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Distributed Resource Allocation via Accelerated Saddle Point Dynamics

IEEE/CAA Journal of Automatica Sinica 2021年第9期8卷 1588-1599页

作者： Wen-Ting Lin Yan-Wu Wang Chaojie Li Xinghuo Yu School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074 Key Laboratory of Image Processing and Intelligent Control Ministry of EducationHuazhong University of Science and TechnologyWuhan 430074China School of Electrical Engineering and Telecommunications University of New South WalesSydney NSW 2052Australia School of Engineering Royal Melbourne Institute of Technology(RMIT University)Melbourne VIC 3000Australia

In this paper,accelerated saddle point dynamics is proposed for distributed resource allocation over a multi-agent network,which enables a hyper-exponential convergence ***,an inertial fast-slow dynamical system with vanishing damping is introduced,based on which the distributed saddle point algorithm is *** dual variables are updated in two time scales,i.e.,the fast manifold and the slow *** the fast manifold,the consensus of the Lagrangian multipliers and the tracking of the constraints are pursued by the consensus *** the slow manifold,the updating of the Lagrangian multipliers is accelerated by inertial ***-exponential stability is defined to characterize a faster convergence of our proposed algorithm in comparison with conventional primal-dual algorithms for distributed resource *** simulation of the application in the energy dispatch problem verifies the result,which demonstrates the fast convergence of the proposed saddle point dynamics.

关键词： Accelerated saddle point dynamic distributed resource allocation hyper-exponential stability power dispatch

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Fast Node Selection of Networked Radar Based on Transfer Reinforcement Learning 5th

Fast Node Selection of Networked Radar Based on Transfer R...

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5th IFIP TC 12 International Conference on Intelligence Science, ICIS 2022

作者： Cao, Yanjun Wang, Yuan Guo, Jingjing Han, Li Zhang, Chao Zhu, Jin Zhang, Tianyang Zhang, Xiangrong The 54th Research Institute of China Electronics Technology Group Corporation Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Shaanxi Xian710071 China National Laboratory of Radar Signal Processing Xidian University Xian710071 China

ISBN: (纸本)9783031149023

The networked radar system can synthesize different echo signals received by various radars and realize the cooperative detection of multiple radars, becoming more and more critical for data fusion sharing and network collaboration. However, due to the large number and wide range of nodes in the networked radar system, there exists a redundancy problem in radar node assignment, which causes additional resource consumption and slows down the task execution speed of radar node selection. To solve the above problem, this paper proposes a fast radar node selection method based on transfer reinforcement learning to quickly select the optimal and minimum node resources. The proposed method devises a novel reward function for the Monte Carlo Tree and a different termination state of iteration to select the minimize the number of radar nodes. In order to further accelerate the selection of radar nodes, transfer reinforcement learning is presented to fully leverage the previous knowledge. Experimental results show that our proposed method can quickly select the optimal and minimum radar nodes in a brief period, significantly improving the speed of radar node selection in the networked radar. © 2022, IFIP International Federation for Information processing.

关键词： Reinforcement learning

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Cooperative Target-Surrounding control of Unmanned Surface Vessels Based on MADDPG

Cooperative Target-Surrounding Control of Unmanned Surface V...

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Annual Conference of Industrial Electronics Society

作者： Taoman Li Fei Zhu Zihan Gan Zexing Zhou Xiaotao Wang Renzhi Lu Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China China Ship Scientific Research Center Wuxi China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan China

This article proposes a multi-agent deep reinforce-ment learning algorithm to control a fleet of unmanned surface vessels (USVs) that encircle and capture sea targets. First, a simulation environment for USVs is established based on a dynamic model; two-dimensional control variables are used to control movements in three directions. Second, the multi-agent deep deterministic policy gradient (MADDPG) algorithm is employed to achieve intelligent control of the USVs, using a reward function based on certain prior knowledge. Finally, centralized training and decentralized execution are used to complete the offline learning of multiple agents, and continuous action decisions are made based on the observations of the USV sensors. Simulations demonstrate that the method can capture stationary moving sea targets with any number of multi-agents under disturbances, and exhibits strong robustness and practicality.

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Dynamic Event-Triggered control for Multi-Agent Consensus with Relative Output Feedback

Dynamic Event-Triggered Control for Multi-Agent Consensus wi...

