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检索条件"机构=Faculty of Electrical Engineering Computer Science and Information Technology Osijek"

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Dataset Ratio Influence on kNN Classification Results 33rd

Dataset Ratio Influence on kNN Classification Results

引用

33rd International Conference on Organization and technology of Maintenance, OTO 2024

作者： Gaće, Marin Galba, Tomislav Baumgartner, Alfonzo Livada, Časlav Faculty of Electrical Engineering Josip Juraj Strossmayer University of Osijek Computer Science and Information Technology Osijek Kneza Trpimira 2B Osijek31000 Croatia

ISBN: (纸本)9783031805967

Classification is used in many fields today, and for most of them machine learning algorithms can be used to make a decision. This article investigates the effects of different sizes of training and test datasets on the accuracy of classification using both classical k-nearest neighbors (kNN) and weighted k-nearest neighbors (wkNN) methods. This study systematically evaluates how different ratios of training to test data affect classification performance and provides a comprehensive analysis of the interplay between the ratios of the datasets and the parameter k. The results show that the ratio of training to test datasets exerts a more significant influence on classification accuracy than the choice of the parameter k. In particular, extreme ratios, where the training dataset is either much larger or much smaller than the test dataset, lead to suboptimal classification results. This emphasizes the importance of carefully selecting an appropriate balance between training and test data to ensure robust and reliable classification results. By highlighting the critical role of the ratio between datasets, this paper provides valuable insights for optimizing kNN and weighted kNN methods in practical applications. The results have far-reaching implications for the design and implementation of machine learning models and emphasize the need for a balanced data distribution to achieve high classification accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： k-nearest neighbors

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Optimal Utilization of Electric Vehicles in the Parking Lot of the faculty Building to Increase Social Welfare 6

Optimal Utilization of Electric Vehicles in the Parking Lot ...

引用

6th IEEE International Conference and Workshop Obuda on electrical and Power engineering, CANDO-EPE 2023

作者： Misljenovic, Nemanja Fekete, Kresimir Knezevic, Goran Pleskalt, Martin Josip Juraj Strossmayer University of Osijek Computer Science and Information Technology Osijek Faculty of Electrical Engineering Osijek Croatia

ISBN: (纸本)9798350328752

This paper strives to investigate the impact of integrating charging stations in the parking lot of the faculty building with an installed photovoltaic power plant (PV) and battery energy storage system (BESS) to maximize social welfare. The integration of charging stations in the parking lot of business buildings would enable savings for owners of electric vehicles (EVs) who live in residential buildings and have to charge their EVs at public charging stations. On the other hand, by integrating charging stations into the parking lot, business buildings could reduce their electricity costs because they use EVs as BESS. Accordingly, the optimization model was developed in the GAMS environment by applying MILP formulation. The research was conducted according to the current market conditions and for a newly proposed market model for charging and discharging batteries of EVs in the parking lot of business buildings. The results show the need to find the optimal prices for charging and discharging batteries of EVs by applying multi-level optimization. © 2023 IEEE.

关键词： Energy management systems

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Using Stable Diffusion Model for Text-Driven Generation in Augmented Reality Applications 33rd

Using Stable Diffusion Model for Text-Driven Generation in A...

引用

33rd International Conference on Organization and technology of Maintenance, OTO 2024

作者： Rücker, Kristian Livada, Časlav Galba, Tomislav Baumgartner, Alfonzo Faculty of Electrical Engineering Josip Juraj Strossmayer University in Osijek Computer Science and Information Technology Osijek Kneza Trpimira 2B Osijek31000 Croatia

ISBN: (纸本)9783031805967

Rapid advances in science and technology have led to the integration of artificial intelligence (AI) in many areas, including augmented reality (AR). This study explores the application of the Stable Diffusion (SD) model to develop an AR application that generates images based on text descriptions. Using Unity, a versatile game development platform, we implemented this AR application, scripting in C# with Visual Studio 2022. The user interface and functionality of the SD model were managed via the UI Stable Diffusion Automatic 1111 tool, allowing seamless integration and execution of SD functions. This work demonstrates the potential of AI-driven models in enhancing AR experiences and introduces a novel approach to dynamic visual content creation and editing. Furthermore, this technology has significant implications for the maintenance of municipal infrastructure and maintenance organizations. By converting text instructions into visual AR overlays, maintenance teams can improve the accuracy and efficiency of tasks such as inspections, repairs, and infrastructure management, streamlining operations and reducing errors. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Augmented reality

