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检索条件"机构=School of Automation and the Key Lab of Measurement and Control of Complex Systems of Engineering"

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Adaptive Leader-Following Consensus for a Class of Time Delay Multi-Agent systems 41

Adaptive Leader-Following Consensus for a Class of Time Dela...

引用

第41届中国控制会议

作者： Jiachun Sun Shiqi Zheng Liangguang Li Tao Song Qing Chang 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,we study the leader-following consensus for multi-agent systems with long input time delay under jointly connected directed switching *** adopt an adaptive distributed observer that can make every agent estimate the information of the *** we propose a new controller utilizing the predictor feedback technique,and then map MAS s into ODE-PDE(Ordinary differntial equations-Partial differntial equations) target systems so that we can utilize Lyapunov-Krasovskii function to analyze the stability of the *** are used to vertify the results in the end.

关键词： Multi-agent system distributed observer long input delay directed switching network

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Finite-time stabilization for high-order nonholonomic systems with full state constraints 41

Finite-time stabilization for high-order nonholonomic system...

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第41届中国控制会议

作者： Liangguang Li Shiqi Zheng Jiachun Sun Tao Song Qing Chang 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper studies the finite-time control problem for a class of high-order nonholonomic systems.A new smooth coupling controller is proposed to make the system states converge to zero in *** order to ensure the states constraints of the nonholonomic systems,a new adding a barrier power integrator technique is *** barrier function is integrated into the traditional adding a power integrator ***,the proposed controller is singular-free and can produce smooth control ***,simulations verify the obtained results.

关键词： Finite time nonholonomic system mobile robots constraints

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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

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Global Practical Sampled-data for a Class of Stochastic Nonlinear systems 41

Global Practical Sampled-data for a Class of Stochastic Nonl...

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第41届中国控制会议

作者： Qing Chang Shiqi Zheng Jiachun Sun Tao Song Liangguang Li 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,we mainly study the global tracking problem for stochastic systems by the sampled data state feedback *** the condition of lower-triangular,the sampled data state feedback controller is constructed to make the states globally bounded,and the error between the system output and the reference signal is less than the specified allowable *** the same time stochastic phenomenon is considered in the nonlinear *** global tracking performance and stochastic stability are both achieved simultaneously.

关键词： Stochastic systems nonlinear systems state feedback sampled-data controllers

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A Survey on IMU-and-Vision-based Human Pose Estimation for Rehabilitation 41

A Survey on IMU-and-Vision-based Human Pose Estimation for R...

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第41届中国控制会议

作者： Yuan Niu Jinhua She Chi Xu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the rapid development of human pose estimation,the human-pose-estimation method applied in rehabilitation therapy has attracted more and more attention in today's aging *** them,inertial measurement units(IMU) and computer vision are two significant methods for human pose *** mainly pay attention to the application of these two methods in the rehabilitation therapy *** this paper,we first summarize the research status of IMU and computer vision in the field of rehabilitation ***,we find that combining the two methods can produce better ***,the main problems of the current research and possible development directions are identified.

关键词： Human Pose Estimation IMU Computer Vision Rehabilitation Therapy

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Task Sequence Planning Algorithm for Cooperative Assembly of Dual-arm Robot

Task Sequence Planning Algorithm for Cooperative Assembly of...

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2021 China automation Congress, CAC 2021

作者： Zhang, Jin Gan, Yahui Wang, Zhengwei Dai, Xianzhong School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (纸本)9781665426473

Dual-arm collaborative robots have gradually become a research hotspot in the field of robotics due to their unique collaboration and flexibility, and they have a wide range of application prospects especially in the assembly manufacturing industry. However, the existing dual-arm robot lacks autonomy in assembly operations, especially when faced with a large number of randomly placed assembly parts, how to make the robot have independent assembly task decision-making ability is a challenge. In response to this problem, this paper proposes a new dual-arm robot collaborative assembly task sequence planning algorithm, which fully considers the constraints of the dual-arm robot's mechanical structure, kinematics, characteristics of objects to be assembled, interference between objects, etc.. Firstly, The algorithm uses the Monte Carlo method to analyze the working space of the left and right arms of the dual-arm robot, while taking into account the structural constraints of the robot. Then, according to the characteristics of the object to be assembled, the idea of interference sphere is proposed to analyze the interference between objects in the assembly task. The influence of the operation, and finally combined with the movement cost of the dual-arm assembly, the assembly task sequence is effectively planned. The algorithm was verified by simulation using the ABB YuMi dual-arm collaborative robot, and the experimental results proved the feasibility and effectiveness of the algorithm. © 2021 IEEE

关键词： Monte Carlo methods

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A Two-step Model for Multi-object Tracking 21

A Two-step Model for Multi-object Tracking

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5th International Conference on Computer Science and Application engineering, CSAE 2021

作者： Zhang, Shuaishuai Lu, Xiaobo Du, Songlin Southeast University Key Laboratory of Measurement and Control of Complex Systems of EngineeringMinistry of Education School of Automation Nanjing China

