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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 75 Documents
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Search results for , issue "Vol 13, No 4: August 2024" : 75 Documents clear
Energy saving performance analysis for future fifth generation millimetre-wave cellular networks Anwar Apandi, Nur Ilyana; Muhammad, Nor Aishah
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.v13i4.5973

Abstract

The deployment of fifth generation (5G) millimetre-wave (mmWave) base stations (BSs) will consume more energy over time due to the limited time available, despite the increasing interest in developing 5G mmWave wireless communication technology. Constructing 5G mmWave cellular network infrastructure can improve energy efficiency, which is a challenge to implement in heterogeneous networks. This paper presents analytical frameworks for monitoring the effectiveness of 5G mmWave cellular networks. Based on the state management of BS, a system model for 2-tier heterogeneous networks is developed, and particle swarm optimization (PSO) is then used to compute the total energy consumption of the heterogeneous networks. Energy consumption was compared and analysed by leveraging state switching and the aggregate delay for three methods: fundamental separation, conventional separation, and a proposed energy-saving method that introduced a sleep state. Simulation shows that the proposed energy-saving method, which is a combination of conventional separation approaches, has the lowest total energy consumption and offers a 9% reduction compared to other related works. The results validate the accuracy of the power usage used in the 5G mmWave cellular network of the proposed method.
Revisiting 5G quality of service in Bangkok metropolitan region: BTS Skytrain station areas Daengsi, Therdpong; Sriamorntrakul, Pakkasit; Chatchalermpun, Surachai; Phanrattanachai, Kritphon
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.v13i4.7337

Abstract

This article compares two of the leading mobile network operators (MNOs) in Thailand’s telecom market in terms of the service quality of Thailand’s fifth generation (5G) networks. The following three factors: download speed, upload speed, and latency, which are frequently considered to be indicators of the quality of Internet networks, were examined. The researchers employed the test results to determine the quality of service (QoS) that was achieved by comparing newly collected data to data that had previously been examined utilizing the same format and application in the middle of May 2021. The average download speed decreased from 196.4 Mbps in 2021 to 140.4 Mbps in 2023, while the average upload speed dropped from 62.6 Mbps in 2021 to 52.0 Mbps in 2023. Furthermore, the average latency increased from 14.9 ms in 2021 to 23.3 ms in 2023. These results show a considerably enhanced service although the test region in this study only comprised BTS stations. Furthermore, this was the case even though the test area in this study only encompassed a small percentage of the total population.
An interior penalty function method for solving fuzzy nonlinear programming problems Govindhasamy, Vanaja; Kandasamy, Ganesan
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.v13i4.7047

Abstract

In this article, we investigate fuzzy interior penalty function method for solving fuzzy nonlinear programming problems (FNLPP) based on a new fuzzy arith-metic, unconstrained optimization, and fuzzy ranking on the parametric form of triangular fuzzy numbers (TFN). The main objective of this paper is to solve constrained fuzzy nonlinear programming problems using interior penalty func-tions (IPF) by converting it into unconstrained optimization problems. We prove an important lemma and a convergence theorem for the interior penalty functions method. Interior penalty function techniques favor sites near the boundary of the feasible region in the interior. We present a numerical example of the suggested method and compare the results to those produced by existing methods.
A system for monitoring human postures, seizures, and falls from bed using radio and surface electromyography signals Intongkum, Chawakorn; Sengchuai, Kiattisak; Booranawong, Apidet
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.v13i4.7241