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Life Cycle Management of Operational technology – OT 33rd

Life Cycle Management of Operational Technology – OT

引用

33rd International Conference on Organization and technology of Maintenance, OTO 2024

作者： Dorić, Dražen Glavaš, Hrvoje Varga, Fran Faculty of Electrical Engineering Josip Juraj Strossmayer University of Osijek Computer Science and Information Technology Osijek Kneza Trpimira 2B Osijek31000 Croatia

ISBN: (纸本)9783031805967

Systematic management of all phases of the lifecycle of an industrial control system, recently referred to as operational technology or OT for short, ensures that the system is up to date, compliant with regulations and in line with changing business requirements. This is especially true for critical infrastructure. In this way, operational resilience is improved, risks are minimized and a proactive approach to maintenance is encouraged, with a focus on safety, efficiency and long-term sustainability. The paper provides an overview of the motivations for systematic lifecycle management of OT systems, offers a description and illustration of lifecycle costs in the CAPEX and OPEX phases, emphasizes the changing role of corrective and preventive maintenance from a lifecycle management perspective, and budgeting for various activities, including cybersecurity and OT system upgrades. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Preventive maintenance

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FireBot - An Autonomous Surveillance Robot for Fire Prevention, Early Detection and Extinguishing 15

FireBot - An Autonomous Surveillance Robot for Fire Preventi...

引用

15th International Conference on computer and Automation engineering, ICCAE 2023

作者： Balen, Josip Damjanovic, Davor Maric, Petar Vdovjak, Kresimir Arlovic, Matej Martinovic, Goran Josip Juraj Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (纸本)9798350396225

Every year, fire is responsible for numerous deaths, as well as huge material losses. Therefore, prevention and early detection of fire have become a priority for society, as well as the main research and development issue for many scientists and various industries. This paper describes our work in the development of FireBot, an autonomous surveillance robot. The Firebot is equipped with modern technologies and state-of-the-art navigational and computer vision methods that enable autonomous navigation, obstacle avoidance, video surveillance, fire prevention and detection, and fire extinguishing. It utilizes both infrared thermal (IRT) and RGB cameras paired with a modern convolutional neural network (CNN) for fault and fire detection, as well as various other sensors for analyzing air composition, processing of surrounding sounds, and detecting irregularities in its environment in general. The best performing CNN was implemented and tested in real-world environments for fire detection purposes, the results of which are presented in this paper. A state-of-the-art SLAM algorithm paired with LiDAR and a depth camera is used for mapping and navigation. The architecture presented in this paper, along with all functionalities planned for future work, represents an innovative autonomous surveillance system that will make a great contribution in the field of fire prevention and detection. © 2023 IEEE.

关键词： Fires

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Impact of the Increasing Integration of Photovoltaic Systems and Charging Stations for Electric Vehicles with Vehicle-To-Grid Concept on Voltage Stability of the Distribution Grid 20

Impact of the Increasing Integration of Photovoltaic Systems...

引用

20th International Conference on Smart Technologies, EUROCON 2023

作者： Kljajic, Ruzica Misljenovic, Nemanja Maric, Predrag Josip Juraj Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (纸本)9781665463973

The increasing integration of renewable energy sources (RES) and variable loads can stress the operation of the distribution grid during certain times of the day. In another way, electric vehicles, which are assumed to take an increasing share in the overall transportation structure, are considered as variable loads with the ability to store energy and thus support stability of the distribution grid. This paper focuses on a model of a prosumer-rich distribution grid with increasing integration of photovoltaic (PV) systems, electric vehicle charging stations (EVCS) as well as vehicle-to-grid (V2G) in the foreseeable future. The impact of integration on voltage stability was analyzed for the different operating scenarios considering voltage profiles and characteristic P-V curves. © 2023 IEEE.

关键词： Vehicle-to-grid

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An ensemble-based framework for biomedical classification problems

An ensemble-based framework for biomedical classification pr...

引用

2023 IEEE Zooming Innovation in Consumer Technologies Conference, ZINC 2023

作者： Dudjak, Mario Zoric, Bruno Bajer, Drazen Josip Juraj Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (纸本)9798350347722

Model selection is an essential step when applying machine learning to classification problems. It is typically carried out by the practitioner who strives to identify the most suitable classifier for a given problem. Given the variety of classifiers available and the difficulty in predicting which one will yield the best performance depending on the characteristics of the problem, this is by no means a simple task. Biomedical problems pose a significant challenge in this regard due to their numerous data intrinsic characteristics that are known to impair classification performance. Given that different classifiers perform well for different biomedical problems, combining them into an ensemble would seem practical. However, the practitioner still needs to determine how to combine them. This paper presents an ensemble-based framework that automates the training and combination of different classifiers in order to relieve practitioners of this burden whilst obtaining highly competitive performance. The effectiveness of the proposed framework was evaluated on several biomedical problems from the literature. © 2023 IEEE.

