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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 57 Documents
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Search results for , issue "Vol 39, No 3: September 2025" : 57 Documents clear
Inertia factor and crossover strategy based particle swarm optimization for feature selection in emotion classification Byreddy, Shilpa Somakalahalli; Revanna, Shashikumar Dandinashivara
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1704-1713

Abstract

Emotion recognition using electroencephalography (EEG) is a better choice because it can’t be easily mimicked like facial expressions or speech signals. The emotion of EEG signals is not the same and vary from human to human, as everyone has different emotional responses to similar stimuli. Existing research has achieved lesser classification accuracy as it relies on whole feature subsets that include irrelevant features for classifying emotions. This research proposes the inertia factor and crossover strategy (IFCS)-based particle swarm optimization (PSO) algorithm to select relevant features for classification, which removes irrelevant features and enhances classification performance. Then, the self-attention with gated recurrent unit (SA-GRU) method is developed to classify the valence and arousal emotion classes, which focuses much on the significant parts of emotions and reaches high classification accuracy. The proposed IFCS-PSO and SA with GRU method achieved an accuracy of 98.79% for the valence class and 98.03% for the arousal class of the DEAP dataset, outperforming traditional approaches such as convolutional neural networks (CNN).
A solar PV-fed MF-DVR for compensation of grid-islanding issues and power-quality issues in grid-connected distribution system Bhavani, Tharinaematam; Rajababu, Durgam; Irfan, Md Mujahid
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1480-1488

Abstract

Difficulties with the quality of power come up as an effect of the inte-conneted renewable energy through grid called as distribution generation (DG) scheme. The voltage harmonics and swell-sag are happened in the utility grid as a result of power quality issues, affecting end-level consumers. Moreover, grid islanding issues is considered the most affected problem in distribution system for affecting the uninterrupted energy-flow to respective load demand. The main aim of this paper provides affective designing of the suitable cost-effective multi-functional dynamic voltage restorer (MF-DVR) has been proposed for resolving the problems. The major objective is mitigation of voltage-interruptions during grid-islanding, voltage-sag, voltage-swell and voltage-harmonics, any voltage quality in the utility grid, by utilizing the solar photovoltaic (PV) integrated MF-DVR as DG scheme through synchronous reference frame (SRF) control theory. Also, it can regulate the voltage and phase of the distribution system during sudden voltage interruptions occurred in grid-islanding. The performance of the proposed SRF controlled MFDVR for power-quality (PQ) improvement and DG integration during grid-islanding has been validated via Matlab/Simulink computing tool; the simulation findings are shown with an appealing comparison analysis.
Sensitivity-based approach for evaluation and enhancement of available transfer capability using FACTS devices Sureban, Manjula S.; Ankaliki, Shekappa G.
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1431-1440

Abstract

Available transfer capability (ATC) plays an important role in the reliable and secure power system operation. It measures the transfer capability available in the transmission system for further trading over and above existing commitments without violating the system limits. The increased demand for electric power in recent years due to increasing population, automation in industries, and use of electricity in transportation, and also the deregulation of power systems results in an overload of the transmission network and hence congestion in the system. Therefore, quick and accurate calculation of ATC and its enhancement is needed for secured and reliable operation. It is possible to enhance ATC by placing the flexible alternating current transmission systems (FACTS) devices of appropriate size and at optimal locations in the system. In this paper, a computationally efficient sensitivity-based approach for evaluation and enhancement of available transfer capability in the presence of of FACTS devices is presented. The developed approach is implemented on the IEEE 14 bus system.
Extended Kalman filter based unconstrained model predictive control of a complex nonlinear system: the quadruple tank process Zidane, Zohra
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1553-1561

