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Communication and Networks (ASIANComNet), Asian Conference on

作者： Iacovos Ioannou Marios Raspopoulos Prabagarane Nagaradjane Christophoros Christophorou Ala’ Khalifeh Vasos Vassiliou Department of Computer Science University of Cyprus and CYENS - Centre of Excellence Cyprus INSPIRE Research Centre University of Central Lancashire Larnaca Cyprus Department of ECE Sri Sivasubramaniya Nadar College of Engineering Chennai India Electrical Engineering and Information Technology German Jordanian University Amman Jordan

ISBN: (数字)9798350367003

ISBN: (纸本)9798350367225

Optimizing device-to-device (D2D) topologies is pivotal for enhancing the performance and efficiency of 6G networks. This paper introduces a novel approach for forming optimal subnet trees within the 6G networks using BDIx agents and advanced minimum-weight spanning tree (MWST/MST) algorithms augmented by graph neural networks (GNNs), and feedforward neural networks (FFNNs). Our solution aims to significantly boost network performance, particularly in highdemand scenarios such as urban areas, large-scale events, and remote locations. Our approach dynamically adapts to changing network conditions, user movements, and traffic patterns by minimizing the power consumption and maximizing the throughput. We implement various MWST algorithms, including Kruskal’s, Prim’s, and Boruvka’s algorithms, and introduce a GNN model to predict edge weights combined with FFNNs to select parent nodes (called GNN-FFNN model), aiding in the construction of minimum-weight spanning trees (MWST). Additionally, a “weighted distance” metric is proposed to analyze network performance comprehensively. The proposed AI/MLdriven solution integrates BDIx agents with MWST algorithms, focusing on optimizing subnets under gNodeB in 6G networks, enhancing data transmission efficiency, reducing latency, and increasing throughput. This research contributes to developing scalable and flexible network management solutions suitable for diverse configurations and architectures.

关键词： 6G mobile communication Adaptation models Network topology Heuristic algorithms Predictive models Prediction algorithms Throughput Graph neural networks Device-to-device communication Topology

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Fuzzy control system design for wheel slip prevention and tracking of desired speed profile in electric trains

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Asian Journal of Control 2022年第1期24卷 388-400页

作者： Moaveni, Bijan Rashidi Fathabadi, Fatemeh Molavi, Ali Center of excellence for Modelling and Control of Complex Systems Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical and Computer Engineering Western Michigan University MI United States Institute of Robotics and Industrial Informatics Polytechnical University of Catalonia Barcelona Spain

This study proposes an anti-slip control system for electric trains based on the fuzzy logic theory, which prevents the wheels from slipping during the acceleration and simultaneously tracks the desired speed profile. To improve the control performance, the train longitudinal velocity and the slip ratio are estimated. By using a field-oriented control (FOC), the angular speed of the traction motor is controlled. The fuzzy control system determines the desired angular speed of the traction motor as the reference input of FOC to obtain the desired slip ratio and track the desired speed profile. Simulation results show the effectiveness of the control system in various wheel–rail surface conditions based on the real parameters of ER24PC locomotive. © 2020 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd

关键词： Traction motors

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Compressing Time Series Towards Lightweight Integrity Commitments 8

Compressing Time Series Towards Lightweight Integrity Commit...

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8th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2023

作者： Katsika, Angeliki Papageorgiou, Konstantinos Fakis, Alexandros Kakarountas, Athanasios Andritsopoulos, Fotis Plagianakos, Vassilis Spathoulas, Georgios University of the Aegean Department of Information and Communication Systems Engineering Samos Greece ITrack Athens Greece University of Thessaly Department of Computer Science and Biomedical Informatics Lamia Greece Department of Information Security and Communication Technology Gjovik Norway

ISBN: (纸本)9798350383676

In the current era of big data, the generation and preservation of time series data has seen widespread adoption in various sectors such as trade, healthcare, and industry. At the same time, data have become a prized asset, as we are witnessing an unprecedented explosion in the volume of data generated and collected. This includes data from various sources, such as sensors, social media, and transactions, offering a competitive edge to organizations that harness the power of data, leading them to informed decisions and rapid responses to changing conditions. However, it is essential to responsibly manage data, addressing privacy and security concerns to ensure their integrity and availability, while efficiently managing the cost of storage and operations. To address these challenges, there is a compelling de-mand for methods that compress these time series while ensuring that the compressed versions faithfully represent the original data and remain secure against tampering or unauthorized alterations. This paper introduces a novel approach to condensing time-series data into lightweight integrity commitments, leveraging polynomial vector commitments as a key component. These integrity commitments are integrated with blockchain technology, serving as a cornerstone in guaranteeing data integrity and authenticity, particularly in supply chain scenarios where trust and transparency are of the utmost importance. © 2023 IEEE.