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American control Conference (ACC)

作者： Sikang Zhan Xianwei Li Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper studies the consensus problem of linear multi-agent systems (MASs) with relative output measurement on undirected graphs. Output-feedback protocols are designed for both leaderless and leader-follower consensus. Different from [1], dynamic event-triggered sampling strategies are integrated to reduce the communication burdens between agents. Compared with most of the existing results which are based absolute output, the proposed protocols are based on sampled relative output. Finally, a simulation example is provided to illustrate the theoretical results.

关键词： Protocols Decentralized control Numerical simulation Topology Closed loop systems Output feedback Multi-agent systems

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Research on Crop Disease and Pest Surveillance based on Big Data and image processing Technologies

Research on Crop Disease and Pest Surveillance based on Big ...

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Information Technology, Communication Ecosystem and Management (ITCEM), International Conference on

作者： Yimei Yang Yujun Yang Huixia Shu Changhua Zhao Shaowei Deng College of Computer and Artificial Intelligence(Software College) Huaihua University Huaihua China Hunan Provincial Key Laboratory of Ecological Agriculture Intelligent Control Technology Huaihua China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua Hunan China

ISBN: (数字)9798331542696

ISBN: (纸本)9798331542702

Recently, with the increasing complexity of agricultural systems and climate change, agricultural pest and disease surveillance and early detection have become critical tasks for guaranteeing food stability and promoting sustainable farming growth. Agricultural pest and disease surveillance is a key component of agricultural production, directly affecting crop yield and quality. As big data advances and image processing technologies rapidly, their application in crop pest and disease surveillance has become increasingly widespread. By integrating multi-source data, such as remote sensing data, meteorological data, ecological data, and pest and disease survey data, researchers can build more accurate and efficient pest and disease surveillance models. This paper reviews the research progress of big data and image processing technologies in crop pest and disease surveillance, analyzes the advantages and challenges of current technologies, and proposes future research directions to enhance the automation and intelligence of systems. Additionally, it discusses the integration of deep learning and other techniques to achieve efficient information mining and accurate retrieval of disaster-related data, aiming to provide scientific guidance and technical support for agricultural production.

关键词： Crops Plant diseases Climate change Pest control Agriculture Big Data Surveillance image processing

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ST-Petri: A Visual Executable Semantic Model for PLC Structured Text Language

ST-Petri: A Visual Executable Semantic Model for PLC Structu...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Xiaoyu Hu Yonghui Liang Shibo Zhu Hui Li Shanying Zhu Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

As very important controllers in automated facto-ries, the correctness and safety of Programmable Logic controller (PLC) and their programs determine the stability of the production process. Structured Text (ST) is one of the most widely used PLC programming languages. However, there are not enough formal semantics and different venders may have their own implementations. In this work, a visual executable semantic model ST-Petri for ST has been proposed. The model uses Petri nets to formally represent ST semantics while using graphs to visualise program control flow. To validate the correctness of the semantic model, we use programs from Github and mutations of existing source programs as a test set to execute and compare the results with the open source OpenPLC platform. Experimental results show that ST-Petri has significant advantages in terms of compilation pass rate and error indication as a semantic model and a ST program compiler.

关键词： Visualization Program processors Semantics Programmable logic devices Petri nets Process control Production Stability analysis Safety Software development management

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Personalized Learning Path Recommendation Driven by Multi-Modal Knowledge Graphs and Large Models Collaboration

Personalized Learning Path Recommendation Driven by Multi-Mo...

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Information Technology, Communication Ecosystem and Management (ITCEM), International Conference on

作者： Changhua Zhao Yimei Yang Yujun Yang Bo Tang Chunqiao Mi College of Computer and Artificial Intelligence(Software College) Huaihua University Huaihua China Hunan Provincial Key Laboratory of Ecological Agriculture Intelligent Control Technology Huaihua China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua Hunan China

ISBN: (数字)9798331542696

ISBN: (纸本)9798331542702

This study examines ways to create personalized learning pathways that are influenced by large collaborative models and multi-modal knowledge graphs. By integrating multi-modal data to construct a comprehensive knowledge network, this study utilizes the generative and comprehension capabilities of large models to achieve personalized customization of learning resources. The focus of the research is on accurately depicting learner characteristics through multi-modal profiling techniques, ensuring the transparency and credibility of the content generated by large models. The personalized learning route suggestion model proposed in this paper includes two parts: recommendations for learning paths within courses and recommendations for learning paths within learning tasks, aiming to enhance learning efficiency and outcomes through the collaborative effects of multi-modal knowledge graphs and large models. Experimental results indicate that the learning path recommendation model driven by multi-modal knowledge graphs and large collaborative models performs excellently in improving learning outcomes and recommendation performance.

