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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
Enhanced proportional integral controller for DSTATCOM with particle swarm optimization algorithm in power system Parag Vijay Datar; Deepak Balkrishna Kulkarni
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.pp1363-1377

Abstract

Power system expansion planning is supported by using distributed generation (DG) instead of installing new main power system generators. The portability and autonomous operation of the DG make it capable of integrating into the distribution system. Power electronics devices for home loads, mainly electric vehicles, mobile phones, and laptops, introduce harmonics and related power quality issues to the distribution system. This load creates power quality issues like an increase in total harmonic distortion (THD) and a reduction in power factor. This paper uses the DG to supply important power electronics devices for the mitigation of power quality problems in distributed systems with non-linear loads, such as electric vehicles, called the “distributed static compensator (DSTATCOM)”. Wind and photovoltaic (PV) generators supply power to a common DC source. To improve power quality D-STATCOM powered by PV and wind energy systems at the DC link is incorporated. The direct axis/quadrature axis method (DQ method) controls the real and reactive components of the power. Proportional integral (PI) and particle swarm optimization (PSO) based PI controller parameter estimation are developed, and results are compared for power quality performance. In terms of improving power quality, the PSO-based parameter estimate of the PI controller performs better than the traditional PI controller. 
Implementation of a low-cost intelligent street light system using internet of things Fatima Outferdine; Khalid Cherifi; Driss Belkhiri; Brahim Bouachrine; Mohamed Ajaamoum
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.pp1387-1396

Abstract

In the contemporary world, science and technology are advancing swiftly to meet the growing human need for electricity. Within this framework, street lighting, serving as the most vital and ubiquitous element of urban lighting infrastructure, contributes significantly to public electricity consumption. Hence, enhancing the operational efficiency of street lamps becomes imperative to conserve energy. Nonetheless, traditional street lighting systems, being manually controlled, consuming excessive power, and entailing high installation expenses, present notable drawbacks and concerns. Leveraging the internet of things (IoT), advanced innovations automate various areas, including health monitoring, traffic management, agricultural irrigation, street lights, and classrooms. The current manual operation of street lights leads to substantial global energy waste. To address this, an integrated hardware and software solution is proposed. Practical implementation of the hardware devices employs a wireless sensor network, while the software focuses on developing an IoT application for data storage, analysis, and visualization. The proposed system enables effective monitoring of parameters such as ambient temperature, current, voltage, and energy consumption of photovoltaic street lights, which are used as an indicator of the lamp status. Using Xbee modules, a configuration by the X-CTU software is necessary to communicate between all street lights wirelessly. These Xbee modules are used as a leading technology for wireless sensor networks due to its low power and low cost. Experimental results demonstrate that the proposed system is energy-efficient and cost-effective, and further can be implemented in real street light systems.
Improving night driving behavior recognition with ResNet50 Muhammad Firdaus Ishak; Fadhlan Hafizhelmi Kamaru Zaman; Ng Kok Mun; Syahrul Afzal Che Abdullah; Ahmad Khushairy Makhtar
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.pp1974-1988

Abstract

The issue of driving behavior at night poses significant challenges due to reduced visibility and increased risk of accidents. Recent works have leveraged deep learning techniques to enhance night-time driving safety. However, the limited availability of high-quality training data and the lack of robustness in existing models present significant problems. In this work, we propose a novel approach to improve driving behavior recognition at night using ResNet50 with contrast limited adapted histogram equalization (CLAHE). We collected a new dataset and developed a more effective and robust model that can accurately recognize driving behaviors under low-illumination conditions, thereby reducing the likelihood of collisions and improving overall road safety. The experimental results demonstrate significant improvements in the deep learning model’s performance compared to conventional methods. Notably, the ResNet50 model delivers the best performance with accuracy rates of 90.73% using NIGHT-VIS-CLAHE data, demonstrating a 16% improvement in accuracy. For benchmark purposes, the InceptionV3, GoogleNet, and MobileNetV2 models also show enhanced accuracy through CLAHE implementation. Furthermore, NIGHT-VIS-CLAHE implementation in ResNet50 achieved 90.29% accuracy, surpassing the best NIGHT-IR InceptionV3 at 89.27%, highlighting the advantage of ResNet50 with CLAHE in low-light conditions even against infra-red sensor.
Enhanced ARIA-based counter mode deterministic random bit generator random number generator implemented in verilog Eugene Rhee; Jihoon Lee
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.pp1416-1423

