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Suppression and synchronization of chaos in uncertain time-delay physical system

applied mathematics(A Journal of Chinese Universities) 2024年第3期39卷 416-437页

作者： Israr Ahmad Muhammad Shafiq Department of Preparatory Studies Centre University of Technology and Applied SciencesNizwaOman Department of Electrical and Computer Engineering Sultan Qaboos UniversityOman

The mechanical horizontal platform(MHP)system exhibits a rich chaotic *** chaotic MHP system has applications in the earthquake and offshore *** article proposes a robust adaptive continuous control(RACC)*** investigates the control and synchronization of chaos in the uncertain MHP system with time-delay in the presence of unknown state-dependent and time-dependent *** closed-loop system contains most of the nonlinear terms that enhance the complexity of the dynamical system;it improves the efficiency of the *** proposed RACC approach(a)accomplishes faster convergence of the perturbed state variables(synchronization errors)to the desired steady-state,(b)eradicates the effect of unknown state-dependent and time-dependent disturbances,and(c)suppresses undesirable chattering in the feedback control *** paper describes a detailed closed-loop stability analysis based on the Lyapunov-Krasovskii functional theory and Lyapunov stability *** provides parameter adaptation laws that confirm the convergence of the uncertain parameters to some constant *** computer simulation results endorse the theoretical findings and provide a comparative performance.

关键词： chaotic horizontal platform system chaos suppression chaos synchronization robust adaptive control Lyapunov stability theory

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Optimizing Crowd Counting in Dense Environments Through Curriculum Learning Training Strategy

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SN computer Science 2024年第6期5卷 683页

作者： Fotia, Lidia Percannella, Gennaro Saggese, Alessia Vento, Mario Department of Information Engineering Electrical Engineering and Applied Mathematics (DIEM) University of Salerno Fisciano Italy

Counting individuals in highly crowded environments, characterized by thousands of people, has garnered significant attention in recent years, due to the high number of vertical markets wherein such algorithms can prove beneficial, ranging from smart city and transportation to retail sectors, among others. Within this context, in this paper we introduce a novel training methodology tailored for estimating the number of people, ensuring precise counting accuracy in both moderately and highly crowded scenarios. The proposed approach exploits a formulation of the problem based on point detection, where each point represents an individual’s head. Our innovative contributions center around the designing of a novel training strategy employing Curriculum Learning (CL), which aims to replicate the gradual learning process observed in human cognition, training on simpler tasks at the beginning and tackling more complex tasks as the training evolves. In order to evaluate the complexity of each sample image, we propose a novel indicator taking into account both the number of people and their distribution within the image. The experimentation phase encompassed 18 publicly available datasets;the obtained results validate the effectiveness of the proposed approach, surpassing the baseline state-of-the-art point detection by 71% and 70% in terms of Mean Absolute Error (MAE) and Mean Squared Error (MSE), respectively. © The Author(s) 2024.

关键词： Curriculum Learning High density crowd counting Point detection

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Enhancing IoT Network Security with Graph Neural Networks for Node Anomaly Detection

Enhancing IoT Network Security with Graph Neural Networks f...

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Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition and Structural and Syntactic Pattern Recognition, S+SSPR 2024

作者： Carletti, Vincenzo Foggia, Pasquale Rosa, Francesco Vento, Mario Department of Information Engineering Electrical Engineering and Applied Mathematics University of Salerno Fisciano Italy

ISBN: (纸本)9783031805066

The widespread deployment of Internet of Things (IoT) devices in homes, industries, and public spaces presents significant cybersecurity challenges, particularly due to their limited computational capabilities and often insecure configurations. Detecting infected devices through network analysis presents a significant challenge given the diversity of network protocols and behaviors. Traditional methods, reliant on packet statistics or binary signatures, show their limits in these complex environments. Recent advancements in machine learning and deep learning offer promising alternatives, also through the use of graph-based representations that capture network topology and facilitate the detection of complex attack patterns. This paper presents a comprehensive analysis in a realistic setup of two state-of-the-art Graph Neural Networks (GNNs) designed for node anomaly detection, applied to a large-scale dataset of IoT network traffic. The dataset, comprising over 240,000 graphs extracted from IoT23, IoTID20, and IoT-Traces, includes both benign and malicious communications. In the analysis we take into account the impact of varying snapshot durations and graph-based representations on the performance achieved by the GNNs. The results suggest that using a state-of-the-art graph autoencoder (DOMINANT) with a computationally efficient representation (TDG) is the best trade-off among the considered constraints and variables. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graph neural networks

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Quantum spin systems: Toroidal classification and geometric duality

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Physical Review B 2024年第14期110卷 L140403页

作者： Vahid Azimi-Mousolou Anders Bergman Anna Delin Olle Eriksson Manuel Pereiro Danny Thonig Erik Sjöqvist Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics Department of Physics and Astronomy Department of Applied Physics School of Engineering Sciences (SeRC) Wallenberg Initiative Materials Science for Sustainability (WISE) Wallenberg Initiative Materials Science WISE School of Science and Technology