关键词： Optimization

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Validation of Machine Learning-Based Lane Lines Detection Methods Using Different Datasets

Validation of Machine Learning-Based Lane Lines Detection Me...

引用

2023 IEEE Zooming Innovation in Consumer Technologies Conference, ZINC 2023

作者： Jukic, Grgur Vranjes, Mario Vajak, Denis Vranjes, Denis TTTech Auto CEE Osijek Croatia Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (纸本)9798350347722

Advanced Driver Assistance System (ADAS) uses various sensors that exist in the vehicle to collect information about the vehicle and its surroundings. One of the most commonly used ADASs is the one designed to recognize driving lane lines. In this paper, analysis, and validation of freely available state-of-The-Art deep learning-based lane lines detection (LLD) methods are done using three different well-known datasets: CULane, TuSimple, and LLAMAS. To perform the validation, as part of this research, two converters of lane line label formats were made. Converters allow testing models that have been previously trained on a dataset that uses different lane line label formats so that their performances can be verified on other datasets with different lane line format types. The experimental results show that the adaptability of the model to the change of the dataset depends on the architecture used as the basis of the model for the LLD, and the similarity of the training set of the initially used dataset and the new, *** dataset. All tested models showed shortcomings when tested on images from datasets on which they were not trained, which confirms that there is still no model that we can confidently say achieves high performance in all possible scenarios. © 2023 IEEE.

关键词： Advanced driver assistance systems

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Modeling and Assessment of RSSI in LoRa WAN Network Using a Neural Network Approach 21

Modeling and Assessment of RSSI in LoRa WAN Network Using a ...

引用

21st IEEE International Symposium on Intelligent Systems and Informatics, SISY 2023

作者： Spisic, Josip Zrnic, Matko Zagar, Drago Crgic, Kresimir Computer Science and Information Technology Department of Communications Faculty of Electrical Engineering Osijek Croatia

ISBN: (纸本)9798350343366

This study employed a l0-fold cross-validation approach to train and validate neural networks to predict the RSSI (received signal strength indicator) in LoRaWAN communication. The model's performance was accurately estimated by averaging results across multiple validation sets and training sequences, and biased outcomes were mitigated. Implementing data normalization techniques, a simple neural network architecture, regularization approaches, and iteration limits ensured robustness within the limitations of a small dataset. The assessment of three neural network models using different optimization algorithms demonstrated their potential for accurate RSSI predictions, particularly for low-power IoT devices like LoRaWAN end devices, where energy efficiency is crucial. Lightweight neural networks on such devices enable efficient data processing while conserving energy, extending their lifespan, and reducing maintenance needs. Experimentation with deeper structures and unconventional activation functions may be necessary for complex problems, further enhancing performance in specific domains like RSSI assessment for LoRaWAN end devices. © 2023 IEEE.

关键词： Iterative methods

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Autonomous Vehicle Control in CARLA Simulator Using Reinforcement Learning 66

Autonomous Vehicle Control in CARLA Simulator Using Reinforc...

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66th International Symposium ELMAR, ELMAR 2024

作者： Cincurak, Dorian Grbic, Ratko Vranjcs, Mario Vranjes, Denis TTTech Auto Central and Eastern Europe OsijekHR-31000 Croatia Computer Science and Information Technology Osijek Faculty of Electrical Engineering OsijekHR-31000 Croatia

ISBN: (纸本)9798350375411

Autonomous vehicles (AVs) have emerged as a transformative technology that promises safer and more efficient transportation systems. Central to the development of autonomous driving is the ability to make real-time decisions in complex and dynamic environments. Reinforcement learning (RL) has gained considerable attention as a promising approach that enables AVs to learn control policies directly from interaction with their environment. In this paper, two RL algorithms for autonomous driving were developed in the CARLA autonomous driving simulator: one based on the Deep Q-Learning (DQN) algorithm and the other based on the Deep Deterministic Policy Gradient (DDPG) algorithm. The evaluation of the developed algorithms was carried out using the user-defined metrics and shows promising results in learning from interaction. © 2024 IEEE.

关键词： Deep reinforcement learning

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