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Switching-Like Event-Based Formation Control for Multi-Agent Systems Against Denial of Service Attacks

IEEE Transactions on Control of Network Systems 2025年

作者： Ru, Tingting Cai, Chenxiao Lin, Hong Rudas, Imre Shi, Peng Nanjing University of Science and Technology School of Automation Nanjing210094 China Shenzhen Polytechnic University Institute of Intelligence Science and Engineering Shenzhen518055 China University Research Innovation and Service Center Óbuda University Antal Bejczy Center for Intelligent Robotics Budapest1034 Hungary University of Adelaide School of Electrical and Mechanical Engineering AdelaideSA5005 Australia

This paper studies formation control for multi-agent systems in the presence of denial-of-service (DoS) attacks. The challenge is to address secure formation tracking and communication bandwidth-saving issues simultaneously. To tackle this, we propose a switching- like event-triggered mechanism that reduces excessive bandwidth consumption during interactions among multiple followers under DoS attacks. The triggering condition is allowed to be switched during the intervals when attacks are activated, ensuring that triggering can normally occur throughout the overall time interval by introducing an auxiliary variable depending on the success of the latest trigger moment. Based on this approach, we construct an event-based time-varying formation control scheme. Additionally, we establish sufficient conditions that reveal the relationship between exponential stability and the frequency/duration of the DoS attacks. Unlike other single-leader methods, a significant feature of the formation control scheme is its ability to achieve the desired formation tracking for multi-agent systems with multiple leaders. Finally, the validity of the proposed method is demonstrated through simulation and experimental results. © 2014 IEEE.

关键词： Multi agent systems

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BCLH2Pro:A novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes

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Energy and AI 2024年第4期18卷 13-26页

作者： Thanadol Tuntiwongwat Sippawit Thammawiset Thongchai Rohitatisha Srinophakun Chawalit Ngamcharussrivichai Somboon Sukpancharoen Department of Mechanical Engineering Faculty of EngineeringKhon Kaen UniversityKhon Kaen 40002Thailand Department of Automation Robotics and Intelligent System Faculty of EngineeringKhon Kaen UniversityKhon Kaen 40002Thailand Department of Chemical Engineering Faculty of EngineeringKasetsart UniversityBangkok 10900Thailand Center of Excellence in Catalysis for Bioenergy and Renewable Chemicals(CBRC) Faculty of ScienceChulalongkorn UniversityPathumwanBangkok 10330Thailand Institute of Nanoscience and Nanotechnology(ION2) Universiti Putra MalaysiaSerdang 43400SelangorMalaysia Department of Agricultural Engineering Faculty of EngineeringKhon Kaen UniversityKhon Kaen 40002Thailand Center for Alternative Energy Research and Development Khon Kaen UniversityKhon Kaen 40002Thailand

This study optimizes biomass chemical looping processes(BCLpro),a technique for converting biomass to energy,through machine learning(ML)for sustainable energy *** study proposes an integrated Fe_(2)O_(3)-based,BCLpro combining steam gasification for H2 *** Plus is used as the primary tool to generate extensive datasets covering 24 biomass types with 18 feature inputs in a supervised model.A methodology involving K-Nearest Neighbors(KNN),Extreme Gradient Boosting(XGB),Light Gradient Boosting Machine(LGBM),Support Vector Machine(SVM),Random Forest(RF),and CatBoost(CB)algorithms was employed to predict H_(2)yields in the BCLpro,utilizing 10-fold cross-validation for robust model *** highlight the CB algorithm’s superior performance,achieving up to 98%predictive accuracy,with carbon content,reducer temperature,and Fe_(2)O_(3)/Al_(2)O_(3)mass ratio identified as crucial *** algorithm has been developed into a user-friendly tool,BCLH2Pro,accessible via a web *** tool is designed to assist in reducing costs,optimizing biomass selection,and planning operational conditions to maximize H2 yield in BCLpro *** to the tool can be obtained through the following link:http://***/.

关键词： Artificial intelligence Supervised learning Process simulation Chemical looping Agricultural waste

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Parameter Identification of Chaotic Systems Based on Least Squares Method and Pauta Criterion

Parameter Identification of Chaotic Systems Based on Least S...

