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Metaverse Computing, Networking and Applications (MetaCom), IEEE International Conference on

作者： Yuna Jiang Jiawen Kang Dusit Niyato Xiaohu Ge Zehui Xiong Minrui Xu Ruilong Deng School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China School of Automation Guangdong University of Technology Guangzhou China School of Computer Science and Engineering Nanyang Technological University Singapore Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

The metaverse is defined as a three-dimensional virtual-real fusion network focused on social connection. Edge computing can empower the metaverse by providing computing resources to realize real-time motion tracking, visual or auditory rendering and virtual scene dynamic change at the edge of networks. Users enjoy the immersive experience provided by the Metaverse Service Provider (MSP), and interact through digital avatars in the virtual world by requesting metaverse services. In this paper, we formulate the edge computing resources pricing, metaverse service rendering bitrate pricing and request problem in the metaverse. Especially, we model the interactions among edge servers, the MSP and Metaverse Service Users (MSUs) as a hierarchical three-stage Stackelberg game. The problem of joining edge-enabled metaverse services is also studied with the consideration of network externality, including the network effect and congestion effect. Based on the backward induction method, we derive the optimal computing resources pricing of edge servers, rendering bitrate pricing of the MSP, and rendering bitrate requests of MSUs. We also prove the existence and uniqueness of the Stackelberg game equilibrium. Simulation results show that the network effect can bring more positive utilities to entities when the social tie coefficient among MSUs is higher. Besides, all entities in the edge-enabled metaverse are affected negatively by the congestion effect.

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A Learning-based Adaptive Compliance Method for Symmetric Bi-manual Manipulation

arXiv

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arXiv 2023年

作者： Cao, Yuxue Zhao, Wenbo Wang, Shengjie Zheng, Xiang Ma, Wenke Wang, Zhaolei Zhang, Tao Beijing Institute of Control Engineering China Department of Automation Tsinghua University China Institute for Interdisciplinary Information Sciences Tsinghua University China Department of Computer Science City University of Hong Kong Hong Kong Qian Xuesen Laboratory of Space Technology China Beijing Aerospace Automatic Control Institute China

Symmetric bi-manual manipulation is an essential skill in on-orbit operationsdue to its potent load capacity. Previous works have applied compliant controlto maintain the stability of manipulations. However, traditional methods haveviewed motion planning and compliant control as two separate modules, which canlead to conflicts with the simultaneous change of the desired trajectory andimpedance parameters in the presence of external forces and ***, the joint usage of these two modules requires experts to manuallyadjust parameters. To achieve high efficiency while enhancing adaptability, wepropose a novel Learning-based Adaptive Compliance algorithm (LAC) thatimproves the efficiency and robustness of symmetric bi-manual ***, the algorithm framework integrates desired trajectory generationand impedance-parameter adjustment under a unified framework to mitigatecontradictions and improve efficiency. Second, we introduce a centralizedActor-Critic framework with LSTM networks preprocessing the force states,enhancing the synchronization of bi-manual manipulation. When evaluated indual-arm peg-in-hole assembly experiments, our method outperforms baselinealgorithms in terms of optimality and robustness. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

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A Self-Organized Constrained Flocking Protocol Design Subject to Sensor Detection Failure

A Self-Organized Constrained Flocking Protocol Design Subjec...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Xiongjun Wu Man Wang Xiangyu Meng National Defense Key Laboratory of Science and Technology on Electromagnetic Scattering The 802 Institute of Shanghai Academy of Space Flight Technology The Eighth Academy of China Aerospace Science and Technology Corporation Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai China Division of Electrical and Computer Engineering Louisiana State University Baton Rouge United States of America

This paper proposes a constrained flocking algorithm to achieve the velocity consensus of multiple agents subject to exogenous disturbances. The Reynolds boids model is adapted to capture the dynamic behavior of agents in flocks. Both collision avoidance and disturbance attenuation are considered in the flocking problem. The collision avoidance is designed to prevent a crash with static obstacles and among agents. The disturbance attenuation is solved by optimizing the energy-to-energy system gain. Stability and system gain conditions are provided in the form of linear matrix inequalities by exploiting the dissipative property of an energy storage function. Illustrative examples of no-fly zone simulations are presented to verify the effectiveness of the suggested approach, as well as the control protocol's feasibility.

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On deployment problem of multi-agent systems with delays

On deployment problem of multi-agent systems with delays

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International Conference on Control, automation, Robotics and Vision (ICARCV)

作者： Katarzyna Topolewicz Carlos E. T. Dórea Sorin Olaru Ewa Girejko Faculty of Computer Science Bialystok University of Technology Białystok Poland Department of Computer Engineering and Automation Universidade Federal do Rio Grande do Norte UFRN-CT-DCA Natal RN Brazil Laboratory of Signals and Systems CentraleSupelec Univesity Paris-Saclay France

ISBN: (纸本)9781665476881

The paper analyzes the decentralized deployment policies for scalar multi-agent systems subject to delays. As a main contribution we obtain explicit conditions on the local feedback gains at the level of subsystems, which ensure safe deployment independent of the delays on the measurement of the positions of the neighboring agents. From the theoretic point of view, positive invariance of sets is used as a main concept for the proofs of the results.

