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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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Exploring the synergy: AI and ML in very large scale integration design and manufacturing Gonsai, Sima K.; Sheth, Kinjal Ravi; Patel, Dhavalkumar N.; Tank, Hardik B.; Desai, Hitesh L.; Rana, Shilpa K.; Bharvad, Suresh Laxmanbhai
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.v13i6.8594

Abstract

With the rapid advancements in very large scale integration (VLSI) and integrated circuit (IC) technology, the complexity of devices has escalated significantly. Designing a VLSI chip is essential for scaling up the capabilities of chips to meet the growing demands of modern applications, like artificial intelligence (AI), IoT, and high-performance computing. Chip testing and verification also emerges as crucial tasks to ensure optimal device functionality. Testing verifies the integrity of a circuit’s gates and connections, ensuring accurate operation. Throughout the chip’s design and development life cycle, design, testing and verification composes a substantial portion of the effort. AI and machine learning (ML) are used in many different research domains to improve predicted accuracy, automate difficult jobs, provide data-driven insights, and optimise workflows. This study aims to showcase the vital role of AI/ML in reducing complexity in VLSI chip design life cycle by automating test pattern generation and fault detection, enhancing efficiency and accuracy, and significantly reducing the time and resources needed for design verification and optimization.
An adaptive neuro-fuzzy inference system-based irrigation sprinkler system for dry season farming Tyokighir, Silas Soo; Mom, Joseph; Eghonghon Ukhurebor, Kingsley; Igwue, Gabriel
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.v13i4.7834

Abstract

In recent years, the management of irrigation systems has emerged as one of the most pressing concerns in the agricultural industry, especially in areas that experience dry seasons. In this research, an adaptive neuro-fuzzy inference system (ANFIS)-based irrigation system that uses a hot and cold sprinkler mechanism is presented. The goal of the system is to reduce the amount of water needed for farming and increase crop output during dry seasons. Adaptive control of water release is achieved via the use of MATLAB and the ANFIS model. This is done in response to changes in soil moisture, ambient temperature, and crop water demand. According to the findings, the suggested system performs noticeably better than conventional irrigation methods in terms of both the amount of water used and the number of crops produced.
Ageing durability of SiR under prolonged voltage stress, contaminant flow and contaminant concentration: a statistical approach Nazir Ali, Nornazurah; Zainuddin, Hidayat; Abd Razak, Jeefferie; Farhani Ambo, Nur
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.v13i5.8141

Abstract

Silicone rubber (SiR) has become a reliable choice for outdoor high voltage (HV) insulation in recent years. However, the durability of these polymeric materials is affected by both electrical and environmental stresses, leading to aging. While prior research aimed at enhancing performance, little focus was placed on identifying the major root causes of aging in these insulators. To delve into the causes, we conducted inclined plane tracking (IPT) tests, varying voltage, flow rate, and contamination concentration. Samples with consistent alumina trihydrate (ATH) filler content were tested under the BSEN 60587 standard. The voltage ranged from 3.5-5.5 kV, flow rate from 0.3-0.9 ml/min, and contamination concentration from 500-4500 µS/cm. A Two-Level Factorial analysis with a design of experiment (DOE) approach was used. The study revealed that contaminant concentration significantly impacted leakage current (LC), followed by contamination flow rate and voltage. Additionally, optimization techniques are used to determine ideal LC values under specific conditions. This study explains how each factor affects LC values, allowing for predictive modelling, which improves our understanding of SiR insulators' durability, optimizes LC in real-world situations, and improves their performance and lifespan.
A survey to build framework for optimize and secure migration and transmission of cloud data Bathini, Ravinder; Vurukonda, Naresh
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.v13i2.5181

