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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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Potentiality of graphene as a base material for impact ionization avalanche transit time diode in high-frequency applications Swain, Mamata Rani; Tripathy, Pravash Ranjan
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.v13i4.6860

Abstract

In this paper, the microwave application potential of graphene is studied using a double-drift-region (DDR) impact ionization avalanche transit time (IMPATT) diode. The simulation of this diode is carried out for the very first time at several different atmospheric window frequencies. Because graphene has unique and special properties, it could be used to make electronic gadgets for the next generation. The device is simulated at a variety of millimeter and sub-millimeter wave frequencies using a model called self-consistent drift diffusion (SCDD), which was developed by the author based on current continuity, Poisson’s equation and space charge equation. When compared to traditional IMPATT devices such as Si, GaAs, InP and GaP, the results demonstrate superior performance in terms of efficiency, and RF power across a wide range of operating conditions. Again, the behavior of noise in graphene IMPATT is studied, and it is found that it makes less noise than Si and GaAs IMPATT. The simulation results open up new avenues for IMPATT diode manufacture and design.
Implementing advance control strategies to improve the performance of a microgrid Rajput, Isha; Verma, Jyoti; Ahuja, Hemant
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.v13i6.7746

Abstract

Integration of flexible and non-dispatchable renewable energy production will influence the operation and future expansion of prevailing power systems. Because of the variations in performance responses between microgrid (MG) and regular generators, including renewable energy sources-based (RES-based) MG into the electrical system may have an influence on stability analysis. The reduction in switching frequency induced by these energy processors electronic interconnected electricity producing sources has a detrimental impact on the system’s structural analysis, potentially leading to stability issues. Power infusion from RES-based MG, on the other hand, increases damping efficiency, reducing transmission line congestion and power shortage. As a result, in light of expanded MG information, it is important to analyse more complex stability problems and regulate the production of a power grid. This study will examine the effect of RES-based MG on the structural analysis and controller of a multimachine multi-area device in various scenarios This paper defines the growth of a one-of-a-kind proportional-integral-derivative (PID-based) power system stabilizer (PSS) type2 fuzzy partial order based on a meta-heuristic hybrid technique for refining the efficiency and robustness of harmonic currents.
Maximum allowable hp rating of 3-phase induction motor fed through a stand-alone constant V/f controlled DFIG via RSC Sharawy, Mohamed; A. Shaltout, Adel; Mohammed Youssef, Omar El-Sayed; A. Al-Ahmar, Mahmoud; Abdel-Rahim, Naser; Sutikno, Tole
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.v13i2.6088

Abstract

This paper presents a scheme to start three-phase induction motors (IMs) directly connected to terminals of constant V/f controlled doubly fed induction generator (DFIG) through the rotor side converter (RSC). The proposed control is achieved by controlling the output voltage and frequency of stand-alone DFIG via controlling an injected voltage into the rotor circuit through the RSC. The control scheme provides a search for maximum rating of the three-phase IM which can be supplied from a DFIG. The search technique is based on using a simplified mathematical model to find the capability limits of the RSC and DFIG. It is found that these parameters depend on the stator frequency and rotor slip. Therefore, an investigation is performed to find the lowest frequency and the corresponding allowable maximum rating for the IM to be safely started. A typical example is provided in the paper for a 15 kW DFIG. It is shown that this generator could supply a three-phase IM with a maximum rating of 1-hp if it operated at nominal outputs, voltage and frequency, during start-up period. While, using the proposed technique, the same generator could start-up a three-phase IM with maximum power rating of 7.25 hp.
FiMoDeAL: pilot study on shortest path heuristics in wireless sensor network for fire detection and alert ensemble Ifeanyi Akazue, Maureen; Efetobore Edje, Abel; Okpor, Margaret Dumebi; Adigwe, Wilfred; Ejeh, Patrick Ogholuwarami; Odiakaose, Christopher Chukwufunaya; Ojugo, Arnold Adimabua; Edim, Edim Bassey; Ako, Rita Erhovwo; Geteloma, Victor Ochuko
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.v13i5.8084

