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Adaptive outer synchronization between two delayed oscillator networks with cross couplings

science China(Information sciences) 2020年第10期63卷 267-269页

作者： Jianbao ZHANG Ancai ZHANG Jinde CAO Jianlong QIU Fuad E.ALSAADI School of Automation Southeast University School of Information Science and Engineering Linyi University Department of Electrical and Computer Engineering Faculty of EngineeringKing Abdulaziz University

Dear editor,Chaos synchronization is a typical kind of collective behaviors induced by external control or mutual couplings between chaotic nodes [1].During the past decades,increasing interest has been devoted to study the interplay between network topology and chaos synchronization [1,2].Many types of synchronization phenomena have been proposed such as complete synchronization,cluster synchronization [3],and outer synchroni-

关键词： Adaptive control Complex networks Lyapunov method Outer synchronization Cross coupling

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The Prediction of COVID-19 Pandemic Situation in Indonesia Using SVR and SIR Algorithm

The Prediction of COVID-19 Pandemic Situation in Indonesia U...

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International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Ainul Yaqin Majid Rahardi Ferian Fauzi Abdulloh Kusnawi Slamet Budiprayitno Siti Fatonah Faculty of Computer Science Universitas Amikom Yogyakarta Yogyakarta Indonesia Department of Electrical and Automation Engineering Vocational Faculty Institut Teknologi Sepuluh Nopember Surabaya Indonesia

The COVID-19 epidemic, which initially surfaced at the end of 2019, has since expanded to every corner of the globe and has profoundly impacted all facets of human existence. This case started to emerge in Indonesia at the end of February 2020, and until this point, there has been a spike in new patients. Researchers have run several models and projections for COVID-19 cases in Indonesia, but the results are not yet entirely reliable. Predictions produced at the national level must take into account these variations in patterns because this is likely related to the distinct patterns in each region. In this study, the prediction process will be conducted for cases of COVID-19 by using the SVR algorithm and mathematical models to predict reproduction numbers. SVR analysis to overcome the problem of nonlinearity of data in model formation. The modeling is done based on the SIR model, whose parameters are estimated based on the data. Testing result by using 3 kernels is different on each test, prediction of data cases and the level of mistake room are by using Kernel ’ RBF ‘ with a value of C = 1E3, and gamma = 0.1 with the value of MAPE and MSE respectively are 4.5% and 4.2.

关键词： COVID-19 Analytical models Sociology Prediction algorithms Mathematical models Data models Kernel

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Open and Closed Source Models for LLM-Generated Metaheuristics Solving Engineering Optimization Problem 28th

Open and Closed Source Models for LLM-Generated Metaheuris...

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28th European Conference on Applications of Evolutionary Computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Senkerik, Roman Viktorin, Adam Kadavy, Tomas Kovac, Jozef Janku, Peter Pekar, Libor Guzowski, Hubert Smolka, Maciej Byrski, Aleksander Pluhacek, Michal A.I.Lab Faculty of Applied Informatics Tomas Bata University Zlin Czech Republic Department of Automation and Control Engineering Faculty of Applied Informatics Tomas Bata University Zlin Czech Republic Faculty of Computer Science AGH University of Krakow Krakow Poland Center of Excellence in Artificial Intelligence AGH University of Krakow Krakow Poland

ISBN: (纸本)9783031900648

This paper explores the applicability of generative AI (genAI), specifically Large Language Models (LLMs), for the automatic generation and configuration of metaheuristic algorithms to address a real-world engineering problem: the optimal parameter estimation of time-delay systems in interconnected heating-cooling loops. The study introduces a pioneering workflow and iterative architecture with feedback within the emerging field of genAI-driven optimization for real optimization problems, eliminating the need for manually crafted or modified algorithms. This automated system empowers domain experts in engineering to solve complex optimization problems with minimal knowledge of optimization algorithms, lowering the barrier to entry for sophisticated algorithm use. We demonstrate how LLMs can generate effective optimizers under conditions like connstrained optimization problems where the solution lies near the boundaries of the search space. Four state-of-the-art LLMs (closed and open-sourced) have been selected for experiments. These are GPT-4o, GPT-4o mini, Claude Sonnet 3.5 and Llama 3.1. All studied LLMs generated metaheuristics that outperformed the initialization baseline optimization method (Random Search and CMA-ES). Notably, the Claude Sonnet 3.5 model generated a metaheuristic with the best mean results, almost matching the performance of the tuned state-of-the-art DISH algorithm, as an example of adaptive Differential Evolution. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Time of arrival