Abstract

In this work, a system for monitoring human postures, seizures, and falls from bed using received signal strength indicator (RSSI) and surface electromyography (sEMG) signals is studied through experiments. In this proposed system, a person who is located inside a wireless link is monitored by considering the change in measured RSSI signals as the 2.4 GHz IEEE 802.15.4 signals received at a receiver. Human motions in bed that affect RSSI levels can be captured. Thus, with this technique, it does not raise a privacy concern compared with vision-based technology. Additionally, sEMG signals associated with muscle movements from human postures are recorded from the human body’s abdominal muscles. Eight different activities, including normal and critical events, are tested and evaluated. Experimental results indicate that the proposed system could automatically monitor different human postures in real-time. RSSI and sEMG signals correlated to each posture have their own patterns. Furthermore, the relationship between human behaviors and RSSI and sEMG levels is summarized.
Pre-slippage detection and counter-slippage for e-pattern omniwheeled cellular conveyor Keek, Joe Siang; Loh, Ser Lee; Hanafi, Ainain Nur; Cheong, Tau Han
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.v13i4.6996

Abstract

This paper presents continuation work of e-pattern omniwheeled cellular conveyor (EOCC) since its first introduction. EOCC is a conveyor that is modular and is made up of omniwheels arranged horizontally and vertically. Although in the last published paper, the EOCC had been proven to be capable of transporting box omnidirectionally and achieving yaw control concurrently, however, due to the natural properties of omniwheel, the performance is jeopardized by slippage. While minor slippage can be negligible, but a major slippage can eventually destroy the whole trajectory tracking performance. Therefore, counter-slippage methods are proposed in this paper. The simulation results show that the proposed counter-slippage method significantly improves the trajectory tracking performance up to 42% of reduction in integral of absolute error. Moreover, in this paper, pre-slippage detection method, which aims to perform early detection of slippage, is being presented as well. Although these proposed methods are simple, but they are proven to have achieved improved tracking performance than conventional controller, as presented in this paper.
Semantic segmentation and thermal imaging for forest fires detection and monitoring by drones Yandouzi, Mimoun; Berrahal, Mohammed; Grari, Mounir; Boukabous, Mohammed; Moussaoui, Omar; Azizi, Mostafa; Ghoumid, Kamal; Kerkour Elmiad, Aissa
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.v13i4.7663

Abstract

Forest ecosystems play a crucial role in providing a wide range of ecological, social, and economic benefits. However, the increasing frequency and severity of forest fires pose a significant threat to the sustainability of forests and their functions, highlighting the need for early detection and swift action to mitigate damage. The combination of drones and artificial intelligence, particularly deep learning, proves to be a cost-effective solution for accurately and efficiently detecting forest fires in real-time. Deep learning-based image segmentation models can not only be employed for forest fire detection but also play a vital role in damage assessment and support reforestation efforts. Furthermore, the integration of thermal cameras on drones can significantly enhance the sensitivity in forest fire detection. This study undertakes an in-depth analysis of recent advancements in deep learning-based semantic segmentation, with a particular focus on model’s mask region convolutional neural network (Mask R-CNN) and you only look once (YOLO) v5, v7, and v8 variants. Emphasis is placed on their suitability for forest fire monitoring using drones equipped with RGB and/or thermal cameras. The conducted experiments have yielded encouraging outcomes across various metrics, underscoring its significance as an invaluable asset for both fire detection and continuous monitoring endeavors.
A novel approach to analyzing the impact of AI, ChatGPT, and chatbot on education using machine learning algorithms Hasan, Nahid; Polin, Johora Akter; Ahmmed, Md. Rayhan; Sakib, Md. Mamun; Jahin, Md. Farhan; Rahman, Md. Mahfuzur
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.v13i4.7158