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Chinese automation Congress (CAC)

作者： Yu Liu Zhihuan Chen Yaru Qiao Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and Technology Wuhan China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China

In this study, an offline parameter identification approach based on the measured time series of chaotic system is proposed. In this approach, least squares method is firstly introduced to minimize the time series error and Pauta criterion is then employed to pick out the wrong extreme value point. Two representative cases are conducted to verify the effectiveness of our proposed approach. Numerical results are in agreement with theoretical analysis. Comparing to other existing methods, our proposed approach just needs to obtain the measured time series to estimate the parameters, which is simple in theorem and easy to be implemented.

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Motion-Guided Dual-Camera Tracker for Endoscope Tracking and Motion Analysis in a Mechanical Gastric Simulator

arXiv

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arXiv 2024年

作者： Zhang, Yuelin Yan, Kim Lam, Chun Ping Fang, Chengyu Xie, Wenxuan Qiu, Yufu Tang, Raymond Shing-Yan Cheng, Shing Shin Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Shenzhen International Graduate School Tsinghua University China Department of Medicine and Therapeutics Institute of Digestive Disease The Chinese University of Hong Kong Hong Kong Department of Mechanical and Automation Engineering T Stone Robotics Institute Shun Hing Institute of Advanced Engineering Multi-Scale Medical Robotics Center Institute of Medical Intelligence and XR The Chinese University of Hong Kong Hong Kong

Flexible endoscope motion tracking and analysis in mechanical simulators have proven useful for endoscopy training. Common motion tracking methods based on electromagnetic tracker are however limited by their high cost and material susceptibility. In this work, the motion-guided dual-camera vision tracker is proposed to provide robust and accurate tracking of the endoscope tip’s 3D position. The tracker addresses several unique challenges of tracking flexible endoscope tip inside a dynamic, life-sized mechanical simulator. To address the appearance variation and keep dual-camera tracking consistency, the cross-camera mutual template strategy (CMT) is proposed by introducing dynamic transient mutual templates. To alleviate large occlusion and light-induced distortion, the Mamba-based motion-guided prediction head (MMH) is presented to aggregate historical motion with visual tracking. The proposed tracker achieves superior performance against state-of-the-art vision trackers, achieving 42% and 72% improvements against the second-best method in average error and maximum error. Further motion analysis involving novice and expert endoscopists also shows that the tip 3D motion provided by the proposed tracker enables more reliable motion analysis and more substantial differentiation between different expertise levels, compared with other trackers. Project page: https://***/PieceZhang/MotionDCTrack © 2024, CC BY.

关键词： Motion tracking

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SheetCopilot: bringing software productivity to the next level through large language models 23

SheetCopilot: bringing software productivity to the next lev...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Hongxin Li Jingran Su Yuntao Chen Qing Li Zhaoxiang Zhang School of Artificial Intelligence University of Chinese Academy of Sciences (UCAS) and State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS) Institute of Automation Chinese Academy of Sciences Center for Artificial Intelligence and Robotics HKISI CAS and The Hong Kong Polytechnic University Center for Artificial Intelligence and Robotics HKISI CAS The Hong Kong Polytechnic University School of Artificial Intelligence University of Chinese Academy of Sciences (UCAS) and State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS) Institute of Automation Chinese Academy of Sciences and Center for Artificial Intelligence and Robotics HKISI CAS and Shanghai Artificial Intelligence Laboratory

Computer end users have spent billions of hours completing daily tasks like tabular data processing and project timeline scheduling. Most of these tasks are repetitive and error-prone, yet most end users lack the skill to automate these burdensome works. With the advent of large language models (LLMs), directing software with natural language user requests become a reachable goal. In this work, we propose a SheetCopilot agent that takes natural language task and control spreadsheet to fulfill the requirements. We propose a set of atomic actions as an abstraction of spreadsheet software functionalities. We further design a state machine-based task planning framework for LLMs to robustly interact with spreadsheets. We curate a representative dataset containing 221 spreadsheet control tasks and establish a fully automated evaluation pipeline for rigorously benchmarking the ability of LLMs in software control tasks. Our SheetCopilot correctly completes 44.3% of tasks for a single generation, outperforming the strong code generation baseline by a wide margin. Our project page: https://***/.

