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Global attracting set and asymptotic behavior for a class of impulsive functional Hopfield neural networks by a novel vector inequality

Global attracting set and asymptotic behavior for a class of...

引用

第35届中国控制与决策会议

作者： Weisong Zhou Guihua Li Wei Zhu Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications

This article is concerned with a class of nonlinear and nonautonomous functional Hopfield neural networks with impulsive *** existence of attracting set and invariant set of the desired impulsive differential equations is ***,we construct a novel vector inequality,which considers the features of *** on the more general vector inequality,we ensure the positive invariant set and global attracting set for a class of the impulsive functional differential *** means of numerical simulation,the effectiveness of our theoretical results is demonstrated.

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Bearing-only Formation of Multi-agent systems via Edge-based Event-triggered control

Bearing-only Formation of Multi-agent Systems via Edge-based...

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第42届中国控制会议

作者： Chenjun Liu Wei Zhu Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications

The event-triggered bearing-only formation of leader-following multi-agent systems is studied in this paper. Based on the neighboring bearing information at event-triggering time instants, the edge-based event-triggered scheme for stationary formation is designed. The event-triggering time instants are iteratively determined by a combined technique, where the Zenobehavior can be avoided directly. A sufficient condition for achieving formation is presented under the assumption that there is no collision among agents. Finally, the numerical simulation is given to illustrate the effectiveness of the theoretical results.

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Road Underground Target Detection in Ground Penetrating Radar Images Based on YOLO Models

Road Underground Target Detection in Ground Penetrating Rada...

引用

2023 China Automation Congress, CAC 2023

作者： Li, Ting Xue, Wei Yang, Xinya Wu, Dongchang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9798350303759

Ground penetrating radar (GPR) is extensively employed for subsurface road target detection, offering benefits such as convenience, nondestructive testing, rapid data acquisition, and superior resolution. Despite these advantages, interpreting GPR data often depends on the expertise of professionals, resulting in low detection efficiency and low accuracy. To address these challenges, this study introduces an intelligent detection technique for GPR images, utilizing an enhanced YOLOv5 framework. First, considering the problems of the small amount of GPR image datasets and the unclear characteristics caused by the complex underground media, a Dense-C3 module is built by utilizing the structure of DenseNet to enhance the network's capability for extracting features. Subsequently, a channel and spatial hybrid attention module is introduced into the backbone for feature refinement and improving the efficiency. Finally, the multi-class focal loss function is devised to enhance the precision in cases of imbalanced sample classes. Experimental results show that the proposed model surpasses the original YOLOv5 model and various contemporary advanced models. © 2023 IEEE.

关键词： Efficiency

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Gesture Recognition Using FMCW Radar Based on Deep Neural Network

Gesture Recognition Using FMCW Radar Based on Deep Neural Ne...

引用

2023 China Automation Congress, CAC 2023

作者： Wang, Rui Xue, Wei Peng, Jiao Yang, Xinya School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9798350303759

In recent years, radar based gesture recognition technology has attracted more and more attention. Among different types of radars, frequency modulated continuous wave (FMCW) radar has the greatest application potential due to its high range and velocity resolution and low cost. However, insufficient gesture information extraction and interference signals affect the performance of FMCW radar gesture recognition. To address the problems, a gesture recognition method based on multi-dimensional features and deep neural network is proposed in this paper. First, the range-time map (RTM) and velocity-time map (VTM) of the gesture are constructed from the range-Doppler map (RDM) with two-dimensional fast Fourier transform (2D-FFT). Then, the angle-time map (ATM) is constructed with the multiple signal classification (MUSIC) algorithm. Subsequently, RTM, VTM and ATM are normalized and adaptively filtered to suppress interference signals, and the three maps are fused to construct the range-velocity-angle-time map (RV ATM) of the gesture. Finally, an improved VGG16 network with self-attention mechanism module is used for feature extraction and recognition. The experimental results show that the proposed method achieves an average accuracy of 98.3% for six gestures, and the improved VGG16 network outperforms other traditional convolutional neural networks. © 2023 IEEE.

