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arXiv 2023年

作者： Petreczky, Mihály Tóth, Roland Mercère, Guillaume Univ. Lille CNRS Centrale Lille UMR 9189 CRIStAL LilleF-59000 France The University of Poitiers Laboratoire d’Informatique et d’Automatique pour les Systèmes France The Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands The Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

The paper makes the first steps towards a behavioral theory of LPV state-space representations with an affine dependency on scheduling, by characterizing minimality of such state-space representations. It is shown that minimality is equivalent to observability, and that minimal realizations of the same behavior are isomorphic. Finally, we establish a formal relationship between minimality of LPV state-space representations with an affine dependence on scheduling and minimality of LPV state-space representations with a dynamic and meromorphic dependence on scheduling. © 2023, CC BY.

关键词： State space methods

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Distributed Online Convex Optimization with Time-Varying Constraints: Tighter Cumulative Constraint Violation Bounds under Slater's Condition

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IEEE Transactions on Automatic control 2025年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Science and Technology China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Nanyang Technological University School of Electrical and Electronic Engineering 50 Nanyang Avenue 639798 Singapore Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with time-varying constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize static network regret and network cumulative constraint violation. A novel distributed online algorithm with a vanishing stepsize is proposed and it achieves an O(Tmax {c,1-c}) static network regret bound and an O(T1-c/2) network cumulative constraint violation bound, where T is the number of rounds and c∈ (0,1) is a user-defined trade-off parameter. When Slater's condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T1-c). Moreover, if the loss functions are strongly convex, then static network regret bound is reduced to O(log (T)), and the network cumulative constraint violation bound is reduced to O(√log (T)T) and O(log (T)) without and with Slater's condition, respectively. To the best of our knowledge, this paper is the first to achieve tighter (network) cumulative constraint violation bounds for (distributed) online convex optimization with time-varying constraints under Slater's condition. Finally, the theoretical results are verified through numerical simulations. © 1963-2012 IEEE.

关键词： Convex optimization

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Robot path guidance algorithm based on compound collaborative filtering

Robot path guidance algorithm based on compound collaborativ...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： WenLin Liu JingLong CHENG XingHua LU YiFu PAN YuNing GAO Computer and Information Engineering Huali College Guangdong University of Technology Guangzhou China Institute of computer control technology Huali College Guangdong University of Technology Guangzhou China

ISBN: (纸本)9781665447300

In order to improve the stability of control and path guidance of rigid-soft hybrid snake-like robot, a crawling path guidance method of rigid-soft hybrid snake-like robot based on composite collaborative filtering is proposed. The physical spatial grid distribution model of rigid-soft hybrid snake-like robot crawling is constructed, the SLAM guidance method is adopted to carry out grid regionalization matching in the obstacle avoidance process of rigid-soft hybrid snake-like robot, the optimization constraint parameters of obstacle avoidance path distribution of rigid-soft hybrid snake-like robot are established, and the path distribution characteristic variables in the obstacle avoidance process of rigid-soft hybrid snake-like robot are analyzed based on the kinematic characteristic analysis method. Parameter optimization design of rigid-soft hybrid snake-like robot crawling path guidance is carried out by local spatial feature distribution detection and recognition method, and optimization control is carried out by composite collaborative filtering algorithm. Adaptive inertial parameter optimization and accurate location are carried out by multi-sensor parameter positioning method. The simulation results show that this method has better adaptive optimization ability and higher robot positioning accuracy, which improves the obstacle avoidance and guidance ability of the rigid-soft hybrid snake robot.

关键词： Legged locomotion Tracking Collaborative filtering Snake robots Signal processing algorithms Planning Compounds

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Toolbox for Discovering Dynamic System Relations via TAG Guided Genetic Programming

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IFAC-PapersOnLine 2021年第7期54卷 379-384页

作者： Ştefan-Cristian Nechita Roland Tóth Dhruv Khandelwal Maarten Schoukens Department of Electrical Engineering Eindhoven University of Technology Eindhoven University of Technology Eindhoven The Netherlands Systems and Control Laboratory Institute for Computer Science and Control Kende u. 13-17 H-1111 Budapest Hungary

Data-driven modeling of nonlinear dynamical systems often requires an expert user to take critical decisions a priori to the identification procedure. Recently, an automated strategy for data driven modeling of single-input single-output (SISO) nonlinear dynamical systems based on genetic programming (GP) and tree adjoining grammars (TAG) was introduced. The current paper extends these latest findings by proposing a multi-input multi-output (MIMO) TAG modeling framework for polynomial NARMAX models. Moreover, we introduce a TAG identification toolbox in Matlab that provides implementation of the proposed methodology to solve multi-input multi-output identification problems under NARMAX noise assumption. The capabilities of the toolbox and the modeling methodology are demonstrated in the identification of two SISO and one MIMO nonlinear dynamical benchmark models.