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DiffusePast: Diffusion-based Generative Replay for Class Incremental Semantic Segmentation

arXiv

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arXiv 2023年

作者： Chen, Jingfan Wang, Yuxi Wang, Pengfei Chen, Xiao Zhang, Zhaoxiang Lei, Zhen Li, Qing The Hong Kong Polytechnic University Hong Kong Center for Artificial Intelligence and Robotics HKISI CAS Hong Kong Institute of Automation Chinese Academy of Sciences China University of Chinese Academy of Sciences China

The Class Incremental Semantic Segmentation (CISS) extends the traditional segmentation task by incrementally learning newly added classes. Previous work has introduced generative replay, which involves replaying old class samples generated from a pre-trained GAN, to address the issues of catastrophic forgetting and privacy concerns. However, the generated images lack semantic precision and exhibit out-of-distribution characteristics, resulting in inaccurate masks that further degrade the segmentation performance. To tackle these challenges, we propose DiffusePast, a novel framework featuring a diffusion-based generative replay module that generates semantically accurate images with more reliable masks guided by different instructions (e.g., text prompts or edge maps). Specifically, DiffusePast introduces a dual-generator paradigm, which focuses on generating old class images that align with the distribution of downstream datasets while preserving the structure and layout of the original images, enabling more precise masks. To adapt to the novel visual concepts of newly added classes continuously, we incorporate class-wise token embedding when updating the dual-generator. Moreover, we assign adequate pseudo-labels of old classes to the background pixels in the new step images, further mitigating the forgetting of previously learned knowledge. Through comprehensive experiments, our method demonstrates competitive performance across mainstream benchmarks, striking a better balance between the performance of old and novel classes. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Optimal Bioprinting Parameters and Experimental Investigation of Acellular Dermal Matrix Scaffold

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Journal of Renewable Materials 2021年第1期9卷 1-16页

作者： Qingxi Hu Yiming Wang Dongchao Yang Haiguang Zhang Zhicheng Song Yan Gu Rapid Manufacturing Engineering Center School of Mechatronic Engineering and AutomationShanghai UniversityShanghaiChina Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai UniversityShanghaiChina National Demonstration Center for Experimental Engineering Training Education Shanghai UniversityShanghaiChina Shanghai Ninth People’s Hospital Shanghai JiaoTong University School of MedicineShanghai200011China

Acellular dermal matrix(ADM)as a biomaterial is currently believed to be promising tissue repair *** the development of tissue engineering,ADM is increasingly used as biological *** explored the feasibility and performance of ADM biological scaffolds that fabricated by 3D *** paper presented our study on the printability of 3D printed ADM scaffolds,with a focus on identifying the influence of printing parameters/conditions on *** characterize the printability,we examined the fiber morphology,pore size,strand diameter,and mechanical property of the printed *** results revealed that the printability could be affected by a number of factors and among them,the most considerable one was related to the nozzle diameter and the composition of *** then evaluated the biocompatibility in terms of cytotoxicity,cell proliferation and *** vitro evaluation of the ADM scaffolds was carried out and the experimental results indicated that cells were viable and proliferative during the period of *** vivo results also indicated that the defect area was well repaired without any noticeable infection,hematoma and other *** conclusion,ADM could be reconstructed with 3D printing technology and ADM biological scaffold has potential applications for tissue engineering.

关键词： Bioprinting acellular dermal matrix scaffolds printability tissue engineering

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Diagnosis of Localized Bearing Defects using Texture Analysis of Vibration Envelope Signals and Machine Learning

Diagnosis of Localized Bearing Defects using Texture Analysi...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Cong Dai Nguyen Fazal E Nasir Shahbaz Khan Sheraz Ali Khan Jong-Myon Kim Faculty of Radio-Electronic Engineering Le Quy Don Technical University Hanoi Vietnam Advanced Robotics and Automation Lab National Center for Robotics and Automation Peshawar Pakistan Institute of Manufacturing University of Engineering and Applied Sciences Swat Pakistan Department of Mechatronics Engineering University of Engineering and Technology Peshawar Pakistan Department of IT Convergence University of Ulsan Ulsan South Korea PD Technology Ulsan South Korea

Bearings are important because they play a critical role in the operation of many machines and devices by reducing friction and enabling smooth motion between two moving parts. They are commonly used in rotating machinery, such as electric motors, turbines, pumps, and gearboxes, where they support and guide the rotating shafts. The primary cause of failure in wind turbines and induction motors is bearing faults, resulting in significant downtime and economic losses. Detecting these faults accurately and promptly is crucial to preventing unexpected shutdowns. Most of the existing methods for bearing fault detection rely on envelope analysis, which involves filtering and demodulating the raw signal, and Fourier analysis of the envelope signal to identify defect frequencies. Recent research has focused on improving the visibility of these frequencies by selecting an optimal band for filtering, using sub-band analysis and spectral kurtosis, which is a complex process and can be time-consuming. This paper presents a new approach to envelope signal analysis using texture analysis of the envelope signals that are projected as 2D grayscale images where each fault generates a unique texture. These textures are encoded using the local binary pattern operator and then used for fault classification. The method is tested on a publicly available seeded fault dataset.