Abstract

This paper proposes the model predictive controller (MPC) based on the Kalman filter for a complicated nonlinear system—the quadruple tank process (QTP). The control of a multivariable and nonlinear system like a QTP is a difficult job. A number of nonlinear design techniques are implemented to ameliorate the pursuit performance of the QTP, however, the nonlinear techniques make implementation composite and computationally unsuitable. In this work, an unconstrained MPC is planed for the QTP experiences and it is controlled for both minimum and non-minimum sentence configurations in order to follow the wanted track. Its performance can be damaged once system is pass from minimum to non-minimum phase region and inversely. The unknown states required for model predictive control design are rebuilt using an extended Kalman filter. The design of model predictive control and extended Kalman filter is based on the QTP and the achievement of the proposed controller is checked for the monitoring of references. All results of simulation are affected using the MATLAB software. The results of the simulation show the capability and power of the suggested controller in respect of monitoring the trajectory and state estimation.
Characterization of binarized neural networks for efficient deployment on resource-limited edge devices Narayana, Ramya Banavara; Singh, Seema
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1815-1825

Abstract

This paper delves into binarized neural networks (BNNs) tailored for resource-constrained edge devices. BNNs harness binary weights and activations to amplify efficiency while upholding accuracy. Across diverse network configurations, BNNs consistently outshine traditional neural networks (NNs). A pioneering BNN architecture is developed in LARQ, achieving an impressive. 61% accuracy on the MNIST dataset through binary quantization, weight clipping, and pointwise convolutions. Implementation on the Xilinx PYNQZ2 FPGA board shows far quicker classification rates, with a maximum inference time of 0.00841 milliseconds per image, approximately 10,000 images being classified in this length of time. The time taken per image represents approximately 0.01% of the total inference time. This underscores BNNs' potential to redefine real-time edge computing applications. The paper makes significant strides by elucidating BNNs' performance superiority, proposing an innovative architecture, and validating its prowess through real-world deployment. These findings underscore BNNs as agile, high-performance models primed for edge computing, fostering a new era of real-time processing innovations.
UniMSE: a unified approach for multimodal sentiment analysis leveraging the CMU-MOSI Dataset Basu, Miriyala Trinath; Saha, Mainak; Gupta, Arpita; Hazra, Sumit; Fatima, Shahin; Sumalakshmi, Chundakath House; Shanvi, Nallagopu; Reddy, Nyalapatla Anush; Abhinav, Nallamalli Venkat; Hemanth, Koganti
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp2032-2042

Abstract

This paper explores multimodal sentiment analysis using the CMU-MOSI dataset to enhance emotion detection through a unified approach called UniMSE. Traditional sentiment analysis, often reliant on single modalities such as text, faces limitations in capturing complex emotional nuances. UniMSE overcomes these challenges by integrating text, audio, and visual cues, significantly improving sentiment classification accuracy. The study reviews key datasets and compares leading models, showcasing the strengths of multimodal approaches. UniMSE leverages task formalization, pre-trained modality fusion, and multimodal contrastive learning, achieving superior performance on widely used benchmarks like MOSI and MOSEI. Additionally, the paper addresses the difficulties in effectively fusing diverse modalities and interpreting non-verbal signals, including sarcasm and tone. Future research directions are proposed to further advance multimodal sentiment analysis, with potential applications in areas like social media monitoring and mental health assessment. This work highlights UniMSE's contribution to developing more empathetic artificial intelligence (AI) systems capable of understanding complex emotional expressions.
Optimizing timing closure and enhancing efficiency in RTL design: a focus on physical design tasks for I2C design blocks Ramegowda, Madhura; Hirebasur Krishnappa, Krutthika; Yamadur Venkatesh, Divyashree; Sreenivasa, Kokila
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1525-1540

