBFRS).
Improving ventilation classification in under-actuated zones: a k-nearest neighbor and data preprocessing approach Yaddarabullah Yaddarabullah; Aedah Abd Rahman; Amna Saad
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp233-244

Abstract

This study investigates the use of k-nearest neighbors (k-NN) for classifying occupant positions in under-actuated zones, aiming to enhance ventilation control. The focus is on evaluating different data preprocessing techniques, particularly cumulative moving average (CMA), Kalman filtering (KF), and their combination, to boost the k-NN model's reliability and accuracy. The research uses received signal strength indicator (RSSI) data in a controlled setting. The methodology involves dividing the dataset into training and testing subsets and using root mean squared error (RMSE) to determine the best k value for model validation. The study performs a comparative analysis of the k-NN model's performance with both original and preprocessed RSSI data, focusing on metrics such as accuracy, precision, recall, F1-score, and RMSE. The findings emphasize the significant impact of the combined CMA-KF preprocessing technique in improving the model's accuracy and reliability. Specifically, this approach achieved an accuracy of 98.58%. The RMSE values are particularly noteworthy, exhibiting a perfect fit (RMSE of 0) for training data and a remarkably low RMSE of 0.119 for testing data, confirming the model's high accuracy and predictive capability.
IoT-based viscometer fabrication using the falling ball method for laboratory applications Alwi Nofriandi; Yulkifli Yulkifli; Asrizal Asrizal; Nur Anisa Sati’at
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp89-97

Abstract

This study outlines the production procedure of internet of things (IoT)- enabled viscometers designed for laboratory use. These viscometers utilize photodiode sensors, lasers, and falling ball techniques. The system is equipped with a temperature sensor that is utilized to quantify the impact of temperature on viscosity. The temperature sensor’s characterization yielded a R-square value of 0.999. The photodiode and laser sensors are utilized to operate a timer within the system, ensuring precise time measurement. The R-square value for the sensor characterization is 0.996. A viscometer equipped with an integrated IoT module for seamless wireless transmission of data. The photodiode timer sensor has an accuracy of 95.76% and a precision of 99.96%, while the temperature sensor has an accuracy of 99.43% and a precision of 99.93%. The viscometer transmits the measured viscosity data to the server using wireless technology. This IoT viscometer has the potential to enhance the efficiency and precision of liquid viscosity measurement in laboratory settings. Additionally, it enables real-time monitoring and data collection for subsequent analysis and research purposes.

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