cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 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. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 65 Documents
 3   4   5   6   7 
Search results for , issue "Vol 12, No 3: June 2023" : 65 Documents clear
Performance analysis for a suitable propagation model in outdoor with 2.5 GHz band Zaenab Shakir; Abbas Al-Thaedan; Ruaa Alsabah; Monera Salah; Ali AlSabbagh; Josko Zec
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.5006

Abstract

As demand for mobile wireless network services continues to rise, network planning and optimization significantly affect development. One of the critical elements in network planning is predicting pathloss. Thus, propagation models predict pathloss in indoor and outdoor environments. Choosing the appropriate propagation model for the area out of existing models is essential for network planning. Selected propagation models suitable with 2.5GHz, such as Friis Free Space Propagation Model (FSPL), Sandford University Interim (SUI), Ericsson, Okumura, and COST-231 HATA models, are utilized for evaluation and compared with empirical data collected from long-term evolution (LTE) networks in urban areas. The best acceptable model is chosen based on statistical results such as mean, standard deviation, and root mean square errors (RMSE). The analytical results show Cost-231 Hata model fits the empirical pathloss with a minimum RMSE of 5.27 dB.
Minimizing electricity cost by optimal location and power of battery energy storage system using wild geese algorithm Thuan Thanh Nguyen; Thang Trung Nguyen; Trung Dung Nguyen
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4779

Abstract

The mismatch between load demand and supply power may increase when distributed generation based on renewable energy sources is connected to the distribution system (DS). This paper shows the optimal battery energy storage system (BESS) placement problem on the DS to minimize the electricity cost. Diverse electricity prices are considered for normal, off-peak and peak hours in a day. Wild geese algorithm (WGA) is applied to optimize the location and power of the BESS. The problem and the efficiency of WGA is validated on the 18-bus DS four scenarios consisting of the DS without BESS placement, the DS with BESS placement, the DS existing photovoltaic system (PVS) without BESS placement and the DS existing PVS with BESS placement. The numerical results show that optimal BESS placement is an effective solution for minimizing electricity cost on the DS with and without PVS. In addition, the results have also shown that WGA is a potential method for the BESS placement problem.
Five parameters extraction of single diode PV model by metaheuristic optimization method by identified built-up data Supriya R. Patil; Prakash G. Burade; Deepak Prakash Kadam
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4876

Abstract

Precision calculation of unknown photovoltaic (PV) modules or single diode models for PV cell specifications under various environmental conditions is needed to build a sunlight-based PV framework. Installing a PV system requires knowledge of all parameters, modeling, and optimization techniques because PV system analysis and configuration help generate renewable energy. This concept requires accurate modeling and calculation of identified and unknown parameters. The single-diode model is simple and accurate for different mathematical equations. Streamlining calculations requires distinguishing this nonlinear model. The current investigation calculated five unknown parameters and compared them with particle swarm optimization (PSO) and wind-driven optimization (WDO) optimization results. The said approach utilizes MATLAB software, analytical as well as optimization methods, and manufacturing data. The suggested method is simple, fast, and accurate for calculating diode ideality factor (A), output currents (Io), series resistance (Rs), Shunt resistance (Rsh), and photocurrent (Iph).
Investigation of coupling loss caused by misalignment in optical fiber Md Ashraful Haque; Mohd Azman Zakariya; Narinderjit Singh Sawaran Singh; Liton Chandra Paul; Md. Fatin Ishraque
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4552

Abstract

In a fiber optic communication system, optical fiber is used as a transmission medium consisting of a flexible filament that guides the optical signal to be transmitted from the transmitter to the receiver or vice versa. Like any other communication medium, the optical fiber cable faces some losses that can be caused by the material and length of the fiber. One of the main reasons for losses in optical communication systems is misalignment during the fiber to fiber joining process. This type of loss is also known as coupling loss, which is caused by an imperfect physical connection between two fibers. The coupling losses are most often caused by three misalignment issues: end gap displacement, lateral displacement, and angular displacement. The main goal of this article is to investigate coupling loss caused by misalignment in optical fiber using the Modicom 6 module. Before we can find a way to reduce the coupling losses in the fiber optic system, we need to have a concrete idea about the nature of coupling losses due to misalignment. An ideal fiber coupler should not lose light and should be insensitive to light dispersion.
Reliability enhancement of radial distribution system by placing the reactive power compensators and distribution systems Manjunatha Babu Pattabhi; Krishna Shanmukha Sundar; Bengaluru Rangappa Lakshmikantha
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4515

