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Archives: Paper Submission

Machine Learning in Phase of Flight Detection

The study integrates a thorough investigation of machine learning approaches with unsupervised clustering algorithms to advance the area of flight phase identification in aviation. We initiate our research with a comprehensive assessment of the literature on machine learning applications for flight phase detection, both scientometrically and conceptually. This review looks at how machine learning techniques have developed to meet theoretical and practical goals in this field, as well as publishing trends, relevant papers and top research institutes.
A significant contribution of our work is the application of several unsupervised clustering algorithms to flight data, which is simulated using Monte Carlo Simulation. This allows us to identify crucial flying phases, namely the straight and level phase and the turn phase. Through the performance evaluation of these techniques, we find that among the models examined, the Gaussian Mixture Model (GMM) provides the most accurate phase detection. This result enhances the accuracy of flight phase identification and demonstrates the advantage of GMM in handling the intrinsic complexity of flight data.
Furthermore, our analysis points out areas of current research deficiency and proposes future directions for investigation, offering a roadmap for the development of machine learning methods in aviation that could improve operational effectiveness, safety and training programs.

Can urban retrofitting achieve a positive energy balance? A case Study of four European Positive Energy District

Abstract— Urban retrofitting has emerged as a key strategy in the transition towards sustainable cities, with Positive Energy Districts (PEDs) serving as a model for achieving energy-positive urban environments. This paper explores the potential for urban retrofitting to achieve a positive energy balance through a case study of four existing districts in European Municipalities: Settimo Torinese (Italy), Großschönau (Austria), Amsterdam (Netherlands), and Resita (Romania). The analysis leverages energy balance simulations, considering various retrofitting scenarios, including building insulation, photovoltaic (PV) installations, and the adoption of flexible grid usage. The findings indicate that while achieving a PED is challenging, it is attainable through a combination of aggressive retrofitting measures, renewable energy integration, and smart energy management. The study highlights the importance of context-specific strategies, as climatic and urban characteristics significantly influence the outcomes. It aims to add to the ongoing discourse on sustainable urban development by providing empirical insights into the pathways and challenges of achieving PEDs through urban retrofitting.
Keywords — Positive Energy Districts, PED, retrofitting, Climate-Neutral Districts

A Study on Reactive Power Control Using Variable Gain and Voltage Limiter for Grid-Forming Converters

In our previous study, an active power control method called variable gain control (VGC) has been proposed for grid-forming (GFM) converters. In the present paper, a reactive power control method for GFM converters to prevent an overcurrent and an alleviate a voltage oscillation under a three-line-to-ground (3LG) fault. In the proposed method, the magnitude of the voltage output of GFM converters is varied in accordance with the terminal voltage. In addition, a novel idea of variable gain and a voltage limiter is applied to avoid overcurrent. Numerical simulations demonstrate the effectiveness of the proposed method in suppressing overcurrent and voltage variation during 3LG faults.

Review of Mathematical Modelling and Interference Minimization Schemes for the Coexistence of 5G and Satellite Radio Access Networks

The aim of the study is to develop a suitable algorithm for interference minimizing in 5G and satellite communication networks coexistence employing Nakagami-m and Shadowed Rician models. Based on this aim, the following research contributes to:
1-Develop a suitable theoretical strategy that evaluates interference scenarios for co-existence between5G and satellite communication networks.
2-Develop an algorithm based on Nakagami-m and Shadowed Rician models for interference minimization in the co-existence between 5G and satellite communication networks.

Improving Generalization in Convolutional Neural Networks with a Dynamic Attention Layer

This paper introduces a novel Dynamic Attention Layer (DAL) that enhances the generalization capabilities of Convolutional Neural Networks in both in-distribution and out-of-distribution scenarios. By dynamically adjusting attention weights based on selected percentiles during training, DAL improves the network’s ability to capture both dominant and subtle features, resulting in better accuracy and robustness across diverse datasets. The study demonstrates DAL’s effectiveness through rigorous testing, showing it outperforms traditional attention mechanisms and data augmentation techniques, offering a valuable advancement in computer vision.