关键词： Deep neural networks

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Trajectory-prediction-based Dynamic Tracking of a UGV to a Moving Target under Multi-disturbed Conditions

Trajectory-prediction-based Dynamic Tracking of a UGV to a M...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jinge Si Bin Li Yongkang Xu Liang Wang Chencheng Deng Shoukun Wang Junzheng Wang State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology School of Automation Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Tracking dynamic targets poses a significant challenge for Unmanned Ground Vehicles (UGVs). Existing methods often lack research on multi-disturbed conditions. To address this issue, we propose a trajectory-prediction-based dynamic tracking scheme, which includes target localization, trajectory prediction, and UGV control. Firstly, an estimation algorithm based on the Extended Kalman Filter (EKF) is employed to mitigate noise and estimate the absolute states of the target accurately. To enhance robustness, we present an Adaptive Trajectory Prediction (ATP) algorithm based on prediction anchors. In this method, a quantization standard for trajectory disturbance is designed for adaptive control. Subsequently, we iteratively solve prediction anchor points based on two motion models to robustly predict the target trajectory even in the presence of unknown disturbances. Finally, the Linear Time-Varying Model Predictive control (LTV-MPC) is utilized in the UGV controller for dynamic tracking. Experimental results demonstrate that the ATP exhibits superior prediction robustness and accuracy in perturbed environments compared to other prediction algorithms. In addition, the proposed scheme effectively achieves dynamic tracking of the Unmanned Aerial Vehicle (UAV) by the UGV under multi-disturbed conditions. Specifically, when the target moves at a speed of 1.0 m/s, the UGV can maintain a tracking error within 0.346 m.

关键词： Target tracking Accuracy Heuristic algorithms Noise Prediction algorithms Autonomous aerial vehicles Robustness

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Pre-trained Language Model - Based Real-time Formation Lithology Identification Method for Drilling Process

Pre-trained Language Model - Based Real-time Formation Litho...

引用

2024 China Automation Congress, CAC 2024

作者： Liu, Jianyang Gan, Chao Cao, Weihua 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)9798350368604

Geological exploration is essential for economic and social progress, with lithology identification being a key technology that improves exploration precision and resource development efficiency. This paper presents a real-time identification method of formation lithology in drilling process based on pre-training language model, which is divided into three stages. In this first stage, the drilling data undergoes rigorous cleaning, feature selection, and normalization to eliminate noise and anomalies. In the second stage, key model inputs such as weight on bit (WOB), rate of penetration (ROP), rotation speed (RPM), torque, and pump volume (P) are identified through detailed drilling mechanism analysis. In the last stage, a real-time identification method of formation lithology in drilling process based on pre-training language model is proposed, which realizes high-precision real-time identification in drilling process. The effectiveness of the proposed method is proved by comparison with common methods. © 2024 IEEE.

关键词： Lithology

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The Optimal Duty Cycle Model-Predictive control of PMSM Used for Satellite Payload 1

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17th Annual Conference of China Electrotechnical Society, CES 2022

作者： Li, Jialin Chen, Zhen Hu, Hengzai Liu, Xiangdong Bao, Liang School of Automatic Beijing Institute of Technology Beijing100081 China Key Laboratory of Drive and Control of Servo Motion System Ministry of Industry and Information Technology Beijing100081 China State Key Laboratory of Intelligent Control and Decision for Complex System Beijing100081 China Beijing Aerospace Automatic Control Institute Beijing100854 China