关键词： Time series

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Grid Export Reduction Based on Time-Scheduled Charging of Residential Battery Energy Storage Systems—A Case Study in cyprus 12th

Grid Export Reduction Based on Time-Scheduled Charging of Re...

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12th KES International Conference on Sustainability and Energy in Buildings, SEB 2020

作者： Chatzigeorgiou, Nikolas G. Yerasimou, Yerasimos P. Florides, Michalis A. Georghiou, George E. FOSS Research Centre for Sustainable Energy/PV Technology Laboratory Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

ISBN: (纸本)9789811587825

Photovoltaic (PV) installations grow exponentially, and the mismatch between generation and demand will adversely affect the stability of the grid. This paper presents a modified operational mode of a grid-connected hybrid PV and battery energy storage system (BESS) in cyprus. The BESS is coupled with residential rooftop PVs and is functioning under an operational mode aiming to decrease the peak power fed into the grid by storing the excess PV generation. The stored energy is then used to cover the mismatch between generation and demand, thus also increasing the on-site PV self-consumption. Given the favourable environmental conditions in cyprus, with increased PV generation especially during summer, a significant amount of excess PV generation exported to the grid is still observed, as the BESS is usually fully charged before noon. For this reason, a modification of the operational mode of the aforementioned BESS was performed, aiming to reduce the building’s interaction with the electricity grid during peak export hours by delaying the BESS charging process. An analysis of the system’s operation during that period and its contribution towards grid interaction limitation is presented. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Battery storage

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Missing Data Imputation: A Comprehensive Review

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Journal of computer and Communications 2024年第11期12卷 53-75页

作者： Majed Alwateer El-Sayed Atlam Mahmoud Mohammed Abd El-Raouf Osama A. Ghoneim Ibrahim Gad Department of Computer Science College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Computer Science Department Faculty of Science Tanta University Tanta Egypt Basic and Applied Science Institute College of Engineering and Technology Arab Academy for Science and Technology (AAST) Alexandria Egypt Department of Computer Science Faculty of Computers and informatics Tanta University Tanta Egypt

Missing data presents a significant challenge in statistical analysis and machine learning, often resulting in biased outcomes and diminished efficiency. This comprehensive review investigates various imputation techniques, categorizing them into three primary approaches: deterministic methods, probabilistic models, and machine learning algorithms. Traditional techniques, including mean or mode imputation, regression imputation, and last observation carried forward, are evaluated alongside more contemporary methods such as multiple imputation, expectation-maximization, and deep learning strategies. The strengths and limitations of each approach are outlined. Key considerations for selecting appropriate methods, based on data characteristics and research objectives, are discussed. The importance of evaluating imputation’s impact on subsequent analyses is emphasized. This synthesis of recent advancements and best practices provides researchers with a robust framework for effectively handling missing data, thereby improving the reliability of empirical findings across diverse disciplines.

关键词： Missing Data Machine Learning Prediction Deep Learning Imputation

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Comparative Study of Machine Learning and Holt-Winters Exponential Smoothing Models for Prediction of CPI's Seasonal Data 2

Comparative Study of Machine Learning and Holt-Winters Expon...

