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Complex Systems and Intelligent science (CSIS-IAC), International Annual Conference on

作者： Jun He Xisheng Dai Rusheng Zhou School of Automation Guangxi University of Science and Technology Institute of Intelligent Systems and Control Guangxi University of Science and Technology Liuzhou China School of Computer Science and Technology Guangxi University of Science and Technology Liuzhou China

This paper focuses on the output trajectory tracking problem for linear parabolic distributed parameter systems (DPSs) using sampled-data iterative learning control (ILC) approach. In addition, in order to reduce the number of controller updates, an event-triggered control (ETC) method is introduced, in which the event triggering condition is generated by comparing the output error of the previous iteration batch. The convergence of the output error of the system at the sampling instant is analyzed through a rigorous mathematical proof process, which concludes that the system output error at the sampling instant converges along the direction of the iteration axis. Finally, numerical simulations are given to prove the effectiveness of the proposed method.

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Protein-Folding Inspired Programmable control System for Robot Swarms

Protein-Folding Inspired Programmable Control System for Rob...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Zhang, Feng Gu, Yuping Wang, Zebin Yuan, Shuai Yang, Yongliang School of Electrical and Control Engineering Shenyang Jianzhu University Liaoning Shenyang110168 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Liaoning Shenyang110106 China School of Computer Science and Engineering Shenyang Jianzhu University Liaoning Shenyang110168 China

ISBN: (纸本)9781665481090

In swarm robotics, most of the control algorithms are inspired by the behaviors of animal swarms. The capability gap between robots and animals, however, hinders the reliability of these algorithms. Toward developing reliable algorithms of robot swarms, a promising solution is learning from the collective behaviors of low-intelligent natural swarms, such as cell collectives and molecular assemblies. Here, we proposed a programmable control system inspired by protein folding. Using our homemade RoboFold platform, we demonstrated a protein-folding inspired programmable control method for robot swarms. By programming and designing the sequence of a thread of protein-like robots, the developed system can mimic the formation of hierarchical structures in robot swarms as the three-level of protein structures. We quantitatively characterized the specific features of self-organized morphologies. The experimental results together demonstrated the feasibility of this method. To summarize, this work successfully translated the principles of protein-folding into programming methods to control the morphology of robot swarms. Considering that the protein folding problem is essentially a physicochemical problem, this system is not only beneficial for the control of swarm robots but also has the potential to develop into a tool for studying the protein folding problem. © 2024 IEEE.

关键词： Robot programming

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Shifting Spotlight for Co-supervision: A Simple yet Efficient Single-branch Network to See Through Camouflage

arXiv

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arXiv 2024年

作者： Hu, Yang Zhang, Jinxia Zhang, Kaihua Yuan, Yin Huang, Jiale Zhan, Zechao Wang, Xin Key Laboratory of Measurement and Control of CSE School of Automation Southeast University China School of Computer Science Nanjing University of Information Science and Technology China Advanced Ocean Institute of Southeast University China Alibaba Group China

Camouflaged object detection (COD) remains a challenging task in computer vision. Existing methods often resort to additional branches for edge supervision, incurring substantial computational costs. To address this, we propose the Co-Supervised Spotlight Shifting Network (CS3Net), a compact single-branch framework inspired by how shifting light source exposes camouflage. Our spotlight shifting strategy replaces multi-branch designs by generating supervisory signals that highlight boundary cues. Within CS3Net, a Projection Aware Attention (PAA) module is devised to strengthen feature extraction, while the Extended Neighbor Connection Decoder (ENCD) enhances final predictions. Extensive experiments on public datasets demonstrate that CS3Net not only achieves superior performance, but also reduces Multiply-Accumulate operations (MACs) by 32.13% compared to state-of-the-art COD methods, striking an optimal balance between efficiency and effectiveness. Copyright © 2024, The Authors. All rights reserved.

关键词： Camouflage

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Enhancement of Two-Photon Fluorescence Microscopy Through Auxiliary Microspheres for Microscopic Observation 14

Enhancement of Two-Photon Fluorescence Microscopy Through Au...

