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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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Braille letter recognition in deep convolutional neural network with horizontal and vertical projection Rahmat, Romi Fadillah; Purnamawati, Sarah; Mardianto, Willy; Faza, Sharfina; Sulaiman, Riza; Nadi, Farhad; Lubis, Arif Ridho
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7222

Abstract

Brail is a written mode of communication utilized by individuals with visual impairments to engage in interpersonal exchanges. The braille writing system consists of patterns printed on specialized paper that feature embossed dots. Braille documents enable the visually impaired to acquire knowledge and information exclusively through the application of their sense of contact. Comprehending braille is not a simple undertaking, particularly for the general populace. Because braille is not a required subject in Indonesian education, the majority of the population lacks proficiency in the language. This may therefore result in a minor communication barrier between visually impaired individuals and non-impaired individuals. In order to address this challenge, the present study employs digital image processing via the deep convolutional neural network (DCNN) technique to facilitate comprehension of braille document contents by non-braille speakers. This study employs a deep learning technique that is highly accurate, effective at image processing, and capable of recognizing complex patterns. This study employed the following image processing methods: grayscaling, filtering, contrast enhancement, thresholding, morphological operation, and resizing. Following testing in this investigation, it was determined that the proposed method accurately identifies embossed braille images with a precision of 99.63%.
Bio-engineered strategies for osteochondral defect repair Alnaimat, Feras; Owida, Hamza Abu; Turab, Nidal M.; Al-Nabulsi, Jamal I.
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7316

Abstract

Due to the absence of blood vessels and nerves, the regenerative potential of articular cartilage is significantly constrained. This implies that the impact of a ruptured cartilage extends to the entire joint. Osteoarthritis, a health condition, may arise due to injury and the progressive breakdown of joint tissues. The progression of osteoarthritis can be accelerated by the extensive degradation of articular cartilage, which is ranked as the third most prevalent musculoskeletal disorder necessitating rehabilitation, following low back pain and fractures. The existing therapeutic interventions for cartilage repair exhibit limited efficacy and seldom achieve complete restoration of both functional capacity and tissue homeostasis. Emerging technological advancements in the field of tissue engineering hold significant promise for the development of viable substitutes for cartilage tissue, capable of exhibiting functional properties. The overarching strategy involves ensuring that the cell source is enriched with bioactive molecules that facilitate cellular differentiation and/or maturation. This review provides a comprehensive summary of recent advancements in the field of cartilage tissue engineering. Additionally, it offers an overview of recent clinical trials that have been conducted to examine the latest research developments and clinical applications pertaining to weakened articular cartilage and osteoarthritis.
New control scheme for a dynamic voltage restorer based on selective harmonic injection technique with repetitive controller Tapre, Pawan C.; Thakre, Mohan P.; Pawase, Ramesh S.; Thorat, Jaywant S.; Dahigaonkar, Dipak J.; Mapari, Rahul G.; Kadlag, Sunil Somnath; Khule, Shridhar
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.5312

Abstract

Repetitive controller and selective harmonic injection technique (SHI) in medium and low voltage distribution networks improve dynamic voltage restorer (DVR) DC bus voltages as well as nullify power quality (PQ) problems. DVRs use sinusoidal pulse width modulation (SPWM) firing control, but DC bus use seems to be limited, affecting density, cost, and power packaging. By adding 1/6th of the 3rd harmonic waveform to the basic waveform, SPWM yields the developed model. According to the findings, 15% of DC bus usage improves and produces high voltage AC. Nevertheless, just control systems perturb PQ. The proposed controller uses feed forward and feedback to enhance transient response and justify stable zero error. 3rd third harmonic injection pulse width modulation (THIPWM) improves total harmonic distortion (THD) in the proposed scheme. Power system computer aided design (PSCAD) simulation produced high accuracy for THIPWM and repetitive controllers.
Sustainability dimensions in enhancing the energy and resource efficiency of big data systems D/O Arunachalam, Aishwharya Raani; Jusoh, Yusmadi Yah; Abdullah, Rusli; Umarova, Zhanat; Akhmetova, Sabira; Iztayev, Zhalgasbek; Zhumatayev, Nurlybek
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8052

Abstract

Big data systems are essential for many businesses to grow, leveraging the vast amounts of data they generate and access. However, big data systems are plagued by significant sustainability challenges. Thus, this study aims to identify metrics that can measure the sustainability of big data systems. This research conducted a comprehensive literature review to identify five key sustainability dimensions: technical, environmental, economic, social, and individual. Then, a set of 29 metrics corresponding to these dimensions was developed. To ensure the relevance and applicability of these metrics, an expert validation session was carried out with five experts in the big data field. The validation process confirmed the appropriateness of our proposed metrics and modification take place. The findings of this study present 30 metrics upon experts’ validation that could enhance the sustainability of big data systems, offering meaningful insights for researchers and practitioners aiming to enhance resource and energy efficiency in this domain.
Long-term performance analysis of operational efficiency of a grid-connected solar power plant under Mauritania climate Elhassene, Issa Cheikh; El Heiba, Bamba; Med Mahmoud, Teyeb; Aoulmi, Zoubir; Youm, Issakha; Mahmoud, Abdelkader
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.7092

Abstract

This work examines a solar power plant connected to the Nouakchott electricity grid in Mauritania. Operating since 2013, the 15 MWp plant's reliability and energy yield have been evaluated using a performance index. The assessment involves three phases. First, the plant's meteorological environment and technical indicators are presented. In the second phase, mathematical performance models specified by the International Energy Agency (IEA) are applied to calculate performance indices using data from the data acquisition system (SCADA). The third phase compares actual production data for 2015, 2017, and 2020 with results simulated for PVsyst for the same years. The obtained results are thoroughly analyzed to highlight relevant physical phenomena. The analysis focuses on the plant's 7-year operating period and its impact on performance indicators for electricity production fed into the grid. This study provides insights into the solar power plant's reliability and energy yield, aiding future operational enhancements. It underscores the importance of performance monitoring and assessment in optimizing solar power generation systems.
Comparative harmonic elimination techniques for supraharmonic reduction in microgrid Siva, Ayyar Subramaniya; Ramesh Kumar, Sakunthala Ganesan; Dhayalini, Karuppiah
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7094