关键词： Sufficient conditions Decentralized control Position measurement Gain measurement Robustness Delays Safety

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Slicing4Meta: An Intelligent Integration Framework with Multi-dimensional Network Resources for Metaverse-as-a-Service in Web 3.0

arXiv

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arXiv 2022年

作者： Liu, Yi-Jing Du, Hongyang Niyato, Dusit Feng, Gang Kang, Jiawen Xiong, Zehui The School of Computer Science and Engineering Nanyang Technological University Singapore The National Laboratory of Communications University of Electronic Science and Technology of China China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

As the next-generation Internet paradigm, Web 3.0 encapsulates the expectations of user-centric immersion and interaction experiences in a decentralized manner. Metaverse, a virtual world interacting with the physical world, is becoming one of the most potential technology to push forward with Web 3.0. In the Metaverse, users expect to tailor immersive and interactive experiences by customizing real-time Metaverse services (e.g., augmented/virtual reality and digital twin) in the three-dimensional virtual world. Nevertheless, there are still no unified solutions for the Metaverse services in terms of orchestration and management. It is calling for a continuous and seamless evolution of mobile systems to support Metaverse services, and thus bring Metaverse into reality. In this paper, to provide scalable subscription solutions for Metaverse services, we propose a new concept, named Metaverse-as-a-Service (MaaS), in which various physical-virtual components and technologies in the Metaverse can be delivered as services. Furthermore, to unify the orchestration and management of MaaS, we propose a novel framework, called Slicing4Meta, to customize Metaverse services by intelligently integrating MaaS models and the associated multi-dimensional resources on the components and technologies. Additionally, we propose the classification for typical Metaverse services based on the quality of experience (QoE) requirements and illustrate how to fulfill the QoE requirements under the Slicing4Meta framework. We then illustrate a virtual travel study case, in which we examine the relationship between the QoE and the multi-dimensional resources by quantitatively modeling the QoE of Metaverse users. Finally, we discuss some open challenges of Slicing4Meta and propose potential solutions to address the challenges. © 2022, CC BY.

关键词： Virtual reality

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Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach

arXiv

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arXiv 2024年

作者： Su, Cheng Wen, Jinbo Kang, Jiawen Wang, Yonghua Su, Yuanjia Pan, Hudan Zhong, Zishao Hossain, M. Shamim School of Automation The GuangdongHongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology Guangzhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Automation The Guangdong Basic Research Center of Excellence for Ecological Security and Green Development Key Laboratory for City Cluster Environmental Safety Green Development The Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation The Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangdong University of Technology Guangzhou510006 China Guangzhou University of Traditional Chinese Medicine State Key Laboratory of Traditional Chinese Medicine Syndrome The Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh12372 Saudi Arabia

Secure data management and effective data sharing have become paramount in the rapidly evolving healthcare landscape, especially with the growing integration of the Internet of Medical Things (IoMT). The rise of generative artificial intelligence has further elevated Multi-modal Large Language Models (MLLMs) as essential tools for managing and optimizing healthcare data in IoMT. MLLMs can support multi-modal inputs and generate diverse types of content by leveraging large-scale training on vast amounts of multi-modal data. However, critical challenges persist in developing medical MLLMs, including security and freshness issues of healthcare data, affecting the output quality of MLLMs. To this end, in this paper, we propose a hybrid Retrieval-Augmented Generation (RAG)-empowered medical MLLM framework for healthcare data management. This framework leverages a hierarchical cross-chain architecture to facilitate secure data training. Moreover, it enhances the output quality of MLLMs through hybrid RAG, which employs multi-modal metrics to filter various unimodal RAG results and incorporates these retrieval results as additional inputs to MLLMs. Additionally, we employ age of information to indirectly evaluate the data freshness impact of MLLMs and utilize contract theory to incentivize healthcare data holders to share their fresh data, mitigating information asymmetry during data sharing. Finally, we utilize a generative diffusion model-based deep reinforcement learning algorithm to identify the optimal contract for efficient data sharing. Numerical results demonstrate the effectiveness of the proposed schemes, which achieve secure and efficient healthcare data management. Copyright © 2024, The Authors. All rights reserved.

