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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 14, No 1: February 2025" : 75 Documents clear
The use of generative adversarial network as a domain adaptation method for cross-corpus speech emotion recognition Farhan Fadhil, Muhammad; Zahra, Amalia
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8339

Abstract

The research of speech emotion recognition (SER) is growing rapidly. However, SER still faces a cross-corpus SER problem which is performance degradation when a single SER model is tested in different domains. This study shows the impact of implementing a generative adversarial network (GAN) model for adapting speech data from different domains and performs emotion classification from the speech features using a 1D convolutional neural network (CNN) model. The results of this study found that the domain adaptation approach using a GAN model could improve the accuracy of emotion classification in speech data from 2 different domain such as the ryerson audio-visual database of emotional speech and song (RAVDESS) speech corpus and the EMO-DB speech corpus ranging from 10.88% to 28.77%, with the highest average performance increase across three different class balancing method reaching 18.433%.
Initial study of general theory of complex systems: physical basis and philosophical understanding Esenovich Suleimenov, Ibragim; Arshavirovich Gabrielyan, Oleg; Serikuly Bakirov, Akhat
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7976

Abstract

The fundamental difference between neural networks containing and not containing feedback between elements is analyzed. It is shown that in the first of these cases, quantitative relationships describing the functioning of the neural network can be obtained based on an analogy with the theory of noise-resistant codes. In the second case, an analogy with electronic circuits that form memory cells (triggers) is valid. It is shown that feedback between elements of even the simplest neural networks can lead to the appearance of multidimensional hysteresis, when, with the same state of inputs, the system can be in several qualitatively different states, the transition between which can be abrupt. In this case, the state of the neural network outputs depends not only on the current state of its inputs, but also on the path along which this state was formed. The results obtained are used for the philosophical substantiation of a new approach to the interpretation of complex systems of various natures, which are considered analogs of neural networks. According to it, a system that can store and processing information should be considered "complex".
Electrical active power optimization of the SEIG-WTS based on perturb and observe method Borja Borja, Mario G.; Lescano, Sergio; Torres Alvarado, Sally; Yancachajlla Tito, Ubaldo; Luyo, Jaime
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.6940

Abstract

This paper proposes an electrical active power optimization for self-excited induction generator-wind turbine system (SEIG-WTS) using a perturb and observe (PO)-based maximum power point tracking. The main advantage is the optimization of the SEIG active power of SEIG-WTS and the simply practical implementation with rotor speed sensor, current sensor and three phase inverter. The active power optimization of SEIG-WTS is achieved by perturbing angular magnetic field speed with stator reference voltage. To test the effectiveness of the proposal, numeric simulations were carried out under very challenging conditions with wind speed profile in steps and actual wind speed profile. The proposal reaches the maximum power in 7 seconds for hardest condition when the system works at high rotor speed. The proposal is useful for the development of maximum power point tracking control (MPPT) controllers due to its simplicity in implementation.This paper proposes an electrical active power optimization for self-excited induction generator-wind turbine system (SEIG-WTS) using a perturb and observe (PO)-based maximum power point tracking. The main advantage is the optimization of the SEIG active power of SEIG-WTS and the simply practical implementation with rotor speed sensor, current sensor and three phase inverter. The active power optimization of SEIG-WTS is achieved by perturbing angular magnetic field speed with stator reference voltage. To test the effectiveness of the proposal, numeric simulations were carried out under very challenging conditions with wind speed profile in steps and actual wind speed profile. The proposal reaches the maximum power in 7 seconds for hardest condition when the system works at high rotor speed. The proposal is useful for the development of maximum power point tracking control (MPPT) controllers due to its simplicity in implementation.
Improving the performance of a U-shaped patch antenna using metamaterials for biomedical applications Siraj, Younes; Foshi, Jaouad; Saidi Alaoui, Kaoutar
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8283

