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3rd International Conference on Advances in Computing Research, ACR 2025

作者： Zacharioudakis, Eleftherios Kakoulli, Elena Evripidou, Salomi Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Pafos Cyprus National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9783031876462

This paper introduces XMeta-OS, a meta-operating system specially designed with Linux as its foundation to unify and optimize resource management for the distributed edge-cloud when dynamic use of GPU resources is possible. This approach offer a new GPU abstraction layer and dynamic edge-cloud allocation model that offer enhanced flexibility and resource utilization over existing systems like Kubernetes and OpenStack. By using likely sensitive data, the model applied cloud-pushed deployment model. XMeta-OS manages computer resources in real-time. Therefore, fulfilling high performance various needs. It allocates CPU and GPU in real-time to workloads requiring massive parallel processing, such as machine learning, scientific simulations, real-time data analytics, and transportation tasks. Fabrication in the laboratory of an edge-cloud environment can help to assess the scalability, fault tolerance and efficiency of the system. It showcases the overall architectural design of XMeta-OS. Changes to the kernel and security solutions for safe and effective distribution are suggested. This article presents the design of the XMeta-OS. The future of XMeta-OS entails usage and testing with which we see latency, energy, computation and other such measurements in labs. XMeta-OS is a solution to cost performance, emerging application, large-scale edge-cloud deployments, and distributed GPU-enabled infrastructures. Meta-Operating System in Edge-Cloud Continuum for dynamic resource management of GPU. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cluster computing

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A Hydroelectric Power Plant Brief: Classification and Application of Artificial Intelligence 17

A Hydroelectric Power Plant Brief: Classification and Applic...

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17th IEEE International Symposium on Applied Computational Intelligence and informatics, SACI 2023

作者： Shahgholian, Ghazanfar Moazzami, Majid Zanjani, Sayed Mohammadali Mosavi, Amir Fathollahi, Arman Islamic Azad University Najafabad Branch Department of Electrical Engineering Najafabad Iran Obuda University John von Neumann Faculty of Informatics Budapest Hungary Aarhus University Department of Electrical and Computer Engineering Aarhus Denmark

ISBN: (纸本)9798350321104

Hydropower is a reliable, clean, and efficient alternative to conventional fossil fuels and other renewable energy sources. The hydro turbine is the core of a hydropower plant, and the proper maintenance and operation of all other components are essential for maximizing energy production. Besides electricity generation, hydropower plants play a critical role in storing irrigation and drinking water and controlling floods. This study presents a concise overview of hydroelectric power plant classification based on the output power generated by peak water drop and storage. Pumped storage water plants are the most applicable classification based on water conditions. The study reviews the application of Artificial Intelligence (AI) in various aspects of hydropower plants, highlighting the potential benefits and challenges of integrating AI technologies in the energy production process. This paper emphasizes the importance of proper maintenance and operation of hydropower plants and provides insights into AI's potential role in optimizing energy production and improving plant efficiency. © 2023 IEEE.

关键词： Hydroelectric power plants

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Uplink OFDMA-Based Random Access Mechanism with Bursty Arrivals for IEEE 802.11ax Systems

IEEE Networking Letters

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IEEE Networking Letters 2022年第1期4卷 34-38页

作者： Cheng, Ray-Guang Yang, Chi-Ming Firmansyah, Bariq S. Harwahyu, Ruki Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei106 Taiwan School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung40132 Indonesia Department of Electrical Engineering University of Indonesia Depok16424 Indonesia

IEEE 802.11ax defines a new channel access scheme of OFDMA-based random access (UORA) for varying number of associated stations (STAs) to send their requests or data to the access point (AP) in a contention manner. This letter presents an analytical model to investigate the performance of UORA in transient condition under bursty arrivals with arbitrary distribution. The key performance metrics of access success probability;average access delay;cumulative distribution function (CDF) for the number of transmissions;and utilization of random access resource units (RA-RUs) were derived. Simulation results verified the accuracy of the proposed analytical model. © 2019 IEEE.

关键词： Analytical models

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Analysis of data from UAVs for surveillance and threat identification in maritime areas 8

Analysis of data from UAVs for surveillance and threat ident...

