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UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 430-445页

作者： Jiawen Kang Junlong Chen Minrui Xu Zehui Xiong Yutao Jiao Luchao Han Dusit Niyato Yongju Tong Shengli Xie IEEE School of Automation Guangdong University of TechnologyGuangzhou 510006China 111 Center for Intelligent Batch Manufacturing based on IoT Technology Guangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China School of Computer Science and Engineering Nanyang Technological UniversitySingaporeSingapore Pillar of Information Systems Technology and Design Singapore University of Technology and DesignSingaporeSingapore College of Communication Engineering Army Engineering University of PLANanjing 210007China National Natural Science Foundation of China Beijing 100085China Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of EducationGuangzhou 510006China Guangdong Key Laboratory of IoT Information Technology Guangzhou 510006China

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach

关键词： Avatar blockchain metaverses multi-agent deep reinforcement learning transformer UAVs

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Underwater RGB-D imaging system with millimetric precision

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Science China(Information Sciences) 2024年第10期67卷 383-385页

作者： Yajun GAO Yang CONG Xu TANG Mingxue LI State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences College of Automation Science and Engineering South China University of Technology

In recent years, underwater fine three-dimensional reconstruction technology has emerged as a focal point in research, exhibiting extensive potential applications, particularly in underwater archaeology and resource e...

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An effective nonlinear dynamic formulation to analyze grasping capability of soft pneumatic robotic gripper

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International Journal of Smart and Nano Materials 2024年第3期15卷 405-431页

作者： Qiping Xu Chenhang Ying Kehang Zhang Huiyu Xie Shiju E Department of Robotics Engineering Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology&Equipment of Zhejiang ProvinceCollege of EngineeringZhejiang Normal UniversityJinhuaP.R.China

Soft pneumatic robotic grippers have found extensive applica-tions across various engineering domains,which prompts active research due to their splendid compliance,high flex-ibility,and safe human-robot interaction over conventional stiff *** simplified rod-based models prin-cipally focused on clarifying overall large deformation and bending postures of soft grippers from static or quasi-static perspectives,whereas it is challenging to elaborate grasping characteristics of soft grippers without considering contact interaction and nonlinear large deformation *** address this,based on absolute nodal coordinate formulation(ANCF),comprehensively allowing for structural complexity,geometric,material and boundary nonlinearities,and incorpor-ating Coulomb’friction law with a multiple-point contact method,we put forward an effective nonlinear dynamic mod-eling approach for exploring grasping capability of soft ***,we solved the established dynamic equations using Generalized-αscheme,and conducted thorough numer-ical simulation analysis on a three-jaw soft pneumatic gripper(SPG)in terms of grasping configurations,displacements and contact *** proposed dynamic approach can accurately both describe complicated deformed configurations along with stress distribution and provide a feasible solution to simulate grasping targets,whose effectiveness and precision were analyzed theoretically and verified experimentally,which may shed new light on devising and optimizing other multi-functional SPGs.

关键词： Nonlinear dynamic formulation soft pneumatic robotic grippers contact interaction grasping capability configuration variations

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Research on the Icing Diagnosis ofWind Turbine Blades Based on FS–XGBoost–EWMA

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Energy engineering 2024年第7期121卷 1739-1758页

作者： Jicai Guo Xiaowen Song Chang Liu Yanfeng Zhang Shijie Guo JianxinWu Chang Cai Qing’an Li College of Mechanical Engineering Inner Mongolia University of TechnologyHohhot010051China Inner Mongolia Key Laboratory of Special Service Intelligent Robotics Hohhot010051China Institute of Engineering Thermophysics Chinese Academy of SciencesBeijing100190China

In winter,wind turbines are susceptible to blade icing,which results in a series of energy losses and safe operation ***,blade icing detection has become a top *** methods primarily rely on sensor monitoring,which is expensive and has limited ***-driven blade icing detection methods have become feasible with the development of artificial ***,the data-driven method is plagued by limited training samples and icing samples;therefore,this paper proposes an icing warning strategy based on the combination of feature selection(FS),eXtreme Gradient Boosting(XGBoost)algorithm,and exponentially weighted moving average(EWMA)*** the training phase,FS is performed using correlation analysis to eliminate redundant features,and the XGBoost algorithm is applied to learn the hidden effective information in supervisory control and data acquisition analysis(SCADA)data to build a normal behavior *** the online monitoring phase,an EWMA analysis is introduced to monitor the abnormal changes in features.A blade icing warning is issued when themonitored features continuously exceed the control limit,and the ambient temperature is below 0℃.This study uses data fromthree icing-affected wind turbines and one normally operating wind turbine for *** experimental results reveal that the strategy can promptly predict the icing trend among wind turbines and stably monitor the normally operating wind turbines.

