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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 58 Documents
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Search results for , issue "Vol 33, No 2: February 2024" : 58 Documents clear
A survey of autonomous vehicles for traffic analysis Ilias Kamal; Khalid Housni; Moulay Youssef Hadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp1016-1029

Abstract

Autonomous vehicles stand at the forefront of technological advancement, promising to alter fundamentally how we navigate, with implications for everything from intercity logistics to daily commuting. The expected benefits of this technology include advancements in safety protocols, streamlining of traffic flow, and superior comfort for those onboard. However, the path to widespread integration is paved with significant barriers that need to be overcome. This article provides a thorough review of the historical development of autonomous driving technologies, beginning with the early vehicles powered by internal combustion engines and extending to the present excitement over Apple’s Project Titan. Rumors suggest this particular project involves the creation of an electric vehicle fully capable of operating without traditional manual controls, including steering wheels and seats. The article concludes by spotlighting the potential for autonomous vehicles to enable disruptive changes in the transport sector, while also considering the myriad of challenges that must be surmounted for their adoption on a grand scale.
A new approximate solution of the fractional trigonometric functions of commensurate order to a regular linear system Djamel Boucherma; Daoud Idiou; Toufik Achour; Mohamed Lotfi Cherrad; Khaled Chettah; Billel Hamadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp879-887

Abstract

This paper introduces a novel approach to the approximate solution of linear differential equations associated with principal fractional trigonometry and the R function. This method proposes a solution that is expressed by adding appropriate fractional linear fundamental functions. Laplace transforms of these functions are irrational. Therefore, we rounded these functions to obtain rational functions in the form of damped cosine, damped sine, cosine, sine and exponential functions. This transformation was achieved by utilizing the concept of fractional commensurate order and, as a result, has direct practical relevance to real-world physics. The precision and effectiveness of the approach are demonstrated through illustrative examples of solving fractional linear systems.
Model development of bond graph based wind turbine Sugiarto Kadiman; Oni Yuliani
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp715-726

Abstract

Just recently, wind energy continues to be one of most important renewable energy resources, by cause of its production is ecologically friendly; for that reason, the technology built for the renewable energy production by way of wind turbines takes excessive challenges in the study. Due to several physical domains prevailing in wind turbine, namely aerodynamical, mechanical and electrical, the modeling of wind turbine is problematic; thus, modeling based on physical techniques has a superior reliability in these circumstances. One of these approaches is bond graph modeling that model system evolved from conservation law of both of mass and energy comprising in the structures. This study presents modeling the parts of bond graph-based wind turbine. Then, sub models are connected together to attain the entire model of wind turbine for simulation based on 20-Sim software. The proposed wind turbine is 2.5 kW of variable velocity wind turbine with three blades, gearbox, tower, and doubly-fed induction generator type. The effectiveness of bond graph modeling system on wind turbine has been proven in simulation results.
Long-term power prediction of photovoltaic panels based on meteorological parameters and support vector machine Saurabh Gupta; Palanisamy Ramasamy; Pandi Maharajan Murugamani; Selvakumar Kuppusamy; Selvabharathi Devadoss; Barath Suresh; Vignesh Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp687-695

Abstract

Solar energy is the most generally accessible energy in the entire globe. Proper solar panel maintenance is necessary to reduce reliance on imported energy. Continuous monitoring of the solar panel's power output is required. The deployment of internet of things (IoT) monitoring of solar panels for maintenance is the basis for the current research. A multi-variable long-term photovoltaic (PV) power production prediction approach based on support vector machine (SVM) is developed in this study with the aim of completely evaluating the influence of PV panels performance and actual operational state factors on the power generation efficiency. This study examines the use of SVM and climatic factors to forecast the long-term output of power from solar panels. A solar power facility in a semi-arid area provided the data utilized in this investigation. Temperature, humidity, wind speed, and sun radiation are some of the meteorological variables that were considered in the study. To anticipate the power generation of the panels, the SVM is trained using the climatic factors and the power generation data. The findings demonstrate that the SVM model consistently predicts the panels' long-term power generation with a high degree of accuracy.
Lexicon-grammar tables standardization and implementation Asmaa Kourtin; Asmaa Amzali; Mohammed Mourchid; Abdelaziz Mouloudi; Samir Mbarki
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp1243-1251

Abstract

The lexicon-grammar approach is a very important linguistic approach in automatic natural language processing (NLP). It allows for the description of the lexicon of the language through readable and intuitive tables for human manual editing. However, the automatic use of the lexicon-grammar tables in the automatic NLP platforms remains difficult, given the incompatibility between the codes used to represent the properties in the lexicon-grammar tables and those used to represent the properties in the automatic NLP platforms. In this work, we present our method of standardizing the lexicon-grammar tables for the French language, since they constitute very rich lexical, syntactic, and semantic linguistic resources. First, we standardize their properties so that they can be compatible with those used in the NLP platforms. Then, to implement the standardized tables, we used a linguistic platform such as NooJ. For that, we describe the process of integrating these tables into this platform through the automatic generation of the dictionaries from these tables. Finally, to test the efficiency of the generated dictionaries, we create for some of them syntactic grammars that take into account all the grammatical, syntactic, and semantic information contained in the dictionaries.
A novel 7-level reduced-switch MLI topology fed PMSM drive for electric vehicle system Chinta Anil Kumar; Kandasamy Jothinathan; Lingineni Shanmukha Rao
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp746-756

