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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 :
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Articles 65 Documents
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Search results for , issue "Vol 31, No 2: August 2023" : 65 Documents clear
Learning color names using least-squares probabilistic classifiers Janya Sainui; Chouvanee Srivisal
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp866-875

Abstract

Color name is one of the important features for computer vision. Many existing methods proposed to classify colors into a small number of color names. In this paper, we propose an alternative method with the goal to improve the accuracy for assigning a color name to an object in the given image. We here use the least-squares probabilistic classifiers (LSPC) with the local scaling parameters for solving this task. The benefit of the LSPC is that its solution can be computed analytically so that the obtained solution is global optimum, while the local scaling parameters play an important role to deal with the data including clusters with different local statistics as appeared in the real-world data. To deal with this task, the LSPC is learned to assign a color name to each pixel with the highest of the class-posterior density distribution. Then, the estimations of the class-posterior density distributions are utilized to compute the scores for predicting a color name to the given object. Lastly, the color name with the highest score is chosen as a predicted color name for that object. The experimental results on the eBay data set show the improvements over previously proposedmethods.
The accessible large-scale renewable energy potential and its projected influence on Tamil Nadu's grid stability Chelladurai Chandarahasan; Edwin Sheeba Percis
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp609-616

Abstract

Due to its inherent geographical potential for wind/solar power and other renewable energy sources (RES) generation, Tamil Nadu is one of the pioneering Indian states in the early development and utilisation of RES. Tamil Nadu accounts for roughly 25% of India's total wind energy capacity. Increased penetration of RES in a power system indicates that RESs have replaced traditional power plants that have historically managed and stabilised the power system, resulting in novel power flow situations. Grid stability must be enhanced in light of the integration of large-scale RES into the grid. The purpose of this research is to analyse and evaluate the impact of large-scale integration of RES by examining mitigating methods from the most recent academic articles and technical reports in this field. This will aid in identifying the key risks affecting the network's stability margin as a result of the widespread integration of RES generation, allowing for a reasonable increase in the network's stability margin. The study's findings will also help to guide the selection of appropriate mitigation strategies. As the stability margin improves, the future fraction of RES additions to the Tamil Nadu extra high voltage (EHV) grid will be increased significantly.
Multi-layer perceptron neural network mobile robot navigator in unknown environment Mohammed Rabeea Hashim Al-Dahhan; Ruqayah Rabeea Al-Dahhan; Ali Tariq Radeef
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp725-733

Abstract

Recently, navigation in an unknown environment without hitting obstacles was considered a big challenge faced by researchers. The difficulty in finding a good mathematical model for the different systems is deciding to use artificial intelligent controllers to control the mobile robot movement. In this paper, designing two multi-layer-perceptron neural networks (MLP-NN) was done to control the movement of mobile robots in an unknown environment. The first MLP-NN is to control the linear velocity on the x-axis and angular velocity of the robot’s movement while the other MLP-NN is designed to avoid the static and dynamic obstacles faced by the robot while navigating in an unknown environment. The results show each controller's advantages in performing navigation tasks and avoiding obstacles in different environments.
Efficient and secure hybrid chaotic key generation for light encryption device block cipher Hussain M. Al-Saadi; Imad S. Alshawi
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp1032-1040

Abstract

Lightweight cryptographic algorithms must develop to ensure the confidentiality and integrity of the data in resource-constrained devices. Keys are vital to every cryptography algorithm because they provide randomness, complexity, unexpected nature, and robustness. A light encryption device (LED) is considered a lighter version of advanced encryption standard (AES), but it is vulnerable to related key attacks due to using the same key during the whole encryption process. This paper presents a hybrid chaotic key generator (HCKG) based on 3D Lorenz, and 2D Henon maps to generate a highly randomized key that combines with the LED to provide a high level of secure encryption on resource-constrained devices. We modified the HCKG every four rounds via simple operations to get the subkeys and XORed it with the state to increase the complexity of the ciphertext. Moreover, the HCKG with subkeys allows us to decrease the total number of LED rounds from 32 to 24 to minimize the calculation cost while maintaining a high level of security. National Institute of Science and Technology (NIST) test suite proves that the proposed LED-HCKG demonstrates a high-performance increase by nearly 0.3283 higher than LED concerning data integrity and secrecy.
Evaluation of additional electricity losses in electric networks using a meter Allaev Kakhramon Rakhimovich; Adeel Saleem; Kholiddinov Ilkhombek Khosiljonovich; Atif Iqbal; Eraliev Khojiakbar Abdinabi ugli
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp617-625

Abstract

In this article, additional electricity losses are calculated using the voltage imbalance factor. There is also a method for predicting extra losses in the case of longitudinal and transverse asymmetry, as well as information on how to measure power quality indicators. It is suggested that home appliances be used to monitor a separate power system's power quality indicators. The findings of investigations on the condition of asymmetry conducted in operational low voltage electrical networks are presented, along with an illustration of how to calculate additional electricity losses in low-voltage networks. In this article for this purpose, the author developed a Malika device, which is fully capable to measure, store, analyze, and draw conclusions from the results of all electrical quality indicators.
An adaptive system for predicting student attentiveness in online classrooms Bhavna Gupta; Richa Sharma; Roli Bansal; Gagan Kumar Soni; Parul Negi; Paawan Purdhani
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp1136-1146

