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Passivity-Based distributed Event-Triggered Flocking Control of Port-Hamiltonian systems

IEEE Control systems Letters 2025年 9卷 342-347页

作者： Aranda-Escolástico, Ernesto Abdelrahim, Mahmoud Fernández-Amorós, David Guinaldo, María Colombo, Leonardo Department of Software and Systems Engineering Universidad Nacional de Educación a Distancia (UNED) Juan del Rosal 16 Madrid 28040 Spain Prince Sultan University Renewable Energy Laboratory College of Engineering Riyadh 11586 Saudi Arabia Computer Science and Automatic Control Department Universidad Nacional de Educación a Distancia (UNED) Juan del Rosal 16 Madrid 28040 Spain Centre for Automation and Robotics (CSIC-UPM) Ctra. M300 Campo Real Km 0200 Madrid Arganda del Rey 28500 Spain

This letter addresses the problem of flocking motion in the context of port-Hamiltonian systems. The port-Hamiltonian framework offers advantages in modeling networks, dissipation, and heterogeneous agents. In this regard, designing flocking controllers in port-Hamiltonian form significantly increases their practical applications. Additionally, event-triggered control has proven effective in reducing bandwidth consumption. However, challenges remain in combining flocking requirements with energy-dissipating terms. Therefore, we propose a novel event-triggered flocking controller suitable for port-Hamiltonian systems. The method is validated through numerical simulations of a group of hexarotors. © 2017 IEEE.

关键词： flocking passivity Port-Hamiltonian systems event-triggered control multi-agent systems

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Service Function Chaining in LEO Satellite Networks via Multi-Agent Reinforcement Learning

Service Function Chaining in LEO Satellite Networks via Mult...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Khai Doan Marios Avgeris Aris Leivadeas Ioannis Lambadaris Wonjae Shin Department of Systems and Computer Engineering Carleton University Ottawa ON Canada Department of Software and IT Engineering École de Technologie Supérieure Montreal QC Canada Korea University School of Electrical Engineering Seoul South Korea

Low-earth-orbit satellite networks (LSNs) offer an enhanced global connectivity and a wide range of applications such as disaster response and military operations, among others. Each specific application can be represented by a service function chain (SFC) in which each function is considered as a task in the application. Our objective is to optimize the long-term system performance by minimizing the average end-to-end delay of SFC deployments in LSNs. To achieve this, we formulate a dynamic programming (DP) problem to derive an optimal placement policy. To overcome the computational intractability, the need for statistical knowledge of SFC requests, and centralized decision-making challenges, we present a multi-agent Q-learning approach where satellites act as independent agents. To facilitate performance convergence in non-stationary agents' environments, we let agents to collaborate by sharing designated learning parameters. In addition, agents update their Q-tables via two distinct rules depending on selected actions. Extensive experimentation shows that our approach achieves convergence and performance relatively close to the optimum obtained by solving the formulated DP equation.

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Enhancing Loan Approval Prediction Through Advanced Machine Learning Models

Enhancing Loan Approval Prediction Through Advanced Machine ...

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Sustainable Computing and Smart systems (ICSCSS), International Conference on

作者： C. Jamunadevi S. Prasath V. E Sathishkumar S. Pandikumar Akshaya J Department of Computer Technology PG Kongu Engineering College Perundurai Erode Department of Computer Science VET Institute of Arts and Science (Co-education) College Thindal School of Engineering and Technology Sunway University Darul Ehsan Malaysia Department of MCA Acharya Institute of Technology Banglore MSc (Software Systems) Kongu Engineering College Perundurai Erode

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Loans generate the majority of revenue forbanks and other financial institutions. Loan approval is a critical process in banking organizations because they can only lend to specific people or organizations due to restricted resources or credit. In order to forecastif a specific individual is eligible for a loan or not, a variety of machine learning techniques are combined with algorithms such as bagging classifiers. These algorithms include logistic regression classifiers, support vector classifiers (SVC), decision trees, and random forest. This system aims to enhance the accuracy and robustness of bank loan approvalpredictions through the implementation of an ensemble machine learning model with 97% accuracy. Leveraging the strengths of Random Forest (RF) and XGBoost (eXtreme Gradient Boosting), this ensemble approach seeks to mitigate the limitations of individual models, providing a more reliable and precise decision-making system for loan approval processes. However, these systems encounter challenges like managing imbalanced datasets and ensuring model interpretability. Furthermore, integrating diverse data sources while maintaining data quality and consistency presents significant difficulties.

