The rising prevalence of electric vehicles (EVs) necessitates advancements in supporting infrastructure, particularly in charging point (CP) management. This paper proposes a solution to determine the maximum power limit for one or more charge points, considering the actual power consumption of the building and charger parameters. The problem formulation highlights the necessity of load balance algorithms due to challenges posed by existing power grid limitations and dynamic consumer demands. The paper reviews existing solutions, introduces a load balance algorithm, presents simulation results, and discusses potential enhancements. The algorithm dynamically adjusts charger power limits based on real-time data, prioritizing efficient charging while ensuring system stability.
The main objective of this research was to explore the consumer attitudes of Generation Z college students towards healthy lifestyles. The primary data collection method was individual interviews. A total of 58 interviews were conducted with students of the Faculty of Foreign Trade at BGE and students of marketing at the Keleti Károly Faculty of Economics at Obuda University. The research covered health maintenance, lifestyle changes, exercise, healthy eating, ways of obtaining information, maintaining mental health and related issues. The qualitative results are not generalisable to the whole population, but provide a good basis for further quantitative studies in the future, where the associations found can be quantified by means of a questionnaire.
Autism spectrum disorder (ASD) is a developmental disability that can cause significant social, communication, and behavioral challenges. People with ASD often have problems with social, emotional, and communication skills. Various challenging behavior events are a problem at the community and family levels, making life difficult for autistic people and their environment. Monitoring the physiological changes associated with negative emotions in people with ASD can support caregivers by giving them insights into the internal emotional changes in a real-time fashion. It makes it possible to act as early as possible to prevent the formulation of behavioral problems. In the current research phase, the design of the unique sensor device required for the preliminary data collection is taking place, for which we define the necessary minimum requirements in the safety area. When defining the requirements, we also consider their specific behavioral characteristics. The purpose of this document is to present the requirements identification method and the output of the process.
The turbulent changes of the 21st century have rewritten the competences and operational frameworks of individuals and organizations. New concepts such as digital awareness and cybersecurity have emerged, but competencies have also been transformed. Agile thinking is also a product of these changes. Many organisations today are moving to agile, ensuring their flexibility and positioning in an increasingly turbulent competitive environment. Agile organisations can only be successful if they have the internal support to do so. Current changes show that agile transformation in organisations has proven to be successful when it is supported by the right environment within the organisation. In our study, we set out to examine the awareness and place of individual agile approaches based on the subjective assessment of respondents. We also undertake a cross-cultural comparison to see how respondents of different ages and educational backgrounds from Hungarian and Arab cultures perceive the agile approach, its knowledge and application. The aim of our study is to present a comprehensive picture and to formulate future goals in order to make agile thinking as a new competence general in the life of every individual.
LoRaWAN communication is one of the most widely used solutions for low power wide area networks (LPW AN). LoRa W An owes its popularity to its low power consumption, robustness, large coverage and relative simplicity. It is, therefore, essential to address additional solutions that are not or only partially covered by standards and recommendations. Research into the exit procedure for LoRa WAnend devices can contribute to the security and reliability of networks and more efficient device management. The exit procedure is more of an implementation issue but has implications for many other areas.
The widespread adoption of Learning Management Systems (LMS) in higher education, coupled with the increasing acceptance of e-learning, distance learning, and hybrid educational models, has significantly raised the expectations and performance metrics for students. This research, conducted at the Faculty of Informatics in Pula, aims to uncover the process models and usage patterns of the LMS in an educational setting, specifically focusing on the online course Business Information Systems. The primary data source is an event log collected from the Moodle LMS used in this course. By employing process mining techniques for process discovery and model analysis, we developed a set of procedures within the Python programming environment to create a process model, process map, and process tree. The findings from this research demonstrate that a better understanding of e-learning usage patterns can contribute to the development of intelligent systems, such as recommendation systems, intelligent agents, and personal assistants, which can enhance the personalization of the learning experience for students.
Vertex-degree-based topological indices have been widely investigated and used in chemical graph theory for describing, predicting and explaining physical, chemical and biological properties of molecules. As opposed to the large body of research on these indices for planar and molecular graphs, little has been done on their computation for 3D crystallographic structures. We fill this gap by computing some of the most widely used vertex-degree-based topological indices of rectangular blocks of unit cells in one of the basic crystallographic structures, the simple cubic grid.
This paper presents a method to recognise fine motor hand movements and visualise them in virtual space by using a smart glove. Sensors mounted on the glove provide data to determine the position of the hands. The presented system is capable of real-time motion recognition by processing continuously incoming sensor data, making it suitable for solving complex problems such as interaction with the VR/ AR world, sign language translation or even as a training tool for physiotherapists. A microcontroller-based glove is built for the project, which monitors head and hand position as well as finger joint curvature, drawing inspiration from open source projects such as OpenGloves and LucidVR. Sensor data from the Oculus Rift VR glasses and the glove is extracted and displayed using the SteamVR extension in the Unity video game engine. The data is streamed to the gesture recognition server, which classifies the motion into one of the predefined hand motion classes. The paper demonstrates the practical utility of the smart glove through a simulated traffic management system. The user can control traffic using the glove by predefined hand movements. The virtual space is based on the U nity3D game engine and Unity VR technology.
