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2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms, PRMVIA 2023

作者： Wang, Yufei Xiao, Baihua Chinese Academy of Sciences University of Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China

ISBN: (纸本)9798350346596

Deep convection can cause a variety of severe weather conditions such as thunderstorms, strong winds, and heavy rainfall. Satellite observations provide all-weather and multi-directional observations, facilitating the timely detection of such weather systems, which is crucial to saving lives and property. However, previous methods based on channel feature extraction and threshold filtering did not make full use of information in satellite images, which led to limitations on such complex problems as strong convection detection. In this study, we propose a novel framework of a deep learning-based model Convection-UNet to detect convection. We use channel 4 to 7 of FY-4B GHI that we select according to the microphysical properties of convection as input and radar reflectivity as label. We combine the detailed training time and test time data augmentation strategies and build a deep neural network to automatically extract spatial context features and achieve end-to-end learning. Results show that the performance of our method far exceeds the previous channel extraction combined with threshold filtering methods such as BT and BTD at least 0.24 on Fi-measure. We also show that our channel selection and data augmentation strategies are of great significance to detect convection. © 2023 IEEE.

关键词： Feature extraction

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Social Radars: Finding Targets in Cyberspace for Cybersecurity

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 279-282页

作者： Lili Fan Changxian Zeng Yutong Wang Jiaqi Ma Fei-Yue Wang School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Science Dalian Minzu UniversityDalian 116600China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Department of Civil and Environmental Engineering University of CaliforniaLos AngelesCA 90024 USA IEEE State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

INSPIRED by the insight from American political scientist Lasswell, who summarized the environmental role in societal surveillance [1], Schramm coined the term “social radar” [2] as it resembles the activities of radar in collecting and processing information, playing a crucial role in helping humans perceive changes in the internal and external environment and promptly adjusting adaptive behaviors.

关键词： prompt summarized insight

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Observer-based event-triggered formation tracking control for second-order multi-agent systems in constrained region

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Science China(Information Sciences) 2025年第2期68卷 151-169页

作者： Fenglan SUN Zhonghua XU Wei ZHU ■ KURTHS Key Lab of Intelligent Analysis and Decision on Complex Systems School of ScienceChongqing University of Posts and Telecommunications Key Lab of Intelligent Air-Ground Cooperative Control for Universities in Chongqing College of AutomationChongqing University of Posts and Telecommunications Department of Complexity Science Potsdam Institute for Climate Impact Research Institute of Physics Humboldt University of Berlin

In this paper, an event-triggered time-varying formation tracking control for a class of second-order nonlinear multiagent systems(MAS) operating within a constrained region is investigated. To mitigate the negative effects of external unknown disturbance, a novel disturbance observer with performance guarantees is proposed, enabling precise disturbance *** the artificial potential field(APF) method, a repulsive potential function is introduced to prevent inter-agent collisions as well as collisions with environmental obstacles. To reduce continuous communication and frequent system updates, a sliding mode technique is incorporated into the formation tracking controller, utilizing an event-triggered mechanism. The controller is also applicable to the formation control of MAS in switching-constrained regions. The achievement of the specified timevarying geometric formation is rigorously demonstrated through the Lyapunov framework. Numerical simulations are presented to validate the effectiveness of the theoretical results.

关键词： formation tracking control region constraints repulsive potential function event-triggered mechanism

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Parallel Vision ■ Image Synthesis/Augmentation

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 782-784页

作者： Wenwen Zhang Wenbo Zheng Qiang Li Fei-Yue Wang the School of Computer Science Shaanxi Normal UniversityXi’an 710119China the School of Computer Science and Technology Wuhan 430070China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Dear Editor,Scene understanding is an essential task in computer *** ultimate objective of scene understanding is to instruct computers to understand and reason about the scenes as humans *** vision is a research fram... 详细信息

关键词： instru explanation computer

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Attention Markets of Blockchain-Based Decentralized Autonomous Organizations

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1370-1380页

作者： Juanjuan Li Rui Qin Sangtian Guan Wenwen Ding Fei Lin Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China IEEE

The attention is a scarce resource in decentralized autonomous organizations(DAOs),as their self-governance relies heavily on the attention-intensive decision-making process of“proposal and voting”.To prevent the negative effects of pro-posers’attention-capturing strategies that contribute to the“tragedy of the commons”and ensure an efficient distribution of attention among multiple proposals,it is necessary to establish a market-driven allocation scheme for DAOs’***,the Harberger tax-based attention markets are designed to facilitate its allocation via continuous and automated trading,where the individualized Harberger tax rate(HTR)determined by the pro-posers’reputation is ***,the Stackelberg game model is formulated in these markets,casting attention to owners in the role of leaders and other competitive proposers as *** equilibrium trading strategies are also discussed to unravel the intricate dynamics of attention ***,utilizing the single-round Stackelberg game as an illustrative example,the existence of Nash equilibrium trading strategies is ***,the impact of individualized HTR on trading strategies is investigated,and results suggest that it has a negative correlation with leaders’self-accessed prices and ownership duration,but its effect on their revenues varies under different *** study is expected to provide valuable insights into leveraging attention resources to improve DAOs’governance and decision-making process.

关键词： Attention decentralized autonomous organizations Harberger tax Stackelberg game.

