检索结果-内蒙古大学图书馆

第41届中国控制会议

作者： Jianhao Li Xiaowei Jiang Hao Chen Shaoying Wang Xianhe Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System College of Science Binzhou University School of Electrical Engineering and Automation Hubei Normal University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,we focus on the tracking performance(TP) limitations of networked control systems(NCS s).The control system considered is a multi-input and multi-output(MIMO) discrete-time network control system with time delay,channel noise,packet dropouts,and logarithmic quantization communication *** modeling the channel,we assumed the channel noise to be additive white Gaussian noise(AWGN) and used a binary random process to model packet dropouts and a logarithmic quantizer to model the quantization *** explicit expression of the TP limitations of NCSs utilizing the two-degree-of-freedom(TDOF) controller is then derived using the frequency-domain approach,coprime decomposition,and Youla parameterization *** results show that the essential characteristics of the plant(locations and directions of nonminimum phase zeros and unstable poles) and network constraints(time delay,packet dropouts,quantization,and noise power spectral density) are closely related to the TP limitations.

关键词： Networked control systems tracking performance limitations packet dropouts logarithmic quantization

来源：评论

学校读者我要写书评

暂无评论

Quantized impulsive consensus of nonlinear Leader-following multi-agent systems 41

Quantized impulsive consensus of nonlinear Leader-following ...

引用

第41届中国控制会议

作者： Gang Li Xiaowei Jiang Le You Xianhe Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education School of Electrical Engineering and Automation Hubei Normal University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This work looks into the quantized impulsive consensus challenges of nonlinear leader-following multi-agent *** graph theory,principle of differential dynamics,logarithmic quantizer,matrix theory,and the Lyapunov function,among other things,are used to investigate the key discoveries on *** appropriate consensus protocols which contain quantized all agents' relative state information and quantized a part of agents' relative state *** the sake of reducing communication consumption and energy,we combine two control strategies and design two control protocols according to the characteristics of quantized control and impulsive *** a series of deductions,we find that under quantized impulsive control,multi-agent systems can achieve consensus as long as certain sufficient conditions are ***,the simulation example demonstrates the validity and correctness of this papers theoretical approach.

关键词： Multi-agent systems Leader-following Quantized impulsive consensus

来源：评论

学校读者我要写书评

暂无评论

Detection of Termite Nests in Dams Using Ground Penetrating Radar Based on Deep Learning 20

Detection of Termite Nests in Dams Using Ground Penetrating ...

引用

20th International Conference on Ground Penetrating Radar, GPR, 2024

作者： Peng, Jiao Xue, Wei Wang, Wenkai Song, Junlei Zhou, Feng Huang, Pengchun Zhang, Zhixuan Zhang, Feng Zhang, Xu Chen, Longjia Sun, Li School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China Dam Safety Monitoring and Termite Control Center of Hubei Provincial Department of Water Resources China Key Laboratory of Termite Control Ministry of Water Resources China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China

Termites have always been one of the major hidden dangers to the operational safety of dams. Traditional detection methods such as manual inspection and termite baiting have limitations in terms of efficiency and accuracy. To solve the problems, paper proposes a termite nest detection method based on ground penetrating radar (GPR) and deep learning. First, the simulated GPR data are generated using gprMax software, and the real data are collected using a GPR at several dams. Subsequently, the YOLOv8 deep neural network is employed for termite nest recognition in GPR images. The experimental results show that the proposed model can achieve the mean average precision (mAP) of 0.96 and a detection speed of 53.76 frames per second (FPS). © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

MADDPG: Multi-agent Deep Deterministic Policy Gradient Algorithm for Formation Elliptical Encirclement and Collision Avoidance 5th

MADDPG: Multi-agent Deep Deterministic Policy Gradient Algor...

引用

5th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2021

作者： Xu, Leixin Chen, Weibin Liu, Xiang Chen, Yang-Yang School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Jiangsu Automation Research Institute Lianyungang222061 China China Tiesiju Civil Engineering Group Co. Ltd. Hefei230023 China

ISBN: (纸本)9789811939976

This paper considers the formation encirclement problem of multi-agent systems with collision avoidance, where the enclosing orbit is an ellipse, the target and the formation are dynamic. The novel rewards, that is the elliptical encirclement reward, the formation reward, the angular velocity reward and collision avoidance reward are designed and a reinforcement learning (RL) algorithm, that is multi-agent deep deterministic policy gradient (MADDPG), is designed based on the novel setting of rewards. Simulation results are given to show the validity of the proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

引用

IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2222-2232页

作者： Juanjuan Li Junqing Li Xiao Wang Rui Qin Yong Yuan Fei-Yue Wang IEEE the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China the School of Artificial Intelligence Anhui UniversityHefei 266114 the Qingdao Academy of Intelligent Industries Qingdao 230031China the School of Mathematics Renmin University of ChinaBeijing 100872China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive *** security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing *** this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary ***,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and ***,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic *** incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading *** temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation ***,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

关键词： Auction data trading markets multi-blockchain pricing mechanisms

来源：评论

学校读者我要写书评

暂无评论

Pipeline Anomaly Detection Based on KPCA and Cosine Distance Prototypical Network 26

Pipeline Anomaly Detection Based on KPCA and Cosine Distance...

