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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Dong, Huijie Zhao, Chunyang Qiao, Tiezhu Wang, Jian Chen, Di Meng, Yan Yu, Junzhi Taiyuan University of Technology Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan030024 China Chinese Academy of Sciences Laboratory of Cognitive and Decision Intelligence for Complex System Institute of Automation Beijing100190 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Guangdong Ocean University College of Electronics and Information Engineering Zhanjiang524088 China

ISBN: (纸本)9798350327182

Underwater supporting robots serving as a relay of energy supplements and communication for other underwater equipment are promising for ocean exploration, development, and protection. This paper proposes a novel autonomous docking system centered on a designed supporting robotic fish named 'CourierFish'. Specifically, CourierFish is capable of docking with a surface dock station for supplying itself and docking with a seafloor platform for supporting equipment in the platform. A visual navigation scheme integrating LED and ArUco markers is presented for accurate localization. The control approach for docking motion is also illustrated. Simulations and aquatic experiments are performed to verify the feasibility of the proposed docking system. The obtained results lay a solid foundation for the construction of various underwater equipment and robot networks. © 2023 IEEE.

关键词： Fish

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Fault detection method of photovoltaic system based on power change of array 4

Fault detection method of photovoltaic system based on power...

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4th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2021

作者： Tan, Chao Li, Chenxi Zhang, Kanjian Wei, Haikun Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China

ISBN: (纸本)9781450385053

The fault detection of photovoltaic power generation system is of great significance in photovoltaic plant management. The conventional fault detection method of photovoltaic power system requires additional sensors, and the fault detection scheme needs to be redesigned if the parameters of the photovoltaic plant change. Therefore, for large photovoltaic plants, it is necessary to develop a method that does not require the use of external sensors or the detailed parameters of the photovoltaic plant. This paper proposes a photovoltaic system fault detection method based on photovoltaic array current and voltage data. This method does not require additional sensor data such as irradiance and temperature. The failure of photovoltaic system can be detected through the change of power, and the abnormal situation of photovoltaic power station can still be detected under cloudy and shaded conditions. The advantages of this method are that there is no need for complex and diverse sensor equipment, the calculation is simple and the fault detection ability is accurate. The experimental data of Padang Poverty Power Station of State Grid has been verified. © 2021 ACM.

关键词： Fault detection

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Optimization of Wide-Baseline Stereo Matching Based on Multi-View Geometry

Optimization of Wide-Baseline Stereo Matching Based on Multi...

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Chinese Automation Congress (CAC)

作者： Yinhui Xu Shuo Shan Gongben Han Shixiong Fang Kanjian Zhang Haikun Wei Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China Shenzhen Research Institute Southeast University Shenzhen China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Stereo vision plays a crucial role in scientific expeditions and facility maintenance. However, fixed-baseline stereo vision lacks accuracy for long-distance measurements, while wide-baseline stereo vision encounters occlusion issues at short distances. In this study, a wide-baseline stereo matching based on multi-view geometric constraints is proposed. Initially, the camera poses of multiple wide-baseline images are estimated, followed by image rectification and depth estimation. Subsequently, the disparity searching is constrained using geometric relationships among the views, resulting in more precise depth maps. Experiments were conducted to verify the superiority of the proposed method. The results indicate that the proposed method achieves an RMSE as low as 0.15 meters, even with invalid depth points. Compared to traditional stereo vision methods, accuracy is improved by approximately 35%, and robustness is significantly enhanced. In the aircraft runway scenario, overall depth map accuracy is higher, and occluded areas are significantly reduced.

关键词： Geometry Meters Accuracy Depth measurement Cameras Robustness Real-time systems Stereo vision Maintenance Optimization

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MALT: Multi-scale Action Learning Transformer for Online Action Detection

MALT: Multi-scale Action Learning Transformer for Online Act...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhipeng Yang Ruoyu Wang Yang Tan Liping Xie The Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China Southeast University Shenzhen Research Institute Shenzhen China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Online action detection (OAD) aims to identify ongoing actions from streaming video in real-time, without access to future frames. Since these actions manifest at varying scales of granularity, ranging from coarse to fine, projecting an entire set of action frames to a single latent encoding may result in a lack of local information, necessitating the acquisition of action features across multiple scales. In this paper, we propose a multi-scale action learning transformer (MALT), which includes a novel recurrent decoder (used for feature fusion) that includes fewer parameters and can be trained more efficiently. A hierarchical encoder with multiple encoding branches is further proposed to capture multi-scale action features. The output from the preceding branch is then incrementally input to the subsequent branch as part of a cross-attention calculation. In this way, output features transition from coarse to fine as the branches deepen. We also introduce an explicit frame scoring mechanism employing sparse attention, which filters irrelevant frames more efficiently, without requiring an additional network. The proposed method achieved state-of-the-art performance on two benchmark datasets (THUMOS’14 and TVSeries), outperforming all existing models used for comparison, with an mAP of 0.2% for THUMOS’14 and an mcAP of 0.1% for TVseries.

关键词： Filters Neural networks Streaming media Transformers Feature extraction Encoding Real-time systems

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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...