Abstract

In the recent era of computational technologies, the internet is needed daily. The data generated is enormous and primarily stored on dedicated servers or clouds. Data migration and transfer are significant tasks for maintaining consistency and updating data. The data is the most critical component in any cloud service. There are various methods to protect data, like secure transfer, encryption, and authentication. These techniques are used as per need and transmission of the data. As data grows on a server or cloud, it must be migrated securely. Here, the exhaustive survey is provided for building a framework for migrating and transmitting cloud data. The framework should be sustainable and adaptable for load-balancing recovery and secure transmission. Various security load balancing parameters must be considered to obtain these state-of-the-art functionalities in the framework. The existing similar frameworks are studied, and findings are proposed in the paper to develop the framework.
A dataset for computer-vision-based fig fruit detection in the wild with benchmarking you only look once model detector Che Ani, Adi Izhar; Hamdani Mohammad Farid, Mohammad Afiq; Firdhaus Kamaruzaman, Ahmad Shukri; Ahmad, Sharaf; Hadi, Mokh Sholihul
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.v13i4.5705

Abstract

The image datasets that are most widely used for training deep learning models are specifically developed for applications. This study introduces a novel dataset aimed at augmenting the existing data for the identification of figs in their natural habitats, specifically in the wilderness. In the present study, researchers have generated numerous image datasets specifically for object detection focus on applications in agriculture. Regrettably, it is exceedingly difficult for us to obtain a specialized dataset specifically designed for detecting figs. To tackle this issue, a grand total of 462 photographs of fig fruits were gathered. The augmentation technique was utilized to substantially increase the size of the dataset. Ultimately, we conduct an examination of the dataset by doing a baseline performance study for bounding-box detection using established object detection methods, specifically you only look once (YOLO) version 3 and YOLOv4. The performance obtained on the test photos of our dataset is satisfactory. For farmers, the capacity to identify and oversee fig fruits in their natural or developed environments can be highly advantageous. The detecting device offers instantaneous data regarding the quantity of mature figs, facilitating decision-making procedures.
A study on the impact of layout change to knowledge distilled indoor positioning systems Mazlan, Aqilah; Ng, Yin Hoe; Tan, Chee Keong
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.v13i6.7157

Abstract

Convolutional neural networks (CNN)-based indoor positioning systems (IPS) have gained significant attention over the past decade due to their ability to provide precise localization accuracy. However, the use of CNNs in these systems comes with a higher computational cost. To tackle this issue, recent studies have introduced knowledge distilled positioning schemes to mitigate the computational burden. Despite the clear possibility of performance degradation due to signal fluctuations, there remains a lack of investigation into the performance of knowledge distilled and CNN based indoor positioning schemes in dynamic indoor environment. To fill this research gap, this paper investigates the practicality of implementing knowledge distilled-based indoor positioning schemes in real-world by analyzing the impact of indoor layout change on these schemes. Results demonstrate that in the case of layout change, the knowledge distilled-based indoor positioning schemes without teaching assistant can still achieve good performance, with an improvement of 11.56% in average positioning error compared to simple CNN model, while taking only 49.05% of the complex CNN model’s execution time. However, the knowledge distilled-based indoor positioning scheme with teaching assistant fails under the same condition as the inclusion of teacher assistant leads to increased error in modeling the received signal strengths (RSS) and locations relationship.
Smart indoor gardening: elevating growth, health, and automation Alrawashdeh, Tawfiq; Alkore Alshalabi, Ibrahim; Al-Jaafreh, Moha'med; Alksasbeh, Malek
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.v13i3.7101

