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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 32, No 3: December 2023" : 64 Documents clear
Adaptive traffic aware clustering and routing model for wireless sensor networks Rudramurthy Veeregowdanadoddi Chandraiah; Aparna Ramalingappa
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1630-1638

Abstract

Designing effective clustering algorithms plays a very important role in improving network lifetime target coverage (NLTC) in heterogeneous wireless sensor networks (HWSNs). However, the current clustering mechanism failed to address hotspot problem resulting in poor coverage and network lifetime performance. Recently, unequal clustering has been used for addressing hotspot problem in HWSNs. However, when performing inter-cluster routing, load balancing is not efficient and the energy degradation is high, which affects the overall network performance. This work introduces an effective model namely adaptive traffic aware clustering and routing (ATACR) model to address hotspot and load balancing issues. The ATACR introduces a novel unequal clustering and ideal distribution of load in different cluster levels. The ATACR model improves network lifetime, reduces control channel overhead, and attains better throughput in comparison with existing routing models.
Characteristic of graphene-based thick film gas sensor for ethanol and acetone vapor detection at room temperature Siti Amaniah Mohd Chachuli; Muhammad Luqman Hakkim Noor; Omer Coban; Nur Hazahsha Shamsudin; Muhammad Idzdihar Idris
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1384-1391

Abstract

Ethanol and acetone are volatile organic compound gases widely used in food processing. Health problems such as irritation of the eyes, nose, and throat can affect human health if exposed to these gases. Two graphene gas sensors were fabricated using a screen-printing technique onto a glass substrate to compare their performance to the acetone and ethanol vapors at room temperature. The graphene paste was prepared by mixing 95 wt.% of the binder with 5 wt.% of graphene nanoflakes. A silver paste was used asthe interdigitated electrode of the gas sensor and becamethe first layer of the gas sensor. The silver paste was deposited on the glass substrate using a screen-printing technique and fired at 150°C for 15 minutes. Next, the graphene paste was depositedonto the interdigitated electrode using a screen-printing technique and becamethe second layer of the gas sensor. The graphene was annealed at 200°C for 30 minutes. Both graphene gas sensors responded well to ethanol and acetone vapor with an n-typed gas sensor at room temperature. As a comparison, the graphene gas sensor showed better characteristics in terms of response and recovery characteristicsto ethanol vapor than acetone vapor at room temperature. The response and response time of the graphene-based thick film gas sensor to ethanol were approximately 21.89 and 24.08, respectively.
Adaptive tuning of PID using chef‑based optimization algorithm for improving automatic voltage regulator Widi Aribowo; Reza Rahmadian; Mahendra Widyartono
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1215-1223

Abstract

This  article  presents  the  proportional-integral-derivative  (PID)  parameter tuning  on  the  automatic  voltage  regulator  (AVR)  using  the chef-based optimization   algorithm   (CBOA).   CBOA   is   modeling   cooking   training activities consisting of students and young chefs in an effort to mature cooking skills.  This  article  uses  other  methods  as  a  comparison  in  measuring  the performance  of  the  proposed  method.  The  methods  used  are grasshopper optimization algorithm (GOA) and cooperation search algorithm (CSA). The simulation results show that the proposed method, namely CBOA, has a better ability in the peak value of overshoot, which is 0.232% compared to the CSA method and 12.99% compared to the GOA method.
Poultry disease early detection methods using deep learning technology Liu Yajie; Md Gapar Md Johar; Asif Iqbal Hajamydeen
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1712-1723

Abstract

Poultry production is a pivotal contributor to global economic growth, playing a central role in promoting human ecosystem sustainability. It offers affordable and readily accessible protein sources, encompassing meat, eggs, and other by-products. Beyond its direct nutritional benefits, poultry production enhances household income, bolsters food security, and aids in poverty reduction, making it integral to worldwide economic advancement. However, as the global population surges, so does the demand for poultry meat and eggs. Concurrently, poultry disease management emerges as a paramount challenge, leading to significant threats to food security and economic stability. Leveraging cutting-edge technology offers promising avenues to devise strategies that not only bolster farm profitability but also mitigate environmental impacts and foster the well-being of both animals and humans. This study systematically reviews the latest literature concerning poultry disease diagnosis based on deep learning techniques, elucidating the clinical manifestations associated with various ailments. The analysis indicates that emerging technological solutions, especially image processing and deep learning (DL), substantially outperform conventional manual inspection methods in early disease detection and warning in the poultry sector. Such innovations underscore their potential for revolutionizing poultry health management and disease mitigation.
Reduced switch cascaded asymmetrical 27 level inverter-STATCOMwith fuzzy logic controller Sundar Ramesh; Vijayakumar Govindaraj; Raja Raman; Shanmugasundaram Venkatarajan; Kamatchi Kannan Vijayarangan
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1288-1297

Abstract

In this study, a 27-levelinverter with a reduced switch asymmetrical cascaded H-bridge (CHB) with fuzzy logic controller (FLC) is proposed. With series connections, a low voltage converter, a middle level voltage converter, and a high voltage converter make up the static synchronous compensator (STATCOM). The configuration of the asymmetrical inverter uses trinary DCsources. To acquire switching signals for the trinary inverter-based STATCOM to compensate for real power, load voltage, reactive power, load current, and power factor under load changing conditions, FLC is constructed. With fewer switches, the suggested arrangement produces greater voltage levels. The performance of the reduced switch asymmetrical cascaded H-bridge inverter-STATCOM with FLCis simulated using the MATLAB Simulink platform under both static and dynamic load conditions. When compared to reduced switch asymmetrical cascaded H-bridge inverter-STATCOM with traditional proportional integral (PI) controller, the FLC result demonstrates efficient unbalanced load compensation. The FLC in the proposed inverter also lowers the total harmonic distortion.
Diabetes mellitus prediction using machine learning within the scope of a generic framework Nidhi Arora; Shilpa Srivastava; Ritu Agarwal; Vandana Mehndiratta; Aprna Tripathi
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1724-1735