关键词： Radio frequency Accuracy Machine learning algorithms Decision making Organizations Predictive models Robustness

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Comparative Analysis of Cryptocurrency Trading Platforms Using the Analytic Hierarchy Process 3

Comparative Analysis of Cryptocurrency Trading Platforms Usi...

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3rd International Workshop on Information Technologies: Theoretical and Applied Problems, ITTAP 2023

作者： Kuznetsov, Oleksandr Kryvinska, Natalia Ilchenko, Oleksii Smirnova, Tetiana Ulianovska, Yuliia Department of Political Sciences Communication and International Relations University of Macerata Via Crescimbeni 30/32 Macerata62100 Italy Department of Information and Communication Systems Security School of Computer Sciences V. N. Karazin Kharkiv National University 4 Svobody Sq. Kharkiv61022 Ukraine Department Department of Information Systems University of Comenius Bratislava Slovakia Oracle Slovensko spol. s r.o. Bratislava Slovakia Department of cyber security and software Central Ukrainian National Technical University 8 University Ave Kropyvnytskyi25006 Ukraine Department of Computer Science and Software Engineering University of Customs and Finance av. Volodymira Vernadskogo 2/4 Dnipro49000 Ukraine

In the rapidly evolving world of cryptocurrency trading, selecting an optimal trading platform is paramount for both novice and seasoned investors. This study aims to provide a comprehensive evaluation of leading cryptocurrency trading platforms using the Analytic Hierarchy Process (AHP). The AHP, a structured technique for organizing and analyzing complex decisions, was employed to rank platforms based on multiple criteria, including UI/UX, security protocols, asset diversity, and more. Initial findings, considering equal importance for all criteria, highlighted Binance as the most favorable platform. However, when varying the importance of criteria based on empirical data, the distinctions between platforms became less pronounced, yet Binance retained its top position. This research not only offers valuable insights for potential investors but also emphasizes the significance of considering multiple criteria in decision-making processes. Furthermore, the study underscores the versatility of the AHP in evaluating multifaceted scenarios, making it a valuable tool for future research in diverse domains. © 2023 CEUR-WS. All rights reserved.

关键词： Analytic hierarchy process

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Implementational Aspects for the Characterization of Low-Inertia in Power systems

Implementational Aspects for the Characterization of Low-Ine...

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Annual North-American Power Symposium

作者： M. D. Baquedano-Aguilar Nader Aljohani Sean P. Meyn Arturo Bretas Department of Electrical & Computer Engineering University of Florida Gainesville Florida Pacific Northwest National Laboratory Distributed Systems Group Richland WA

ISBN: (纸本)9781665499224

The replacement of conventional generating units with non-conventional renewable energy resources impacts the reliability of power systems. With high integration of renewable-based generation resources, there is a reduction in total system inertia, which could lead to systems with less time to react for frequency restoration in case of unexpected unit failures. Though several power system testbeds are available for studying and analysing different stability phenomena, none are suitable for characterizing, or analyzing low-inertia conditions. In this paper, a power system simulation environment is introduced for investigating control architectures for power grids, with special attention to the impact of high penetration of inverter-based generation resources (IBRs). The testbed is applied to the WSCC 9- bus test system, modified so that a range of IBR penetration levels can be analyzed (from zero to 80%). The testbed is suitable for studying both fast and slow electromechanical phenomena. A conclusion from numerical experiments is that high levels of IBRs do not necessarily result in low-inertia. This is due in part to the layers of control in the grid already in place to avoid frequency excursions.

关键词： Renewable energy sources Time-frequency analysis System dynamics Simulation Power system stability Power grids Stability analysis

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Brain-Controlled computer Tasks for Paralyzed Persons: Framework Overview

Brain-Controlled Computer Tasks for Paralyzed Persons: Frame...