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GAS TURBINE VIBRATION MONITORING BASED ON REAL DATA AND NEURO-FUZZY SYSTEM

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Diagnostyka 2024年第1期25卷

作者： Nail, Bachir Djaidir, Benrabeh Tibermacine, Imad Eddine Napoli, Christian Haidour, Nabil Abdelaziz, Rabehi Faculty of Science and Technology University of Djelfa Djelfa17000 DZ Algeria Department of Computer Automation and Management Engineering Sapienza University of Rome Italy Institute for Systems Analysis and Computer Science Italian National Research Council Italy Department of Computational Intelligence Czestochowa University of Technology Czestochowa Poland L'École Supérieure de Physique et de Chimie Industrielles Paris France

The gas turbine is considered to be a very complex piece of machinery because of both its static structure and the dynamic behavior that results from the occurrence of vibration phenomena. It is required to adopt monitoring and diagnostic procedures for the identification and localization of vibration flaws in order to ensure the appropriate operation of large rotating equipment such as gas turbines. This is necessary in order to avoid catastrophic failures and deterioration and to ensure that proper operation occurs. Utilizing an approach that is based on spectrum analysis, the purpose of this study is to provide a model for the monitoring and diagnosis of vibrations in a GE MS3002 gas turbine and its driven centrifugal compressor. This will be done by utilizing the technique. Following that, the collection of vibration measurements for a model of the centrifugal compressor served as a suggestion for an additional method. This method is based on the neuro-fuzzy approach type ANFIS, and it aims to create an equivalent system that is able to make decisions without consulting a human being for the purpose of detecting vibratory defects. In spite of the fact that the compressor that was investigated has flaws, this procedure produced satisfactory results. © 2024 by the Authors.

关键词： Vibration analysis

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RIEMANNIAN GEOMETRY-BASED EEG APPROACHES: A LITERATURE REVIEW

arXiv

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arXiv 2024年

作者： Tibermacine, Imad Eddine Russo, Samuele Tibermacine, Ahmed Rabehi, Abdelaziz Nail, Bachir Kadri, Kamel Napoli, Christian Department of Computer Automation and Management Engineering Sapienza University of Rome Rome Italy Department of Psychology Sapienza University of Rome Rome Italy Department of Computer Science University of Biskra Biskra Algeria Faculty of Science and Technology University of Djelfa Djelfa Algeria Polytechnic Military School Algiers Algeria Institute for Systems Analysis and Computer Science Italian National Research Council Italy Department of Computational Intelligence Czestochowa University of Technology Poland

The application of Riemannian geometry in the decoding of brain-computer interfaces (BCIs) has swiftly garnered attention because of its straightforwardness, precision, and resilience, along with its aptitude for transfer learning, which has been demonstrated through significant achievements in global BCI competitions. This paper presents a comprehensive review of recent advancements in the integration of deep learning with Riemannian geometry to enhance EEG signal decoding in BCIs. Our review updates the findings since the last major review in 2017, comparing modern approaches that utilize deep learning to improve the handling of non-Euclidean data structures inherent in EEG signals. We discuss how these approaches not only tackle the traditional challenges of noise sensitivity, non-stationarity, and lengthy calibration times but also introduce novel classification frameworks and signal processing techniques to reduce these limitations significantly. Furthermore, we identify current shortcomings and propose future research directions in manifold learning and riemannian-based classification, focusing on practical implementations and theoretical expansions, such as feature tracking on manifolds, multitask learning, feature extraction, and transfer learning. This review aims to bridge the gap between theoretical research and practical, real-world applications, making sophisticated mathematical approaches accessible and actionable for BCI enhancements. © 2024, CC BY.