Abstract

The effectiveness of teaching methods is determined by whether a student is attentive in a lecture or not. In face-to-face classroom teaching, a teacher is able to judge whether students are understanding the subject, based on their facial expressions. However, since the uprise of COVID-19 pandemic, virtual classrooms have found a holding in the field of education and detecting attentiveness of students is a challenge in the same. This paper proposes a student attentiveness model that would detect and monitor a student’s eye state to determine their level of attentiveness and provide a real-time feedback mechanism to the teacher. The proposed model employs a histogram of oriented gradient (HOG) method in conjunction with support vector machine (SVM) algorithm for face recognition. It then computes an adaptive eye aspect ratio (AEAR) for each individual student to determine their level of attentiveness. The model is tested on a real-time dataset and validated using classifiers (SVM, decision tree, and random forest). The results of the classifiers verify that the model produces an accuracy of more than 92%.
A review on contact lens inspection Nur Alifah Megat Abd Mana; Lim Chee Chin; Chong Yen Fook; Haniza Yazid; Yusnita Mohd Ali
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp700-712

Abstract

Over the year, contact lens detection has attracted attention and interest from many researchers to study further in this field of inspection. This paper provides a comprehensive review of the existing literature surrounding contact lens inspection methods. In this paper, contact lens-related, defects-related, and inspection methods related are described in detail. To detect contact lenses in a single image and also multi-image, numerous techniques have been developed and this paper is aimed at classifying and evaluating these algorithms. Also, contact lens inspection based on conventional and artificial intelligence methods will be discussed in detail. The industrial production process of contact lenses probably needs to be constructed with advanced tools based on recent technologies so that they can help in the inspection system to achieve accurate results of the inspection and reduce processing time. 
Optimizing multimedia communication in internet of thing network for improving quality of service Sathya Vijaykumar; Shiva Prakash Thyagaraj
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp1201-1210

Abstract

The internet of things (IoT) has revolutionized the way we interact with technology, with the proliferation of interconnected devices leading to an increase in the volume of data transmitted over the network. One of the key applications of IoT is in multimedia communication, where real-time audio and video data is transmitted over the network. However, the diverse nature of applications and the sheer volume of data in IoT networks can lead to network congestion, latency, and variable quality of service (QoS) at the internet side, resulting in a degradation of the overall QoS for multimedia traffic. In this paper, we propose a cross-layer multimedia optimization solution for multi-point to point IoT networks that incorporates service differentiation and bandwidth reservation techniques to improve the QoS of multimedia traffic. We evaluate the performance of our proposed solution using simulations and compare it with existing solutions. Our results show that our proposed solution can significantly improve the QoS of multimedia traffic in IoT networks, even during periods of high network congestion or variable QoS at the internet side.
Optimum yearly and seasonal tilt angle of solar system in the center of Babylon/Iraq using PVsyst software Mohanad Aljanabi; Muhammed Salah Sadiq Al-Kafaji; Ahmed Hussein Duhis
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp626-635

Abstract

The photovoltaic system tilt angle is one of the more significant factors for obtaining the maximum solar energy that will fall on the PV panel. Consequently, then obtain maximum power output, the solar array needs to be angled properly. The analysis for a stand-alone system is simulated and modelled using PVsyst software version-7.2 for system power 3,120 Wp to obtain the optimum value of the tilt angles of maximum solar irradiation in the center of Babylon Governorate, Iraq. The ideal tilt angle for the south has been found for both yearly and the seasons. Different tilt angles were taken which were (29°, 30°, and 31°) annually, azimuth angle for all is (0°). The incident global irradiation in the collector plane, Incident beam irradiation in the collector plane is maximum at tilt angle (29°) that produces maximum available energy 5,132 kWh/kWp/year. same processing for annual different tilt angle was taken for seasonally; the results show that the angle (49°) for summer and (13°) for winter are the optimum tilt angle which obtains the maximum incident global irradiation in the collector plane, maximum incident beam irradiation in the collector plane at this angle then product maximum available energy 5,343 kWh/kWp/year.
Predictive analytics on COVID-19 data using Hive based on Hadoop cluster Ali Abbood Khaleel; Ali Noori Kareem; Laith Hikmet Mahdi
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp945-956

Abstract

COVID-19 pandemic has received a serious attention from academia, industry and governments to stop the huge number of deaths and economic disruptions around the world. Many techniques have been used to control the spread of the pandemic by understanding its characteristics and behavior. However, because of the large amounts and complex characteristics of COVID-19 data, the querying and analysis of such data using conventional tools have become a challenging task. As a result, powerful and distributed tools are highly required for querying and analyzing this data effectively. In this paper, distributed system using Hive based on Hadoop cluster is used to query and analyze COVID-19 data to obtain meaningful information. Hadoop is employed as a scalable and reliable framework to accommodate such large amounts of data. Hive is used as a data warehouse that run on Hadoop cluster to perform querying and predictive analytics on huge COVID-19 datasets. Several experiments are performed to evaluate the performance of proposed system. Experiments show that the proposed system outperforms relational database management system (RDBMS) in terms of query processing time. Experiments also show that the proposed system has a better efficiency in terms of data load, I/O operation, reading and writing data.

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