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2023 International Conference on Industry Sciences and computer Science Innovation, iSCSi 2023

作者： Imran, Amna Chaudhary, Haider Tariq, Maimoona Ali, Zahra Ahad, Abdul Naqvi, Hammad Coelho, Paulo Jorge Pires, Ivan Miguel Knowledge Unit of Science and Technology University of Management and Technology Sialkot Pakistan School of Software Northwestern Polytechnical University Shaanxi Xian710072 China Department of Computer Science Istanbul Technical University Turkey Polytechnic of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra Coimbra Portugal Instituto de Telecomunicações Universidade da Beira Interior Covilhã Portugal

To bridge the digital barrier between those with disabilities and those without impairments, several applications can be operated by brain signals. It highlights the importance of employing brain-computer interfaces (BCI) and mentions a specific system called the "Brain-Controlled computer System" that can control a computer cursor and perform mouse actions using brain signals.. The system can control the cursor and provide mouse left/right click with brain signals using a low-cost Brainlink Lite device. Based on this system, we employed machine learning and deep learning methodologies to develop a system that classifies brain signals for cursor movement. The methodology involved gathering user input through surveys, interviews, and comparison studies, as well as utilizing pre-developed datasets collected using a mind sensor. We initially trained a model on a previous dataset, achieving approximately 70% accuracy. However, when applied to the new dataset, the accuracy dropped to 49%. To improve the results, we experimented with more complex models, including additional layers and a dropout layer to prevent overfitting. Despite these enhancements, the accuracy only reached a maximum of 52%. The research team collected a dataset tailored to their specific application. They made a groundbreaking discovery by applying a convolutional neural network (CNN), typically used for image analysis, to process continuous brainwave data. Despite this non-standard use, their hypothesis proved correct, with the CNN model achieving an impressive 80% accuracy in classifying brain signals for controlling a cursor. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Brain computer interface

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Stereo Image Super-Resolution via Disparity Estimation and Domain Diffusion 9th

Stereo Image Super-Resolution via Disparity Estimation and D...

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9th International Symposium on Artificial Intelligence and Robotics, ISAIR 2024

作者： Wang, Wanjun Ma, Chunyan Zhu, Hongjun Xu, Kai Han, Huihui School of Computer and Software Engineering Anhui Institute of Information Technology WuHu241199 China School of Artificial Intelligence Department of Information Hohai University NanJing210024 China Platforms of Automotive Intelligent Software and Systems Anhui Institute of Information Technology WuHu241199 China

ISBN: (纸本)9789819629107

Image super-resolution (SR) and disparity estimation are closely related in stereo images, with effective utilization of disparity maps enhancing SR performance. This paper proposes a stereo image super-resolution reconstruction network utilizing disparity estimation and a domain diffusion model. The network integrates disparity estimation with diffusion models for feature summation and domain space construction, enabling interaction between forward and backward diffusion memory units. To further improve reconstructed image quality, we introduce a novel memory unit inspired by long short-term memory concepts. Disparity loss and structural similarity are included in the loss function to achieve more accurate results. Experiments show that our method outperforms existing techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Stereo image processing

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RAT: A Refactoring-Aware Traceability Model for Bug Localization

RAT: A Refactoring-Aware Traceability Model for Bug Localiza...

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International Conference on software engineering (ICSE)

作者： Feifei Niu Wesley K. G. Assunção LiGuo Huang Christoph Mayr-Dorn Jidong Ge Bin Luo Alexander Egyed State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Institute for Software Systems Engineering Johannes Kepler University Linz Austria Department of Computer Science and Engineering Southern Methodist University Dallas Texas USA

A large number of bug reports are created during the evolution of a software system. Locating the source code files that need to be changed in order to fix these bugs is a challenging task. Information retrieval-based bug localization techniques do so by correlating bug reports with historical information about the source code (e.g., previously resolved bug reports, commit logs). These techniques have shown to be efficient and easy to use. However, one flaw that is nearly omnipresent in all these techniques is that they ignore code refactorings. Code refactorings are common during software system evolution, but from the perspective of typical version control systems, they break the code history. For example, a class when renamed then appears as two separate classes with separate histories. Obviously, this is a problem that affects any technique that leverages code history. This paper proposes a refactoring-aware traceability model to keep track of the code evolution history. With this model, we reconstruct the code history by analyzing the impact of code refactorings to correctly stitch together what would otherwise be a fragmented history. To demonstrate that a refactoring aware history is indeed beneficial, we investigated three widely adopted bug localization techniques that make use of code history, which are important components in existing approaches. Our evaluation on 11 open source projects shows that taking code refactorings into account significantly improves the results of these bug localization techniques without significant changes to the techniques themselves. The more refactorings are used in a project, the stronger the benefit we observed. Based on our findings, we believe that much of the state of the art leveraging code history should benefit from our work.

