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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 66 Documents
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Search results for , issue "Vol 11, No 6: December 2022" : 66 Documents clear
Finger knuckle pattern person identification system based on LDP-NPE and machine learning methods Ali Mohsin Aljuboori; Mohammed Hamzah Abed
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4236

Abstract

Biometric-based individual distinguishing proof is a successful strategy for consequently perceiving, with high certainty, an individual's character. The utilization of finger knuckle pictures for individual ID has shown promising outcomes and produced a ton of interest in biometrics. By seeing that the surface example delivered by twisting the finger knuckle is profoundly particular, in this paper we present a new biometric validation framework utilizing finger-knuckle-print (FKP) imaging. In this paper, another methodology in view of neighborhood surface examples is proposed. Local derivative pattern (LDP) histogram is investigated for FKP description. Then based on neighborhood preserving embedding (NPE) is used for dimension reduction to the feature vector. The feature extraction method is computed and evaluated in the identification framework task. The machine learning methods (multiclass support vector machine (MSVM), random forest (RF), k-nearest neighbor (KNN)) are proposed for classification. The system is tested on the PolyU finger knuckle database. The empirical results proved that the proposed model has the best results with RF. Moreover, our proposed LDP-NPE model has been evaluated and the results show remarkable efficiency in comparison with previous work. Experimentally, the proposed model has better accuracy as reflected by 99.65%.
K-Means clustering-based semi-supervised for DDoS attacks classification Mahdi Nsaif Jasim; Methaq Talib Gaata
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4353

Abstract

Network attacks of the distributed denial of service (DDoS) form are used to disrupt server replies and services. It is popular because it is easy to set up and challenging to detect. We can identify DDoS attacks on network traffic in a variety of ways. However, the most effective methods for detecting and identifying a DDoS attack are machine learning approaches. This attack is considered to be among the most dangerous internet threats. In order for supervised machine learning algorithms to function, there needs to be tagged network traffic data sets. On the other hand, an unsupervised method uses network traffic analysis to find assaults. In this research, the K-Means clustering algorithm was developed as a semi-supervised approach for DDoS classification. The proposed algorithm is trained and tested with the CICIDS2017 dataset. After using the proposed hybrid feature selection methods and applying multiple training, testing, and carefully sorting DDoS traffic through a series of experiments, the optimum 2 centroids were found to be DDoS and normal. The generated centroids can be used to classify network traffic. So the proposed method succeeded to cluster the network traffic to safe and theat.
A comprehensive overview of the ADALINE method applied to rapid voltage sags detection in multi-motors drive systems Mounir Bensaid; Abdellfattah Ba-Razzouk; Mustapha Elharoussi
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4141

Abstract

Several strategies have been developed for identifying power quality issues, monitoring them, and compensating for relevant disturbances. In this field, online estimate of amplitudes and phase angles of network voltages and currents is commonly used. The adaptive linear neuron (ADALINE)-based voltage sag detection algorithm with least mean square (LMS) adaptation allows for rapid convergence of estimate techniques based on artificial neural networks (ANN). This approach has the advantage of being straightforward to implement on hardware and based on simple calculations (essentially multiply and accumulate "MAC"). This paper gives a comparison of the performance of two ADALINE approaches ("with" and "without" error supervision) for detecting and estimating voltage dips. The described techniques and models of a two-coupled motor system were implemented in MATLAB/Simulink/SimPowerSystems to run simulations under various fault scenarios in order to create the three-phase voltage sag alarm signal. The simulation outcomes are presented and debated.
Anti-disturbance GITSMC with quick reaching law for speed control of PMSM drive Salah Eddine Halledj; Amar Bouafassa
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4222

Abstract

In this article, in order to minimize response time and enhance anti-disturbance performance of parmanent magnet synchronous motor, a global integral terminal sliding mode control based on improved quick reaching law (GITSMC-QRL) is developed. This novel reaching law has two terms which play a key role of bringing state trajectory to sliding surface as quick as possible whenever the system is close to or far from the manifold. The proposed controller cannot only speed up the convergence rate, but also has ability to suppress the chattering and ensure finite time stability. In order to avoid the chattering phenomenon caused by load disturbances and high switching gain of sliding mode control, an extended hyperbolic tangent state observer is designed as feedforward compensation compensator that is added to GITSMC. Finally, the novel scheme is validated on paremanent magnet synchronous motor (PMSM) drive through simulation, and the comparative results in various conditions show the robustness, the feasibility and the effectiveness of the proposed controller.
Spoken language identification on 4 Indonesian local languages using deep learning Panji Wijonarko; Amalia Zahra
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4166

Abstract

Language identification is at the forefront of assistance in many applications, including multilingual speech systems, spoken language translation, multilingual speech recognition, and human-machine interaction via voice. The identification of indonesian local languages using spoken language identification technology has enormous potential to advance tourism potential and digital content in Indonesia. The goal of this study is to identify four Indonesian local languages: Javanese, Sundanese, Minangkabau, and Buginese, utilizing deep learning classification techniques such as artificial neural network (ANN), convolutional neural network (CNN), and long-term short memory (LSTM). The selected extraction feature for audio data extraction employs mel-frequency cepstral coefficient (MFCC). The results showed that the LSTM model had the highest accuracy for each speech duration (3 s, 10 s, and 30 s), followed by the CNN and ANN models.
Economic dispatch problem in smart grid system with considerations for pumped storage Adil Rizki; Rachid Habachi; Karim Tahiry; Abdelwahed Echchatbi
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4207

