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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 2,901 Documents
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Classifying thai news headlines using an artificial neural network Chanakot, Benjamin; Sanrach, Charun
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4228

Abstract

This research aimed to measure the effectiveness of Thai news headlines classification using an artificial neural network (ANN). The headlines consisted of i) political news, ii) sports news, iii) economic news, and iv) crime news, 1,200 headlines in total. The distribution of headlines was measured by using chi-square, information gain, and term frequency inverse class frequency (TFICF). Threshold default value was set in relation to terms of headlines before cross-validation was employed to categorize the data to examine the efficiency of the model using a neural network algorithm in classifying the headlines. The investigation of the news headline classification efficiency revealed that the 15-fold data division using TFICF was the most accurate in classifying headlines, with the accuracy rate of 99.60% and F-measure rate of 99.05%. Moreover, it was found that when more news headlines were provided as the learning data, the news headline classification became more accurate. Likewise, appropriate threshold value determination facilitated the selection of appropriate features in the headlines and resulted in more effective and accurate classification. Hence, it can be concluded that headline classification will be more accurate if the appropriate amount of learning data exists, and appropriate threshold value was set.
Comparison between boost and positive output super lift Luo converters to improve the performance of photovoltaic system Ream Mohammed Jassim; Kadhim H. Hassan; Issa Ahmed Abed
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3941

Abstract

Employment DC-DC switching power converters in many different areas become is very important. In this paper, three different models of the photovoltaic solar module were proposed in order to designing, implementation, and simulated them in MATLAB/Simulink with the boost converter circuit first and then with the positive output super lift Luo (POSLL) converter circuit again. A comparison was made between the two circuits, as well as a theoretical and simulation values were made and compared between them (in the same standard conditions) for each of these selected models. So as to improving solar system performance and clarify the functions played by POSLL in power electronic circuits.
An approach of anchor link prediction using graph attention mechanism Van-Vang Le; Phuong Nguyen Huy Pham; Tran Kim Toai; Vaclav Snasel
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4274

Abstract

Nowadays social networks such as Twitter, LinkedIn, and Facebook are a popular and necessary platform. It is considered a miniature of an actual social network because of its advantages in connecting and sharing information between users. The analysis of data on online social networks has become a field that has attracted a lot of attention from the research community and anchor link prediction is one of the main research directions in this field. Depending on demand, a user can simultaneously participate in many different online social networks, anchor link prediction is a kind of task that determines the identity of a user on many different social networks. In this article, we proposed an algorithm that determines missing/future anchor links between users from two different online social networks. Our algorithm utilizes the graph attention technique to represent the source and target network into the low-dimension embedding spaces, we then apply the canonical correlation analysis to recline their embeddings into same latent spaces for final prediction.
Signal multiple encodings by using autoencoder deep learning Anaz, Ammar Sameer; Al-Ridha, Moatasem Yaseen; Al-Nima, Raid Rafi Omar
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4229

Abstract

Encryption is a substantial phase in information security. It permits only approved persons to get private information. This study suggests a signal multi-encryptions system (SMES) technique for coding and decoding signals created by a deep autoencoder network (DAN). The DAN of four layers is employed for a coding package of signals multiple times before decoding or restructuring the original signals again. The suggested SMES offers a high level of security as it can produce and exploit multiple encryptions for signals. Many statistical calculations are applied to measure the reliability of the system. The outcomes are promising where noteworthy encryptions-decryptions are obtained.
Optimization, design and control of a photovoltaic/wind turbine/battery system in Mediterranean climate conditions Nabil Mezzai; Saloua Belaid; Djamila Rekioua; Toufik Rekioua
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3872

Abstract

The goal of this study is to design and optimize a photovoltaic/wind turbine/batteries system. The application is made in the area of Bejaia (Algeria), a Mediterranean region where the solar and wind energy are extremely exploitable in this area due to its geographical location. The total incident energy approach was used to develop the device under consideration. To optimize power, the fuzzy logic control (FLC) is applied and to highlight the benefits of this maximum power point tracking (MPPT) strategy, it was compared to perturb and observe (P and O) method. A power management control has been applied. The findings from the three different days are displayed and analyzed to demonstrate the applicability of the suggested system. The examined system is assessed using the Homer software to demonstrate the best feasible integration of the several sources at the Bejaia location. There has been an increase in renewable power, which is one of the study's key novelty and objectives, so less stress on batteries in a PV/wind system. This is owing to the suggested accurate sizing procedure and to the FLC algorithm. The findings of the suggested study under various solar irradiation and wind speed velocity profiles are presented to demonstrate its applicability.
Complex predictive analysis for health care: a comprehensive review Srivastava, Dolley; Pandey, Himanshu; Agarwal, Ambuj Kumar
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4373