关键词： Nonlinear system identification Equation discovery Tree Adjoining Grammar Genetic Programming Data-driven system modeling

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Transfer Learning with Demonstration Forgetting for Robotic Manipulator

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Procedia computer Science 2021年 186卷 374-380页

作者： Ermek Aitygulov Aleksandr I. Panov Moscow Institute of Physics and Technology Moscow Russia Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences Moscow Russia

Deep learning and especially deep reinforcement learning usually require huge amount of data for training and simulators using is perspective approach to provide this data. Model trained in simulator can be transferred on real robot without wasting a lot of time to collect data. Training in simulator also allows using of different techniques to speed up convergence and increase resulting performance. One of them is to train feature-based model with access to the whole information about the environment and use it as expert for main image-based model. This reduces earning time and the computational costs that are necessary to obtain quality results with image-based model. In this work we improve idea of behaviour cloning feature agent and make it more flexible with using of expert demonstrations forgetting. We conducted experiments on transfer learning for a robotic manipulator that interacts with complex objects and compared them with classic off-policy approaches.

关键词： reinforcement learning learning from demonstrations robotic manipulator simulation forgetting

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The Residential Rat Monitoring and Detection System based on Object Detection

The Residential Rat Monitoring and Detection System based on...

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Image Processing, computer Vision and Machine Learning (ICICML), International Conference on

作者： Yubin Song Guangyu Cao Wenyan Ma Zhanmeng Yang Borui Wang Lele Li School of Cyber Security Hangzhou Dianzi University Hangzhou China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Computer Science Hangzhou Dianzi University Hangzhou China Sydney Smart Technology College Northeastern University Qinhuangdao China School of Science Harbin Institute of Technology Harbin China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

With the popularity of automatic monitoring systems in home environments, animal detection techniques based on computer vision have gradually become a research hotspot. In this article, a rat detection method based on the improved YOLOv5 algorithm is proposed, aiming to improve the detection accuracy of the model in complex environments, especially in low-light and occlusion situations. Firstly, to address the limitations of YOLOv5 in small object detection, this article employs data enhancement techniques and incorporates high-resolution image inputs to improve the detection of rat, a small target. Second, a post-processing method based on adaptive thresholding is proposed to reduce the false detection of low-confidence targets while enhancing the robustness of the model in complex backgrounds. In addition, the feature extraction capability of YOLOv5 is optimised by introducing a new attention mechanism model, which improves the accuracy of rats detection. The experimental results show that the improved YOLOv5 model exhibits higher accuracy and stronger robustness in the rats detection task, providing an effective solution for mouse monitoring in home environments.

关键词： YOLO Adaptation models computer vision Accuracy Attention mechanisms Surveillance Smart homes Rats Feature extraction Robustness

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An Architecture Modeling Framework for Distributed Automation Systems Using SysML and Semantic Web Technologies

An Architecture Modeling Framework for Distributed Automatio...

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IEEE International Conference on Mechatronics, Robotics and Automation (ICMRA)

作者： Dan Wang Xiaofeng Li Bin Gu Yue Cao Yusheng Liu Academy of Aerospace Propulsion Technology Innovation Center for Liquid Rocket Engine Digital Research and Development CNSA Xian China Beijing Institute of Control Engineering Beijing China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China State Key Lab. Of CAD&CG Zhejiang University Hangzhou China

ISBN: (数字)9798350307535

ISBN: (纸本)9798350307542

The rising interdisciplinarity and complexity of the Distributed Automation Systems (DASs) require the systems to be modeled in an unambiguous and high-level abstract way for cross-discipline/stage communication and interoperability in the Model-Driven Development (MDD) lifecycle. The concept of the System Architecture Model in systems engineering has been adopted for this challenge. To support the creation and analysis of this model, a modeling framework with a modeling methodology, modeling language, knowledge base, and related toolkit is established based on SysML and Semantic Web Technologies. A modeling methodology which is the core of the framework for modeling the architecture of DASs is formally defined with domain-specificity, comprehensiveness, discipline-neutrality, and platform-independency. Based on it, the SysML-DAS modeling language is extended from SysML, and the System Architecture Ontology is built with the help of the Knowledge Extraction Tool. This ontology works as the knowledge base not only to provide a unified and unambiguous view of the system but also to support the automated accomplishment of tasks in the MDD process. As a typical task, the semantic correctness and integrity of the system architecture model can be assessed by the Knowledge Analysis Tool in this framework.