Abstract

Achieving precise timing closure in integrated circuit (IC) design is a significant challenge, especially with today's rapid technology advancements and intricate design specifications. Even with intense post-synthesis optimization, timing violations persist particularly in multi-corner, multi-mode designs. This research work emphasizes the necessity for power-efficient methods and streamlined approaches to boost timing closure and physical verification. Modern IC design thrives on effective physical design optimization strategies, usually tackled top-down. Clock tree synthesis (CTS) is transformative which effectively addresses clock deviation, latency, transition time, and insertion delay. This investigation mainly focuses on improving timing closure for inter integrated circuit (I2C) design blocks using custom-designed ccopt_spec and mmmc.tcl files to support multi-corner, multi-mode settings and significantly reduces register-to-register path violations from 80 to. 0. Additionally, the development and the usage of mmmc.tcl and global files are highlighted as critical components in the design process.
A comparative study of CNN architectures for the detection of tomato leaf diseases Benkrama, Soumia; Ahmed, Benyamina; Hemdani, Nour El Houda
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1587-1594

Abstract

Recent advancements in computer vision and machine learning (ML) have revolutionised various sectors, including precision agriculture (PA). In our study, we focused on detecting tomato leaf diseases (TLD) using deep learning (DL) techniques. Using a convolutional neural network (CNN) model, we developed an agricultural image index to accurately detect TLD. By utilizing available datasets from Kaggle, we trained our model to recognize various TLDs. To determine the most effective one, we compared multiple architectures, including VGG, ResNet, and EfficientNetB1. The obtained results demonstrated a classification accuracy of over 99% on the test set. This approach has allowed us to accelerate and enhance the disease detection process, positively impacting agricultural communities by reducing crop losses and enabling early intervention in case of disease outbreaks. Our study highlights the effectiveness of CNN models in the detection of TLD, paving the way for future applications in PA.
Electrical system load re-phasing: a case of a university building in the Philippines Marcos, Ferdinand L.; Domingo, Enalyn T.; Lapuz, Cid L.
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1441-1448

Abstract

In the pursuit of attaining energy efficiency, administrators must delve deeply into the electrical system of a facility, especially if it is an old structure. As years go by, renovations and the addition of new equipment may lead to an imbalance in the electrical system. These imbalances may lead to inefficiencies, contributing to damage to equipment. This study aimed to investigate the electrical system of a university building by determining whether the percent current and voltage unbalance values in the system meet the standards. For non-conforming electrical branches, re-phasing schemes were proposed. Data revealed that the majority of the panelboards in the building have voltage imbalances that are within the allowable limit, while there is a considerable number of panelboards with above-the-minimum current unbalance value. The original configurations of some panelboards were retained to avoid further increase in the percentage of current imbalance associated with re-phasing. Merging certain panelboards, however, resulted in a reduction of current imbalances within the acceptable limit. If the re-phasing and merging of loads are to be implemented, a cost-benefit analysis and a study on the improvements in energy efficiency may be considered for further research.
Deep belief network classification model for accurate breast cancer detection and diagnosis Amirthayogam, G.; R., Deepak; Ram, M. Preethi; J., Nithya; Basha H., Anwar; B., Sriman; Sundar, R.
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1900-1912

Abstract

Breast cancer is still one of the common malignancies and endemics that are fatal to women across the globe. Early-stage diagnosis helps reduce the percentage of deaths because treatment outcomes are much better at that stage. As the contemporary approaches in machine learning (ML) and deep learning (DL) emerged, the automatic detection of breast cancer has received a great consideration for their ability to improve diagnosis and treatment. We present a new deep belief network (DBN) based breast cancer detection system to increase the accuracy and the dependability of the diagnosis of breast cancer. The major modules of the system are image preprocessing, feature extraction and the DBN-based classification to guarantee accurate detection and classification of malignant and benign breast lesions. We compared the proposed DBN model with the existing DL models like convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and generative adversarial networks (GANs). It is with respect to critical features of the model performance which includes accuracy, precision, recall, specificity and F1-score. The methodologies used in this study show that the performance of the proposed DBN model is significantly better than these conventional algorithms in accuracy and sensitivity where the DBN model is an ideal method for the early detection of breast cancer. Through extensive experimentation, we compared the proposed DBN model with existing DL techniques such as CNNs, RNNs, LSTMs, and GANs. Our results show that the proposed DBN model outperforms these models in several key performance metrics.

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