关键词： Information management

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Towards Energy-Efficiency: Integrating MATD3 Reinforcement Learning Method for Computational Offloading in RIS-Aided UAV-MEC Environments

引用

IEEE Internet of Things Journal 2025年

作者： Wu, Liangshun Zhang, Cong Zhang, Bin Du, Jianbo Qu, Junsuo Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai201210 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Xi'an Key Laboratory of Advanced Control and Intelligent Process School of Automation Xi'an University of Posts & Telecommunications Xi'an710061 China Xinjiang University of Political Science and Law Tumushuke832003 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China Department of Computer City University of Hong Kong 999077 Hong Kong School of Communications and Information Engineering Xi'an University of Posts & Telecommunications Xi'an710061 China

With the proliferation of IoT devices, there is an escalating demand for enhanced computing and communication capabilities. Mobile Edge Computing (MEC) addresses this need by relocating computing resources to the network edge, thereby delivering swifter and more efficient services. This paper introduces a computation offloading and energy consumption optimization framework that leverages Reconfigurable Intelligent Surfaces (RIS), Unmanned Aerial Vehicles (UAVs), and MEC. The scheme aims to maximize energy efficiency through the optimization of task allocation, RIS phase shifts, and UAV trajectories. By employing the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) reinforcement learning algorithm, the paper further refines UAV trajectories and RIS configurations. The simulation results indicate that the proposed method surpasses the traditional Concave-Convex Procedure (CCCP) algorithm in both UAV trajectory control and RIS configuration, demonstrating quicker convergence and enhanced stability. The method proves to be adaptable to diverse environments and tasks, showcasing notable benefits in RIS-assisted interference suppression, particularly with large RIS, thereby enhancing UAV data reception rates. Additionally, MATD3 exhibits faster and smoother convergence for extended task durations and smaller RIS scenarios. Simulation results reveal that UAVs tend to move closer to RIS, with energy efficiency falling as IoT tasks increase, affirming the proposed algorithm's high energy efficiency and effectiveness. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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GF-2 Image Blue Roof Building Extraction Method Based on Object-Oriented Classification Technology 1

引用

6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Zhang, Dezheng Yin, Xinyu Xie, Yonghong He, Zheyu Wang, Li School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (数字)9789811580833

ISBN: (纸本)9789811580826

Remote sensing image classification method is the focus of current academic field. Most studies use complex machine learning and deep learning methods to extract ground object, but lack simple and effective methods to extract ground object with distinctive features. In this paper, using rule-based object-oriented classification technology, through remote sensing image preprocessing, multi-scale segmentation, extraction rule formulation and other steps, using object-oriented classification method, taking Jinfeng District, Xixia District and Xingqing District of Yinchuan City as an example, The blue roof buildings in the range were extracted, and the results showed that the method can effectively extract the target features. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Roofs

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Image Super-Resolution Reconstruction with Dense Residual Attention Module 1

引用

6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Wang, Li Chen, Ziyi Li, Hui Pan, Chengwei Zhang, Dezheng School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China

ISBN: (数字)9789811580833

ISBN: (纸本)9789811580826

Deep convolutional neural networks have recently achieved great success in the field of image super-resolution. However, most of the super-resolution methods based on deep neural network do not make full use of the multi-level features which extracted from low-resolution images, and do not pay attention to the high-frequency information which needs to be reconstructed in the image, so the performances are relatively poor. Aiming at these problems, we propose a dense residual attention module to improve the image reconstruction performance. The dense residual attention module proposed in this paper makes full use of low-level image feature, and the channel spatial attention mechanism makes the network pay more attention to the high-frequency information that the image needs to be repaired, and uses the sub-pixel convolution to complete the image. Experiments were carried out on five benchmark datasets Set5, Set14, BSD100, Urban100 and DIV2K100. When the magnification was 4, the PSNR and SSIM are 32.47 dB/0.8986, 29.72 dB/0.8004, 27.73 dB/0.7423, 26.63 dB/0.8030, 29.43 dB/0.9023. Compared with other methods, we obtain the expected results. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Adaptive Estimation for Environmental Monitoring Using an Autonomous Underwater Vehicle

Adaptive Estimation for Environmental Monitoring Using an Au...

引用

IEEE Conference on Decision and Control

作者： Ziwen Yang Joana Fonseca Shanying Zhu Cailian Chen Xinping Guan Karl H. Johansson 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 Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures

This paper considers the problem of monitoring and adaptively estimating an environmental field, such as temperature or salinity, using an autonomous underwater vehicle (AUV). The AUV moves in the field and persistently measures environmental scalars and its position in its local coordinate frame. The environmental scalars are approximately linearly distributed over the region of interest, and an adaptive estimator is designed to estimate the gradient. By orthogonal decomposition of the velocity of the AUV, a linear time-varying system is equivalently constructed, and the sufficient conditions on the motion of the AUV are established, under which the global exponential stability of the estimation error system is rigorously proved. Furthermore, an estimate of the exponential convergence rate is given, and a reference trajectory that maximizes the estimate of the convergence rate is obtained for the AUV to track. Numerical examples verify the stability and efficiency of the system.

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