关键词： Geometry

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DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing

arXiv

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arXiv 2024年

作者： Sun, Li Huang, Zhenhao Wu, Hua Ye, Junda Peng, Hao Yu, Zhengtao Yu, Philip S. School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Department of Computer Science University of Illinois ChicagoIL United States

Graph Neural Networks (GNNs) have shown great power for learning and mining on graphs, and Graph Structure Learning (GSL) plays an important role in boosting GNNs with a refined graph. In the literature, most GSL solutions either primarily focus on structure refinement with task-specific supervision (i.e., node classification), or overlook the inherent weakness of GNNs themselves (e.g., over-squashing), resulting in suboptimal performance despite sophisticated designs. In light of these limitations, we propose to study self-supervised graph structure-feature co-refinement for effectively alleviating the issue of over-squashing in typical GNNs. In this paper, we take a fundamentally different perspective of the Ricci curvature in Riemannian geometry, in which we encounter the challenges of modeling, utilizing and computing Ricci curvature. To tackle these challenges, we present a self-supervised Riemannian model, DeepRicci. Specifically, we introduce a latent Riemannian space of heterogeneous curvatures to model various Ricci curvatures, and propose a gyrovector feature mapping to utilize Ricci curvature for typical GNNs. Thereafter, we refine node features by geometric contrastive learning among different geometric views, and simultaneously refine graph structure by backward Ricci flow based on a novel formulation of differentiable Ricci curvature. Finally, extensive experiments on public datasets show the superiority of DeepRicci, and the connection between backward Ricci flow and over-squashing. Codes of our work are given in https://***/RiemanGraph/. © 2024, CC BY-NC-SA.

关键词： Graph neural networks

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Practical Engineering Education: Use of collaborative robots in the context of Industry 5.0

Practical Engineering Education: Use of collaborative robots...

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World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC)

作者： Christian Vogel Fabian Lindner Alexander Kratzsch Faculty of Electrical Engineering and Computer Science Zittau/Görlitz University of Applied Science Zittau Germany Faculty of Business Administration and Engineering Zittau/Görlitz University of Applied Science Zittau Germany Institute of Process Technology Process Automation and Measuring Technology Zittau/Görlitz University of Applied Science Zittau Germany

This scientific paper examines the use of collaborative robots in the context of Industry 5.0 and its impact on practical engineering education. The aim of this paper is to highlight the importance as well as the challenges of using robots in Industry 5.0 and to analyze how this can be integrated into engineering education. The paper discusses various aspects such as the technological advances, the need for interaction between humans and robots, and the competencies that engineers need in an increasingly digitalized working world. Furthermore, practical methods and approaches in engineering education at the Zittau/Görlitz University of Applied sciences are presented to prepare students for the requirements of Industry 5.0. Additionally, a draft of a future interdisciplinary learning factory 5.0 will be presented, in which networked technologies with the focus on robotics run through various scenarios in the context of Industry 5.0 at different locations.

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Discrete-time Formation Control With Attitude Alignment: A Geometric Approach under Ergodic Products

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IEEE Transactions on Control of Network Systems 2025年

作者： Li, Zhen Tang, Yang Zhang, Wenbing Fan, Yongqing Huang, Tingwen Xi-an University of Posts and Telecommunications School of Automation Xian710121 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Shanghai200237 China Yangzhou University Department of Mathematics Jiangsu225002 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