Abstract

This paper discusses the performance improvement of a patch antenna using metamaterials (MTM). The suggested antenna is a U-shaped patch antenna with a modified ground plane dedicated to biomedical applications. The size of the antenna is 40×20 mm2 with a FR4 substrate (εr=4.3, tanδ=0.02, H=1.6 mm) designed for operation at 2.4 GHz (ISM Band) and 6.23 GHz frequencies. The proposed MTM is 2×2 array positioned under the antenna at a distance of 2 mm. The integration of the MTM enhances clearly the antenna performance especially the return loss, voltage standing wave ratio (VSWR) and the gain. However, the reflection coefficient was enhanced from -10.71 dB to -36.63 dB at 2.45 GHz and from -13.88 dB to -36.54 dB at 6.23 GHz, the VSWR improved from 1.66 to 1.03 at 2.45 GHz and from 1.75 to 1.04 at 6.23 GHz. Additionally, the peak gain also was increased from 1.77 dB to 3.48 dB. The obtained results confirm the suitability of the suggested antenna for biomedical applications.
Fusing Xception and ResNet50 features for robust grape leaf disease classification Vo, Hoang-Tu; Chau Mui, Kheo; Nguyen Thien, Nhon
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7269

Abstract

Grapes are one of the most widely cultivated fruits worldwide, and their economic and nutritional value makes them a significant crop in agriculture. However, grape plants are vulnerable to various diseases that can have detrimental effects on crop yield and quality. Accurate and timely identification of grape leaf diseases is crucial for efficient disease control and ensuring sustainable viticulture practices. In this study, we present a disease classification model specifically designed for grape leaves. The model incorporates bilinear pooling, utilizing the intermediate features extracted from two powerful convolutional neural network (CNN) models, Xception, and ResNet50. The outer product operation is applied to the extracted features, enabling the capture of intricate interactions and relationships between the features. The accurate classification of grape leaf diseases provided by our model offers significant benefits for grape farmers, vineyard owners, and agricultural researchers. It facilitates early disease detection, enabling proactive disease management strategies. Additionally, it assists in optimizing crop health, minimizing yield losses, and ensuring sustainable grape production.
Leveraging pretrained transformers for enhanced air quality index prediction model Velusamy, Santhana Lakshmi; Madhaya Shanmugam, Vijaya
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7968

Abstract

Air pollution mitigation is essential to ensure sustainable development, as it directly affects climate change, economic productivity, and social well-being. Despite the availability of numerous prediction techniques, machine learning (ML) remains the optimal solution for forecasting air pollution. Constructing a prediction model for a region with limited data poses a challenge. This study presents a novel technique that combines temporal fusion transformer (TFT) with transfer learning to create an inventive air quality index (AQI) prediction model, effectively utilizing temporal insights and prior knowledge. The TFT is an advanced deep neural architecture engineered to enhance time series forecasting through the fusion of sequence modelling and global temporal patterns. By fusing TFT with transfer learning, the research pioneers a fresh approach to AQI prediction for region with data scarcity issue, capitalizing on cross-domain knowledge transfer. Utilizing meteorological and pollutant data from the Cochin region, a hybrid AQI prediction model is constructed through TFT and transfer learning. Employing a preexisting TFT model trained on Trivandrum data, transfer learning technique is utilized to adapt the model for predicting AQI in the Cochin region. The study demonstrates that integrating TFT with transfer learning yields superior accuracy compared to an exclusive TFT-based approach.
SeeAround: an offline mobile live support system for the visually impaired Sebban, Othmane; Azough, Ahmed; Lamrini, Mohamed
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7904