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14th IEEE International Conference on Cyber Technology in automation, control, and Intelligent Systems, CYBER 2024

作者： Zhang, Feng Jin, Zijian Jiang, Chaodi Yuan, Shuai Yang, Yongliang Wang, Xiaoduo School of Electrical & Control Engineering Shenyang Jianzhu University Liaoning Province Shenyang110168 China School of Computer Science and Engineering Shenyang Jianzhu University Liaoning Province Shenyang110168 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9798331506056

At present, it is still a great challenge to improve the observation accuracy of two-photon fluorescence microscope. A new method that using auxiliary microspheres as movable components on the sample is proposed to solve this challenge in this study, it can improve high-resolution imaging. In experiments using two-photon fluorescence microscopy, the half-width height (FWHM) of red fluorescent nanoparticles with a diameter of 20 nm was observed to be 296 nm using a primitive microscope system. In this paper by combining barium titanate microspheres with two-photon fluorescence microscopy, the accuracy of the point spread function of the nanoparticles can be improved by a factor of about 1.65, which results in a significant reduction of their half-width height measurements to 179 nm. The research is of great significance to improve the resolution of two-photon fluorescence microscope. This progress is very valuable for studying biological tissues and cell structures, it provides a new impetus for the sustainable development in the microscopic imaging field. © 2024 IEEE.

关键词： Microspheres

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Study on Landslide Susceptibility Evaluation Based on Improved Multivariable Weighted Model: A Case Study of Lushan County, China

Study on Landslide Susceptibility Evaluation Based on Improv...

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Software Engineering and Artificial Intelligence (SEAI), IEEE International Conference on

作者： Li Zhao Gonghao Duan School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

The evaluation of regional geological hazard susceptibility is of great significance to the prevention and control of geological hazard. In this paper, the "4-20" Lushan earthquake disaster area as the research area, combined with GIS and characteristics of the research area, through correlation analysis, selected 9 influencing factors as the evaluation factors. The study was conducted using a weighted information volume-logistic regression model (WI-LR). The results were classified into five sensitivity levels: very low, low, medium, high and very high. The results show that the landslide prone areas in Lushan County are concentrated in the area below the middle of the county seat, mostly near rivers, faults and areas with peak acceleration greater than 0.4. Among them, WI-LR model (AUC=0.918) > deterministic factor model (CF =0.908) > weighted information quantity model (WI) (AUC=0.896) > information quantity model I (AUC=0.853) > information quantity logistic regression model I-LR (AUC=0.799). It indicates that WI-LR model has high evaluation accuracy.

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells 32

Application of Deep Learning Method to Estimate Bottomhole P...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Cheng, Haibo Li, Shichao Zeng, Peng Vyatkin, Valeriy State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Luleå University of Technology Electrical and Space Engineering Department of Computer Science Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350399714

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry. © 2023 IEEE.

关键词： Long short-term memory

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Learning impedance control of robots with enhanced transient and steady-state control performances

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science China(Information sciences) 2020年第9期63卷 204-216页

作者： Tairen SUN Long CHENG Liang PENG Zengguang HOU Yongping PAN State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Data and Computer Science Sun Yat-sen University

This study proposes a learning impedance controller comprising a proportional feedback control term, a composite-learning-based uncertainty estimation term, and a robot-environment interaction control term. The impedance control problem is converted into a particular reference-trajectory tracking problem based on a generated reference trajectory. The proposed controller ensures the exponential convergence of the auxiliary tracking error and the uncertainty estimation error. The interaction control term improves the transient control performance through suppression/encouragement of the incorrect/correct robot *** composite-learning update law enhances the transient and steady-state control performances based on the exponential convergence of the uncertainty estimation error and auxiliary tracking error. Finally, the effectiveness and advantages of the proposed impedance controller are validated by theoretical analysis and simulations on a parallel robot.