Abstract

There are two significant issues with the incorporation of smart grid technology in power system operating studies including the economic emission, unit commitment problem (UCP). Economic dispatch problem (EDP) is a UCP sub-problem which find the optimum output for a given combination of running units. When using electro-energy systems to strategically distribute the power produced by all plants, the power economic dispatch problem is especially important. Pumped storage units that have the capacity to store energy can provide spinning reserves, which will lower overall costs and emissions. The general goal of this study is to develop control and optimization algorithms that are appropriate for managing new generation electrical networks. In this research work, the economic dispatch issue in a ten-unit smart grid system is resolved using the crow search algorithm (CSA), which acts as a local optimizer of the eagle strategy (ES). The outcomes of the ES-CSA program are compared to those found in the literature. The results of simulations suggest that adopting ES-CSA can lead to the generation of reliable and enough power that can meet the needs of both civil and industrial areas.
Strategy to reduce solar power fluctuations by using battery energy storage system for UTeM’s grid-connected solar system Wei Hown Tee; Yen Hoe Yee; Chin Kim Gan; Kyairul Azmi Baharin; Pi Hua Tan
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.3862

Abstract

Recent years have witnessed the increasing uptake of solar photovoltaic (PV) installations, ranging from a few kilowatts for residential rooftops to a few megawatts for large-scale solar farms. One of the key challenges for the solar PV systems is its dependency on the solar energy, which is intermittent in nature and highly unpredictable. In this regard, battery energy storage system (BESS) is regarded as the effective solution that can smoothen the output power fluctuation from the solar PV system. Hence, this work utilized BESS that had fast response time with high power and energy density to reduce the solar output fluctuations from a real grid-connected solar system installed at the campus rooftop. The characteristic of the PV power fluctuation and the BESS storage requirement to smooth out the fluctuation within the allowable limit were determined and analyzed. More importantly, actual solar irradiance data with an interval of one minute was utilized in this work. The findings suggest that BESS with 66% of the installed solar capacity and 21% of the average daily solar generation of the installed system are required to smoothen the solar fluctuation that exceeds the ramp rate limit of 10%/min.
Enabling unmanned aerial vehicle to serve ground users in downlink NOMA system Nhat-Tien Nguyen; Hong-Nhu Nguyen; Leminh Thien Huynh; Miroslav Voznak
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.3945

Abstract

The emergence of internet-of-things (IoT) devices in homes and industry, has resulted in the current and future generation of wireless communications facing unique challenges in spectral efficiency, energy efficiency, and massive connectivity issues. Non-orthogonal multiple access (NOMA) has been proposed as a viable solution to address these challenges as it offers low-latency, spectral efficiency, and massive connectivity capabilities, which are key requirements in upcoming next-generation networks. In addition, another technology that has emerged as a solution to spectral efficiency and coverage is an unmanned aerial vehicle (UAV). Therefore, the combination of UAVs with NOMA has great potential to minimize the challenges and maximize the benefits. Specifically, we investigate the outage performance of the NOMA-UAV network over Nakagami-m channel fading. To this end, we derive a closed-form outage performance metric. The formulated framework is validated using simulations to verify the effectiveness of the proposed solution.
Analysis of a new voltage stability pointer for line contingency ranking in a power network Tayo Uthman Badrudeen; Funso Kehinde Ariyo; Ayodeji Olalekan Salau; Sepiribo Lucky Braide
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4266

Abstract

Improper management of reactive power in a power network could lead to voltage instability. This paper presents a well-detailed study on voltage instability due to violation of power equilibrium in a power network and introduces a new voltage stability pointer (NVSP). The proposed NVSP is developed from a reduced 2-bus interconnected network to predict the sensistivity of voltage stability to reactive power variation. The simulation results from MATLAB were evaluated on IEEE 14-bus test system. The contingency ranking was achieved by varying the reactive power on the load buses to its maximum loading limit. The maximum reactive power point was taken at each load bus and the critical lines were ranked according to their vulnerability to voltage collapse. The results were compared with other notable voltage stability indices. The results prove that the NVSP is an essential tool in predicting voltage collapse.
Machine learning approaches in the diagnosis of infectious diseases: a review Smriti Mishra; Ranjan Kumar; Sanjay Kumar Tiwari; Priya Ranjan
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4225

Abstract

Infectious diseases are a group of medical conditions caused by infectious agents such as parasites, bacteria, viruses, or fungus. Patients who are undiagnosed may unwittingly spread the disease to others. Because of the transmission of these agents, epidemics, if not pandemics, are possible. Early detection can help to prevent the spread of an outbreak or put an end to it. Infectious disease prevention, early identification, and management can be aided by machine learning (ML) methods. The implementation of ML algorithms such as logistic regression, support vector machine, Naive Bayes, decision tree, random forest, K-nearest neighbor, artificial neural network, convolutional neural network, and ensemble techniques to automate the process of infectious disease diagnosis is investigated in this study. We examined a number of ML models for tuberculosis (TB), influenza, human immunodeficiency virus (HIV), dengue fever, COVID-19, cystitis, and nonspecific urethritis. Existing models have constraints in data handling concerns such data types, amount, quality, temporality, and availability. Based on the research, ensemble approaches, rather than a typical ML classifier, can be used to improve the overall performance of diagnosis. We highlight the need of having enough diverse data in the database to create a model or representation that closely mimics reality.

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