Assuming agents possess sensing capabilities and dynamics relative to their body coordinate frames, this paper addresses a displacement-based strategy for discrete-time formation control through attitude synchronization. Unlike the Eulerian approximation, the first challenge lies in developing discrete-time models that capture both position and attitude motions, thereby complementing existing continuous-time systems with a discretized version. Then, two leader-following control protocols are designed, incorporating a jointly-connected topology to relax the connectivity requirements. The analysis of these systems often relies on the Lyapunov function, the existence of which may be hard to guarantee. Consequently, geometric properties of several polytopes are explored under the dynamic topologies and time-varying parameters. By extending the ergodicity results to leader-following scenarios, these properties are characterized as positively invariant sets. This invariance, which is independent of the existence of the Lyapunov function or the solvability of its associated criteria, provides a beneficial tool for analyzing time-variant systems. Utilizing this invariance and some mathematical techniques, the desired formation shape can be achieved in the cases where the attitudes of the followers synchronize with the leader. Finally, a numerical simulation validates the results. © 2014 IEEE.

关键词： Lyapunov functions

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Human Body Motion Tracking for Rehabilitation

Human Body Motion Tracking for Rehabilitation

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IEEE International Conference on Intelligent Engineering Systems (INES)

作者： Moraru Andrei Eva-H. Dulf Lehel Dénes-Fazakas Levente Kovacs Department of Automation And Applied Informatics Faculty of Automation and Computer Science Technical University of Cluj-Napoca Cluj-Napoca Romania Obuda University University Research and Innovation Center Physiological Controls Research Center Obuda University John von Neumann Faculty of Informatics Biomatics and Applied Artificial Intelligence Institute Obuda University Applied Informatics and Applied Mathematics Doctoral School Obuda University

ISBN: (数字)9798350367591

ISBN: (纸本)9798350367607

Human body motion tracking is a key technique in robotics, virtual reality and other human–computer interaction fields, for interactive mixed reality applications. Human motion tracking for rehabilitation is widely discussed by researchers in the last period, being motivated by the increased number of patients with motor function disability. For such patients rehabilitation is an important step to restore their functional capability to normal or close to normal. To reach this target, a patients’ activities need to be continuously monitored, and subsequently corrected. This involves accurate identification, tracking, and post-processing of movement. This paper thus proposes an open loop real time angle computation algorithm designed in Matlab®. Its efficacy was tested using an Arduino Nano® coupled with the MPU-6050 as the Inertial Measurement Units (IMU). The advantages of the proposed algorithm are presented by comparison with the already existing and widely used methods.

关键词： Target tracking Quaternions Virtual reality Patient rehabilitation Sensor fusion Time measurement Sensor systems

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Multi-Domains Personalized Local Differential Privacy Frequency Estimation Mechanism for Utility Optimization

SSRN

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SSRN 2024年

作者： Li, Yunfei Fu, Xiaodong Liu, Li Ding, Jiaman Peng, Wei Jia, Lianyin Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Graduate School Yunnan University of Finance and Economics Kunming650221 China Yunnan Provincial Key Laboratory of Computer Technology Application Kunming University of Science and Technology Kunming650500 China

Local Differential Privacy (LDP) has garnered considerable attention in recent years because it does not rely on trusted third parties and has low interactivity and high operational efficiency. However, current LDP frequency estimation mechanisms aggregate data using different privacy budgets within the same data domain, overlooking the aggregation requirements across different data domains. This limits the potential for enhancing the data utility under fixed privacy budgets and meeting user preferences in multiple data domains and privacy budgets. To address this issue, we define a Multi-Domains Personalized Local Differential Privacy (MDPLDP) model that allows users to freely choose data domains and privacy budgets according to their privacy preferences. Furthermore, based on the MDPLDP model, two new frequency estimation mechanisms are proposed: MDPLDP-Generalized Randomized Response and MDPLDP-basic Randomized Aggregatable Privacy-Preserving Ordinal Response. These mechanisms support cross-domains data aggregation and optimize data utility by adjusting the data domains and increasing privacy budgets. Theoretical analysis reveals that these new mechanisms have lower estimation errors than the traditional LDP mechanisms. Experiments on real and synthetic datasets demonstrate that the proposed mechanisms effectively reduce estimation errors and enhance the utility of data-frequency estimation. © 2024, The Authors. All rights reserved.

关键词： Frequency estimation

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