Bulletin of Electrical Engineering and Informatics
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Fixed and fair power allocation in downlink and uplink NOMA: outage probability analysis and bit error rate comparative study
Get PDFNon-orthogonal multiple access (NOMA) is a crucial technology for upcoming radio access networks since it allows several users to use the same time and frequency resources. It is positioned as a viable option for next-generation communication systems because to its capabilities to increase system capacity and spectrum efficiency. This essay investigates the effects of fair and fixed power allocation (PA) techniques on NOMA systems' uplink and downlink performance. It specifically assesses bit error rate (BER) and outage probability (OP), two crucial performance parameters. The paper provides a thorough comparison of the fixed and fair PA approaches, highlighting the advantages, and disadvantages of each. While fixed PA is easier to deploy, results show that it performs poorly in dynamic situations, increasing BER and OP, particularly for users with less reliable channels. Fair PA, on the other hand, improves system dependability, and user fairness by dynamically allocating power depending on user situations, thus reducing OP and BER. Future wireless networks will benefit greatly from its enhanced spectrum efficiency and up to 78% reduction in outage likelihood. With fair PA's higher flexibility and effectiveness in real-world, varied circumstances, the results underline the significance of selecting appropriate PA techniques for NOMA systems
Text clustering for analyzing scientific article using pre-trained language model and k-means algorithm
Get PDFText clustering is a technique in data mining that can be used for analyzing scientific articles. In Indonesia-accredited journals, SINTA, there are two languages used, Indonesian and English. This is the first research focusing on clustering Indonesian and English texts into one cluster. In this research, bidirectional encoder representations from transformers (BERT) and IndoBERT are used to represent text data into fixed feature vectors. BERT and IndoBERT are pre-trained language models (PLMs) that can produce vector representations that take care of the position and context in a sentence. To cluster the articles, the K-Means algorithm is implemented. This algorithm has good convergence and adapts to the new examples, which helps in improved clustering performance. The best k-value in the K-Means algorithm is defined by using the silhouette score, the elbow method, and the Davies-Bouldin index (DBI). The experiment shows that the silhouette score can produce the most optimal k-value in clustering the articles, which has a mean score of 0.597. The mean score for the elbow method is 0.425, and for the DBI is 0.412. Therefore, the silhouette score optimizes the performance of PLMs and the K-Means algorithm in analyzing scientific articles to determine whether in scope or out of scope
Embedded system with automatic control for solar energy capture using photovoltaic panels
Get PDFSolar energy harvesting addresses challenges related to environmental variability and the limitations of fixed systems, which affect the energy yield obtained from photovoltaic panels. To improve efficiency, tracking systems are being developed using control algorithms or algorithmic energy management strategies. Reviewed research has explored methodologies such as software modeling, experimental testing, and integration of embedded systems with fuzzy logic and internet of things (IoT) for energy monitoring and management, using both single-axis and dual-axis technologies, demonstrating improvements in energy harvesting efficiency. This paper presents the development of a microcontroller-based system with automatic control to optimize solar energy capture in photovoltaic panels using light-dependent sensors and integrating control algorithms into low-cost hardware. The tests carried out demonstrated the operation of the tracking algorithm, confirming that the integration of light-dependent sensors, servo motors and the Arduino UNO microcontroller orient the solar panel based on the detected light, determining that with the generation of 900 mA with 6.98 V in full sunlight, the 5 V and 4400 mAh battery is charged, obtaining an autonomy of up to 3.65 days without solar recharging
Hybrid 3D CNN–transformer model for early brain tumor detection with multi-modal magnetic resonance imaging
Get PDFAccurate and early diagnosis of brain tumors using multi-modal magnetic resonance imaging (MRI) remains a critical challenge due to tumor heterogeneity and complex spatial representation. This study proposes a novel hybrid deep learning framework that integrates a 3D convolutional neural network (3D CNN) with swin transformer blocks and an attention-based feature fusion module (ABFFM). The model leverages multi-modal MRI inputs—T1, T1Gd, T2, and fluid-attenuated inversion recovery (FLAIR)—and features a dual-branch classification head for binary tumor detection and multi-label tumor sub-region classification: enhancing tumor (ET), tumor core (TC), and whole tumor (WT). Experiments conducted on the BraTS2023-GLI dataset demonstrate that the proposed model achieves a superior classification accuracy of 96.51%, with precision of 97.98%, recall of 97.04%, and F1-score of 97.61%, outperforming state-of-the-art methods. Furthermore, intrinsic attention weights offer interpretability by highlighting modality-specific contributions. The proposed model establishes a clinically promising approach for brain tumor analysis, with strong implications for early diagnosis and treatment planning
Design type-2 fuzzy for superconducting magnetic energy storage to enhance frequency transient response
Get PDFRenewable energy has become a new trend in power systems. Renewable-based power plants such as wind power systems and photovoltaics. This paper proposed a novel method for inertia emulation based on superconducting magnetic energy storage (SMES). To get better inertia support for the system, a type-2 fuzzy controller is used as the SMES controller. An area power system is used as the test system to investigate the performance of type-2 fuzzy controller on SMES. Time domain simulation is carried out to show the efficacy of the proposed method. From the simulation results, it is found that the proposed controller can reduce the overshoot of frequency by up to 20% compared to the type-1 fuzzy controller. It is also hoped that the proposed method can be used as a reference of the Industrial people
An overview of 33 years of trends in space weather research: a bibliometric analysis (1988-2021)