关键词： Vibrations Fault diagnosis Shafts Time-frequency analysis Filtering Pumps Machine learning

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Design and Development of Cascaded Current Control in DC Motor Variable Speed Drive using dSPACE 8

Design and Development of Cascaded Current Control in DC Mot...

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8th International Conference for Man Machine Systems, ICoMMS 2023

作者： Ahmad Firdaus, A.Z. Davendren, T. Azmi, S.A. Hwai, Leong Jenn Kamarudin, Kamarulzaman Hassan, Azlini Ali, Hasimah Azalan, Mohd Shuhanaz Zanar Mamat, Normahira Faculty of Electrical Engineering and Technology Universiti Malaysia Perlis Kampus Pauh Putra Perlis Malaysia Center of Intelligent Robotics and Automation Universiti Malaysia Perlis Kampus Pauh Putra Perlis Malaysia Faculty of Business and Communication Universiti Malaysia Perlis Pusat Perniagaan Pengkalan Jaya Perlis Kangar Malaysia Faculty of Electronic Engineering and Technology Universiti Malaysia Perlis Kampus Pauh Putra Perlis Malaysia

Even today, DC motors are still used in variety of applications, including home appliances, transportation, as well as industrial crane and rolling machine. However, achieving precise speed and torque control in DC drives at industry level could be challenging, as instability and reduced efficiency remains at large. This project focuses on developing a cascaded control system for a Separately Excited Brushed DC motor using dSPACE platform. The cascaded control system, designed using MATLAB Simulink, incorporates a proportional-integral (PI) controller at the speed loop and a Hysteresis controller at the current loop to improve robustness and dynamic performance. The experimental setup utilizes the dSPACE 1104 platform, an asymmetric bridge converter board, gate driver, and electrical load. Speed measurement is done using an incremental encoder, while current is measured using the ACS712 current sensor. The drive system was tested in alternate low and high speed cycle on various load level to test for stability, robustness and dynamic performance. The proposed control system was compared with PI-closed-loop control and open-loop control determine the best drive performance. Experimental results showed significant improvement in term of transient response and ripple reduction of speed and current for proposed cascaded current control over the closed-loop design. © Published under licence by IOP Publishing Ltd.

关键词： Electric current control

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Supervision policies can shape long-term risk management in general-purpose AI models

arXiv

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arXiv 2025年

作者： Cebrian, Manuel Gómez, Emilia Llorca, David Fernández Department of Statistics Universidad Carlos III de Madrid Spain Center for Automation and Robotics Spanish National Research Council Madrid Spain European Commission Joint Research Centre Seville Spain Computer Engineering Department University of Alcalá Alcalá de Henares Spain

The rapid proliferation and deployment of General-Purpose AI (GPAI) models, including large language models (LLMs), present unprecedented challenges for AI supervisory entities. We hypothesise that these entities will need to navigate an emergent ecosystem of risk and incident reporting, likely to exceed their supervision capacity. To investigate this, we develop a simulation framework parametrised by features extracted from the diverse landscape of risk, incident or hazard reporting ecosystems—including community-driven platforms, crowdsourcing initiatives, and expert assessments. We evaluate four supervision policies: non-prioritised (first-come, first-served), random selection, priority-based (addressing the highest-priority risks first), and diversity-prioritised (balancing high-priority risks with comprehensive coverage across risk types). Our results indicate that while priority-based and diversity-prioritised policies are more effective at mitigating high-impact risks—particularly those identified by experts—they may inadvertently neglect systemic issues reported by the broader community. This oversight can create feedback loops that amplify certain types of reporting while discouraging others, leading to a skewed perception of the overall risk landscape. We validate our simulation results with several real-world datasets, including one with over a million ChatGPT interactions, including more than 150K conversations identified as risky. This validation underscores the complex trade-offs inherent in AI risk supervision and highlights how the choice of risk management policies can shape the future landscape of AI risks across diverse GPAI models used in society. © 2025, CC BY.

关键词： Digital elevation model

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