Abstract

Recently, indoor systems for growing plants have emerged as a promising approach to address the problems related to extreme weather conditions outdoors. However, such systems must manage the plants surrounding environments to satisfy the environmental and the economical requirements. In this line, any proposed solution must address challenging factors such as plants diseases and unordinary climate situations. In this paper, propose an internet of things (IoT) indoor system that can be used to facilitate the plant growing process. The proposed system is designed to provide alternatives for outdoor climate dependency such as the vitamins provided through sunlight. Moreover, renewable energy sources (sunlight) are employed to reduce the impact on the environment. With the help of several types of sensors, the system continuously monitors the plants through their growing journey. Whereas actuator devices are employed to control the plant-feeding process based on the sensors’ reported values. All the collected data will be uploaded to the cloud for analysis, utilizing a website. Additionally, the architecture of the provided system eliminates the need for human involvement, which has a degrading effect on the plant growing process.
YOLO-based object detection performance evaluation for automatic target aimbot in first-person shooter games Asmara, Rosa Andrie; Rahmat Samudra Anugrah, Muhammad; Wibowo, Dimas Wahyu; Arai, Kohei; Burhanuddin, Mohd Aboobaider; Handayani, Anik Nur; Damayanti, Farradila Ayu
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.v13i4.6895

Abstract

First-person shooter (FPS) focuses on first-person perspective action gameplay, with gunfights usually giving the player a choice of weapons, significantly impacting how the player approaches or strategies. General military-themed FPS games have realistic models with actual weapons’ shapes and characteristics. This type of game requires high aiming accuracy while using a mouse on a PC. However, not all players have a fast response time in knowing the surrounding situation. New players may need aid when targeting enemies in the FPS world. One popular yet underhanded method is injecting a program code using a dynamic-link library (DLL) to manipulate memory and asset data from the game. Instead of DLL, we promote a novel approach using the player’s real-time game screen, detecting the person without injecting program code into the game. The you only look once (YOLO) algorithm is used as an object detector model since it can process images in real time for up to 45 frames per second. The proposed object detection has an outstanding performance with 65% accuracy, 98% precision, and 61% recall of 51 tests for each game. YOLO’s fastest detection speed produces an average of 35 FPS on the YOLO tiny variant using a mixed precision (half) graphics processing unit (GPU).
Representative power distribution network: a review of available models Masdzarif, Nur Diana Izzani; Ibrahim, Khairul Anwar; Gan, Chin Kim; Au, Mau Teng
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.v13i6.7787

Abstract

In recent decades, there has been an increasing penetration of new smart grids (SG) and distributed generations (DG) that are connected to the distribution network (DN). Thus, it is critical for utilities to analyze and assess their impact on the power system networks, which often necessitates major decisions about network operation and planning. Consequently, researchers are constantly developing new and improved methods of advanced control and operation to address these challenges. Unfortunately, there are a limited number of realistic DN models that are made publicly available by the utilities for the development, testing, and evaluation of such new methods. This is mainly caused by the utilities' concerns and reluctance to reveal the public's real and “sensitive” network information. Although international standard test systems such as IEEE and CIGRE are publicly available, these test network models are customized based on the US DN and are not representative of the other networks that operate under different network settings. This paper presents a brief literature survey of existing and prominent representative DNs with a special emphasis on identifying the general description, and application, as a comparison for future development of test network in Malaysia.
A deep learning-based intelligent decision-making model for tumor and cancer cell identification Durga, Putta; Godavarthi, Deepthi
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6469

Abstract

In the current era, the prevalence of common ailments is leading to an increasing number of fatalities. Various infections, viruses, and other pathogens can cause these illnesses. Some illnesses can give rise to tumors that seriously threaten human health. Distinct forms of tumors exist, including benign, premalignant, and malignant, with cancer being present only in malignant forms. Deep learning (DL) algorithms have emerged as one of the most promising methods for detecting cancers within the human body. However, existing models face criticism for their limitations, such as lack of support for large datasets, and reliance on a limited number of attributes from input images. To address these limitations and enable efficient cancer detection throughout the human body, an intelligent decision-making approach model (IDMA) is proposed. The IDMA is combined with the pre-trained VGG19 for improved training. The IDMA analyses convolutional neural network (CNN) layer images for signs of malignancy and rules out false positives. Various performance indicators, like sensitivity, precision, recall, and F1-score, are used to assess the system's performance. The suggested system has been evaluated and proven to outperform similar current systems, achieving an impressive 98.67% accuracy in detecting cancer cells.

Page 233 of 291 | Total Record : 2901

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