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11th International Conference on Advanced informatics: Concept, Theory and Application, ICAICTA 2024

作者： Basuki, Setio Sari, Zamah Indrabayu, Rizky Fauzan, Reza Wardhana, Aulia Arif Tsuchiya, Masatoshi Universitas Muhammadiyah Malang Informatics Engineering Malang Indonesia Politeknik Negeri Banjarmasin Department of Electical Engineering Banjarmasin Indonesia Doctoral School Wroclaw University of Science and Technology Wroclaw Poland Toyohashi University of Technology Department of Computer Science and Engineering Toyohashi Japan

ISBN: (纸本)9798331520311

This paper develops a Machine Learning model to estimate the citation counts of research papers. The model uses citation functions, representing the intentions of the paper's author when making citations of previous works, to estimate the number of citations. These intentions include introducing a research topic, comparing, and criticizing previous works, etc. Three predictors have been developed based on citation functions: citing sentence, regular sentence, and reference. The prediction is seen as a regression and classification problem by pre-grouping the number of citations into high-count, medium-count, and low-count. The dataset was obtained from the International Conference on Learning Representations (ICLR) 2017-2020 containing 5,156 accepted and rejected papers. This paper uses only the accepted papers since the main task is to predict the number of citations of accepted/published papers. To obtain the number of citations one year after publication, this paper uses the API provided by Semantic Scholar. According to experiments, the best results in classification reach 98.33% accuracy, and in regression, the results reach 0.3 on both RMSE and MAE. The feature labeled 'citing paper dominant,' which reflects the superiority of the citing paper over the cited paper, has proven highly effective in delivering the best prediction results, even though it is sparsely represented in the dataset. In conclusion, citation function-based predictors are effective in estimating the future impact of a paper. © 2024 IEEE.

关键词： Contrastive Learning

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Enhancing QoL with IoT Interoperability for Diabetes and Hypertension Management 29

Enhancing QoL with IoT Interoperability for Diabetes and Hyp...

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29th IEEE International Workshop on computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024

作者： Andreou, Andreas Mavromoustakis, Constandinos X. Markakis, Evangelos Bourdena, Athina Mastorakis, George Cyprus and Hellenic Mediterranean University Department of Computer Science University of Nicosia Nicosia Crete Heraklion Greece University of Nicosia Department of Computer Science Nicosia Cyprus Hellenic Mediterranean University Department of Electrical and Computer Engineering Crete Heraklion Greece Hellenic Mediterranean University Department of Business Administration and Tourism Crete Heraklion Greece Hellenic Mediterranean University Department of Management Science and Technology Crete Agios Nikolaos Greece

ISBN: (纸本)9798350377644

This demonstration highlights the use of IoT to enhance the QoL for older adults, particularly those managing diabetes and high blood pressure. By deploying digital solutions, we can remotely monitor health conditions, allowing for early detection of issues like hyper/hypoglycemia and hypertension. Our experimental results demonstrate how healthcare providers can utilize real-time data visualization to interpret abnormal vital signs, ultimately improving treatment adherence and health outcomes. This approach showcases the significant potential of IoT in fostering smart and healthy living environments for individuals and communities. © 2024 IEEE.

关键词： Electronic health record

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Advanced Short-Term Net Load Forecasting for Renewable-Based Microgrids 8

Advanced Short-Term Net Load Forecasting for Renewable-Based...

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8th IEEE International Smart Cities Conference, ISC2 2022

作者： Tziolis, Georgios Koumis, Anastasios Theocharides, Spyros Livera, Andreas Lopez-Lorente, Javier Makrides, George Georghiou, George E. University of Cyprus Pv Technology Laboratory Foss Research Centre for Sustainable Energy Department of Electrical and Computer Engineering Nicosia Cyprus Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki Greece

ISBN: (数字)9781665485616

ISBN: (纸本)9781665485616

Net load forecasting is essential for the reliable, safe and cost-effective operation of modern power systems incorporating variable renewable technologies. This paper proposes a short-term net load forecasting (STNLF) methodology based on Bayesian neural networks, applicable to microgrids with embedded photovoltaic (PV) systems. Input feature selection and determination of hidden nodes were performed to develop an optimally performing forecasting model. To validate the performance of the model, historical net load site-specific and aggregated data from buildings within the university of cyprus microgrid (with integrated PV shares of 26%) were used. The developed STNLF model demonstrated a normalized root mean square error of 4.81 % and 3.98% for the solar-integrated building and the microgrid, respectively. Finally, the capability of the developed machine learning forecasting model to yield reliable forecasts was benchmarked against baseline naïve persistence forecasts, achieving skill score improvements of up to 18.61 % at microgrid level. © 2022 IEEE.