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

作者： Afrah, Ashri Shabrina Sari, Nur Fitriyah Ayu Tunjung Utama, Shoffin Nahwa Holle, Khadijah Fahmi Hayati Lestandy, Merinda Sintiya, Endah Septa Rizdania Universitas Islam Negeri Maulana Malik Ibrahim Malang Department of Informatics Engineering Malang Indonesia Universitas Muhammadiyah Malang Department of Electrical Engineering Malang Indonesia State Polytechnic of Malang Information Technology Department Malang Indonesia University of Pgri Wiranegara Computer Science Department Pasuruan Indonesia

ISBN: (纸本)9798350317503

Inflation is one of the factors influencing price stability. Inflation affects people's purchasing power and impacts their decisions as economic actors. Consumer Price Index (CPI) is one of the factors used by economists to measure the inflation or deflation in a country. This research focuses on comparing the prediction results of the CPI for the Education Goods and Services using the Holt's Winters Exponential Smoothing and Machine Learning Methods, namely Long Short-Term Memory (LSTM), Extreme Learning Machine (ELM), Ridge, and Least Absolute Shrinkage and Selection Operator (LASSO). The data used is univariate data on the CPI for the Education Goods and Services in Malang City in 1996-2013, which was obtained from the publication of the Statistics Indonesia (BPS), entitled 'Malang City in Figures' which was published in 1997-2014. The results of this research show that the Ridge Method produces the smallest Mean Absolute Percentage Error (MAPE) value compared to other Machine Learning Methods and the Multiplicative Holt-Winters Exponential Smoothing Method, with MAPE= 2.10723%%. Machine Learning models that have been simulated have very good accuracy values, with MAPE values © 2024 IEEE.

关键词： Forecasting

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Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

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computers, Materials & Continua 2023年第2期74卷 3263-3278页

作者： Sagheer Abbas Syed Ali Raza MAKhan Muhammad Adnan Khan Atta-ur-Rahman Kiran Sultan Amir Mosavi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120Korea Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah31261Saudi Arabia John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

File labeling techniques have a long history in analyzing the anthological trends in computational *** situation becomes worse in the case of files downloaded into systems from the ***,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user ***,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic ***,one major drawback of current topic modeling approaches is better *** rely on specific language types and domain similarity of the *** this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the *** results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

关键词： Automated file labeling file organization machine learning topic modeling

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Parameterized Saga of First-Fit and Last-Fit Coloring 42

Parameterized Saga of First-Fit and Last-Fit Coloring

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42nd International Symposium on Theoretical Aspects of computer Science, STACS 2025

作者： Agrawal, Akanksha Lokshtanov, Daniel Panolan, Fahad Saurabh, Saket Verma, Shaily Indian Institute of Technology Madras India University of California Santa BarbaraCA United States School of Computer Science University of Leeds United Kingdom The Institute of Mathematical Sciences HBNI Chennai India Department of Informatics University of Bergen Norway Algorithm Engineering Group Hasso Plattner Institute Potsdam Germany

ISBN: (纸本)9783959773652

The classic greedy coloring algorithm considers the vertices of an input graph G in a given order and assigns the first available color to each vertex v in G. In the Grundy Coloring problem, the task is to find an ordering of the vertices that will force the greedy algorithm to use as many colors as possible. In the Partial Grundy Coloring, the task is also to color the graph using as many colors as possible. This time, however, we may select both the ordering in which the vertices are considered and which color to assign the vertex. The only constraint is that the color assigned to a vertex v is a color previously used for another vertex if such a color is available. Whether Grundy Coloring and Partial Grundy Coloring admit fixed-parameter tractable (FPT) algorithms, algorithms with running time f(k)nO(1), where k is the number of colors, was posed as an open problem by Zaker and by Effantin et al., respectively. Recently, Aboulker et al. (STACS 2020 and Algorithmica 2022) resolved the question for Grundy Coloring in the negative by showing that the problem is W[1]-hard. For Partial Grundy Coloring, they obtain an FPT algorithm on graphs that do not contain Ki,j as a subgraph (a.k.a. Ki,j-free graphs). Aboulker et al. re-iterate the question of whether there exists an FPT algorithm for Partial Grundy Coloring on general graphs and also asks whether Grundy Coloring admits an FPT algorithm on Ki,j-free graphs. We give FPT algorithms for Partial Grundy Coloring on general graphs and for Grundy Coloring on Ki,j-free graphs, resolving both the questions in the affirmative. We believe that our new structural theorems for partial Grundy coloring and "representative-family" like sets for Ki,j-free graphs that we use in obtaining our results may have wider algorithmic applications. © Akanksha Agrawal, Daniel Lokshtanov, Fahad Panolan, Saket Saurabh, and Shaily Verma.