Get PDFSpace weather (SpW) is a phenomenon caused by a variety of solar events and has the potential to disrupt infrastructure systems and technology, putting them at risk. Despite SpW’s immense impact, there has been a notable absence of bibliometric analysis studies to understand the research trends, regional distribution, social structure, conceptual structure, and knowledge gaps. This review synthesized scopus documents of SpW domain from 1988 to 2021. In this study, three tools were used, such as Microsoft Excel, VOSviewer, and Harzing’s Publish or Perish for statistical analysis, graphical presentation, and citation metrics, respectively. Based on the 3,956 articles, roughly 70% of the articles were published in the last ten years, reveals a rapid growth in SpW research. The study discovered that China ranked third in publication volume, following the United States and the United Kingdom with Russian Federation following closely in fourth place. This study also presents six key findings, including the growth pattern of publications, contributions, and authorship collaboration by countries, most productive and influenced authors, co-authorship status, most influenced journals and articles, research cluster and new SpW subtopics discovered. These findings provide useful insight and aid in the advancement and progress of this field
Enhancing detection of zero-day phishing email attacks in the Indonesian language using deep learning algorithms
Get PDFEmail phishing is a manipulative technique aimed at compromising information security and user privacy. To overcome the limitations of traditional detection methods, such as blacklists, this research proposes a phishing detection model that leverages natural language processing (NLP) and deep learning technologies to analyze Indonesian email headers. The primary objective is to more efficiently detect zero-day phishing attacks by focusing on the unique linguistic and cultural context of the Indonesian language. This enables the development of models capable of recognizing phishing attack patterns that differ from those in other language contexts. Four models are tested, combining Indonesian bidirectional encoder representation of transformers (IndoBERT) and FastText feature extraction techniques with convolutional neural network (CNN) and long short-term memory (LSTM) deep learning algorithms. The results indicate that the combination of FastText and CNN achieved the highest performance in accuracy, precision, and F1-score metrics, each at 98.4375%. Meanwhile, the FastText model with LSTM showed the best performance in recall, with a score of 98.9583%. The research suggests exploring deeper into email content or integrating analysis between headers and email content in future studies to further improve accuracy and effectiveness in phishing email detection
Leveraging pretrained transformers for enhanced air quality index prediction model
Get PDFAir pollution mitigation is essential to ensure sustainable development, as it directly affects climate change, economic productivity, and social well-being. Despite the availability of numerous prediction techniques, machine learning (ML) remains the optimal solution for forecasting air pollution. Constructing a prediction model for a region with limited data poses a challenge. This study presents a novel technique that combines temporal fusion transformer (TFT) with transfer learning to create an inventive air quality index (AQI) prediction model, effectively utilizing temporal insights and prior knowledge. The TFT is an advanced deep neural architecture engineered to enhance time series forecasting through the fusion of sequence modelling and global temporal patterns. By fusing TFT with transfer learning, the research pioneers a fresh approach to AQI prediction for region with data scarcity issue, capitalizing on cross-domain knowledge transfer. Utilizing meteorological and pollutant data from the Cochin region, a hybrid AQI prediction model is constructed through TFT and transfer learning. Employing a preexisting TFT model trained on Trivandrum data, transfer learning technique is utilized to adapt the model for predicting AQI in the Cochin region. The study demonstrates that integrating TFT with transfer learning yields superior accuracy compared to an exclusive TFT-based approach
Optimizing neural radiance field: a comprehensive review of the impact of different optimizers on neural radiance fields
Get PDFNeural radiance field (NeRF) is a form of deep learning model that may be used to depict 3D scenes from a collection of photos. It has been demonstrated that NeRF can produce photorealistic photographs of fresh perspectives on a scene even from a small number of input images. However, the optimizer that is employed can have a significant impact on the quality of the final reconstruction. Finding an effective optimizer is one of the biggest challenges while learning NeRF models. The optimizer is responsible for making changes to the model's parameters to minimize the discrepancy between the model's predictions and the actual data. We cover the many optimizers that have been used to train NeRF models in this study. We present research results contrasting the effectiveness of multiple optimizers and examine the benefits and drawbacks of each optimizer. For training NeRF models, four different optimizers viz. Adaptive moment estimation (Adam), AdamW, root mean square propagation (RMSProp), and adaptive gradient (Adagrad) are trained. The most effective optimizer for a given assignment will vary depending on a variety of elements, including the size of the dataset, the complexity of the scene, and the level of accuracy that is required
Analysis of alternatives methodology for large-scale information system implementation
Get PDFAccording to the Presidential Decree, central and local governments must implement electronic-based government systems or sistem pemerintahan berbasis elektronik (SPBE). However, the independent implementations have created various similar applications to support the same field of governmental activities. The situation creates difficulties in achieving effectiveness, integration, sustainability, efficiency, accountability, interoperability, and security of governmental services. Therefore, a common application will be developed for each governmental activity to improve interoperability and data integration. On the other hand, central or local governments must consider the suitable implementation of their public service information systems. This manuscript guides the determination of alternatives using cost, benefit, and risk analysis. We use the proposed guidance for a case study because sistem pengelolaan pengaduan pelayanan publik nasional-layanan aspirasi dan pengaduan online rakyat (SP4N-LAPOR!) has been regulated as the common application for Public Service Complaints Management using PermenPANRB No. 680, 2020. The application of the proposed guidance shows that it can help the stakeholder quantitatively decide on an alternative implementation of the application for the public service complaints management system