The care provided by family members to the elderly or chronically ill loved ones is gaining increasing importance in ageing societies. University students caring for family members may have increased emotional and academic career risks due to their caregiving tasks. In line with the environmental, social and corporate governance (ESG) initiatives, social sustainability is crucial for higher education institutions. Hence developing university strategies to identify and support family caregiving students at risk, understanding their preparedness to use digital health technologies in homecare, and investigate how innovative digital solutions can be used to support them, are urgent challenges of higher education in the digital age. We present a small exploratory study from Hungary in which care- and health-related quality of life, and capability well-being of caregiving university students (N=15; mean age 32.6 years) were assessed with standard measurement tools. Caregivers' and care recipients' digital knowledge, habits, access to and use of IT technologies were recorded. Our study highlights important areas for interventions and further research, and the usefulness of using standard quality of life measurement tools in this area.
Predicting the parameters of a Combined Diesel-Electric and Gas (CODLAG) propulsion system is crucial for optimizing the design, performance, and reliability of these complex engineering systems. This paper presents the implementation of the Kolmogorov-Arnold Network (KAN) for predicting CODLAG system parameters. The KAN, based on Kolmogorov's superposition theorem, can accurately approximate nonlinear relationships within the system. We provide a comprehensive overview of CODLAG systems, their operational principles, and the challenges in parameter prediction. Experimental results demonstrate that increasing hidden layers, optimizing learning rates, and adjusting batch sizes and epochs significantly improve prediction accuracy. The findings highlight the robustness of KAN in modeling complex systems and pave the way for future research to enhance the predictive capabilities of engineering systems.
This study presents an innovative method to improve the accuracy of indoor mapping by deploying and integrating a sensor that transmits two-dimensional data with a three-dimensional mesh created by another device. The Iterative Closest Point algorithm was used to coordinate the different data sets and transform their different coordinate systems into a common indoor map. Prior to this, rigorous image cleaning and matching processes are conducted to ensure the quality of the data used. The effectiveness of this methodology has been validated through multiple tests using a Lidar scanner and a HoloLens 2 Augmented Reality device. This method not only offers potential for enhancing indoor navigation and safety protocols like emergency evacuation but also presents opportunities for advancing asset tracking and other applications.
This paper outlines the development of an artificial intelligence spam detection solution using PyTorch framework. The neural network itself is based on a feed forward neural network where the model will be trained on different datasets to show the overall results of the spam detection. Also the model architecture, used parameters and the training process will be shown from zero to finish and what algorithms used too. At the end of the paper, it will show the developed model efficiently identified and detected messages that exhibited suspicious characteristics.
Modeling dynamic systems is an important part in analysing and control of various systems arising either in health sciences or in the engineering word. Recent approaches to learn models from data are the so-called kernel-based methods and SVMs. There are strong relations to the theory of reproducing kernel Hilbert space (RKHS), to principal component analysis and canonic correlation analysis known previously from statistics. In recent form their use was extended from statistics to obtain models for dynamic systems. First we summarise the basics for the reproducing kernel based Hilbert space (RKHS) and the support vector machine (SVM) approaches. Following this it will be shown how some frequently used nonlinear models can be obtained by using these concepts. In the last part we discuss the structure estimation problem, i.e. how to determine the (least) number of features (observables) to describe the nonlinear system with a sparse representation.
In the era of autonomous vehicles, state estimation is crucial for planning and control. While various sensors like GNSS, IMU, cameras, and wheel encoders provide essential data, each has limitations. Sensor fusion, especially combining GNSS and IMU, addresses these challenges. Recently, data-based techniques using machine learning tools have emerged to enhance tuning processes. However, if an architecture that has a practical impact is utilized, the training of the neural net results in a complicated task. This paper explores different state estimation architectures in the era of data-based techniques and proposes an algorithm generating reference tuning values. The presented methods are tested with real vehicle measurements.
This article explores the application of Polycam, a mobile-based 3D scanning app, in capturing high-quality 3D models for various fields. It outlines a structured workflow involving preparation, data capture, processing, examination, and refinement, emphasizing best practices for each phase. The comparison between Photo Mode and LiDAR Mode within the Polycam app is highlighted to give a clearer overview of their respective advantages and applications. A case study on scanning handmade outdoor furniture illustrates practical challenges and solutions, demonstrating Polycam's utility in the absence of digital documentation. The article also highlights applications in education, environmental science, and cultural heritage preservation, showcasing the transformative potential of 3D scanning technologies. These insights offer valuable guidance for researchers, educators, and professionals seeking to leverage 3D scanning in their work.
