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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 64 Documents
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Search results for , issue "Vol 33, No 3: March 2024" : 64 Documents clear
Fractional-order PID controller tuned by particle swarm optimization algorithm for a planar CDPR control Hemama Aboud; Ammar Amouri; Abdelhakim Cherfia; Abdelaziz Mahmoud Bouchelaghem
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1500-1510

Abstract

The use of cable-driven parallel robots (CDPRs) has been steadily increasing across various sectors due to their expansive workspaces, impressive payload-to-mass ratios, and cost-effective designs. Controlling these robots, particularly those with substantial actuation redundancy, can present challenges. This research paper proposes the implementation of a fractionalorder proportional-integral-derivative (FOPID) controller to effectively regulate the end-effector of a planar CDPR with four actuation cables. The parameters of the controller are fine-tuned using the particle swarm optimization (PSO) algorithm to ensure optimal performance. The proposed controller's performance is evaluated through two numerical experiments: target tracking and trajectory tracking using a point-to-point approach. Furthermore, a comparative study is conducted to highlight the controller's performance, comparing the proposed FOPID controller with both the classical PID controller and an optimized PID controller. The achieved results demonstrate that the proposed controller exhibits superior performance in terms of tracking accuracy and smoothness of control signals when compared to the other controllers under investigation. As a result, the proposed controller design represents a substantial advancement in control performance and can be regarded as a promising control strategy for CDPRs.
Enhancing the performance of sustainable energy management of buildings in smart cities Mule Pala Prasad Reddy; Mamidala Vijay Karthik; Chava Sunil Kumar; Katuri Rayudu; Gurrala Madhusudhana Rao
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1315-1326

Abstract

Energy utilization has been the most influential parameter in recent decades, especially in the smart city model. The energy management system has been a more attractive research problem due to its utility, ability, and applications. This paper has an objective that the article discusses innovative energy management methods for sustainability and highlights the potential for integrated smart energy sources. The discussion also touches on the understanding of energy management and production, various storage systems, and their potential future applications. This paper explores challenges in sustainable smart energy management, focusing on methodologies like smart energy systems, PV calculations, electric grid models, and energy management strategies in smart cities. The passive infrared receiver (PIR) sensor has been used in real-time energy management systems to integrate these methodologies into the city's infrastructure. The energy management design aims to coordinate electrical appliances such as fans and lights to minimize energy consumption. The article proposes new energy management and security techniques based on data sources to enhance city intelligence, adaptability, and sustainability by reducing human involvement in controlling electrical appliances in residential buildings. The proposed design and development system optimizes energy utilization more efficiently and effectively than conventional systems, meeting real-time energy management objectives.
Emotion detection using Word2Vec and convolution neural networks Anil Kumar Jadon; Suresh Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1812-1819

Abstract

Emotion detection from text plays a very critical role in different domains, including customer service, social media analysis, healthcare, financial services, education, human-to-computer interaction, psychology, and many more. Nowadays, deep learning techniques become popular due to their capabilities to capture inherent complex insights and patterns from raw data. In this paper, we have used the Word2Vec embedding approach that takes care of the semantic and contextual understanding of text making it more realistic while detecting emotions. These embeddings act as input to the convolution neural network (CNN) to capture insights using feature maps. The Word2Vec and CNN models applied to the international survey on emotion antecedents and reactions (ISEAR) dataset outperform the models in the literature in terms of accuracy and F1-score as model evaluation metrics. The proposed approach not only obtains high accuracy in emotion detection tasks but also generates interpretable representations that contribute to the understanding of emotions in textual data. These findings carry significant implications for applications in diverse domains, such as social media analysis, market research, clinical assessment and counseling, and tailored recommendation systems.
Deep neural networks optimization for resource-constrained environments: techniques and models Raafi Careem; Md Gapar Md Johar; Ali Khatibi
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1843-1854