关键词： Graph algorithms

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Liver Tumor Decision Support System on Human Magnetic Resonance Images:A Comparative Study

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computer Systems Science & engineering 2023年第8期46卷 1653-1671页

作者： Hiam Alquran Yazan Al-Issa Mohammed Alslatie Isam Abu-Qasmieh Amin Alqudah Wan Azani Mustafa Yasmin Mohd Yacob Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Computer Engineering Yarmouk University 556Irbid21163Jordan The of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Faculty of Electrical Engineering&Technology Campus Pauh PutraUniversiti Malaysia PerlisArau02600PerlisMalaysia Faculty of Electronic Engineering&Technology Campus Pauh PutraUniversiti Malaysia PerlisArau02600PerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia PerlisArau02600PerlisMalaysia

Liver cancer is the second leading cause of cancer death *** tumor detection may help identify suitable treatment and increase the survival *** imaging is a non-invasive tool that can help uncover abnormalities in human *** Resonance Imaging(MRI),in particular,uses magnetic fields and radio waves to differentiate internal human organs ***,the interpretation of medical images requires the subjective expertise of a radiologist and ***,building an automated diagnosis computer-based system can help specialists reduce incorrect *** paper proposes a hybrid automated system to compare the performance of 3D features and 2D features in classifying magnetic resonance liver tumor *** paper proposed two models;the first one employed the 3D features while the second exploited the 2D *** first system uses 3D texture attributes,3D shape features,and 3D graphical deep descriptors beside an ensemble classifier to differentiate between four 3D tumor *** top of that,the proposed method is applied to 2D slices for comparison *** proposed approach attained 100%accuracy in discriminating between all types of tumors,100%Area Under the Curve(AUC),100%sensitivity,and 100%specificity and precision as well in 3D liver *** the other hand,the performance is lower in 2D *** maximum accuracy reached 96.4%for two classes and 92.1%for four *** top-class performance of the proposed system can be attributed to the exploitation of various types of feature selection methods besides utilizing the ReliefF features selection technique to choose the most relevant features associated with different *** novelty of this work appeared in building a highly accurate system under specific circumstances without any processing for the images and human input,besides comparing the performance between 2D and 3D *** the future,the presented work can be extended to be

关键词： Liver tumors ensemble classifier 3D shape features 3D cooccurrence matrix ResNet101

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Impact Assessment of NILM Methods for an Enhanced Observability of Low Voltage Distribution Networks 8

Impact Assessment of NILM Methods for an Enhanced Observabil...

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8th IEEE International Smart Cities Conference, ISC2 2022

作者： Moreno Jaramillo, Andres F Lopez-Lorente, Javier Brogan, Paul Laverty, David Del Rincon, Jesus Martinez Foley, Aoife School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast United Kingdom Pv Technology Laboratory Foss Research Centre for Sustainable Energy University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus Applications Strategist Phasora Ltd Belfast United Kingdom School of Mechanical and Aerospace Engineering Queen's University Belfast Belfast United Kingdom

ISBN: (数字)9781665485616

ISBN: (纸本)9781665485616

The migration to low-carbon technologies in secondary (low-voltage) distribution networks relies on high integration of distributed energy resources (DER), including distributed generation and low-carbon loads. The electrification of power and transport sectors has led to an increased deployment of solar photovoltaic systems and electric vehicles causing unconventional dynamics in distribution networks. Thus, distribution network operators are facing new technical challenges such as reverse power flow, higher load peaks, and power quality issues. This has attracted research interest to investigate the implementation of innovative techniques such as non-intrusive load monitoring (NILM) methods. These techniques contribute to an enhanced and scalable monitoring of DER in electrical networks. Current literature based NILM models have achieved significantly high performance for both electromobility and photovoltaic systems, which makes them eligible for deployment from a technical standpoint. However, a critical analysis of the considerations for a feasible implementation of these methodologies has not been covered. Thus, this work studies the techno-economic implications for the large-scale implementation of NILM methods at the customer level and low voltage side of distribution transformers. While a substantial investment is associated with a better monitored low voltage distribution network, there are multiple benefits related to technical, economic, social, and environmental aspects. © 2022 IEEE.

关键词： Electric power transmission networks

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