When solving Many-objective Optimization Problems (MaOPs), selection strategies are very important for Evolutionary Algorithms (EAs) and affect great influence to optimization result. The selection strategy is required to improve convergence while maintaining the diversity of the solution set or the population. This paper proposes two novel selection indicators, hyper volume difference and hyper conical volume, for Many-objective Optimization Evolutionary Algorithms (MaOEAs). NSGA, one of the MaOEAs, ranks the solutions using non-dominated sorting, which is a very powerful selection strategy to fill in a population within a predetermined number of solutions, and then selects the remaining solutions to fill the free space of the population using additional selection. However, when the number of objectives is large, the non-dominated sorting cannot determine the superiority of the solutions, and in the end, optimization is often performed by repeating only the additional selections. In such a case, the selection indicator of the additional selection greatly infuences or failure of the optimization. NSGA-II effectively solve multi-objective optimization problems (MOPs) which has less than or equal to three objectives by using crowding distance in the target front set as an indicator in the additional selection, but it does not work effectively for MaOPs which has four or more objectives. On the other hand, NSGA-III performs niche selection based on a large number of uniformly distributed reference points in the additional selection, allowing the optimization to proceed effectively while sustaining high diversity of the population, but consuming huge amount of computation cost. In order to reduce the computational cost, the proposed selection indicators are based on the hyper volume difference and the hyper conical volume of the search points of the target front set respectively. NSGA with the proposed indicators in its additional selection, NSGA-II and NSGA-III are applied to well-known many-objective test problems and verify the effectiveness.
One of today's most pressing issues is the safety of our children in cyberspace. The world of the internet harbours a number of serious threats that can negatively affect children's emotional, social and mental health. This paper focuses on known and emerging technologies of deep forgery. The mechanisms behind deepfake technology, in particular Generative Adversarial Networks (GAN models), were examined, and its harmful effects on children were explored. Serious ethical and moral issues raised by this technology were highlighted through real examples. It was also shown how deepfake technology has been used as a tool for cyberbullying and its psychological, social, and educational consequences were discussed. Finally, awareness-raising initiatives and legal measures are proposed as possible solutions to alleviate these problems.
The increasing complexity and diversity of laboratory automation call for more adaptable and integrated solutions. This paper presents a real-life implementation of the Laboratory Automation Plug and Play (LAPP) concept, leveraging the SiLA 2 protocol to enable seamless robotic integration in heterogeneous laboratory environments. We introduce the mobERT mobile manipulator, a collaborative robot system designed to handle standard labware, such as ANSI/SLAS microtiter plates, across multiple workstations. Our approach employs a hierarchical workflow decomposition and a system architecture model to facilitate plug-and-play configuration. We implemented this system using Biosero's GBG scheduler, ensuring scalable and standardized interoperability. The implementation demonstrates the practical application of LAPP in a pharmaceutical laboratory setting, specifically automating the sample preparation workflow for High-performance Liquid Chromatography (HPLC). This work highlights the feasibility of modular, low-level control agnostic solutions in advancing laboratory automation towards higher efficiency and flexibility.
This study investigates personalized EEG-based action recognition models, exploring various training methods, model architectures, and amounts of training data. Drawing from data sourced from a previous study, we simulate scenarios akin to newly joined participants to assess model personalization performance across diverse experimental setups. Our primary aim is to determine the minimal requirements of data amount and training iterations essential for obtaining a personalized model. The analysis reveals that models incorporating Batch Normalization layers achieve superior accuracy when trained on a subset of available data, with only 20% of the training data yielding competitive results. Notably, this 20% of data translates to approximately seven seconds of brain activity recordings for each action, a finding accentuating the potential for efficiency in data utilization. Furthermore, we find that traditional finetuning and transfer learning techniques exhibit limitations in scenarios characterized by significant inter-individual variability in EEG signals. These methods often necessitated higher numbers of training iterations and larger amounts of training data to achieve comparable results, indicating challenges in knowledge transferability between individuals. Our findings underscore the importance of personalized modeling approaches tailored to individual participants, emphasizing efficient data utilization and model optimization.
University North is the sole higher education institution in Croatia that took part in the Energy Renovation Program for Public Sector Buildings between 2014 and 2020. After a thorough renovation and the installation of measurement equipment in the buildings, a study was launched to explore how user behavior affects energy consumption. This was done by monitoring meteorological conditions and indoor comfort parameters such as temperature, relative humidity (RH), carbon dioxide (CO2) levels, and volatile organic compounds (VOC). The objective was to enhance the existing energy performance contracting model (ESCO model). Data collected throughout the 2022/2023 heating season (from October to May) was used as input for training and evaluating a feedforward artificial neural network (ANN). The ANN was designed to estimate the reference energy consumption for heating and to forecast energy use under varying future meteorological and occupancy scenarios.
