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2023 IEEE International Conference on Unmanned systems, ICUS 2023

作者： Du, Meiyi Gao, Zhijun Yang, Liying Jiang, Han Huang, Zhaoxiong School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350316308

Accidents involving hazardous gas leakage from chemical plants occur frequently, highlighting the critical importance of accurately locating air pollution sources. Rapid localization of pollution sources through the collaborative efforts of multiple unmanned aerial vehicles (UAVs) can help prevent major disasters. To address this, this paper presents an air pollution source tracking algorithm based on an improved particle swarm optimization (IPSO) algorithm. By enhancing the inertia weight coefficient, the search efficiency and accuracy of the UAV swarm are significantly improved. The simulation environment considers a Gaussian plume model, which incorporates various atmospheric constraints such as temperature and wind speed, for UAV swarm navigation. The simulation results demonstrate that employing the particle swarm optimization algorithm to guide UAVs in locating pollution sources enables them to accurately identify the sources within a short period, exhibiting a high level of effectiveness and robustness. © 2023 IEEE.

关键词： Antennas

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An edge-aware-oriented semantic association retrieval method for industrial data 6

An edge-aware-oriented semantic association retrieval method...

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6th International Workshop on Advanced Algorithms and control Engineering, IWAACE 2022

作者： Liu, Yang Zhang, Tianshi Xiao, Jinchao Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110000 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9781510657755

The development of the Industrial Internet has caused a change that traditional Internet technologies gradually sink into the field of industrial manufacturing. Due to the high degree of business coupling at the edge of industrial production, the relationships between the data are complex and difficult to comprehensively analyze and apply. This paper proposes a data semantic association retrieval method for industrial data at the industrial edge. Firstly, the framework of semantic association retrieval of industrial data is established, and then the construction method of inverted table in association retrieval is explained, including the formation method of keyword set and concept set, and the algorithm for realizing semantic association retrieval is given. The algorithm is verified in the actual scene of the test production line, and the results show that the algorithm in this paper is feasible and effective in relational retrieval. © 2022 SPIE.

关键词： Edge computing

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Deployment Schemes of In-Band Management Mode Based on Industrial SDN Network

Deployment Schemes of In-Band Management Mode Based on Indus...

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2022 International Conference on 6G Communications and IoT Technologies, 6GIoTT 2022

作者： Piao, Jinnan Li, Dong Yang, Ming Liu, Jindi Yu, Xueting Li, Zhibo Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9781665454841

When software defined network (SDN) is used to deploy the industrial control network, the deployment schemes are different in actual according to the different industrial production business, environment and capital cost. In this paper, we use the in-band management mode to deploy the SDN network in an industrial environment. We design four deployment schemes of the in-band management mode for the industrial SDN network according to the specific requirements of industrial environment and propose an algorithm for controller location selection in the in-band management network For the four schemes and the algorithm, we use the controller and switches to build a simulation environment according to different schemes and compare the performance between the in-band management and out-of-band management, which shows that the in-band management mode can set up a normal SDN industrial network with applicable network scale and throughput. © 2022 IEEE.

关键词： Internet of things

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Parallel Learning:Overview and Perspective for Computational Learning Across Syn2Real and Sim2Real

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 603-631页

作者： Qinghai Miao Yisheng Lv Min Huang Xiao Wang Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China School of Artificial Intelligence Anhui UniversityHefei 266114China Qingdao Academy of Intelligent Industries Qingdao 230031China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE

The virtual-to-real paradigm,i.e.,training models on virtual data and then applying them to solve real-world problems,has attracted more and more attention from various domains by successfully alleviating the data shortage problem in machine *** summarize the advances in recent years,this survey comprehensively reviews the literature,from the viewport of parallel ***,an extended parallel learning framework is proposed to cover main domains including computer vision,natural language processing,robotics,and autonomous ***,a multi-dimensional taxonomy is designed to organize the literature in a hierarchical ***,the related virtual-toreal works are analyzed and compared according to the three principles of parallel learning known as description,prediction,and prescription,which cover the methods for constructing virtual worlds,generating labeled data,domain transferring,model training and testing,as well as optimizing the strategies to guide the task-oriented data generator for better learning *** issues remained in virtual-to-real are ***,the future research directions from the viewpoint of parallel learning are suggested.

关键词： Machine learning parallel learning parallel systems sim-to-real syn-to-real virtual-to-real

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A Task Offloading Method for Smart Instruments Based on Edge Computing 7

A Task Offloading Method for Smart Instruments Based on Edge...

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7th International Conference on Computer and Information Processing Technology, ISCIPT 2022

作者： Liu, Yang Zhang, Tianshi Xiao, Jinchao Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110000 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

The development of edge computing technology has enabled the promotion of traditional industrial instruments to smart instruments with collaborative working capabilities. Smart instruments are widely distributed in industrial production sites and can monitor working conditions in real time. Implementing multi-instruments collaborative computing on the edge side will help improve the responsiveness of industrial systems and reduce instrument maintenance costs. This paper proposes a task offloading method for smart instruments based on edge computing. Firstly, the instrument capability is evaluated based on the characteristics of the smart instrument. Then, the task scheduling of smart instruments is optimized based on Lyapunov algorithm. Finally the simulation verification is carried out. The experimental results show that the algorithm in this paper has a significant effect on improving computing power and reducing resource occupancy. © 2022 Copyright for this paper by its authors.