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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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Smoky Vehicle Detection Based on Improved Vision Transformer 21

Smoky Vehicle Detection Based on Improved Vision Transformer

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

作者： Yuan, Li Tong, Shuzhen Lu, Xiaobo School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (纸本)9781450389853

The harmful exhaust emissions of fuel vehicles in theworld are damaging to human health and the environment, thus detecting smoky vehicles from real road environment is significant. At present, methods of smoky vehicle detection based on deep learning have the problem of high false-positive rate. To improve the performance, a two-stage video smoky vehicle detection algorithm based on the smoke classification in the core region from detected vehicle object boxes is proposed in this paper. Specifically, the vehicle object detection is realized by the algorithm based on YOLOv3. The smoke classification is realized by combining Vision Transformer and distillation, and the loss function is optimized in the training process. Experimental results on our smoky vehicle dataset have shown that the improved model achieves an F1 score over 0.4, precision over 0.4, recall nearly 0.1 improvement compared with the basic model, which can effectively reduce the false-positive rate during detection.. © 2021 Association for Computing Machinery. All rights reserved.

关键词： Smoke

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Stabilization of Port-controlled Hamiltonian systems subject to exogenous disturbances via compensation control approach

Stabilization of Port-Controlled Hamiltonian systems subject...

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Data Driven control and Learning systems (DDCLS)

作者： Baozeng Fu Qingzhi Wang Yongchao Liu Shandong Key Laboratory of Industrial Control Technology School of Automation Qingdao University Qingdao China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China

The stabilization of Port-controlled Hamiltonian (PCH) systems under exogenous disturbances by designing a compensation control approach is studied in the paper. Firstly, a baseline feedback control is proposed utilizing the damping injection method. To estimate disturbances, then a novel exogenous disturbance observer is constructed, based on which a feedforward compensation control is developed. Next, a composite control law is proposed based on the feedforward compensation and the feedback control. For the Hamiltonian system, an asymptotic stability analysis is followed finally. The feasibility of the compensation control strategy is revealed by a numerical simulation example.

关键词：

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A compensation method for the packet loss deviation in system identification with event-triggered binary-valued observations

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Science China(Information Sciences) 2020年第12期63卷 266-268页

作者： Jing-Dong DIAO Jin GUO Changyin SUN School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation Southeast University Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education

Dear editor,With the rapid development of network and communication technology, group systems interrelated in terms of both time and space are commonly encountered, such as sensor networks, multi-agent systems, and sm... 详细信息

关键词： system identification event-triggered binary-valued observations packet loss convergence

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Robust control Analysis With Model Transformation for Interval Type-2 Fuzzy systems

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IEEE Transactions on Fuzzy systems 2025年

作者： Yang, Jie Gai, Shaoyan Da, Feipeng Xie, Wenbo Southeast University School of Automation Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing210096 China Shanghai University and Shanghai Artificial Intelligence Laboratory School of Mechatronic Engineering and Automation Shanghai200444 China

To address the problem of robust stability analysis for interval type-2 fuzzy systems (IT2FSs), this paper proposes an innovative analysis approach based on model transformation. Firstly, a classical piecewise linear approximation method is utilized to process the upper boundary membership functions (UMFs) and lower boundary membership functions (LMFs) of the footprint of uncertainty (FOU) in IT2FSs, resulting in linear boundary membership functions (MFs) that are more convenient for analysis, along with the corresponding approximation error functions. Subsequently, a novel error model transformation method is introduced to handle these error terms. By constructing new fuzzy rules and MFs, the boundary error terms are converted into a new fuzzy model, thereby incorporating more information about the error functions into the stability analysis. Based on this model, a robust stability condition in the form of linear matrix inequalities (LMIs) are derived, achieving improved robustness. Finally, the effectiveness of the proposed method is validated through simulations on real-world systems, and its superiority is demonstrated by comparison with existing methods. © 2025 IEEE.

关键词： Membership functions

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A Delay-free control Method Based On Function Approximation And Broadcast For Robotic Surface And Multiactuator systems

arXiv

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arXiv 2024年

作者： Zhao, Yuchen School of Automation Southeast University Jiangsu Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Jiangsu Nanjing210096 China

Robotic surface consisting of many actuators can change shape to perform tasks, such as facilitating human-machine interactions and transporting objects. Increasing the number of actuators can enhance the robot's capacity, but controlling them requires communication bandwidth to increase equally in order to avoid time delays. We propose a novel control method that has constant time delays no matter how many actuators are in the robot. Having a distributed nature, the method first approximates target shapes, then broadcasts the approximation coefficients to the actuators, and relies on themselves to compute the inputs. We build a robotic pin array and measure the time delay as a function of the number of actuators to confirm the system size-independent scaling behavior. The shape-changing ability is achieved based on function approximation algorithms, i.e. discrete cosine transform or matching pursuit. We perform experiments to approximate target shapes and make quantitative comparison with those obtained from standard sequential control method. A good agreement between the experiments and theoretical predictions is achieved, and our method is more efficient in the sense that it requires less control messages to generate shapes with the same accuracy. Our method is also capable of dynamic tasks such as object manipulation. © 2024, CC BY.

关键词： Time delay

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