Abstract

With the incessant outbreak of fire, the heavy loss to both lives and properties in the society fire has since become a critical issue and challenge that needs our daily attention to be resolved. Loss of lives and properties to fire outbreak in 2021 alone as occurring in major Nigerian markets and residential homes was estimated at over 3 trillion Naira. Our study proposes a wireless sensor network internet of things (IoT) based ensemble to aid the effective monitoring, detection and alerting of residents and fire service departments. With cost as a major issue and the requisite installation of fire and smoke detectors in many houses our ensemble can efficiently integrate into the existing system using the ESP8285-controller to create a comprehensive access control system. The system provides real time monitor and control capabilities that will allow administrators to track and manage fire monitor and detection within a facility. Thus, enhances system's efficiency and performance.
Unmanned aerial vehicle path planning in a 3D environment using a hybrid algorithm Kareem, Abbas Abdulrazzaq; Mohamed, Mohamed Jasim; Oleiwi, Bashra Kadhim
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.v13i2.6020

Abstract

The optimal unmanned aerial vehicle (UAV) path planning using bio-inspired algorithms requires high computation and low convergence in a complex 3D environment. To solve this problem, a hybrid A*-FPA algorithm was proposed that combines the A* algorithm with a flower pollination algorithm (FPA). The main idea of this algorithm is to balance the high speed of the A* exploration ability with the FPA exploitation ability to find an optimal 3D UAV path. At first, the algorithm starts by finding the locally optimal path based on a grid map, and the result is a set of path nodes. The algorithm will select three discovered nodes and set the FPA's initial population. Finally, the FPA is applied to obtain the optimal path. The proposed algorithm's performance was compared with the A*, FPA, genetic algorithm (GA), and partical swarm optimization (PSO) algorithms, where the comparison is done based on four factors: the best path, mean path, standard deviation, and worst path length. The simulation results showed that the proposed algorithm outperformed all previously mentioned algorithms in finding the optimal path in all scenarios, significantly improving the best path length and mean path length of 79.3% and 147.8%, respectively.
Comparative analysis and validation of advanced control modules for standalone renewable micro grid with droop controller Swathi, Savitri; Kumar, Bhaskaruni Suresh; Upendar, Jalla
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.v13i2.5849

Abstract

A micro grid system with renewable source operation control is a complex part as each source operates at different parameters. This renewable micro grid with multiple sources like solar plants, wind farm, fuel cell, battery backup has to be operated in both grid connected and standalone condition. During grid connection the micro grid, inverter has to inject power to the grid and compensate load in synchronization to the grid voltages. And during standalone condition the inverter is controlled with droop control module which stabilizes the voltage and frequency of the system even during grid disconnection. The droop control module is further updated with new advanced controllers like fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) replacing the traditional proportional integral derivative (PID) and proportional integral (PI) controllers improving the response rate and for achieving better stabilization. This paper has comparative analysis of the micro grid system with different droop controllers under various operating conditions. Parameters like voltage magnitude (Vmag), frequency (F), load and inverter powers (Pload and Pinv) of the test system are compared with different controllers. A numeric comparison table is given to determine the optimal controller for the inverter operation. The analysis is carried out in MATLAB/Simulink software with graphical and parametric validations.
Customer data prediction and analysis in e-commerce using machine learning Al Rahib, Md Abdullah; Saha, Nirjhor; Mia, Raju; Sattar, Abdus
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.v13i4.6420