ISBN: (数字)9789819904082

ISBN: (纸本)9789819904075

Model-predictive control is emerging as an advanced control strategy for its feature that the state variables can be predicted according to the math model of the control system. In the PMSM drive application, conventional model-predictive control strategy induces current ripple due to the fact that the optimal voltage vector always acts on the system for the whole control period. In order to restrain the current ripple, the idea of duty cycle control is taken into MPC by inserting a zero vector into the optimal non-zero vector and regulating the duty cycle of non-zero vector. In this paper, the optimal duty cycle FSC-MPC strategy based on two different principles is investigated. Principle A is that the estimation is equal to the reference at the end of control cycle, and principle B is that the area under trace of the estimation is equal to that of reference. The calculation of duty based on the two different principles and the flow chart of the strategy are introduced in detail. Finally, the effectiveness of the optimal duty cycle FSC-MPC is firstly verified by the simulation, and then verified by the experimental results, which is implemented on the PMSM driver of the DQ-1 satellite payload. © 2023, Beijing Paike Culture Commu. Co., Ltd.

关键词： Model predictive control

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Non-Convex Operating Zone Establishment and Alarm Limit Design for Multivariate Alarm systems Based on Grid Clustering 43

Non-Convex Operating Zone Establishment and Alarm Limit Desi...

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43rd Chinese control Conference, CCC 2024

作者： Wei, Yujie Hu, Wenkai Li, Yupeng Cao, Weihua China University of Geosciences School of Automation 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)9789887581581

Alarm systems are core components in large complex industrial facilities to ensure safe and efficient operation. Traditional univariate alarm system design methods ignore the correlation of process variables and thus may generate massive false alarms and missed alarms. An effective way of designing a multivariate alarm system is to establish the normal operating zones (NOZs) based on historical data under normal conditions. These NOZs serve as the basis for setting alarm limits in multivariate alarm systems. Given that the actual correlations between related variables are frequently unknown, a robust modeling approach is necessary to effectively handle both convex and non-convex NOZs. Thus, this paper presents a systematic method for nonconvex operating zone establishment and alarm limit design for multivariate alarm systems. A multivariate non-convex NOZ is established based on grid clustering. Then, the multidimensional grid boundary is extracted and used to determine the alarm limits for real-time samples. A numerical example is used to demonstrate the effectiveness of the proposed method. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Alarm systems

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A Review on 3D Hand Pose and Shape Reconstruction from Color Images 43

A Review on 3D Hand Pose and Shape Reconstruction from Color...

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43rd Chinese control Conference, CCC 2024

作者： Leng, Huajian Ge, Yuting Xu, Chi China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China

ISBN: (纸本)9789887581581

The task of hand reconstruction aims to estimate the shape and pose of hands from the input images. Deep learning algorithms have shown remarkable performance in the field of computer vision in recent years, promoting the development of related algorithms. This article reviews the 3D hand reconstruction technologies, introducing the research background, value, and directions, summarizing the characteristics of datasets and evaluation metrics, and discussing the types of parametric, non-parametric, and joint mesh representation methods;it then outlines research directions in single-hand, hand-object interaction, and two-hand reconstruction, and analyzes their progress. Finally, it summarizes both the hand datasets and the algorithms, and provides a perspective on the future development of hand reconstruction. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： 3D reconstruction

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Multi user computation offloading decision in edge networks 43

Multi user computation offloading decision in edge networks

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43rd Chinese control Conference, CCC 2024

作者： Liu, Chang Yuan, Yan Cao, Weihua Hu, Yabin China University of Geosciences School of Automation 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)9789887581581

Computing offload is a key technology in mobile edge computing (MEC). By offloading tasks to the edge servers, it solves the problem that terminal devices in the edge network cannot handle high computing and time sensitive tasks. However, in multi-user scenarios, the resources of edge servers are very limited, and without good offloading decisions, the performance of MEC systems cannot be guaranteed. In this paper, the computation offloading problem of multi-user MEC system is analyzed and modeled and a batch offloading method is proposed. And in response to the shortcomings of the existing algorithms, a neural network combined with genetic algorithm for offloading decision is proposed. Simulation results show that the proposed algorithm performs better than existing algorithms in terms of time complexity and accuracy. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Computation offloading

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