Abstract

This paper presents a study aimed at effectively implementing a deterministic random bit generator (DRBG) IP in verilog language, based on the standard encryption algorithm. By controlling the existing round generation and key generation blocks, the internal modules of the counter mode deterministic random bit generator (CTR-DRBG) were successfully implemented and operated, ensuring the secure and efficient generation of random bit sequences. The research focused on parallel operation of modules and optimized module placement to achieve improved clock frequencies. By concurrently operating two modules in the derivation and internal update modules of CTR-DRBG, the processing speed was enhanced compared to the conventional algorithm. Additionally, integrating the reseeding and initialization modules of CTR-DRBG into a single module successfully reduced size. Furthermore, this IP supports the special function register (SFR) interface. The safety of the CTR-DRBG was validated through known answer test (KAT) verification utilizing test vectors from certification. Future research should explore additional studies on CTR-DRBG operating on real FPGA or ASIC, not only using normal algorithm but also employing other block cipher algorithms.
Monte carlo simulation with bilstm for day-ahead stock portfolio management Zakir Mujeeb Shaikh; Suguna Ramadass
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.pp1903-1914

Abstract

Predicting stock price movement and optimizing day-ahead stock portfolios are challenging tasks due to the inherent complexity and volatility of financial markets. This study proposes a novel approach that combines bidirectional long short-term memory (BiLSTM) neural networks with monte carlo simulation (MCS) to enhance day-ahead stock portfolio management. In the proposed methodology, historical data of the top-performing 10 stocks from different sectors of the National Stock Exchange of India (NSEI) is obtained from 1 January 2004 to 30 June 2023 and utilized to train a BiLSTM model. This model effectively extracts intricate patterns and trends from the time series, leading to more accurate and robust stock price predictions. MCS generates different scenarios, considering various market conditions and uncertainties. These scenarios provide a comprehensive view of the portfolio’s performance under different conditions, thus mitigating the risk of relying solely on a single prediction. The study evaluates the proposed framework and compares its performance against traditional portfolio management strategies. Results demonstrate that the MCS with the BiLSTM approach outperforms traditional methods in terms of risk-adjusted returns and portfolio stability.
Review of battery models and experimental parameter identification for lithium-ion battery equivalent circuit models Nouhaila Belmajdoub; Rachid Lajouad; Abdelmounime El Magri; Soukaina Boudoudouh; Mohamed Hicham Zaggaf
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.pp1336-1346

Abstract

The growing use of electric vehicles has led to an ever-increasing demand for efficient and reliable management systems to control the behavior of lithium-ion batteries, especially with respect to heat generation and state-of-charge. Understanding these patterns constitutes a major new challenge for these batteries, as remaining ignorant of their behavior can result in decreased performance, shorter service life and even safety dangers. This review provides an overview of the different modeling techniques applied to simulate battery behavior. Different methods using equivalent electrical circuit models are discussed, covering both simple battery models and more complex equivalent electrical circuit models, with a focus on the 2RC-Thévenin circuit model. In this context, parameter approach methods for these systems are reviewed. In addition, laboratory tests are run to identify the various model parameters for a lithium-ion battery. This comprehensive study is designed to guide scientists and engineers in the selection and use of suitable tools for state-of-charge and battery health studies.
Novel printed passive ultra high frequency radio frequency identification antenna using meander technique Latifa El Ahmar; Ahmed Errkik; Jamal Zbitou; Ilham Bouzida; Aziz Oukaira; Ahmed Lakhssassi
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.pp1474-1485