关键词： robot adaptive control neural network impedance control parameter convergence

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Designing a Secure Smart Remote Patient Monitoring and Warning System using Big Data and Internet of Things Ecosystems 10

Designing a Secure Smart Remote Patient Monitoring and Warni...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Ramesh, S.P. Abdulwahid, Ali Hadi Anjum, Afsana Venkatesh, N. Singh, Rajesh Chakravarthi, M. Kalyan Amet University Department of Computer Science and Engineering Tamil Nadu Kanathur India Southern Technical University Engineering Technical College Department of Control and Automation Basra Iraq Jazan University Department of Information Technology and Security Jazan Saudi Arabia School of Computer Science and Artificial Intelligence Sr University Telangana Warangal India Uttaranchal Institute of Technology Uttaranchal University India School of Electronics Engineering VIT-AP University Amaravathi India

ISBN: (纸本)9789380544472

The demand on healthcare is growing as a result of the expanding global population, rising demands for successful treatment, and an underlying improvement in life quality. Consequently, healthcare remains one of the most significant social and economic problems in the world, necessitating the development of new and more sophisticated scientific and technological treatments.E-health services are provided via personalised healthcare systems to meet the medical and support needs of the ageing population. An important development in the Big Data age is the Internet of Things, which facilitates several real-time engineering solutions through improved services. Healthcare systems are now using analytics over IoT data streams as a stream of user data to find new information, forecast rapid recognition, and make decisions on life-threatening situations. In this study, researchers have carried out a thorough review of the hottest recent revolutionary technologies in bespoke healthcare systems, with a focus on hypervisor, cloud infrastructure, big data analytic, the Web of Things, and smartphone apps. In order to make early disease detection and diagnosis possible, it is important to develop a better healthcare system. We have examined these problems and suggested potential solutions while offering secure e-health services. The purpose of this study is to design a smart and secure remote monitoring and warning system using the latest emerging technologies in 21stcentury such as big data analytics and Internet of Things (IoT) ecosystems. In this study the researchers discussed a detailed research methodology to meet the outcomes of the reerach. All the relevant findings and analysis are explained in descriptive manner. Moreover, this study highlights the constantly expanding requirements for better medical systems in the present and offers potential directions for future research. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Internet of things

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Online B-Spline Based Trajectory Planning for Swarm of Agents Using Distributed Model Predictive control

Online B-Spline Based Trajectory Planning for Swarm of Agent...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Cong Khanh Dinh Ionela Prodan Florin Stoican Univ. Grenoble Alpes Grenoble INP† LCIS Valence France The Faculty of Automation Control and Computer Science Institute of Engineering and Management Univ. Grenoble Alpes University Politehnica of Bucharest Romania

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

This paper deals with the motion planing in real-time for swarm of agents using distributed Model Predictive control (DMPC). Optimization-based control helps both to stabilize the motion dynamics of the robots and to enforce the system's constraints. However, the price is a significant increase in complexity as the scale of the system surges. To improve the scalability for a large formation, we propose a DMPC framework that considers B-spline parameterizations for the agents' trajectories. The proposed approach has great promise for multi-drones system, as illustrated with simulation examples using a state of the art quadcopter model.

关键词： Trajectory planning Scalability Prediction algorithms Real-time systems Trajectory Splines (mathematics) Surges

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FedEFM: Federated Endovascular Foundation Model with Unseen Data

arXiv

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arXiv 2025年

作者： Do, Tuong Vu, Nghia Jianu, Tudor Huang, Baoru Vu, Minh Su, Jionglong Tjiputra, Erman Tran, Quang D. Chiu, Te-Chuan Nguyen, Anh Deparment of Computer Science University of Liverpool United Kingdom AIOZ Ltd. Singapore Automation & Control Institute TU Wien Austria Xi’an Jiaotong-Liverpool University China National Tsing Hua University Taiwan

In endovascular surgery, the precise identification of catheters and guidewires in X-ray images is essential for reducing intervention risks. However, accurately segmenting catheter and guidewire structures is challenging due to the limited availability of labeled data. Foundation models offer a promising solution by enabling the collection of similar-domain data to train models whose weights can be fine-tuned for downstream tasks. Nonetheless, large-scale data collection for training is constrained by the necessity of maintaining patient privacy. This paper proposes a new method to train a foundation model in a decentralized federated learning setting for endovascular intervention. To ensure the feasibility of the training, we tackle the unseen data issue using differentiable Earth Mover’s Distance within a knowledge distillation framework. Once trained, our foundation model’s weights provide valuable initialization for downstream tasks, thereby enhancing task-specific performance. Intensive experiments show that our approach achieves new state-of-the-art results, contributing to advancements in endovascular intervention and robotic-assisted endovascular surgery, while addressing the critical issue of data sharing in the medical domain. © 2025, CC BY.

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