Abstract

Artificial intelligence (AI) is one of the most common and essential technologies in this modern era, especially in the education and research sectors. It mimics machine-processed human intellect. In modern times, ChatGPT is one of the most effective and beneficial tools developed by OpenAI. Provides prompt answers and feedback to help academics and researchers. Using ChatGPT has various advantages, including improving methods of instruction, preparing interactive lessons, assessment, and advanced problem-solving. Threats against ChatGPT, however, include diminishing creativity, and analytical thinking. Additionally, students would adopt unfair procedures when submitting any tests or assignments online, which would increase their dependency on AI systems rather than thinking analytically. In this study, we have demonstrated arguments on both sides of AI technology. We believe that our study would provide a depth of knowledge and more informed discussion. Data is collected via an offline platform and then machine learning algorithms such as K-nearest neighbour (K-NN), support vector machine (SVM), naive bayes (NB), decision tree (DT), and random forest (RF) are used to analyze the data which helps to improve teaching and learning techniques where SVM shows best performance. The results of the study would offer several significant learning and research directions as well as ensure safe and responsible adoption.
An intelligent obfuscated mobile malware detection using deep supervised learning algorithms Ganapathi, Padmavathi; Arumugam, Roshni; Dhathathri, Shanmugapriya
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.v13i4.6974

Abstract

Obfuscated mobile malware (OMM) is a malicious software in mobile that hides to avoid detection and annihilation. These types of malwares are thorny to identify due to their inevitable nature. Deep learning (DL) algorithms are the most desirable to detect obfuscated malware based on the ‘n’ number of iterations with adjustable weights and neurons. This study investigates the accurate detection of OMM using significant DL algorithms such as multi-layer perceptron (MLP), self-organizing maps (SOM), long short-term memory (LSTM) networks, auto encoders (AE), and convolutional neural network (CNN) based on appropriate parameter tuning. The dataset taken for the study is CICMalMem2022 that contains 58,596 samples with 57 features which is basically designed for OMM detection. The dataset comprises Spyware, Ransomware, Trojan horse, and Benign. The DL models are evaluated based on performance metrics such as precision, recall, accuracy, training accuracy, test accuracy, validation accuracy, training loss, validation loss and receiver operating characteristic (ROC) curve. Based on the experimental evaluation, the study reveals that LSTM outperforms with 100% accuracy and MLP achieves 99.9% accuracy in detecting and classifying the OMM using deep supervised learning (SL) mechanism.
A flexible paper based strain sensors drawn by pencil for low-cost pressure sensing applications Mat Nawi, Mohd Norzaidi; Ho Lau, Jim Tze
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.v13i4.8001

Abstract

Paper-based strain sensors, offering a cost-effective and environmentally friendly solution, are in demand for pressure sensing applications. Here, we present a simple sensor design comprising a piece of paper, a graphite pencil, and a copper plate. The proposed fabrication process is simple and eco-friendly. Beyond design and fabrication, our study explores the performance of paper-based sensors in effectively measuring and monitoring pressure changes induced by varying deflection angles. Our findings show that as the deflection angle increases, the sensor exhibits a proportional increase in the relative change in resistance. Furthermore, the practical applicability of the fabricated sensor is demonstrated through real-world testing on a human finger, considering different positions. In essence, our research positions paper-based strain sensors as a promising and practical choice for affordable, eco-friendly, and responsive pressure sensing.
Pre-trained Bi-LSTM model for automated classification of ventricular arrhythmias using 1-D and 2-D ECG Chaitanya, M Krishna; Sharma, Lakhan Dev
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.v13i4.6705

Abstract

Number of cardiac conditions have been associated with abnormal heartbeat (arrhythmia) such as ventricular fibrillation (Vfib), ventricular flutter (Vfl), and ventricular tachycardia (Vta). This is a difficult and essential job for timely clinical assessment and identification of these potentially life-threatening heart arrhythmias. With the aid of a one-dimensional electrocardiogram (ECG) signal and its associated two-dimensional image, the suggested method provides a strategy for the detection of time-frequency interpretation (Vfib, Vfl, and Vta). A four-stage cascaded Savitzky-Golay (SG) filter is used after a 2-stage median filter to preprocess the ECG signal. This technique employs z-score normalisation after brief (2 sec) ECG readings. The classification of these ECG segments (1-D) and associated time-frequency representation pictures (2-D) was explored separately using a bi-directional long short-term memory-based network. Eight distinct categorization scenarios were examined, and then an average accuracy of 99.67% for 1-D ECG and 99.87% for 2-D ECG signal was attained.

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