Abstract

Distribution systems (DSs) are highly stressed with addition of newer loads like electric vehicle charging stations and lower scope for expansion due to urbanization. Any line outage could cause interruption to major loads. Reliability studies have gained importance for lowering the frequency and lowering the duration of interruption for supply systems. In this paper a bi-stage method for optimum placement of reactive power compensation devices and distributed generations (DGs) for enhancing voltage stability and system reliability. A new method named delta analysis method is used to optimally locate the reactive power compensation devices and DGs. IEEE-33 radial DS, which is taken as experimental system. Based on the study, the fixing of reactive power compensation devices and DGs are to increase voltage outline of buses and decrease power fatalities. After the placement of DGs, the enhancement in reliability indices following line contingency is studied using MATLAB simulation.
Curriculum learning based overcomplete U-Net for liver tumor segmentation from computed tomography images Bindu Madhavi Tummala; Soubhagya Sankar Barpanda
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4676

Abstract

In this paper, we have proposed an overcomplete U-Net to perform liver tumor segmentation jointly using a curriculum learning strategy. Liver tumor segmentation is the most prominent and primary step in treating liver cancer and can also help doctors with proper diagnosis and therapy planning. However, it is challenging because of variations in shape, position, and depth of tumors and adjacent boundaries with internal organs around the liver. We have presented a promising solution by designing a U-Net-based segmentation network with two branches: an overcomplete branch to fine grade the small structures and an undercomplete branch to fine grade the high-level structures. This combination allows the network to learn all types of tumor artifacts more accurately. We also changed the conventional learning paradigm to curriculum learning where the input images are fed to the network from easy to hard ones to achieve faster convergence. Finally, our network segments the tumors directly from the whole medical images without the need for segmented liver region of interests (ROIs). The proposed network achieved a DICE score of 75% in tumor segmentation which is a decent value when compared with some existing deep learning methods for liver tumor segmentation.
Identifying clickbait in online news using deep learning Andry Chowanda; Nadia Nadia; Lie Maximilianus Maria Kolbe
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4444

Abstract

Several industries use clickbait techniques as their strategy to increase the number of readers for their news. Some news companies implement catchy headlines and images in their news article links, with the expectation that the readers will be interested in reading the news and click the provided link. The majority of the news is not hoax news. However, the content might not be as grand as the catchy headlines and images provided to the readers. This research aims to explore the classification model using machine learning to identify if the headlines are classified as clickbait in online news. This research explores several machine learning techniques to classify clickbait in online news and comprehensively explain the results. Several popular machine learning techniques were implemented and explored in this research. The results demonstrate that the model trained with fast large margin provides the best accuracy and classification error (90% and 10%, respectively). Moreover, to improve the performance, bidirectional encoder representations from transformers architecture was used to model clickbait in online news. The best BERT model achieved 98.86% in the test accuracy. BERT model requires more time to train (0.9 hour) compared to machine learning (0.4 hour).
Real-time monitoring system based on integration of internet of things and global system of mobile using Raspberry Pi Hamzah H. Qasim; Ali M. Jasim; Khalid A. Hashim
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4699

Abstract

Security and safety of homes remain critical issues in all countries. The majority of individuals have to deal with significant issues like fire and theft at some point in their lives, particularly in families that spend the majority of their time and engage in most of their activities outside the house. There is a pressing need to use cutting-edge technology in order to upgrade and strengthen the security system, as well as to remotely monitor the living environment for potential mishaps. In this paper, we proposed two integrated techniques, which are the internet of things (IoT) and short message service (SMS), to monitor the home for hazards to take the necessary actions, by Raspberry Pi 4 model B as a controller and phone app to monitor. Global system of mobile (GSM) sends SMS alerts to users, and the Blynk application monitors the data of sensors. Our outcome of this demonstrates that the proposed had the capability and high efficiency to monitor and detect undesirable situations in real-time before disasters occur.
Comparative analysis of predictive machine learning algorithms for diabetes mellitus Kirti Kangra; Jaswinder Singh
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4412

