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Chinese automation Congress (CAC)

作者： Ziyu Wang Xin Xin Yannian Liu School of Automation Southeast University Nanjing China Faculty of Computer Science and Systems Engineering Okayama Prefectural University Okayama Janpan

Strong stabilization refers to designing stable feedback controllers that stabilize a given plant. The second-order stable stabilizing controller design of two-link underactuated planar robots around their UEP (Upright Equilibrium Point) is concerned in this paper. The proposed method can obtain explicit transfer function of the controller by solving a set of inequalities, which is relatively more concise and the final set of stable controllers is larger than the existing results. Simulation results of both the Acrobot and the Pendubot are presented to verify the effectiveness and robustness of proposed stabilizing controllers.

关键词： automation Simulation Transfer functions Robustness Adaptive control Robots

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FSIW Cavity-Based Self-Packaging Filtering Slot Antennas with Wide-Stopband and High Frequency Selectivity

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IEEE Transactions on Antennas and Propagation 2025年

作者： Zhou, Xin Zhang, Gang Tam, Kam-Weng Pang, Ziyu Zhang, Zhuowei Lin, Huawei Hong, Jiasheng University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering China Nanjing Normal University School of Electrical and Automation Engineering Nanjing210046 China Nanjing Normal University Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing School of Electrical and Automation Engineering Nanjing210046 China Heriot-Watt University School of Engineering and Physical Sciences Department of Electrical Electronic and Computer Engineering EdinburghEH14 4AS United Kingdom

This article proposes a novel design approach for miniaturized, highly selective, self-packaged, and wide-stopband filtering slot antennas based on C- and T-type folded substrate integrated waveguide (C-/T-FSIW) cavities. Firstly, two forms of FSIW cavity are explored and analyzed. A new coupled C-type half-mode FSIW (C-HMFSIW) cavities are developed. A filtering antenna is proposed, employing a C-HMFSIW cavity for feeding and a slot-loaded C-FSIW cavity for radiation. By introducing cross-coupling between different resonators, a radiation null (RN) is achieved. To further enhance bandwidth and frequency selectivity, a new antenna configuration is developed using a C-HMFSIW cavity for feeding and a slot-loaded T-FSIW cavity for radiation. After loading the radiation slot, the T-FSIW cavity serves as two parts: one as the main radiator and the other as a parasitic radiator opposite to it. This configuration increases antenna bandwidth and generates an additional RN. Interestingly, adjusting the position of the radiation slot allows the RN to switch between the upper and lower stopbands. The antennas proposed in this work exhibit good stopband performance and compact size. Three filtering antenna prototypes are designed, fabricated, and measured, showing consistent results between measurements and simulations. © 1963-2012 IEEE.

关键词： Radiators

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Adaptive outer synchronization between two delayed oscillator networks with cross couplings

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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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Code Converters Based on a Dynamic Optical RAM for Information System Facilities

Code Converters Based on a Dynamic Optical RAM for Informati...

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IEEE International Conference on Electronics and Nanotechnology (ELNANO)

作者： Andriy Semenov Serhii Tsyrulnyk Olena Semenova Anton Khloba Andrii Rudyk Serhii Baraban Faculty of Information Electronic Systems Vinnytsia National Technical University Vinnytsia Ukraine Department of Computer Science and Economic Cybernetics Vinnytsia National Agrarian University Vinnytsia Ukraine Department of Automation Electrical Engineering and Computer-Integrated Technologies National University of Water and Environmental Engineering Rivne Ukraine Faculty of Computing and Telecommunications Poznan University of Technology Poznan Poland

ISBN: (数字)9798350368178

ISBN: (纸本)9798350368185

In this paper, the authors propose a new dynamic optical RAM based on a fiber-optic line. This device significantly increases the speed of information flows by reducing the time required to operate the RAM by 10. The proposed optical RAM increases the productivity and efficiency of information exchange between slow and fast devices of information and communication systems without software costs, which reduces the probability of failure by two times with the same buffer memory capacity. The authors proposed variants of the practical application of the new dynamic optical RAM. On its basis, a shift register, a serial-to-parallel code converter, a parallel-to-serial code converter, and a TDM switch for information system facilities were created. The time characteristics of signals in code converters based on a dynamic optical RAM are presented.

关键词： Optical fibers Codes Optical switches Optical buffering Random access memory Time division multiplexing Shift registers Optical fiber devices High-speed optical techniques Information systems

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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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Effect of loads on temperature distribution characteristics of oil-immersed transformer winding

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Distributed Generation and Alternative Energy Journal 2022年第2期37卷 237-254页

作者： Zhao, Zhengang Jiang, Zhangnan Li, Yang Li, Chuan Zhang, Dacheng Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650000 China Yunnan Key Laboratory of Computer Technology Applications Kunming650000 China

The temperature of the hot-spots on windings is a crucial factor that can limit the overload capacity of the transformer. Few studies consider the impact of the load on the hot-spot when studying the hot-spot temperature and its location. In this paper, a thermal circuit model based on the thermoelectric analogy method is built to simulate the transformer winding and transformer oil temperature distribution. The hot-spot temperature and its location under different loads are qualitatively analyzed, and the hot-spot location is analyzed and compared to the experimental results. The results show that the hot-spot position on the winding under the rated power appears at 85.88% of the winding height, and the hot-spot position of the winding moves down by 5% in turn at 1.3, 1.48, and 1.73 times the rated power respectively. © 2021 River Publishers

关键词： Temperature distribution

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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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