Abstract

This paper aims to present a comprehensive review of advanced techniques and models with a specific focus on deep neural network (DNN) for resource-constrained environments (RCE). The paper contributes by highlighting the RCE devices, analyzing challenges, reviewing a broad range of optimization techniques and DNN models, and offering a comparative assessment. The findings provide potential optimization techniques and recommend a baseline model for future development. It encompasses a broad range of DNN optimization techniques, including network pruning, weight quantization, knowledge distillation, depthwise separable convolution, residual connections, factorization, dense connections, and compound scaling. Moreover, the review analyzes the established optimization models which utilizes the above optimization techniques. A comprehensive analysis is conducted for each technique and model, considering its specific attributes, usability, strengths, and limitations in the context of effective deployment in RCEs. The review also presents a comparative assessment of advanced DNN models’ deployment for image classification, employing key evaluation metrics such as accuracy and efficiency factors like memory and inference time. The article concludes with the finding that combining depthwise separable convolution, weight quantization, and pruning represents potential optimization techniques, while also recommending EfficientNetB1 as a baseline model for the future development of optimization models in RCE image classification. 
Towards a consulting model for a good urban generation of social housing districts in terms of equipment Lamyae Alaoui; sakina elhadi; Abdellah Lakhouili; Abdelaziz Merzak
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1866-1875

Abstract

Social housing in Morocco is housing built with state assistance. It is subject to precise rules of construction, management and allocation and governed by specifications defining requirements in a manual manner. This work which is done manually can produce errors either at the level of calculations or at the level of equipment proposal. The objective of this article is to computerize this area as there is a lack of tools and platforms. To solve this problem, we have created an advisory platform for good urban planning for residents which will help engineers. Based on mathematical formulas. This tool will allow architects and urban planners to design autonomous populations in terms of equipment.
Smart airbag vest with integrated light turn signaling and location tracking Alecsandra Marjorie G. Cerda; Mark Joseph B. Enojas; Gil Jr. Abengaña; Mylene B. Laid; Mark Joshua T. Revedizo; Rimmel James Torrenueva
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1465-1473

Abstract

Research on road safety is continuously developing with the applications of sensors and technologies. Another area that draws the attention of the developers for road safety are in the road accident response. As the number of riders and cyclists increase, so do the accidents particularly at night where visibility is limited. This study presents a method of integration of inflatable safety vests for riders with light emitting diode LED signaling strips embedded in its front and back, and sensors to send the location of the rider when an accident happens. The LED strips are controlled using a wireless remote switch to make the rider more visible other than that embedded in the motorcycle or bicycle. The airbag will activate once an accident occurs and or the rider is detached from the motorcycle. A force-sensitive resistor (FSR) is used as a triggering device attached to the vest when an accident happens where a global positioning system (GPS) module will send the location and a map to a specific mobile number for response. Three trials were conducted to test the functionality of the LED lights for signaling. The device functioned well, that both the left, right, and standby mode were activated. The functionality of the location tracker is also tested in three different locations. The FSR was triggered and it gave the exact location by sending the coordinates and a link to view it on google maps with an average transmit and receive time of 3 seconds. It is recommended that the prototype be developed using light-weight materials and batteries.
Gym’s hybrid system for off-grid renewable energy solutions Abdelfattah El Azzab; Abdelmounime El Magri; Rachid Lajouad; Ilyass El Myasse; Aziz Watil; Hassan Ouabi
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1378-1386

Abstract

The primary objectives behind transitioning from fossil fuels to green energy sources, with a particular focus on reducing both electricity costs and carbon emissions. This transition has prompted various sectors and sports bikes, to embrace renewable energy alternatives, with a specific emphasis on technologies such as photovoltaic systems, energy storage solutions, and power generation from machines. The core subject of investigation in this paper is the application of renewable energy sources within sports bikes, with a particular emphasis on a hybrid system. This hybrid system incorporates DC/DC, AC/DC, and DC/AC converters to meet the energy requirements of the facility. The central aim of the research is to identify the most economically efficient scale for a self-sufficient hybrid photovoltaic system that integrates stationary generators and battery storage. The research seeks to optimize the balance between cost-effectiveness and sustainable energy provision in the context of sports facilities.
SEM and TEM images’ dehazing using multiscale progressive feature fusion techniques Chellapilla V. K. N. S. N. Moorthy; Mukesh Kumar Tripathi; Suvarna Joshi; Ashwini Shinde; Tejaswini Kishor Zope; Vaibhavi Umesh Avachat
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp2007-2014