Abstract

Diabetes mellitus (DM) is a serious worldwide health issue, and its prevalence is rapidly growing. It is a spectrum of metabolic illnesses defined by perpetually increased blood glucose levels. Undiagnosed diabetes can lead to a variety of problems, including retinopathy, nephropathy, neuropathy, and other vascular abnormalities. In this context, machine learning (ML) technologies may be particularly useful for early disease identification, diagnosis, and therapy monitoring. The core idea of this study is to identify the strong ML algorithm to predict it. For this several ML algorithms were chosen i.e., support vector machine (SVM), Naïve Bayes (NB), K nearest neighbor (KNN), random forest (RF), logistic regression (LR), and decision tree (DT), according to studied work. Two, Pima Indian diabetic (PID) and Germany diabetes datasets were used and the experiment was performed using Waikato environment for knowledge analysis (WEKA) 3.8.6 tool. This article discussed about performance matrices and error rates of classifiers for both datasets. The results showed that for PID database (PIDD), SVM works better with an accuracy of 74% whereas for Germany KNN and RF work better with 98.7% accuracy. This study can aid healthcare facilities and researchers in comprehending the value and application of ML algorithms in predicting diabetes at an early stage.
A novel pulse charger with intelligent battery management system for fast charging of electric vehicle Sunil Somnath Kadlag; Mohan P. Thakre; Rahul Mapari; Rakesh Shriwastava; Pawan C. Tapre; Deepak P. Kadam
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4890

Abstract

Electric vehicles contribute a major role in building an eco-friendly environment. Li-ion batteries are most widely used in electric vehicles. It is very important to maintain the operation of Li-ion batteries within their “safety operation area (SOA)”. Hence implementing a battery management system (BMS) becomes a necessity while using Li-ion batteries. This paper proposes an intelligent BMS for electric vehicles using proportional integral derivative (PID) control action along with artificial neural network (ANN). It prefers the improved pulse charging technique. The design consists of a battery pack containing four 12 V Li-ion batteries, MOSFETs, Arduino Uno, a transformer, a temperature sensor, a liquid-crystal displays (LCD), a cooling fan, and four relay circuit are used. Arduino Uno is used as a master controller for controlling the whole operation. Using this design approximately 38 minutes are required to fully charge the battery. Implementation results validate the system performance and efficiency of the design.

Page 6 of 7 | Total Record : 65

 3   4   5   6   7 

Filter by Year

2023 2023


Reset
Filter By Issues
All Issue Vol 14, No 6: December 2025 Vol 14, No 5: October 2025 Vol 14, No 4: August 2025 Vol 14, No 3: June 2025 Vol 14, No 2: April 2025 Vol 14, No 1: February 2025 Vol 13, No 6: December 2024 Vol 13, No 5: October 2024 Vol 13, No 4: August 2024 Vol 13, No 3: June 2024 Vol 13, No 2: April 2024 Vol 13, No 1: February 2024 Vol 12, No 6: December 2023 Vol 12, No 5: October 2023 Vol 12, No 4: August 2023 Vol 12, No 3: June 2023 Vol 12, No 2: April 2023 Vol 12, No 1: February 2023 Vol 11, No 6: December 2022 Vol 11, No 5: October 2022 Vol 11, No 4: August 2022 Vol 11, No 3: June 2022 Vol 11, No 2: April 2022 Vol 11, No 1: February 2022 Vol 10, No 6: December 2021 Vol 10, No 5: October 2021 Vol 10, No 4: August 2021 Vol 10, No 3: June 2021 Vol 10, No 2: April 2021 Vol 10, No 1: February 2021 Vol 9, No 6: December 2020 Vol 9, No 5: October 2020 Vol 9, No 4: August 2020 Vol 9, No 3: June 2020 Vol 9, No 2: April 2020 Vol 9, No 1: February 2020 Vol 8, No 4: December 2019 Vol 8, No 3: September 2019 Vol 8, No 2: June 2019 Vol 8, No 1: March 2019 Vol 7, No 4: December 2018 Vol 7, No 3: September 2018 Vol 7, No 2: June 2018 Vol 7, No 1: March 2018 Vol 6, No 4: December 2017 Vol 6, No 3: September 2017 Vol 6, No 2: June 2017 Vol 6, No 1: March 2017 Vol 5, No 4: December 2016 Vol 5, No 3: September 2016 Vol 5, No 2: June 2016 Vol 5, No 1: March 2016 Vol 4, No 4: December 2015 Vol 4, No 3: September 2015 Vol 4, No 2: June 2015 Vol 4, No 1: March 2015 Vol 3, No 4: December 2014 Vol 3, No 3: September 2014 Vol 3, No 2: June 2014 Vol 3, No 1: March 2014 Vol 2, No 4: December 2013 Vol 2, No 3: September 2013 Vol 2, No 2: June 2013 Vol 2, No 1: March 2013 Vol 1, No 4: December 2012 Vol 1, No 3: September 2012 Vol 1, No 2: June 2012 Vol 1, No 1: March 2012 List of Accepted Papers (with minor revisions) More Issue