We demonstrate a toroidal classification for quantum spin systems, revealing an intrinsic geometric duality within this structure. Through our classification and duality, we reveal that various bipartite quantum features in magnon systems can manifest equivalently in both bipartite ferromagnetic and antiferromagnetic materials, based upon the availability of relevant Hamiltonian parameters. Additionally, the results highlight the antiferromagnetic regime as an ultrafast dual counterpart to the ferromagnetic regime, both exhibiting identical capabilities for quantum spintronics and technological applications. Concrete illustrations are provided, demonstrating how splitting and squeezing types of two-mode magnon quantum correlations can be realized across ferro- and antiferromagnetic regimes.

关键词： Magnons Quantum correlations in quantum information Quantum information processing Quantum information theory Spintronics

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Certifying IoT Data with Verifiable Credentials 22

Certifying IoT Data with Verifiable Credentials

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22nd International Symposium on Network Computing and Applications, NCA 2024

作者： Mazzocca, Carlo Allevi, Stefano Montanari, Rebecca Department of Information and Electrical Engineering and Applied Mathematics University of Salerno Salerno Italy Department of Computer Science and Engineering University of Bologna Bologna Italy

ISBN: (纸本)9798331510183

The Internet of Things (IoT) is a major contributor to the vast amount of data generated worldwide, significantly impacting the big data market. However, this data holds value only when utilized for insights and applications. Many organizations hesitate to use third-party data due to concerns about accuracy, reliability, and integrity. Enhancing trustworthiness in data is crucial to unlock their full potential, especially in critical domains such as healthcare where erroneous data can have severe consequences. The Identity of Things (IDoT) paradigm addresses this need by identifying trustworthy devices using Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs), offering promising solutions for digital identification. Although DIDs and VCs have primarily been used for mutual trust and access control, their potential for data certification in IoT remains underexplored. This paper is the first to investigate the feasibility of IoT devices to use VCs for certifying data. We evaluated devices with varying capabilities, focusing on the latency, computing, and storage requirements for issuing VCs. Our findings demonstrate that IoT devices can issue VCs with up to 100 claims in less than 90 ms, with storage requirements growing linearly and remaining below 4 KB. Using VCs for data certification does not introduce significant computational overhead, suggesting its practicality for IoT environments. ©2024 IEEE.

关键词： Data integrity

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Regression analysis of squeezing-induced hybrid nanofluid flow in Darcy-Forchheimer porous medium

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applied mathematics and Mechanics(English Edition) 2025年第1期46卷 193-208页

作者： K.MUHAMMAD M.SARFRAZ Department of Humanities and Sciences School of Electrical Engineering and Computer ScienceNational University of Sciences and TechnologyIslamabad 44000Pakistan Department of Mathematics Quaid-i-Azam UniversityIslamabad 44000Pakistan

This article presents a mathematical model addressing a scenario involving a hybrid nanofluid flow between two infinite parallel *** plate remains stationary,while the other moves downward at a squeezing *** space between these plates contains a Darcy-Forchheimer porous medium.A mixture of water-based fluid with gold(Au)and silicon dioxide(Si O2)nanoparticles is *** contrast to the conventional Fourier's heat flux equation,this study employs the Cattaneo-Christov heat flux equation.A uniform magnetic field is applied perpendicular to the flow direction,invoking magnetohydrodynamic(MHD)***,the model accounts for Joule heating,which is the heat generated when an electric current passes through the *** problem is solved via NDSolve in *** and statistical analyses are conducted to provide insights into the behavior of the nanomaterials between the parallel plates with respect to the flow,energy transport,and skin *** findings of this study have potential applications in enhancing cooling systems and optimizing thermal management *** is observed that the squeezing motion generates additional pressure gradients within the fluid,which enhances the flow rate but reduces the frictional ***,the fluid is pushed more vigorously between the plates,increasing the flow *** the fluid experiences higher flow rates due to the increased squeezing effect,it spends less time in the region between the *** thermal relaxation,however,abruptly changes the temperature,leading to a decrease in the temperature fluctuations.

关键词： convective boundary condition Darcy-Forchheimer medium hybrid nanofuid Joule heating magnetohydrodynamic(MHD) numerical solution squeezing flow regression analysis

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Combining Deep Networks with Model-Based Scene Segmentation for Reliably Detecting Rocks on Railway Tracks 8

Combining Deep Networks with Model-Based Scene Segmentation ...