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Blind Image Despeckling Using a Multiscale Attention-Guided Neural Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 205-216页

作者： Guo, Yu Lu, Yuxu Liu, Ryan Wen Zhu, Fenghua Wuhan University of Technology School of Navigation Wuhan430063 China Wuhan University of Technology Chongqing Research Institute Chongqing401151 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China

Coherent imaging systems have been applied in the detection of target of interest, natural resource exploration, ailment diagnosis, etc. However, it is easy to generate speckle-degraded images due to the coherent interference of reflected echoes, restricting these practical applications. Speckle noise is a granular interference that affects the observed reflectivity. It is often modeled as multiplicative noise with a negative exponential distribution. This nonlinear property makes despeckling of imaging data an intractable problem. To enhance the despeckling performance, we propose to blindly remove speckle noise using an intelligent computing-enabled multiscale attention-guided neural network (termed MSANN). In particular, we first introduce the logarithmic transformation to convert the multiplicative speckle noise model to an additive version. Our MSANN, essentially a feature pyramid network, is then exploited to restore degraded images in the logarithmic domain. To enhance the generalization ability of the MSANN, a multiscale feature enhancement attention module is incorporated into the MSANN to extract multiscale features for improving the imaging quality. A multiscale mixed loss function is further presented to increase the network robustness during training. The final despeckled images are naturally equivalent to the exponential versions of the output of the MSANN. Experimental results have shown that the MSANN has the capacity of effectively removing the speckle noise while preserving essential structures. It can achieve superior despeckling results in terms of visual quality and quantitative measures. © 2020 IEEE.

关键词： Additives

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The TAO of Blockchain Intelligence for Intelligent Web 3.0

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IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2183-2186页

作者： Juanjuan Li Fei-Yue Wang IEEE the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

WE are in an exciting new intelligent era where various Web 3.0 systems emerge and flourish.[1]–[3].In this new epoch,the collaboration of data and knowledge,humans and machines,actual and virtual worlds is undergoing an unprecedented diversification and community-driven transformation,unveiling an open future full of boundless ***,the value of dispersed data extends far beyond passive storage and application.

关键词： Web TAO knowledge

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Open Set Domain Adaptation with Zero-shot Learning on Graph 23

Open Set Domain Adaptation with Zero-shot Learning on Graph

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4th International Conference on Computing, Networks and Internet of Things, CNIOT 2023

作者： Zhang, Xinyue Yang, Xu Liu, Zhiyong School of Artificial Intelligence University of Chinese Academy of Sciences China and State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400700705

Open set domain adaptation focuses on transferring the information from a richly labeled domain called source domain to a scarcely labeled domain called target domain, while classifying the unseen target samples as one unknown class in an unsupervised way. Compared with the close set domain adaptation, where the source domain and the target domain share the same class space, the classification of the unknown class makes it easy to adapt to the real environment. Particularly, after the recognition of the unknown samples, the model can either ask for manually labeling or further develop the classification ability of the unknown classes based on pre-stored knowledge. Inspired by this idea, we propose a model for open set domain adaptation with zero-shot learning on the unknown classes in this paper. We utilize adversarial learning to align the two domains while rejecting the unknown classes. Then the knowledge graph is introduced to generate the classifiers for the unknown classes with the employment of the graph convolution network (GCN). Thus the classification ability of the source domain is transferred to the target domain, and the model can distinguish the unknown classes in detail with prior knowledge. We evaluate our model on digits datasets and the result shows superior performance. © 2023 Owner/Author.

关键词： Zero-shot learning

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Vision-based control in the open racing car simulator with deep and reinforcement learning

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 15673-15685页

作者： Zhu, Yuanheng Zhao, Dongbin State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

With decades of development, computer intelligence has now reached a really high level. Especially deep learning (DL) and reinforcement learning (RL) endow computers the perception and decision abilities. This paper aims to design a vision-based system that is able to play The Open Racing Car Simulator (TORCS) like a human player that uses images. With the DL-trained perception module, useful and low-dimensional information is extracted from first-person images. Based on that, the RL-trained module further manipulates the simulated car in the middle of the lane. The two modules are separately trained, and both DL and RL advantages are maximally utilized. Experiments on different tracks show the promising performance of the method. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Reinforcement learning

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Photoelectric Intelligent Detection Platform Based on Multifunctional Edge Computing Equipment 43

Photoelectric Intelligent Detection Platform Based on Multif...

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43rd Chinese control Conference, CCC 2024

作者： Xu, Qianjing Chen, Wenjie Xue, Zhijun State Key Lab of Intelligent Control and Decision of Complex Systems Beijing10081 China Beijing Institute of Technology School of Automation Beijing100081 China

ISBN: (纸本)9789887581581

In order to study to design a photoelectric intelligent platform based on multifunctional edge computing equipment by using photoelectric detection equipment TC505C, RK-Series Development Kits computing platform and Nvidia Jetson Series. The photoelectric intelligent platform can be used to run multiple target detection algorithms after design and implementation. Completed as follows. First of all, after the first stage of the photoelectric intelligent platform is completed, the host computer can establish communication with the photoelectric detection equipment, carry out automatic or manual servo control on the photoelectric detection equipment, switch its visible and infrared field of view and view it in real time through the monitor, and realize simple tracking function through the image processing box. Secondly, after the second stage of the photoelectric intelligent platform is built, under the premise of ensuring the normal availability of servo function, a variety of target detection algorithms can be debugged on multifunctional edge computing equipment. Overall, this paper uses the modular design concept to design, debug and implement the system in stages. Pay attention to the synchronous debugging of hardware and software functions of each module to ensure the stability of each module. Finally completed the design goals of each stage. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Program debugging

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