Abstract

Healthcare organizations accept information technology in a management system. A huge volume of data is gathered by healthcare system. Analytics offers tools and approaches for mining information from this complicated and huge data. The extracted information is converted into data which assist decision-making in healthcare. The use of big data analytics helps achievement of improved service quality and reduces cost. Both data mining and big data analytics are applied to pharma co-vigilance and methodological perspectives. Using effective load balancing and as little resources as possible, obtained data is accessible to improve analysis. Data prediction analysis is performed throughout the patient data extraction procedure to achieve prospective outcomes. Data aggregation from huge datasets is used for patient information prediction. Most current studies attempt to improve the accuracy of patient risk prediction by using a commercial model facilitated by big data analytics. Privacy concerns, security risks, limited resources, and the difficulty of dealing with massive amounts of data have all slowed the adoption of big data analytics in the healthcare industry. This paper reviews the various effective predictive analytics methods for diverse diseases like heart disease, blood pressure, and diabetes.
Design and analysis of microstrip patch antenna for 5G wireless communication systems Md. Sohel Rana; Md. Mostafizur Rahman Smieee
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.3955

Abstract

Due to lower latency, greater transmission speed, wider bandwidth, and the possibility to connect with greater multiple devices, fifth-generation (5G) networks are far better than 4G. In this study, a microstrip patch antenna operating at 28 GHz is investigated and modeled for future 5G communication technologies. The substrate used in this work for the antenna is Rogers RT/Duroid5880. Dielectric of the substrate is 2.2 and thickness is 0.3451 mm. CST software is used to simulate the antenna as it is convenient to use. From the simulation, the return loss, gain, radiation efficiency, side-lobe level was found to be -38.348 dB, 8.198dB, 77%, and -18.3 dB respectively. The result found from this simulation is better than the works took place in the past. As a result, it can be utilized as a capable candidate for 5G wireless technology. The results of this proposed antenna are superior to those of existing antennas published in recent scientific journals. As a result, it's likely that this antenna will meet the needs of 5G wireless communication systems.
Real-time multiple face mask and fever detection using YOLOv3 and TensorFlow lite platforms Ali A. Abed; Alaa Al-Ibadi; Issa Ahmed Abed
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4227

Abstract

COVID-19 has caused disruptions to many aspects of everyday life. To reduce the impact of this pandemic, its spreading must be controlled via face mask wearing. Manually mask-checking for everybody is embarrassing and uncontrollable. Hence, the proposed technique is used to help for automatic mask-checking based on deep learning platforms with real-time surveillance live infra-red (IR) camera. In this paper, two recent object detection platforms, named, you only look once version 3 (YOLOv3) and TensorFlow lite are adopted to accomplish this task. The two models are trained with a dataset consisting of images of persons with/without masks. This work is simulated with Google Colab then tested in real-time on an embedded device mated with fast GPU called Raspberry Pi 4 model B, 8 GB RAM. A comparison is made between the two models to verify their performance in relation to their precision rate and processing time. The work of this paper is also succeeded to realize multiple face masks real-time detection up to 10 facemasks in a single scene with high inference speed. Temperature is also measured using IR touchless sensor for each person with sound alarming to alert fever. The presented detector is cheap, light, small, and fast, with 99% accuracy rate during training and testing.
Energy management strategy with smart building control system to reduction electrical load using ANN Bareq Musaab Jalal; Rashid Hamid Al-Rubayi
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4087

Abstract

Buildings have long been large energy consumers, and inadequate control of heating, ventilation, and air conditioning kinds of variable refrigeration flow (HVAC-VRF) and lighting systems. To reduce energy consumption by using a smart building control system (SBCS) in a building was created using occupant control, daylight sensors, weather condition variations, load consumed, and changes in solar power. The model was tested using MATLAB/Simulink, and it was then utilized to investigate the impact of an integrated system on energy usage based on two scenarios. The first scenario was tested in a simulation of building occupant behavior, meteorological variables, daylight sensors, temperature, and load control. This resulted in energy savings for the HVAC system (23% on summer days and 16% on winter days), and lighting system energy savings (22% on summer days and 15% on winter days). In the second scenario, the building was tested to integrate PV system power with load consumption by using the artificial neural network (ANN) algorithm to manage building load consumption by PV, grid, and diesel generator. As a result, the energy savings were 56% on a summer day and 65% on a winter day of the combined energy utilized by the HVAC and lights.
Apply three metaheuristic algorithms for energy storage-based integrated power system to reduce generation cost Dao Trong Tran; Phu Trieu Ha; Hung Duc Nguyen; Thang Trung Nguyen
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4544

Abstract

This research applies new computing methods to optimize the operation of a typical hydrothermal system for one day. The system consists of one thermal power plant (TPP) and one pumped storage hydropower plant (PSHP). The main target of the research is to determine the amount of water that must be discharged or pumped back to the reservoir to reduce the total electricity production cost (TEPC) of TTP. The volumes of water storage in the reservoir at the beginning and end points of the schedule must be the same. Three meta-heuristic algorithms are applied, including Coot optimizer (COOT), aquila optimizer (AO), and particle swarm optimizations (PSO) in which COOT and AO were proposed at early 2021. The results show that the effectiveness of COOT is better than AO, PSO and several methods in previous studies. Hence, COOT is considered a powerful computing tool for the problem.

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