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8th IEEE International Forum on Research and Technologies for Society and Industry Innovation, RTSI 2024

作者： Carletti, Vincenzo Foggia, Pasquale Ranieri, Speranza Saggese, Alessia Spingola, Camilla Vento, Mario University of Salerno Department of Information and Electrical Engineering and applied Mathematics Italy

ISBN: (纸本)9798350362138

Railway transportation is crucial to global mobility, serving as a sustainable conduit for transporting goods and passengers;unfortunately, it is vulnerable to different threats, like the presence of rocks on railway tracks. Accidents caused by rocks can disrupt transport schedules, damage infrastructure, and even lead to injuries or fatalities. Addressing these safety challenges is very important for ensuring the operation of rail networks, which in turn enhances overall mobility and minimizes delays, but also for protecting lives. In this paper, we propose a novel approach to detect rocks on the railway tracks using a camera mounted on the train's front-end. In particular, we exploit domain-specific knowledge regarding intrinsic characteristics of the camera, its positioning with respect to the rails and rail-track characteristics, together with the output of a deep neural network for semantic segmentation, to identify the railway track in the image. Once the track area is identified, it is partitioned into patches and a convolutional neural network is applied to analyze these patches and to detect the presence of rocks;furthermore, in order to enhance the robustness of the detection process, we employ a majority voting mechanism across a sequence of video frames. The experimentation, conducted over six videos augmented with the presence of rocks, confirm the effectiveness of the proposed approach. © 2024 IEEE.

关键词： Semantic Segmentation

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Geyser Inspired Algorithm:A New Geological-inspired Meta-heuristic for Real-parameter and Constrained engineering Optimization

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Journal of Bionic engineering 2024年第1期21卷 374-408页

作者： Mojtaba Ghasemi Mohsen Zare Amir Zahedi Mohammad-Amin Akbari Seyedali Mirjalili Laith Abualigah Department of Electronics and Electrical Engineering Shiraz University of TechnologyShirazIran Department of Electrical Engineering Faculty of EngineeringJahrom UniversityJahromFrasIran Department of Electrical and Computer Engineering Tarbiat Modares UniversityTehranIran Department of Electrical and Computer Engineering University ofCyprusNicosiaCyprus Centre for Artificial Intelligence Research and Optimisation Torrens University AustraliaBrisbaneQLD 4006Australia University Research and Innovation Center Obuda University1034 BudapestHungary Department of Electrical and Computer Engineering Lebanese American UniversityByblos 13-5053Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied Science Research Center Applied Science Private UniversityAmman 11931Jordan

Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world *** study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization *** efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark ***,GEA has been applied to several real-parameter engineering optimization problems to evaluate its *** addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization *** results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and *** that the source code of the GEA is publicly available at https://***/projects/gea.

关键词： Nature-inspired algorithms Real-world and engineering optimization Mathematical modeling Geyser algorithm(GEA)

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An infrastructure software perspective toward computation offloading between executable specifications and foundation models

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Science China(Information Sciences) 2025年第4期68卷 380-382页

作者： Dezhi RAN Mengzhou WU Yuan CAO Assaf MARRON David HAREL Tao XIE Key Laboratory of High Confidence Software Technologies (PKU) Ministry of Education School of Computer SciencePeking University School of Electronics Engineering and Computer Science Peking University Department of Computer Science and Applied Mathematics Weizmann Institute of Science

Foundation models(FMs) [1] have revolutionized software development and become the core components of large software systems. This paradigm shift, however, demands fundamental re-imagining of software engineering theories and methodologies [2]. Instead of replacing existing software modules implemented by symbolic logic, incorporating FMs' capabilities to build software systems requires entirely new modules that leverage the unique capabilities of ***, while FMs excel at handling uncertainty, recognizing patterns, and processing unstructured data, we need new engineering theories that support the paradigm shift from explicitly programming and maintaining user-defined symbolic logic to creating rich, expressive requirements that FMs can accurately perceive and implement.

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Research on Campus Public Opinion Registration and Management System Based on KMP Algorithm 24

Research on Campus Public Opinion Registration and Managemen...

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2nd International Conference on Educational Knowledge and Informatization, EKI 2024

作者： Su, Jianna Zhao, Lin Wei, Xinyao Department of Mathematics and Computer Engineering Ordos Institute of Applied Technology China

ISBN: (纸本)9798400710230

In the context of the digital age, online public opinion has rapidly emerged as a key manifestation of social sentiment. The spread of campus public opinion is primarily through the internet, characterized by high attention, rapid dissemination, and a special group, which can easily lead to secondary social consequences and has a profound impact on school management, student mental health, and the formulation of educational policies. This requires school administrators to be able to capture and effectively analyze public opinion dynamics in a timely manner to make appropriate responses. However, in the face of massive amounts of public opinion data, traditional monitoring and management methods are no longer sufficient. This paper proposes a campus public opinion registration and management system based on the Knuth-Morris-Pratt (KMP) algorithm, aiming to improve the efficiency and accuracy of public opinion monitoring. © 2024 Copyright held by the owner/author(s).

关键词： Economic and social effects

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