A Market, Economic, and Technical Analysis of a Community Solar Electricity Aggregator Approach for Local Governments to meet their Net Zero Emissions Energy Needs

This research can enhance the level of understanding of local governments in Western Australia on possible approaches to meet their net zero emissions targets.

Advancing Brain Tumor Detection via ViRCNN: A Fusion of Vision Transformers and Faster R-CNN

In the field of cancer diagnosis, especially detection of brain tumors, achieving highly accurate detection is very im- portant. Deep learning, with its remarkable capabilities in object detection, has emerged as a valuable tool for identifying brain tumors. We introduce a novel approach called ViRCNN that combines the strengths of Faster R-CNN and Vision Transformer (ViT), referred to as ViRCNN. This method enhances both the accuracy and efficiency of brain tumor detection in magnetic resonance image (MRI) images. To evaluate the effectiveness of ViRCNN, we employed the Br35H dataset, which includes 801 MRI images for training, validation, and testing. Our approach demonstrates significant improvements in the Mean Average Precision 50 (MAP50) and Recall metrics compared to previous methods. Notably, ViRCNN achieves a 0.9% improvement in the MAP50 score while maintaining a parameter count of only 19 million, substantially lower than the over 80 million parameters typical of state-of-the-art methods.

Assessing and Predicting Air Pollution in Asia: A Regional and Temporal Study (2018-2023)

This study provides a comprehensive temporal and geospatial analysis of PM2.5 levels across Asian countries from 2018 to 2023. Utilizing time series modeling (ARIMA) for predicting future pollution trends and evaluating multiple metrics for model performance, the research highlights significant regional variations in air quality and identifies key patterns in pollution trends. By categorizing countries into different pollution level clusters, the study presents a nuanced understanding of air quality dynamics, facilitating targeted policy recommendations for environmental management and public health interventions in Asia. This work contributes to the field by combining predictive modeling with spatial analysis to address a critical environmental issue.

Energy Consumption Forecasting Using Ensemble Machine Learning Models in Smart Grid

Energy Consumption Forecasting Using Ensemble Machine Learning Models in Smart GridAccurately predicting medical charges is crucial for healthcare providers, insurance companies, and policymakers to manage costs and allocate resources efficiently. This study conducts a comparative analysis of five machine learning algorithms—Linear Regression, Decision Tree Regressor, Random Forest Regressor, XGBoost Regressor, and Gradient Boosting Regressor—to evaluate their performance in predicting medical insurance charges. Utilizing a dataset of patient demographics, health indicators, and lifestyle factors, we identify the key variables that most significantly influence medical expenses. Our findings reveal that certain algorithms outperform others in predictive accuracy, with XGBoost Regressor showing the highest accuracy (R² = 0.94). Additionally, the study highlights the most critical factors contributing to medical charges, are Smoking status, BMI, and Age. The analysis of feature importance across different models provides valuable insights into the underlying drivers of healthcare costs. This research contributes to the growing body of literature on healthcare analytics by offering a dual focus on predictive modeling and variable importance. The results underscore the potential of machine learning to enhance decision-making in the healthcare industry, particularly in optimizing resource allocation and cost management.

Construction of a Regional Public Transportation Management Support System Using the Cloud

Public transportation is an indispensable part of residents’ daily lives, including commuting, shopping, and hospital visits. Our research group provides support activities for regional public transportation systems, mainly for community buses operated by local governments. The primary support services include the development of the General Transit Feed Specification (GTFS), bus location that displays the location of buses on a map, and the measurement of the number of passengers. We are supporting the DX of regional public transportation by constructing and providing an infrastructure system to realize these services. Until now, the infrastructure system was built on a server in a university laboratory to provide these services. However, the service was often interrupted due to power outages for legal inspections several times a year and network failures within the university, resulting in constant complaints from the regional public transportation operators they support. The conversion of the infrastructure system to the cloud solves these problems. A comparison of communication speeds showed that the on-premise environment was faster. However, by converting the infrastructure system to AWS, the security risk of converting their servers and the risk of power outages due to legal inspections can be reduced or eliminated, so these speed differences are considered acceptable.