Abstract

We present a highly effective algorithm for image dehazing that leverages the valuable information within the hazy image to guide the haze removal process. Our proposed algorithm begins by employing a neural network that has been trained to establish a mapping between hazy images and their corresponding clear versions. This network learns to identify the shared structural elements and patterns between hazy and clear images through the training process. To enhance the utilization of guidance information from the generated reference image, we introduce a progressive feature fusion module that combines the features extracted from the hazy image and the reference image. Our proposed algorithm is an effective solution for image dehazing, as it capitalizes on the guidance information in the hazy appearance. By combining the strengths of deep learning, progressive feature fusion, and end-to-end training, we achieve impressive results in restoring clear images from hazy counterparts. The practical applicability of our algorithm is further validated by its success on benchmark data sets and real-world SEM and TEM images.
An ensemble deep learning model for automatic classification of cotton leaves diseases Hirenkumar Kukadiya; Nidhi Arora; Divyakant Meva; Shilpa Srivastava
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1942-1949

Abstract

Cotton plant (Gossypium herbaceum), is one of the significant fiber crop grown worldwide. However, the crop is quite prone to leaves diseases, for which deep learning (DL) techniques can be utilized for early disease prediction and prevent stakeholders from losing the harvest. The objective of this paper is to develop a novel ensemble based deep convolutional neural network (DCNN) model developed on two base pretrained models named: VGG16 and InceptionV3 for early detection of cotton leaves diseases. The proposed ensemble model trained on cotton leaves dataset reports higher training and testing prediction accuracies as compared to the base pretrained models. Given that, deep learning architectures have hyper-parameters, this paper presents exhaustive experimental evaluations on ensemble model to tune hyper-parameters named learning rate, optimizer and no of epochs. The suggested hyper-parameter settings can be directly utilized while employing the ensemble model for cotton plant leaves disease detection and prediction. With suggested hyper-parameters settings of learning rate 0.0001, 20 epochs and stochastic gradient descent (SGD) optimizer, ensemble model reported training and testing accuracies of 98% and 95% respectively, which was higher than the training and testing accuracies of VGG16 and InceptionV3 pretrained DCNN models.
A hybrid data mining for predicting scholarship recipient students by combining K-means and C4.5 methods Halifia Hendri; Harkamsyah Andrianof; Riska Robianto; Hasri Awal; Okta Andrica Putra; Romi Wijaya; Aggy Pramana Gusman; Muhammad Hafizh; Muhammad Pondrinal
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1726-1735

Abstract

This scholarly investigation delves into the strong desire for academic scholarships within the student body, especially prominent among socioeconomically disadvantaged individuals. The study aims to formulate a hybrid data mining paradigm by synergizing the K-means and C4.5 methodologies. K-means is applied for clusterization, while C4.5 facilitates prediction and decision tree instantiation. The research unfolds in sequential phases, commencing with data input and progressing through meticulous pre-processing, encompassing data selection, cleaning, and transformation. The novelty lies in successfully integrating the K-means and C4.5 methodologies, culminating in the hybrid data mining method. The dataset comprises 200 students seeking scholarships, revealing effective stratification into three clusters—cluster 0, cluster 1, and cluster 2—with 119, 48, and 33 students, respectively. The K-means method proves highly suitable, especially when combined with C4.5, for predicting scholarship recipients. A subset of 81 students from clusters 1 and 2 undergoes predictive modeling using C4.5, resulting in a commendable 85% accuracy, with 17 accurate forecasts and 3 minor inaccuracies. This research significantly enhances scholarship selection efficiency, particularly benefiting socioeconomically disadvantaged students.

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