Abstract

The inability of blind or partially-sighted people to understand visual content and real-life situations reduces their standard of living, especially in a world mainly tailored for sighted individuals. Despite the progress made by certain devices to assist them in using touch, sound, or other senses, these solutions often fall short of bridging the comprehension gap. Our work proposes an intuitive, user-friendly mobile-based framework named "SeeAround" that is capable of automatically providing real-time audio descriptions of the user's immediate visual surroundings. Our solution addresses this challenge by leveraging key point detection, image captioning, text-to-speech (TTS), optical character recognition (OCR), and translation algorithms to offer comprehensive support for visually impaired individuals. Our system architecture relies on convolutional neural networks (CNNs) such as Inception-V3, Inception-V4, and ResNet152-V2 to extract detailed features from images and employs a multi-gated recurrent unit (GRU) decoder to generate word-by-word natural language descriptions. Our framework was integrated into mobile applications and optimized with TensorFlow lite pre-trained models for easy integration on the Android platform.
Web system to enhance technical supervision of incidents at the hydrocarbons regulatory institution in Lima–2023 Caceres Sanchez, Cynthia Elvia; Diburga Evangelista, Luis Alfredo; Cano Lengua, Miguel Angel; Rosas Culcos, Fredy Robert
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7701

Abstract

In this article, the implementation of a web system was carried out to improve the process of technical supervision of incidents of a hydrocarbon regulatory company because time was lost in carrying out each process; this research was developed using the SCRUM methodology as it is an agile methodology and adapted to our research. Using the process, events and artifact, it was possible to design the prototypes of the system, architecture, and database. Finally, the implementation was carried out among other important points obtained as results; the average level of optimization of the incident assignment process, derived from the observations, is 91.05% efficiency in assigning incidents to specialists. Regarding the 95% confidence interval for this indicator, it is between 88.98% and 93.11% efficiency, representing two standard deviations with respect to the mean. Regarding the average response time to incidents in all states, obtained from observations, it is 15 days. The 95% confidence interval for this indicator ranges between 14 and 18 days, which represents two standard deviations from the mean. The system is intuitive and not complex. With the implementation of the web system, processes are automated and end user satisfaction is obtained.
Human-centered design approach for enhancing supply chain management systems in SMEs: insights from Malaysia Ahmad, Sabrina; Zainudin, Nurhamizah; Ermahani A. Jalil, Intan; Hefri Ariyanto, Hepy; Ahmad, Mazida
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7751

Abstract

A reliable supply chain management (SCM) system is crucial to small and medium enterprises (SMEs) to meet the increasing demands of supply chain operations. However, the misalignment between the functional SCM system and the complex, dynamic, and diverse needs of the supply chain stakeholders is paramount. This paper presents an effort to adopt human-centered design (HCD) in the process of identifying requirements for a SCM system, aimed at providing valuable support to SMEs. The HCD places a strong emphasis on shaping design choices in alignment with users' tasks, needs, and preferences, instead of requiring users to adapt their behaviors to fit the system. The survey method is employed to get the SMEs' perspectives on the potential benefits of incorporating HCD into the requirements of the SCM system. The findings showed that a minimum of 80% of the respondents agreed that HCD brings numerous benefits to the development of SCM systems for SMEs in Malaysia.
Non-centroid-based discrete differential evolution for data clustering Poonthong, Tanapon; Wetweerapong, Jeerayut
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8811

Abstract

Data clustering can find similarities and hidden patterns within data. Given a predefined number of groups, most partitional clustering algorithms use representative centers to determine their corresponding clusters. These algorithms, such as K-means and optimization-based algorithms, create and update centroids to give (hyper) spherical shape clusters. This research proposes a non-centroid-based discrete differential evolution (NCDDE) algorithm to solve clustering problems and provide non-spherical shape clusters. The algorithm directs the population of discrete vectors to search for data group labels. It uses a novel discrete mutation strategy analogous to the continuous mutation in classical differential evolution. It also combines a sorting mutation to enhance convergence speed. The algorithm adaptively selects crossover rates in high and low ranges. We use the UCI datasets to compare the NCDDE with other continuous centroid-based algorithms by intra-cluster distance and clustering accuracy. The results show that NCDDE outperforms the compared algorithms overall by intra-cluster distance and achieves the best accuracy for several datasets.

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