关键词： Cost effectiveness

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Enhanced Redundant Scheme Based on Weighted Cluster-Head Selection for Critical 6G Infrastructures

Enhanced Redundant Scheme Based on Weighted Cluster-Head Sel...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Khayat, Grace Mavromoustakis, Constandinos X Pitsillides, Andreas Batalla, Jordi Mongay Markakis, Evangelos K. Andreou, Andreas Department of Computer Science Nicosia Cyprus University of Cyprus Department of Computer Science Cyprus University of Johannesburg Department of Electrical and Electronic Engineering Science South Africa Warsaw University of Technology Warsaw Poland Hellenic Mediteranean University Department of Electrical and Computer Engineering Estavromenos Heraklion Greece University of Nicosia Research Foundation Department of Computer Science Nicosia Cyprus

ISBN: (纸本)9781665459754

Flying Ad hoc Network (FANET) consists of a group of unmanned aerial vehicles (UAVs) that fulfill a certain mission in various applications. The UAV targets uploading data to the 6G networks through communication to other UAVs or to the base station. UAVs collaborate for a purpose to achieve a certain mission in a faster and more reliable way compared to a single UAV. In critical cases such as flooding or earthquakes, one or more UAVs can be non-functional thus emerging a disconnected network. One of the most reliable routing schemes is clustering which splits the network into clusters. Every cluster incorporates a cluster-head and cluster-members. The selection of the cluster-head possesses a high influence on the routing scheme's performance as it is responsible for all intra-cluster communication. A new clustered weighted scheme with a redundant cluster-head is proposed to ensure end-to-end communication in critical 6G infrastructures. The parameters of the weighted formula used for cluster establishment and primary cluster-head selection are the distance, the performance index, the speed, and the energy. A secondary cluster-head that automatically replaces the primary cluster-head whenever it is non-functional is selected based on the link reliability. The promising results obtained from MATLAB simulation are discussed in section IV. © 2022 IEEE.

关键词： MATLAB

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Alapana Generation using Finite State Machines and Generative Adversarial Networks

Alapana Generation using Finite State Machines and Generativ...

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2023 International Research Conference on Smart Computing and Systems engineering, SCSE 2023

作者： Jayatharan, Vithushigan Alwis, Dileeka University of Westminster Computer Science and Engineering London United Kingdom Informatics Institute of Technology Department of Computing Colombo Sri Lanka

ISBN: (纸本)9798350341454

The recent advancements in deep learning techniques and computational power have promoted the development of novel approaches for music generation. In this study, generating alapana, an improvisational form of Carnatic music was proposed, by leveraging Generative Adversarial Networks (GANs) and Finite State Machines (FSM). The goal is to create melodious alapana sequences that follow a given input Raga, ensuring continuity and coherence throughout the generated musical piece. The proposed approach incorporates Carnatic music theory rules into the generation process to enhance the structural coherence of the generated alapana. Additionally, various hyperparameter settings were explored to achieve the best performance. The Fréchet Audio Distance, Percentage of Correct Pitches and the Subjective evaluation through human listeners are the evaluation metrics of this approach. The result of this study demonstrates the potential of using GANs and FSM for generating continuous and pleasing alapana sequences in Carnatic music, contributing to the growing body of research in computational music generation. © 2023 IEEE.

关键词： Music

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Plugged-in electric vehicle-assisted demand response strategy for residential energy management

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Energy informatics 2023年第1期6卷 6页

作者： Alfaverh, Khaldoon Alfaverh, Fayiz Szamel, Laszlo Department of Electric Power Engineering Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics P.O.B. 91 Budapest1521 Hungary School of Engineering and Computer Science University of Hertfordshire HatfieldAL10 9AB United Kingdom

Demand response (DR) management systems are a potentially growing market due to their ability to maximize energy savings by allowing customers to manage their energy consumption at times of peak demand in response to financial incentives from the electricity supplier. Successful execution of a demand response program requires an effective management system where the home energy management system (HEMS) is a promising solution nowadays. HEMS is developed to manage energy use in households and to conduct the management of energy supply, either from the grid or the alternative energy sources like solar or wind power plants. With the increase of vehicle electrification, in order to achieve a more reliable and efficient smart grid (SG), cooperation between electric vehicles (EVs) and residential systems is required. This cooperation could involve not only vehicle to grid (V2G) operation but a vehicle to home (V2H) too. V2H operation is used to transfer the power and relevant data between EVs and residential systems. This paper provides an efficient HEMS enhanced by smart scheduling and an optimally designed charging and discharging strategy for plugged-in electric vehicles (PEVs). The proposed design uses a fuzzy logic controller (FLC) for smart scheduling and to take the charging (from the grid)/discharging (supply the household appliances) decision without compromising the driving needs. Simulations are presented to demonstrate how the proposed strategies can help to reduce electricity costs by 19.28% and 14.27% with 30% and 80% state of charge (SOC) of the PEV respectively compared to the case where G2V operation only used along with the photovoltaic (PV) production, improve energy utilization by smoothing the energy consumption profile and satisfy the user’s needs by ensuring enough EV battery SOC for each planned trip. © 2023, The Author(s).