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Sentiment Analysis of Covid-19 Twitter Data using Deep Learning Algorithm

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Procedia computer Science 2024年 235卷 3397-3407页

作者： B. Valarmathi N. Srinivasa Gupta V. Karthick T. Chellatamilan K. Santhi Dhanush Chalicheemala Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Vellore Tamil Nadu India. Department of Manufacturing School of Mechanical Engineering Vellore Institute of Technology Vellore Tamil Nadu India. Assistant Systems Engineer Tata Consultancy Services Chennai Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India. Department of Information Technology School of Information Technology and Engineering Vellore Institute of Technology Vellore Tamil Nadu India

A key method used in the study of Natural Language Processing (NLP) is sentiment analysis, or emotion analysis, plays a pivotal role in text analysis. Its primary function is to discern and categorize the underlying emotions within textual content, classifying them as positive, neutral, or negative sentiments. A variety of textual inputs can be used with this process, including entire documents, individual sentences, and more. Understanding the sentiments expressed by individuals is of paramount importance to organizations in today’s digital age. Clients and customers now have the means to express their thoughts and emotions with greater ease and immediacy than ever before. This wealth of sentiment data can offer valuable insights that organizations can leverage to enhance their products, services, and overall customer experience. The present study focuses on the sentiment analysis of Twitter data related to the COVID-19 pandemic, employing the Long Short-Term Memory (LSTM) algorithm. To improve the accuracy of sentiment analysis, a pre-processing procedure is introduced, which involves the use of the Neat Text module in Python to clean the tweets. In this research endeavor, a dataset comprising 1,79,107 COVID-19-related tweets is subjected to sentiment analysis using the proposed pre-processing module and LSTM. The results demonstrate an impressive accuracy rate of 96% in accurately determining the sentiments conveyed in these COVID-19 tweets. In contrast, the existing algorithm, based on Artificial Neural Network (ANN), achieved a significantly lower accuracy rate of 76%. This research not only showcases the effectiveness of LSTM and pre-processing in sentiment analysis but also highlights the significance of sentiment analysis in gaining valuable insights from vast amounts of textual data, especially when important events like the COVID-19 pandemic are involved.

关键词： COVID-19 Natural Language Processing Text Mining Long Short-Term Memory WordCloud Opinion mining Sentiment analysis Opinion extraction

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A Systematic Review of Approaches for Reviewing Security-Related Aspects in Agile Requirements Specification of Web Applications

A Systematic Review of Approaches for Reviewing Security-Rel...

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computer Science, computer engineering, & Applied Computing (CSCE), Congress in

作者： Aqsa Munir Mohammed Assiri Saba Naveed Alam Muzna Khan Wasi Haider Butt Mamoona Humayun Department of Computer and Software Engineering College of Electrical and Mechanical Engineering (EME) National University of Science and Technology (NUST) Islamabad Pakistan Department of Computer Science College of Sciences and Humanities Aflaj Prince Sattam bin Abdulaziz University Saudi Arabia Department of Software Engineering College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Saudi Arabia Department of Information Systems College of Computer and Information Sciences Jouf University Al-Jouf Saudi Arabia

Imperfections in requirement specification can cause serious issues during software development life cycle. It might bring about inferior quality products due to missing attributes, for example, security. Specifically, Web Applications are considered as obvious target for getting significant information. Security aspects have gotten hard to manage in web applications because security requirements are not regularly seen appropriately and often details are missing which lead into ill-characterized security-related aspects. With the help of systemic literature review (SLR), we have identified 32 major research works which were published in period of year 2010 to 2020. We identified 38 security-related aspects of web applications and also 13 techniques and tools for security related aspects in agile requirement specification. We have also analyzed 22 challenges in reviewing security related aspects in agile development life cycle.

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