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8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023

作者： Lampropoulos, Fotis Daramouskas, Ioannis Perikos, Isidoros Paraskevas, Michael University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece Hellenic Coast Guard Patras Greece University of Patras Computer Technology Institute and Press "diophantus" Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press "diophantus" Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350373615

In this paper, we present a methodology for drones for recognizing different types of objects in maritime areas. The concept and the aim is to assist the national maritime surveillance authorities in the identification of treats and the recognition of the exact type of the treat. The methodology relies on the use of deep learning networks and YOLO framework as well as regression models. The YOLO model detects where each object is and which label should be applied. In this way, object detection is subject to the analysis of Machine Learning-based approaches and Deep Learning-based approaches providing more information about the video or an image than traditional approaches to recognition. These approaches are used to identify groups of pixels that may individually belong to an object. This then feeds a regression model with the help of a convolutional neural network and more specifically, R-CNN, Fast R-CNN, Faster R-CNN and after that object detection algorithm approximates the location of the object and gives its label at the same time. The purpose of the application is to detect objects with an emphasis on identifying one or more targets of interest from data of a video or an image capture. An experimental study was conducted in real-world conditions and revealed quite interesting findings. © 2023 IEEE.

关键词： computer vision

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The Deep Learning-based Intelligent System for Extracting Information from e-KTP 2

The Deep Learning-based Intelligent System for Extracting In...

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2nd International Conference on Software engineering and Information Technology, ICoSEIT 2024

作者： Fuadi, Zaki Muchtar, Kahlil Subianto, Muhammad Riza, Hammam Oktiana, Maulisa Nizamuddin Universitas Syiah Kuala Department of Informatics Banda Aceh Indonesia Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Jakarta10340 Indonesia

ISBN: (纸本)9798350317503

This research implements an Intelligent Information System for Text Recognition on Indonesian Identity Cards (eKTP) based on Deep Learning architecture. The aim of this research is to develop a system capable of accurately and efficiently detecting and recognizing text on e-KTP images. The first phase of this research involves selecting ideal images from the e-KTP dataset. Subsequently, pre-processing is conducted to crop the image sides and merge them with background images to create a more diverse dataset and obtain the e-KTP image masks as training data. After selecting the ideal images, the total training data amounts to 144 images. The U-Net architecture is chosen as the deep learning method for image segmentation in this research, while for text detection and recognition, two neural networks, CRAFT and TRBA (TPS-ResNet-BiLSTM-Attention), are employed. The testing, based on accuracy values, Dice coefficients, and IoU for the segmentation process, results in a U-Net model accuracy of 99.52%. Text detection and recognition follow confidence values. © 2024 IEEE.

关键词： Intelligent systems

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DLMetaChain: An IoT Data Lake Architecture Based on the Blockchain 13

DLMetaChain: An IoT Data Lake Architecture Based on the Bloc...

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13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022

作者： Pingos, Michalis Christodoulou, Panayiotis Andreou, Andreas Cyprus University of Technology Computer Engineering and Informatics Department of Electrical Engineering Limassol Cyprus Neapolis University Department of Computer Science Pafos Cyprus

ISBN: (数字)9781665463904

ISBN: (纸本)9781665463904

Nowadays, the IoT ecosystem is evolving rapidly, with multiple heterogeneous sources producing high volumes of data and processes transforming this data into meaningful or 'smart' information. These volumes of data, including IoT data, need to be stored in repositories that can host raw, unprocessed, relational and non-relational types of data, such as Data Lakes. Due to the weakness of metadata management, security & access control is one of the main challenges of Big Data storage architectures as Data Lakes can be replaced without oversight of the contents. Recently, the Blockchain technology has been introduced as an effective solution to build trust between different entities, where trust is either nonexistent or unproven, and to address security and privacy concerns. In this paper we introduce DLMetaChain, an extended Data Lake metadata mechanism that consists of data from heterogeneous data sources which interact with IoT data. The extended mechanism mainly focuses on developing an architecture to ensure that the data in the Data Lake is not modified or altered by taking into advantage the capabilities of the Blockchain. © 2022 IEEE.

关键词： Smart contract

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A Machine Learning-Based Privacy-Preserving Approach to Incorporate Distributed Generators in AC Optimal Power Flow

A Machine Learning-Based Privacy-Preserving Approach to Inco...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Dindar, Burak Saner, Can Berk Polat, Dogukan Yigit Çakmak, Huseyin Kemâl Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany National University of Singapore Department of Electrical and Computer Engineering Singapore National University of Singapore School of Computing Singapore