Abstract

In order to reduce voltage distortion and supraharmonic (SH) emission in microgrid (MG) systems with electric vehicle (EV) charging stations, this research compares several harmonic elimination approaches. The increasing deployment of EVs has led to the integration of EV charging stations within MG systems, presents challenges in maintaining a high power quality (PQ). Voltage distortions and SH emissions are caused due to non-linear loads and the intermittent nature of EV charging, which have an effect on the performance and dependability of the MG. In order to solve these problems, multilevel converters (MLCs) are used to produce high-quality waveforms. MLCs use harmonic elimination methods to cut down on SH emissions, which improves the PQ overall. Sinusoidal pulse width modulation (PWM), selective harmonic elimination (SHE), space vector modulation (SVM), and random-PWM (RPWM) techniques are among the harmonic elimination methods compared and analyzed. The results will enable the selection of the most appropriate strategy for minimizing voltage distortion and SH emission in MG systems, while providing valuable insights into the effectiveness of each method.
Predicting lung cancer risk using explainable artificial intelligence Shoukat Makubhai, Shahin; Pathak, Ganesh R.; Chandre, Pankaj R.
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.6280

Abstract

Lung cancer is a lethal disease that claims numerous lives annually, and early detection is essential for improving survival rates. Machine learning has shown promise in predicting lung cancer risk, but the lack of transparency and interpretability in black-box models impedes the understanding of factors that contribute to risk. Explainable artificial intelligence (XAI) can overcome this limitation by providing a clear and understandable approach to machine learning. In this study, we will use a large patient record dataset to train an XAI-based model that considers various patient information, including lifestyle factors, clinical data, and medical history, for predicting lung cancer risk. We will use different XAI techniques, including decision trees, partial dependence plots, and feature importance, to interpret the model’s predictions. These methods will provide healthcare professionals with a transparent and interpretable framework for screening and treatment decisions concerning lung cancer risk.
Citrus leaf disease detection through deep learning approach Islam, Sk. Fahmida; Chakrabarty, Nayan; Uddin, Mohammad Shorif
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.4521

Abstract

The majority of people in the world directly or indirectly depend on agriculture. Plant diseases are a significant threat to agricultural production and food security. Due to its high nutritional value, citrus fruit is one of the most abundant fruits in the world. However, different diseases are responsible for degraded citrus production as well as financial losses to the farmers. Traditionally, visual observation by experts has been attended to diagnose plant diseases. Usually, plant leaf disease recognition methods mainly rely on expert experiences to manually extract the colour, composition, and other features of diseased leaf images. Black spot, greening, canker, and melanoses are four common citrus leaf diseases. Rapid and accurate diagnosis of these diseases is a demand of time. Deep learning is a promising solution to these problems. There are different types of deep learning architecture like ImageNet, GoogleNet, VGG16, ResNet50, and InceptionV3, which show promising results in different object detection. Though most of these benchmark models give almost similar accuracy. However, this paper uses two deep learning models to find the better ones for the detection of citrus leaf disease detection. Hence, InceptionV3 outperforms VGG16 in terms of accuracy.
Solar photovoltaic integrated load frequency control of power system using variable structure fuzzy controller Masikana, Sboniso; Sharma, Gulshan; Sharma, Sachin
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7806

Abstract

The incorporation of renewable energy sources into modern power systems is on the upswing, intending to produce and deliver cost-efficient electricity to meet the ever-increasing demands of today’s world. Solar energy stands out as a plentiful and robust solution for meeting current electricity requirements. However, integrating solar photovoltaic (PV) generated power into the contemporary power system introduces complexity, necessitating the development of suitable control design to ensure effective regulation of load frequency control (LFC). This research paper concentrates on the mathematical modeling and integration of solar PV generated electricity into a hydrothermal system. In addition, this study also evaluates the performance of the variable structure fuzzy (VSF) control with reduced rule base for hydrothermal system concerning varying degrees of disturbances in one or in both regions of the power system. Moreover, the research reveals that the integration of PV power into hydro-thermal systems can improve the LFC outputs and mitigate system deviations in the face of different disturbance scenarios.
Enhancing Arabic offensive language detection with BERT-BiGRU model Bensoltane, Rajae; Zaki, Taher
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.6530

Abstract

With the advent of Web 2.0, various platforms and tools have been developed to allow internet users to express their opinions and thoughts on diverse topics and occurrences. Nevertheless, certain users misuse these platforms by sharing hateful and offensive speeches, which has a negative impact on the mental health of internet society. Thus, the detection of offensive language has become an active area of research in the field of natural language processing. Rapidly detecting offensive language on the internet and preventing it from spreading is of great practical significance in reducing cyberbullying and self-harm behaviors. Despite the crucial importance of this task, limited work has been done in this field for nonEnglish languages such as Arabic. Therefore, in this paper, we aim to improve the results of Arabic offensive language detection without the need for laborious preprocessing or feature engineering work. To achieve this, we combine the bidirectional encoder representations from transformers (BERT) model model with a bidirectional gated recurrent unit (BiGRU) layer to further enhance the extracted context and semantic features. The experiments were conducted on the Arabic dataset provided by the SemEval 2020 Task 12. The evaluation results show the effectiveness of our model compared to the baseline and related work models by achieving a macro F1- score of 93.16%.

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