关键词： Edge computing

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A History Information-Based A* Algorithm for the Path Planning with Multiple Alternative Destinations

A History Information-Based A* Algorithm for the Path Planni...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Ke Wang Wei Liang Huaguang Shi Jialin Zhang Qi Wang 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 University of Chinese Academy of Sciences Beijing China School of Artificial Intelligence Henan University Zhengzhou China

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

As a cornerstone technology in automation, path planning plays a crucial role across various domains, including logistics, autonomous vehicles, and robotics. A novel path planning challenge, focusing on routes with multiple alternative destinations, has emerged as a focal point in industrial settings. Traditional approaches such as breadth-first and heuristic searches lack specific optimizations for this challenge, resulting in suboptimal efficiency. This paper introduces a new approach to the path planning problem with multiple alternative destinations that utilizes historical data. It features three heuristic strategies: leveraging historical information for search acceleration, pruning based on the length of the current shortest path, and searching in ascending order of the Manhattan distance. These strategies enhance the search efficiency by leveraging historical information for faster searches and pruning based on real-time search outcomes. Simulation results show that the proposed algorithm outperforms the existing works in terms of the computational cost.

关键词： Service robots Navigation Simulation Focusing Path planning Real-time systems Computational efficiency

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Non-Overshooting Output Regulation of Linear systems via Positive systems Method

Non-Overshooting Output Regulation of Linear Systems via Pos...

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International Conference on Electronic Engineering and Informatics (EEI)

作者： Xuanjin Deng Wei Liu Key Laboratory of Autonomous Systems and Networked Control of Ministry of Education School of Automation Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

This paper investigates the non-overshooting output regulation problem by using the analysis and synthesis approaches of positive systems. Combining linear programming with gain matrix decomposition technique, a state feedback control law with two different kinds of design parameters is designed to ensure the stability and positivity of the corresponding closed-loop systems, which leads to the solvability of the non-overshooting output regulation problem of linear systems. Finally, an example is given to verify the effectiveness of the proposed design.

关键词： Linear systems state feedback Programming Linear programming Regulation Stability analysis Matrix decomposition Closed loop systems Nonlinear systems Lyapunov methods

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An Online Strategy for Nonlinear Vector Autoregression

An Online Strategy for Nonlinear Vector Autoregression

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robotics, control and Automation Engineering (RCAE), International Conference on

作者： Gang Fang Yiqi Liu Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science & Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

Nonlinear vector autoregression (NVAR), as an effective alternative to traditional reservoir computing (RC), has garnered significant attention in the field of time series prediction in recent years. Unlike conventional RC, NVAR does not require the use of random sampling matrices to define the underlying recurrent neural network, and it features fewer hyperparameters. However, NVAR is an offline model, making it challenging to meet the real-time monitoring requirements of industrial processes. Inspired by the Woodbury matrix identity, this paper proposes an online NVAR that can update the model using data arriving in blocks. Subsequently, this study constructs a soft sensing model based on the online NVAR to perform real-time monitoring of industrial processes. Finally, the effectiveness of the proposed method is validated using a real-world dataset from a wastewater treatment plant.

关键词： Recurrent neural networks Soft sensors Time series analysis Reservoir computing Predictive models Robot sensing systems Vectors Real-time systems Data models Wastewater treatment

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Stabilization of Continuous-Time systems Against Stochastic Network Uncertainties:Fundamental Variance Bounds

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Journal of systems Science & Complexity 2021年第5期34卷 1858-1878页

作者： QI Tian CHEN Jie School of Automation Science and Engineering Key Laboratory of Autonomous Systems and Networked ControlSouth China University of TechnologyGuangzhou 510640China Department of Electrical Engineering City University of Hong KongHong KongChina

This paper studies the stabilizability and stabilization of continuous-time systems in the presence of stochastic multiplicative *** authors consider multi-input,multi-output(MIMO)linear time-invariant systems subject to multiple static,structured stochastic uncertainties,and seek to derive fundamental conditions to ensure that a system can be stabilized under a mean-square *** the stochastic control framework,this problem can be considered as one of optimal control under state-or input-dependent random noises,while in the networked control setting,a problem of networked feedback stabilization over lossy communication *** authors adopt a mean-square small gain analysis approach,and obtain necessary and sufficient conditions for a system to be meansquare stabilizable via output *** single-input,single-output(SISO)systems,the condition provides an analytical bound,demonstrating explicitly how plant unstable poles,nonminimum phase zeros,and time delay may impose a limit on the uncertainty variance required for mean-square *** MIMO minimum phase systems with possible delays,the condition amounts to solving a generalized eigenvalue problem,readily solvable using linear matrix inequality optimization techniques.

关键词： Mean-square small gain theorem multiplicative stochastic uncertainty networked control

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Blockchain-based Dependable Task Offloading and Resource Allocation for IIoT via Multi-Agent Deep Reinforcement Learning

Blockchain-based Dependable Task Offloading and Resource All...

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IEEE Conference on Vehicular Technology (VTC)

作者： Peifeng Zhang Chi Xu Changqing Xia Xi Jin 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 University of Chinese Academy of Sciences Beijing China

Task offloading and resource allocation are fundamental and crucial for the edge computing-enhanced industrial Internet of things, where the security and credibility among massive heterogeneous devices are being challenged. This paper first proposes a novel blockchain consensus scheme named replicated and Byzantine fault tolerant, which can enhance the trust among nodes with the low communication cost. Then, with the objective of minimizing the task completion time, which includes credible verification, task offloading and transaction record, a joint task offloading and resource allocation problem with respect to blockchain verification ratio, offloading decision, communication and computing resources is formulated. Due to its non-convexity and the decentralized characteristic of blockchain, a multi-agent deep reinforcement learning algorithm with deterministic policy gradient is proposed to appropriate the optimal solution. Experiment results confirm the effectiveness of the proposed scheme in guaranteeing the timeliness and security.

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