Abstract

In this paper, a novel radio frequency identification (RFID) antenna using a meander technique associated with a slotted patch is studied for RFID applications in the ultra high frequency (UHF) band [867.5-868 MHz]. The proposed RFID antenna is designed on a Kapton substrate with dielectric constant 3.5 and loss of 0.0027. It consists of two opposite meander line antennas of 8 turns each one and interconnected to ALIEH H3 microchip associated to two slotted patch’s with a global size 105×25×0.1 mm3. The proposed RFID antenna is designed and simulated using CST MWS as an electromagnetic solver. The results of the simulation show a return loss of -22.64 dB at 868 MHz, a reading distance of around 5 m, and a simulated input impedance of the antenna are 31.72+j109.68 Ω at the operating frequency 868 MHz.
Artificial intelligence-based Karawo motif formation using genetic algorithm Mukhlisulfatih Latief; Syahrul Syahrul; Abdul Muis Mappalotteng
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.pp1820-1828

Abstract

This research explores the application of artificial intelligence in generating Karawo motifs, a traditional Indonesian pattern. The research involves collecting a dataset of existing Karawo motifs and utilizing genetic algorithms to evolve and create novel pattern variations. The generated motifs are evaluated based on their adherence to traditional design principles and aesthetic appeal. The formation of Karawo motifs begins with randomly selecting image data from a database. Then, the selection of transformation treatments is performed by optimizing the fitness function within the genetic algorithm. The applied types of transformations include geometric transformations, Boolean transformations, and arithmetic transformations. The outlined genetic algorithm steps include determining the fitness function, performing its evaluation, selecting fitness values, applying crossover, implementing mutation, managing survivor selection, and terminating iterations. The results indicate that the developed system is capable of creating diverse and appealing Karawo motif patterns, showcasing the potential of combining traditional artistry with artificial intelligence. This study has the potential to expand the possibilities of Karawo motif design and contribute to the preservation and promotion of Indonesian cultural heritage.
An ensemble approach for electrocardiogram and lip features based biometric authentication by using grey wolf optimization Latha Krishnamoorthy; Ammasandra Sadashivaiah Raju
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.pp1524-1535

Abstract

In the pursuit of fortified security measures, the convergence of multimodal biometric authentication and ensemble learning techniques have emerged as a pivotal domain of research. This study explores the integration of multimodal biometric authentication and ensemble learning techniques to enhance security. Focusing on lip movement and electrocardiogram (ECG) data, the research combines their distinct characteristics for advanced authentication. Ensemble learning merges diverse models, achieving increased accuracy and resilience in multimodal fusion. Harmonizing lip and ECG modalities establishes a robust authentication system, countering vulnerabilities in unimodal methods. This approach leverages ECG's robustness against spoofing attacks and lip's fine-grained behavioral cues for comprehensive authentication. Ensemble learning techniques, from majority voting to advanced methods, harness the strengths of individual models, improving accuracy, reliability, and generalization. Moreover, ensemble learning detects anomalies, enhancing security. The study incorporates ECG signal filtering and lip region extraction as preprocessing, uses wavelet transform for ECG features, SIFT for lip image features, and employs greywolf optimization for feature selection. Ultimately, a voting-based ensemble classifier is applied for classification, showcasing the potential of this integrated approach in fortified security measures.
Software component selection methods and techniques: a systematic review Ahmad Nabot
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.pp1802-1811

Abstract

Software component selection is critical in software engineering due to its vital role in reducing software development cost and time. This study analyzes software component selection research studies on methodologies, criteria, and multi-criteria decision-making (MCDM) techniques. The key study findings are: first, comprehensive standardized criteria for software component selection are lacking, with ambiguous terminology used in research. Second, current ad hoc selection processes need streamlining to reduce time, cost, and effort. Thus, an integrated approach is required to aid decision-makers. The review suggests developing automated tools or decision support systems combining multiple criteria decision methods to improve selection accuracy and efficiency. Standardized criteria catalogs can also assist software developers in the evaluation. The findings highlight that despite extensive academic research, component selection in practice remains sub-optimal. By informing future research and tool development, this review can benefit practitioners to systematically select the most appropriate software components meeting software requirements.

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