ISBN: (纸本)9781450389853

Multi-object tracking is widely used in video analysis. However, due to the limitation of detector performance, many multi-object tracking models have the problem of detecting two objects into one object in some occlusion scenes. In this paper, we propose a twostep model for handling this problem. In the first step model, the non-occlusion targets are detected and embeddings are extracted, while the occlusion areas are identified. The second step model processes the occlusion areas to obtain occlusion targets' accurate positions and embeddings. Finally, we integrate and optimize the output results of the two steps models. Experiments show that the number of false positives and missed positives in our model's object detection is significantly reduced. The multi-object tracking performance (MOTA metric) is improved by nearly 3% compared with other models.. © 2021 Association for Computing Machinery. All rights reserved.

关键词： Embeddings

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Effective Indoor Fire Detection with Channel Shuffle Module 21

Effective Indoor Fire Detection with Channel Shuffle Module

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5th International Conference on Computer Science and Application engineering, CSAE 2021

作者： Ge, Haotian Cao, Yichao Lu, Xiaobo School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (纸本)9781450389853

In recent years, methods based on computer vision and deep learning become the mainstream approaches in fire detection. However, the expensive computation cost of 3D convolutional neutral network (CNN) is unbearable and it is difficult for them to capture the fire regions of videos in time. In this paper, we design a module named channel shuffle module (CSM) based on 2D CNN to keep the balance between computation cost and accuracy. By fusing RGB frame and differential frame, CSM improves the ability of 2D CNN in temporal information extraction which much less cost than methods based on 3D CNN. Four different structures of CSM are proposed and we choose the best one by experiment results. Also, experiments prove that the performances of TSN and TSM are improved with CSM in sequence classification. The accuracy of TSM with CSM is 99.2045%, false positive rate reaches 0.7890% and false negative rate reaches 0.4530%, which demonstrates the efficiency of CSM in temporal feature modeling.. © 2021 Association for Computing Machinery. All rights reserved.

关键词： Fires

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Multi Scale Attention Network for Crowd Counting 21

Multi Scale Attention Network for Crowd Counting

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5th International Conference on Computer Science and Application engineering, CSAE 2021

作者： Yang, Xiangpeng Lu, Xiaobo Key Laboratory of Measurement and Control of Complex Systems of Engineering School of Automation Southeast University Ministry of Education Nanjing China

ISBN: (纸本)9781450389853

Reasonable management and control of extra crowded scenes have become a hot topic in recent years. Counting people from density map generated from the object location annotations is an effective way to analyze crowd information and control crowds in severely congested scenes. In this paper, we propose a novel endto- end crowd counting method called MSANet for crowd counting. MSANet consists of the VGG16 backbone as the fronted part, two branches as the back-end part, including the attention map extractor to predict crowd states (means with people or not), and density map branch to regress the density map. What is more, to obtain high-resolution density map, we combine different scale maps from the front part to the back-end part. On the design of the loss function, to enhance the resolution of the predicted map and its structural similarity to ground truth, we proposed a new loss function for crowd counting. The test result based on the public dataset ShanghaiTech and Subway Crowd Counting Dataset supported by the Nanjing Metro demonstrates the effectiveness of our method.. © 2021 Association for Computing Machinery. All rights reserved.

关键词： Statistical tests

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DeepTQ: Predictive TSN Switch Queue Length Based on Deep Learning 1

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Jia, Yanxin Xu, Long Xiong, Wei Wang, Xin Shang, Zhijun Yuan, Zijun 3onedata Co. Ltd. Shenzhen China Ministry of Education School of Automation and the Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Nanjing China 3onedata Qitong Co. Ltd. Shanghai China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9789819733361

ISBN: (纸本)9789819733354

With the rapid development of network technology, TSN (Time-Sensitive Networking) is in the stage of rapid development. It needs to ensure the deterministic transmission of time-sensitive flow, further improve the network throughput, and meet the increasing traffic demand. SDN (Software-Defined Networking) can realize the automatic management of network resources by decoupling the control plane and the data plane. Through global network information, it uses efficient route scheduling to achieve load balancing and improve network throughput. However, the prerequisite for route scheduling is that accurately predict the queue length of TSN switch, so that route scheduling can be carried out according to the predicted results. To address the queue length prediction problem of TSN switch, DeepTQ (Predictive TSN Switch Queue Length Based on Deep Learning) is proposed, which predicts the queue length of TSN switches in the next time slot using deep learning methods. It consists of the feature selection phase and the model prediction phase. In DeepTQ, the feature selection phase uses the Spearman rank correlation coefficient to remove redundant features in the datasets. The model prediction phase uses an improved Transformer Encoder and GRU (Gate Recurrent Unit) to construct the prediction model. Simulation results demonstrate that DeepTQ achieves superior performance compared to existing methods and individual components contribute to its overall effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Software defined networking

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