Abstract

Artificial intelligence (AI) based automated disease prediction has recently taken a significant place in the field of health informatics. However, due to unavailability of real time large scale medical data, the dynamic learning of prediction models remains principally subsided. This paper, therefore proposes a dynamic predictive modelling framework for chronic diseases prediction in real-time. The framework premise suggests creation of a centralized patient-indexed medical database to dynamically train machine learning (ML) models and predict risk levels of chronic diseases in real time. In this study, comprehensive empirical evaluations to train seven state-of-the-art ML models for diabetes risk prediction are performed in context of phase 2 of the suggested framework. The selected optimal model can then be dynamically applied to predict diabetes in phase 3 of the framework. Various metrics such as accuracy, precision, Recall, F1-score and receiver operating characteristic (ROC) curve are employed for evaluating performances of the trained models. Parameter tunings using different type of kernels, different number of neighbors and estimators are rigorously performed in order to create a suggestive literature for healthcare prediction ecosystem. Comparative analysis indicates high prediction accuracies on diabetes test data records for neural network and support vector machine (SVM) models as compared to other applied models.
A study of stereo matching algorithm on low texture and depth discontinuity regions Melvin Gan Yeou Wei; Rostam Affendi Hamzah; Nik Syahrim Nik Anwar; Adi Irwan Herman
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1512-1520

Abstract

This article studies the performance of the proposed stereo matchingalgorithm on complex regions. These regions are areas with very limitedinformation for the matching process which are low texture, and depthdiscontinuity regions. In this study, each algorithm uses different matchingcost computation (MCC) techniques, but for cost aggregation (CA), disparityoptimization (DO) and disparity refinement (DR), the technique remains thesame. The MCC areabsolute difference(AD), the combination ofabsolutedifference and gradient matching(AD+GM) andcensus transform(CT).Then, for CA, DO and DR, they areminimum spanning tree(MST),winnertake all(WTA) andbilateral filter(BF), respectively. The results are presentedand discussed in this article. Hence, thru this study the robust method can beestimated at the MCC stage.
Yoga pose annotation and classification by using time-distributed convolutional neural network Somashekhar S. Dhanyal; Suvarna S. Nandya
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1639-1647

Abstract

In India, people have been practicing yoga for thousands of years to improve their health and well-being on all levels. As the pace of technological development increases, this presents a great opening for computational probing across all areas of social domains. Nevertheless, it remains difficult to integrate artificial intelligence (AI) and machine learning (ML) methods to an interdisciplinary field like yoga. The proposed study aims to develop a yoga pose annotation and classification for yogasana recognition in real time. The study considers TensorFlow for better implementation of data automation, performance monitoring. TensorFlow yields better numerical computation and hat helps ML and efficiently develops the neural network. The proposed composed of time-distributed convolutional neural network (CNN) through the Softmax function. Also, a poseNet algorithm is considered to estimate the user’s real-time yoga pose. The use of a database i.e., poseTrack in the proposed method offers annotation to the evaluation of yoga pose and tracking of it. The performance analysis of the proposed yoga pose annotation and classification model suggests that it offers higher accuracy than traditional, support vector machines (SVM) and K-nearest neighbor (KNN).
Modelling and design of grid voltage oriented vector control scheme for DC railway recuperating system Chuen Ling Toh; Chee Wei Tan
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1816-1824

Abstract

The braking energy harvested by a railway vehicle can be restored to the utility grid with a power recuperating system. A grid connected voltage source inverter (VSI) is commonly used as a grid-feeding converter in the recuperating system. This paper proposes to integrate grid voltage oriented vector control (GVOVC) and third harmonic voltage injection pulse width modulation (THVI-PWM) technique for the VSI to ensure grid voltage and frequency synchronization. A simulation study is carried out to evaluate the feasibility of the proposed control and modulation schemes using MATLAB/Simulink. The results show that the proposed controller may reach steady-state operating mode within 7 ms by producing good quality AC voltages and currents waveforms. With the independent control of voltage quantities in dq reference frame, the regulation of active and reactive power could be realized.
FedLANE: a federated U-Net architecture for lane detection Santhiya Santhiya; Immanuel Johnraja Jebadurai; Getzi Jeba Leelipushpam Paulraj; Polisetti Pavan Venkata Vamsi; Madireddy Aravind Reddy; Praveen Poulraju
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1621-1629

Abstract

Lane detection is a crucial module for today’s autonomous driving cars. Detecting road lanes is a challenging task as it varies in color, texture, boundaries and markings. Traditional lane detection techniques detect the lane by applying a model trained with centralized data. As roads vary in urban and rural areas, a more localized and decentralized training technique is desired for accurate and personalized lane detection. Federated learning has recently proved to be a promising technology that trains and prunes the model using local data. Applying federated learning-based lane detection improves the accuracy of detection and also ensures the security and privacy of autonomous cars. This paper proposes FedLANE, a federated learning-based lane detection technique. U-Net, U-Net long short-term memory (LSTM) and AU-Net architectures were explored using a federated learning approach. Experimental analysis using TuSimple and CuLane dataset shows that the FedLANE based lane detection performs similar to that of the traditional deep learning lane detection models.

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