引用

26th International Conference on Industrial Technology, ICIT 2025

作者： Yi, Kang Liu, Xin Hu, Wenkai Li, Yupeng School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9798331521950

Accurately and promptly detecting the pipeline anomaly is crucial to the safe operation of pipeline systems, while a difficulty lies in that many existing methods require massive data for training models. However, pipelines are running under normal state for the most of the time, and labeled pipeline anomaly data is usually scarce. Among the commonly used sensors, vibration sensors are widely utilized in pipeline detection because of their advantages such as easy installation and high sensitivity. However, the vibration signal shows non-stationary characteristics when anomalies occur, and are contaminated by noises, making it difficult to represent the actual state with features extracted from either the time or frequency domain. Accordingly, this paper proposes a pipeline anomaly detection method based on the KPCA (kernel principal component analysis) and cosine distance prototypical network. First, features are extracted from original signals;then, the feature dimension is reduced by KPCA;last, the cosine distance is introduced to the prototypical network for anomaly detection. The effectiveness of the proposed method is demonstrated by case studies involving experimental data. © 2025 IEEE.

关键词： Frequency domain analysis

来源：评论

学校读者我要写书评

暂无评论

Relative Localization for UAV-UGV Based on Correlation of Bearing and Distance 20

Relative Localization for UAV-UGV Based on Correlation of Be...

引用

20th IEEE International Conference on Mechatronics and automation, ICMA 2023

作者： Guo, Jia Hu, Kang Liu, Jinting State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology No.5 Zhongguancun South Street Beijing100081 China

ISBN: (纸本)9798350320831

To ensure reliable localization for Unmanned Aerial Vehicles(UAVs) in the presence of uncertain speed of the Unmanned Ground Vehicle (UGV), this study examines the relative localization problem of UAV-UGV using distance and bearing measurements. A Correlation of Bearing and Distance-based Relative Localization (CBDRL) algorithm is proposed in this paper under this scenario. The estimation of altitude, distance, and angle are simplified into a representation of the relative positioning between the UAV and UGV. The relative height difference is measured using the barometer in the algorithm. To determine the relative distance, Time of Arrival (TOA) ranging and Ultra Wide Band (UWB) communication are utilized. The relative direction measurement is then determined using the correlations of bearing and distance. We integrate these observations with height, direction, and distance data in an Extended Kalman Filter(EKF) to provide accurate and reliable relative position estimates that allow the UAV to track the target. The simulation results indicate that the CBDRL method developed in this study is superior to previous relative localization algorithms that rely on multi-sensor fusion, and can significantly enhance the accuracy of UAV positioning provided that range and angle measurements are precise enough. © 2023 IEEE.

关键词： Antennas

来源：评论

学校读者我要写书评

暂无评论

RTPEIR: A Reverse Trajectory Prediction Enhanced Intent Recognition Algorithm for Multi-Agent systems*

RTPEIR: A Reverse Trajectory Prediction Enhanced Intent Reco...

引用

International Conference on control, automation, Robotics and Vision (ICARCV)

作者： Jun-Yang Cai Qin Liu Xin-Yu Xu Kelin Lu Yang-Yang Chen Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

This paper proposes a novel Reverse Trajectory Prediction Enhanced Intent Recognition Algorithm (RTPEIR) that utilizes historical data to predict earlier trajectories and enhance intent recognition accuracy. The RTPEIR algorithm incorporates two main enhancements. Firstly, it leverages a combination of past and present trajectory data to reconstruct previous agent states, thereby providing a deeper understanding of agents' strategic developments. Secondly, it integrates these reconstructed trajectories with ongoing strategy recognition processes, significantly refining the predictive accuracy. Sim-ulation experiments conducted on the StarCraft Multi-Agent Challenge (SMAC) platform show that RTPEIR outperforms existing forward trajectory prediction models by 6.5%, and existing LSTM and GRU models by 13.3% and 14.4%, respectively, in terms of intent recognition accuracy. Furthermore, when combined with deep reinforcement learning algorithms, RTPEIR demonstrates a notable improvement in win rates, highlighting its effectiveness in complex multi-agent environments.

关键词： Accuracy Intent recognition Refining Predictive models Prediction algorithms Deep reinforcement learning Trajectory Robots Long short term memory Multi-agent systems

来源：评论

学校读者我要写书评

暂无评论

Stability Analysis of a New Constant Current to Constant Voltage Power Supply System 42

Stability Analysis of a New Constant Current to Constant Vol...

引用

42nd Chinese control Conference, CCC 2023

作者： Song, Xiaoxian Fang, Zhijian Xia, Yuangeng Cai, Jie Wei, Zihao School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9789887581543

A high-reliability constant current to constant voltage power supply system has the advantages of small volume of switching power supply, high power density, high efficiency was proposed. This paper use two controllers to control the shunt regulator(SR) circuit and single-end flyback converter part, and separate the two parts for small signal modeling and give the parameters to stabilize the closed loop. The state space average modeling idea was used to solve the state equations for the modes of the converters in a switching cycle. In order to ensure the stability of cascade system, this paper collaborative optimization of hardware filter parameters and the appropriate PI parameter design. The experimentals verify the correctness of our theory, and the system has good stability under closed-loop conditions. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Stability

来源：评论

学校读者我要写书评

暂无评论

Nash Equilibrium Estimation of Non-Cooperative Games with Time-Varying Delay

Nash Equilibrium Estimation of Non-Cooperative Games with Ti...

引用

2023 China automation Congress, CAC 2023

作者： Liu, Ziqi Zhai, Chao China University of Geosciences School of Automation Wuhan Campus China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China

ISBN: (纸本)9798350303759

Time delay has great impacts on the stability and the reliability and real-time of the communication of multi-agent systems. In multi-agent communication network, due to network congestion, transmission distance and other factors, there are various communication delays. In this paper, we study the deviation of convergence value after adding time-varying delays under gradient descent method, and the upper bound related to delay time is estimated. This upper bound can be used to analyze the magnitude of deviation under different time delays and minimize the loss caused by delays, and provide more explicit information for system optimization and resource allocation. Numerical simulation is conducted to verify the proposed approach. © 2023 IEEE.

关键词： Time delay

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：