Abstract

Customer churn is a major challenge faced by e-commerce companies, as it leads to loss of revenue and decreased customer loyalty. In recent years, for predicting and reducing client churn machine learning techniques are powerful tools. This research aims to explore the use of machine learning algorithms for predicting customer churn, annual spending, and product on-time delivery in e-commerce. The study first conducted a comprehensive review of the literature on customer churn in machine learning. The literature showed that customer churn has been predicted successfully using a variety of machine learning algorithms, including support vector machine (SVM), random forest, and decision tree in various industries. To address this gap in the literature, the study conducted an empirical analysis of customer churn in e-commerce using machine learning algorithms. The data were then pre-processed and analyzed utilizing machine learning techniques for prediction. According to the study’s findings, machine learning algorithms are effective in predicting customer churn, and product on-time delivery in e-commerce. The best-performing algorithm SVM achieved an accuracy of 83.45% in predicting customer churn and 68.42% for product on-time delivery prediction.
Computationally efficient ResNet based Telugu handwritten text detection Revathi, Buddaraju; Prasad, M. V. D.; Gattim, Naveen Kishore
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.v13i6.8170

Abstract

Optical character recognition (OCR) is a technological process that converts diverse document formats into editable and searchable data. Recognition of Telugu characters through OCR poses a challenge because of compound characters. Identifying handwritten Telugu text proves difficult due to the substantial number of characters, their similarities, and overlapping forms. To handle overlapping characters, we implemented a segmentation algorithm that efficiently separates these characters, consequently enhancing the model’s accuracy. Feature extraction is a crucial phase in recognizing a broader range of characters, especially those that are similar in appearance. So, we have employed a light weighted ResNet 34 model that effectively addresses these challenges and handles deep networks without declining accuracy as the network’s depth increases. We have achieved a word level recognition rate of 81.5%. In addition, the parameters required by the model are less when compared to its counterpart inception V1, making it computationally efficient.
Palmprint recognition system using VR-LBP and KAZE features for better recognition accuracy A. Khalid, Noor Aldeen; Imran Ahmad, Muhammad; Shie Chow, Tan; H. Mandeel, Thulfiqar; Majid Mohammed, Ibrahim; Kadhim Alsaeedi, Mokhalad Abdulameer
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.v13i2.4739

Abstract

The palmprint recognition system has gained significant attention in security and law enforcement due to its unique features, such as principle lines, ridges, and wrinkles. However, many existing methods for extracting these features have limited accuracy, especially when the image illumination varies or the size of the processed pixels increases. Previous studies have shown that the local binary patterns (LBP) algorithm is effective for palmprint recognition due to the rich texture characteristics of a palmprint. In this paper, we propose a new technique for a robust contact-based palmprint identification system using vertical-LBP and KAZE feature detection. Our technique aims to improve recognition accuracy by using KAZE, which is a nonlinear diffusion approach that extracts nonlinear features from the evolution of the illuminance of an image. We also utilize principal component analysis (PCA) to reduce the dimensionality of the generated descriptor vector elements. The proposed method was tested on the PolyU database and achieved recognition accuracy of 99.7%.
Skin cancer classification using EfficientNet architecture Harahap, Mawaddah; Husein, Amir Mahmud; Kwok, Shane Christian; Wizley, Vincent; Leonardi, Jocelyn; Ong, Derrick Kenji; Ginting, Deskianta; Silitonga, Benny Art
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.v13i4.7159

Abstract

Skin cancer is one of the most common deadly diseases worldwide. Hence, skin cancer classification is becoming increasingly important because treatment in the early stages of skin cancer is much more effective and efficient. This study focuses on the classification of three common types of skin cancer, namely basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma using EfficientNet architecture. The dataset is preprocessed and each image in the dataset is resized to 256×256 pixels prior to incorporation in later stages. We then train all types of EfficientNet starting from EfficientNet-B0 to EfficientNet-B7 and compare their performances. Based on the test results, all trained EfficientNet models are capable of producing good accuracy, precision, recall, and F1-score in skin cancer classification. Particularly, our designed EfficientNet-B4 model achieves 79.69% accuracy, 81.67% precision, 76.56% recall, and 79.03% F1-score as the highest among others. These results confirm that EfficientNet architecture can be utilized to classify skin cancer properly.

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