ISBN: (纸本)9789531842976

In order to achieve the goal of a carbon-neutral power system, significant changes to the power grid are underway, necessitating enhanced interoperability between Transmission System Operators (TSOs) and Distribution System Operators (DSOs) for effective grid operation, particularly in light of the growing number of distributed generators (DGs). Data privacy concerns, however, complicate decision-making processes, particularly when integrating DGs into system-wide dispatch decisions. In this paper, we propose a machine-learning (ML)-based method to incorporate DGs located within the distribution system (DS) into dispatch decisions, adhering to data privacy by mitigating the exchange of sensitive data, such as system topology and demand profiles, between TSOs and DSOs. The methodology involves training three ML models to represent the behavior of the DS, thereby replacing the standard power flow model that contains sensitive information. Notably, we utilize a novel tailored neural network (NN) architecture to enhance computational efficiency in mapping the feasible region of the DS. Additionally, we employ open-source data to construct the Baden-Wü rttemberg (Germany) electricity grid, allowing to test our method not only on standard systems but also on a model that more accurately represents real-world power systems. The numerical case studies verify that the proposed method achieves results comparable to standard AC optimal power flow (AC-OPF) in both cost-optimality and computational time. © 2024 IEEE.

关键词： Differential privacy

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(ω1,ω2)-temporal random hyperbolic graphs

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Physical Review E 2024年第2期110卷 024309-024309页

作者： Sofoclis Zambirinis Fragkiskos Papadopoulos Department of Electrical Engineering Computer Engineering and Informatics <a href="https://***/05qt8tf94">Cyprus University of Technology</a> 3036 Limassol Cyprus.

We extend a recent model of temporal random hyperbolic graphs by allowing connections and disconnections to persist across network snapshots with different probabilities ω1 and ω2. This extension, while conceptually simple, poses analytical challenges involving the Appell F1 series. Despite these challenges, we are able to analyze key properties of the model, which include the distributions of contact and intercontact durations, as well as the expected time-aggregated degree. The incorporation of ω1 and ω2 enables more flexible tuning of the average contact and intercontact durations, and of the average time-aggregated degree, providing a finer control for exploring the effect of temporal network dynamics on dynamical processes. Overall, our results provide new insights into the analysis of temporal networks and contribute to a more general representation of real-world scenarios.

关键词： Evolving networks Networks & random structures Random graphs Evolving network models

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Hybrid Local Cache Model for Enhancing Performance and Memory Footprint of Mobile Heap Allocator 12

Hybrid Local Cache Model for Enhancing Performance and Memor...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Lee, Kyung-Seok Lee, Teahyung Eom, Young Ik Sungkyunkwan University Department of Digital Media and Communications Engineering Suwon Korea Republic of Sungkyunkwan University Department of Computer Science and Engineering Suwon Korea Republic of Sungkyunkwan University College of Computing and Informatics Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350340181

The representative mobile heap allocator that has been used in Android system, called SCUDO, supports two local cache models: the shared model and the exclusive model. However, neither the shared model nor the exclusive model cannot satisfy both memory usage and performance requirements simultaneously since mobile applications have diverse memory allocation/free patterns and the number of threads varies from single to several tens. This paper suggests a hybrid local cache model that aims to adaptively meet memory usage and performance goals. Our evaluation results show that our hybrid local cache model outperforms the shared model by up to 22% on allocation time, while preserving the memory usage. © 2023 IEEE.

关键词： Android (operating system)

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Implementing a Robust and Scalable Detect-and-Avoid Algorithm for UAV Navigation in ROS 15

Implementing a Robust and Scalable Detect-and-Avoid Algorith...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Meimetis, Dimitrios Daramouskas, Ioannis Lappas, Vaios Perikos, Isidoros Kapoulas, Vaggelis Paraskevas, Michael Nkua Department of Aerospace Science & Technology Athens Greece Computer Technology Institute and Press 'Diophantus' Patras Greece UoP Applied Mechanics Lab Patras Greece UoP Computer Engineering and Informatics Department Patras Greece Computer Technology Institute and Press 'Diophantus' University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350368833

Unmanned aerial vehicles (UAVs) have garnered significant attention from the research community during the last decade, due to their diverse capabilities and potential applications. One of the most critical functions that drones must execute efficiently is navigation in real-world environments. This paper presents a decentralized approach for enabling unmanned aerial vehicles (UAVs) to navigate safely in unknown environments and avoid obstacles. Leveraging the Optimal Reciprocal Collision Avoidance (ORCA) algorithm, implemented in the Robot Operating System (ROS), our method facilitates conflict detection and resolution in 2D environments. Through simulations using ROS, Gazebo, and Iris drones, we validate the effectiveness of our approach in scenarios with initial trajectory conflicts. Our work addresses the pressing need for UAVs to autonomously plan and execute safe flights, laying the groundwork for enhanced UAV capabilities in various real-world applications. The simulation results demonstrate the efficiency and robustness of our approach. © 2024 IEEE.

关键词： Drones

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