Abstract

The compact and efficient design motivates the renewable energy-powered permanent magnet synchronous motor drive for electric vehicle applications. The available renewable energy is interfaced with a power drive by employing an electronic commutator such as a conventional 3-level inverter. But, the multilevel inverter produces favorable merits by producing staircase output voltage from several input direct current (DC) sources. The cascaded H-bridge multilevel inverter plays a significant role in many applications, but it was developed only for limited voltage levels. The major problem in cascaded h-bridge multilevel inverter (CHBMLI) requires more switching devices for higher voltage levels, which increases the size, cost and space of the electric vehicle (EV). To overcome above-mentioned problems, a new objective has been developed by employing the novel Reduced-switch multilevel inverter topology for higher voltage levels. This improves the voltage quality and reducing the harmonic level, and common-mode voltage issues. The main contribution of this work is, developing the novel 5-level, 7-level reduced-switch multilevel inverter (RSMLI) topologies with reduced switching devices with favourable merits over CHBMLI topology. Finally, the performance of proposed novel 5-level and 7-level RSMLI topologies fed PMSM drive for EV system has been verified, by using MATLAB/Simulink computing tool, and simulation results are presented with comparisons.
A low-cost dual bandpass planar filter for WiMAX and mobile communications Amal Kadiri; Abdelali Tajmouati; Jamal Zbitou; Ahmed Lakhssassi
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp757-766

Abstract

This study proposes a new design of low-cost dual-bandpass filter for worldwide interoperability and microwave access (WiMAX) band at 3.50 GHz and mobile communications band at 1.19 GHz. A high pass filter and a stopband filter make up the new dual-bandpass filter structure. Different theoretical studies were carried out for the design of the proposed filter. This filter’s base is a RO5880 substrate with a dielectric permittivity constant of 2.2, loss tangent of 0.0009 and 1.6 mm thickness. High mashing density was used to validate the various simulated structures while accounting for two numerical methods: the moment technique and the finite integration method. The final circuit's overall dimensions are 60×178,3 mm2.
Enhancing fault tolerance: dual Q-learning with dynamic scheduling Chetankumar Kalaskar; Thangam Somasundaram
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp1150-1168

Abstract

Cloud computing has revolutionized IT delivery by offering scalable on-demand internet services encompassing software, platforms, and infrastructure. However, cloud services face significant performance challenges due to their susceptibility to failures given their vast operational scale. Implementing fault tolerance in dynamic cloud services is a key challenge, with complex configurations and dependencies complicating deployment. This paper introduces an innovative approach that combines double deep Q-learning (DDQL) with a dynamic fault-tolerant real-time scheduling algorithm (DFTRTSA) to enhance fault tolerance in real-time systems. DDQL, an extension of deep Q-learning, optimizes the fault-tolerance decision-making process. The algorithm adjusts scheduling strategies dynamically based on system conditions and errors. The fusion of DDQL and DFTRTSA aims to create a resilient and adaptive fault-tolerant mechanism, ensuring uninterrupted operation while meeting real-time requirements. This adaptive approach efficiently manages resources, meets deadlines, and gracefully handles errors, as demonstrated through experiments. Our DDQL-DFTRTSA method outperforms conventional fault-tolerant mechanisms in defect tolerance, energy efficiency, downtime reduction, and system dependability. It proves to be an ideal solution for real-time systems in dynamic and unpredictable environments.
Acoustic and visual geometry descriptor for multi-modal emotion recognition fromvideos Kummari Ramyasree; Chennupati Sumanth Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp960-970

Abstract

Recognizing human emotions simultaneously from multiple data modalities (e.g., face, and speech) has drawn significant research interest, and numerous research contributions have been investigated in the affective computing community. However, most methods concentrate less on facial alignment and keyframe selection for audio-visual input. Hence, this paper proposed a new audio-visual descriptor, mainly concentrating on describing the emotion through only a few frames. For this purpose, we proposed a new self-similarity distance matrix (SSDM), which computes the spatial, and temporal distances through landmark points on the facial image. The audio signal is described through an asset of composite features, including statistical features, spectral features, formant frequencies, and energies. A support vector machine (SVM) algorithm is employed to classify both models, and the final results are fused to predict the emotion. Surrey audio-visual expressed emotion (SAVEE) and Ryerson multimedia research lab (RML) datasets are utilized for experimental validation, and the proposed method has shown significant improvement from the state of art methods.
An efficient smart grid stability prediction system based on machine learning and deep learning fusion model Annemneedi Lakshmanarao; Ampalam Srisaila; Tummala Srinivasa Ravi Kiran; Kamathamu Vasanth Kumar; Chandra Sekhar Koppireddy
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp1293-1301

Abstract

A smart grid is a modern power system that allows for bidirectional communication, driven mostly by the idea of demand responsiveness. Predicting the stability of the smart grid is necessary for improving its dependability and maximizing the efficacy and regularity of electricity delivery. Predicting smart grid stability is difficult owing to the various elements that impact it, including consumer and producer engagement, which may contribute to smart grid stability. This research work proposes machine learning (ML) and deep learning (DL) approaches for predicting smart grid sustainability. Five ML algorithms, namely support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), random forest (RF), and logistic regression (LR), were applied for the prediction of smart grid stability. Later, the stacking ensemble and voting ensemble of ML algorithms were also applied for prediction. To further increase accuracy, a novel fusion model with DL artifical neural networks (ANN) and ML SVM was applied and achieved an accuracy of 98.92%. The experiment results show that the proposed model outperformed existing models for smart grid stability prediction.

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