关键词： Knowledge engineering Analytical models Accuracy Biological system modeling Education Ecosystems Collaboration Knowledge graphs Data models Information technology

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GLAND SEGMENTATION VIA DUAL ENCODERS AND BOUNDARY-ENHANCED ATTENTION

arXiv

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arXiv 2024年

作者： Wang, Huadeng Yu, Jiejiang Li, Bingbing Pan, Xipeng Liu, Zhenbing Lan, Rushi Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Department of Pathology Ganzhou Municipal Hospital Ganzhou China

Accurate and automated gland segmentation on pathological images can assist pathologists in diagnosing the malignancy of colorectal adenocarcinoma. However, due to various gland shapes, severe deformation of malignant glands, and overlapping adhesions between glands. Gland segmentation has always been very challenging. To address these problems, we propose a DEA model. This model consists of two branches: the backbone encoding and decoding network and the local semantic extraction network. The backbone encoding and decoding network extracts advanced Semantic features, uses the proposed feature decoder to restore feature space information, and then enhances the boundary features of the gland through boundary enhancement attention. The local semantic extraction network uses the pre-trained DeepLabv3+ as a Local semantic-guided encoder to realize the extraction of edge features. Experimental results on two public datasets, GlaS and CRAG, confirm that the performance of our method is better than other gland segmentation methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Extraction

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The Deployment of Microservice at Edge based on MQTT for Low Latency

The Deployment of Microservice at Edge based on MQTT for Low...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Mengqi Han Bo Yang Yuxiang Liu Sicheng Liu Qi Liu Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

With the rapid development of smart manufacturing, existing microservice deployment strategies based on the TCP protocol tend to encounter problems such as network latency, excessive header length, and port exhaustion in high concurrency scenarios. MQTT Broker can relay data between microservices, effectively alleviating these issues. However, the communication overhead of the Broker is significantly related to its deployment and the dependency of microservices. An appropriate deployment solution can significantly improve service quality. To address this issue, this paper models the deployment of MQTT Broker and microservices as a graph with cycles. By introducing auxiliary variables, the NP-hard problem is transformed into a mixed-integer quadratic programming problem, improving problem-solving efficiency and optimizing the microservices deployment strategy to reduce communication overhead. Meanwhile, a real-world heterogeneous cluster was constructed to experiment on fault detection applications, elaborating on the superiority of the microservice deployment strategy designed in this paper. Experimental results demonstrate that our approach reduces communication overhead by 20% in comparison to both the greedy strategy and the K8s strategy. This suggests that achieving an optimal balance between the number of Brokers and microservices is vital for ensuring efficient resource utilization and maintaining low communication latency, particularly as the scale of services expands.

关键词： Protocols Fault detection Microservice architectures Transforms Programming Quadratic programming Resource management Problem-solving Relays Smart manufacturing

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Tracing Intricate Cues in Dialogue: Joint Graph Structure and Sentiment Dynamics for Multimodal Emotion Recognition

arXiv

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arXiv 2024年

作者： Li, Jiang Wang, Xiaoping Zeng, Zhigang The School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Institute of Artificial Intelligence Huazhong University of Science and Technology Wuhan430074 China The Hubei Key Laboratory of Brain-Inspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China

Multimodal emotion recognition in conversation (MERC) has garnered substantial research attention recently. Existing MERC methods face several challenges: (1) they fail to fully harness direct inter-modal cues, possibly leading to less-than-thorough cross-modal modeling;(2) they concurrently extract information from the same and different modalities at each network layer, potentially triggering conflicts from the fusion of multi-source data;(3) they lack the agility required to detect dynamic sentimental changes, perhaps resulting in inaccurate classification of utterances with abrupt sentiment shifts. To address these issues, a novel approach named GraphSmile is proposed for tracking intricate emotional cues in multimodal dialogues. GraphSmile comprises two key components, i.e., GSF and SDP modules. GSF ingeniously leverages graph structures to alternately assimilate inter-modal and intra-modal emotional dependencies layer by layer, adequately capturing cross-modal cues while effectively circumventing fusion conflicts. SDP is an auxiliary task to explicitly delineate the sentiment dynamics between utterances, promoting the model’s ability to distinguish sentimental discrepancies. Furthermore, GraphSmile is effortlessly applied to multimodal sentiment analysis in conversation (MSAC), forging a unified multimodal affective model capable of executing MERC and MSAC tasks. Empirical results on multiple benchmarks demonstrate that GraphSmile can handle complex emotional and sentimental patterns, significantly outperforming baseline models. Copyright © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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