关键词： Semantic Web Analytical models Automation Mechatronics Knowledge based systems Semantics Systems architecture Ontologies Service-oriented architecture Model-driven development

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Recent Development, Trends and Challenges in IoT Security 7th

Recent Development, Trends and Challenges in IoT Security

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Talezari, Morteza Tu, Shanshan Rehman, Sadaqat ur Chuangbai, Xiao Ahmad, Basharat Waqas, Muhammad Rehman, Obaid ur Research Center of Intelligent Perception and Autonomous Control Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Namal Institute Mianwali42250 Pakistan Department of Electronic Engineering Tsinghua University Beijing China Institute of Engineering Sciences and Technology Topi23460 Pakistan Department of EE Sarhad University of Science and IT Peshawar Pakistan

ISBN: (纸本)9783030786113

A large amount of data that IoT applications deal, is users’ Private data, and their privacy is a significant issue. However, if users are not sure about the security of their data, they will not desire to use such applications. The goal of this study is to improve the security and privacy of users in the Internet of Things (IoTs) setting. Since, the application of IoT technology is growing day by day. Also, the importance of users’ data security is always top of the list and difficult to achieve. In this study, we studied latest development in the security of Internet of Things (IoTs) so that we can use it to investigate its challenges and advantages. © 2021, Springer Nature Switzerland AG.

关键词： Internet of things

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Research on Cardiac Medical Image Segmentation Based on an Improved U-Net Model

Research on Cardiac Medical Image Segmentation Based on an I...

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Image Processing, computer Vision and Machine Learning (ICICML), International Conference on

作者： Yuqing Wu Jun Wang Rui Wang Jian Wang Kecheng Zhou Xiaoxin Zhang School of Computer Science and Technology Shenyang University of Chemical Technology Key Laboratory of Industrial Intelligence Technology on Chemical Process Liaoning Province Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Department of Emergency Zhongyi Northeast International Hospital Co. Ltd. Shenyang China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

This paper presents a cardiac MRI image segmentation model based on an improved U-Net architecture. Accurate segmentation of cardiac MRI images is critical for the diagnosis and treatment of cardiovascular diseases, yet existing U-Net models exhibit limitations in handling complex cardiac structures and multi-scale features. To address these challenges, this paper proposes two key enhancements. First, we propose a Multi-Dimensional Context Attention module, designed to improve the integration of global and local information within the skip connections, thereby enhancing segmentation accuracy for intricate cardiac structures. Furthermore, we propose a Reverse Feature Modulation module, which generates reverse masks and dynamically adjusts feature weights across different classes using adaptive weighting, effectively mitigating class imbalance issues in multi-class segmentation tasks and improving focus on difficult-to-segment regions. Experimental results demonstrate that the proposed model outperforms the standard U-Net on the ACDC dataset, achieving significant improvements in evaluation metrics such as the Dice coefficient. These enhancements underscore the model's efficacy and robustness in complex cardiac image segmentation tasks, offering new technical support for automated cardiovascular disease diagnosis.

关键词： Image segmentation Accuracy Magnetic resonance imaging Computational modeling Modulation Context awareness Robustness Medical diagnosis Cardiovascular diseases Context modeling

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Fully Decentralized Design of Initialization-free Distributed Network Size Estimation

arXiv

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arXiv 2024年

作者： Lee, Donggil Kim, Taekyoo Lee, Seungjoon Shim, Hyungbo Center for Intelligent and Interactive Robotics Korea Institute of Science and Technology Seoul02792 Korea Republic of The Train Control and Communication Team Korea Railroad Research Institute Uiwang16105 Korea Republic of NAVER Labs Seongnam13161 Korea Republic of ASRI Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of

In this paper, we propose a distributed scheme for estimating the network size, which refers to the total number of agents in a network. By leveraging a synchronization technique for multi-agent systems, we devise an agent dynamics that ensures convergence to an equilibrium point located near the network size regardless of its initial condition. Our approach is based on an assumption that each agent has a unique identifier, and an estimation algorithm for obtaining the largest identifier value. By adopting this approach, we successfully implement the agent dynamics in a fully decentralized manner, ensuring accurate network size estimation even when some agents join or leave the network. © 2024, CC BY-NC-ND.

关键词： Multi agent systems

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