关键词： Energy utilization

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Efficient Cloud Resource Scheduling with an Optimized Throttled Load Balancing Approach

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computers, Materials & Continua 2023年第11期77卷 2179-2188页

作者： V.Dhilip Kumar J.Praveenchandar Muhammad Arif Adrian Brezulianu Oana Geman Atif Ikram Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Computer Science and Engineering Karunya Institute of Technology and SciencesCoimbatoreTamil Nadu641114India Department of Computer Science Superior UniversityLahorePakistan Faculty of Electronics Telecommunications and Information Technology Gheorghe Asachi Technical University of IasiIa¸si700050Romania Department of Health and Human Development Stefan cel Mare University of SuceavaSuceava720229Romania Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia

Cloud technology is a new platform that offers on-demand computing Peripheral such as storage,processing power,and other computer system *** is also referred to as a system that will let the consumers utilize computational resources like databases,servers,storage,and intelligence over the *** a cloud network,load balancing is the process of dividing network traffic among a cluster of available servers to increase *** is also known as a server pool or server *** a single node is overwhelmed,balancing the workload is needed to manage unpredictable *** load balancer sends the load to another free node in this *** focus on the Balancing of workflows with the proposed approach,and we present a novel method to balance the load that manages the dynamic scheduling *** of the preexisting load balancing techniques is considered,however it is somewhat modified to fit the scenario at *** on the experimentation’s findings,it is concluded that this suggested approach improves load balancing consistency,response time,and throughput by 6%.

关键词： Load balancing throttled algorithm efficient resource allocation

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Synthetic generation of hourly photovoltaic power forecasts using Bayesian Regularised Neural Networks 2

Synthetic generation of hourly photovoltaic power forecasts ...

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2nd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2022

作者： Theocharides, Spyros Lopez-Lorente, Javier Makrides, George Georghiou, George E. University of Cyprus Pv Technology Laboratory Foss Research Center for Sustainable Energy Department of Electrical and Computer Engineering Nicosia1678 Cyprus

ISBN: (数字)9781665461078

ISBN: (纸本)9781665461078

Accurate day-ahead photovoltaic (PV) power output forecasting techniques are important for grid and plant operators. In this study, a methodology for PV power forecasting is proposed based on a synthetic generation of multiple Bayesian regularised neural network (BRNN) models. Specifically, different BRNN models were developed for every hour of each day within the power production period. The final PV power production forecasting was provided by combining the outputs of all the developed models. The accuracy of the developed model was experimentally validated by deploying it to a test system installed in cyprus. The day ahead power production forecasting performance accuracy of the proposed methodology in terms of normalised root mean square error and mean absolute percentage error was 5.80% and 4.49%, respectively. © 2022 IEEE.

关键词： Neural network models

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Assessing the Impact of Primary Frequency Support from IBRs in Low Inertia Isolated Power Systems

Assessing the Impact of Primary Frequency Support from IBRs ...

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IEEE General Meeting Power& Energy Society

作者： Phivos Therapontos Rogiros Tapakis Petros Aristidou Distribution System Operator Electricity Authority of Cyprus Nicosia Cyprus Transmission System Operator of Cyprus Nicosia Cyprus Dept. of Electrical & Computer Engineering & Informatics Cyprus University of Technology Limassol Cyprus

The anticipated massive penetration of Renewable Energy Sources (RES) will significantly impact the operation of electric power systems. The effect will be more prominent on low-inertia, isolated, power systems. Modern grid code requirements can be used to ensure that RES will remain connected to the system during abnormal or transient operating conditions and provide support. In this paper, the cyprus transmission system is used to evaluate the impact of primary frequency support (PFS) from Inverter-Based Resources (IBRs), both during low- and high-loading conditions. A sensitivity analysis of the major parameters of PFS has been performed to evaluate their impact on the frequency response in low inertia conditions. It is demonstrated that PFS provided by IBRs during low loading conditions positively affects the power system frequency stability without any additional curtailments. During high-loading conditions, PFS from RES also improves the system performance, however, significant RES curtailments from the IBGs participating in PFS are required which may raise fairness concerns.

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A Low-Cost Real-Time Cyber Physical System for Overcoming Excess Braking Issues In Race Cars

A Low-Cost Real-Time Cyber Physical System for Overcoming Ex...

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International Conference on Modern Circuits and Systems Technologies (MOCAST)

作者： V. Samaras K. Tatas A. Lontos Department of Electrical Engineering Computer Engineering and Informatics Frederick University Nicosia Cyprus Department of Mechanical Engineering Frederick University Nicosia Cyprus

This paper presents the development of proof of concept for a low-cost, real-time Cyber Physical System (CPS)/IoT that deals with observed excess braking issues in Formula Student (FS) race cars. The goal is to utilize telemetry aspects through a smart monitoring system under the FS low budget competition restrictions, with low-cost materials, open-source software, Long Range wireless communication (LoRa) and cloud services. The paper also introduces an algorithm for implementing an adaptive sampling rate based on the proximity of monitored parameters to warning levels to minimize unnecessary data transmission. The system prototype was tested under realistic driving conditions, obtaining useful preliminary data that can be used to guide both driving behavior and future vehicle design. The results demonstrate that the proposed approach can be useful for the race team in FS conditions and may be possible to expand it to other similar driving scenarios outside FS.

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