关键词： Wind turbine blade icing feature selection XGBoost EWMA

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A practical PID variable stiffness control and its enhancement for compliant force-tracking interactions with unknown environments

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Science China(Technological Sciences) 2023年第10期66卷 2882-2896页

作者： ZHANG Xin ZHOU Hao LIU JinGuo JU ZhaoJie LENG YuQuan YANG ChenGuang State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Beijing 100049China School of Computing University of PortsmouthPortsmouth PO13HEUK Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy EngineeringSouthern University of Science and TechnologyShenzhen 518055China Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510640China

Compliant interaction control is a key technology for robots performing contact-rich manipulation *** design of the compliant controller needs to consider the robot hardware because complex control algorithms may not be compatible with the hardware performance,especially for some industrial robots with low bandwidth *** paper focuses on effective and easy-to-use compliant control algorithms for position/velocity-controlled *** by human arm stiffness adaptation behavior,a novel variable target stiffness(NVTS)admittance control strategy is proposed for adaptive force tracking,in which a proportional integral derivative(PID)variable stiffness law is designed to update the stiffness coefficient of the admittance function by the force and position ***,its stability and force-tracking capability are theoretically *** addition,an impact compensator(Impc)is integrated into the NVTS controller to enhance its disturbance-suppression capability when the robot is subjected to strong vibration disturbances in complicated surface polishing *** proposed controllers are validated through four groups of experimental tests using different robots and the corresponding results demonstrate that they have high-accuracy tracking capability and strong adaptability in unknown environments.

关键词： compliant interaction control force tracking PID variable stiffness impact compensator unknown environments

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Local Path Planning of Unmanned Surface Vehicle Based on Improved DWA Algorithm

Local Path Planning of Unmanned Surface Vehicle Based on Imp...

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International Conference on Guidance, Navigation and Control, ICGNC 2024

作者： Ji, Chenjia Zhang, Lanyong College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin150001 China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9789819622351

An improved dynamic window method is proposed to solve the obstacle avoidance problem of intelligent ships in offshore waters that cannot effectively avoid when encountering unknown dynamic obstacles. Firstly, an autonomous collision avoidance decision-making model was constructed to judge whether the unmanned boat had a collision hazard, and the encounter situation was reclassified. To ensure that the USV can safely pass through the moving obstacle vessel, a collision risk assessment function considering the velocity of dynamic obstacles is introduced. Moreover, a collision avoidance rule function is incorporated into the evaluation function of the collision avoidance algorithm to ensure that the USV adheres to collision avoidance regulations. Ultimately, the simulation results confirm that the enhanced dynamic window method can safely avoid collisions while adhering to collision avoidance rules. This paper proposes an effective solution for collision avoidance behavior of intelligent vessels in near shore waters when facing unknown dynamic obstacles, providing insightful recommendations for subsequent research in maritime sectors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unmanned surface vehicles

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Additive manufacturing of high-quality tritium breeding pebbles using ceramic precursors

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Journal of the European Ceramic Society 2024年第7期44卷 4719-4730页

作者： Su, Fang Li, Yuzeng Xing, Huanhuan Liu, Yu Liu, Zhiyuan Liu, Changyong Jiang, Cho-Pei Chen, Zhangwei Additive Manufacturing Institute Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robotics Shenzhen University Shenzhen518060 China College of Mechanical & Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Xi'an710049 China

In this study, the fabrication of advanced lithium orthosilicate (Li4SiO4) ceramic tritium breeding pebbles through photopolymerization using ceramic precursors (i.e., SiO2 and Li2CO3) is reported for the first time. The pebbles exhibit excellent mechanical and physical properties. Through optimization of manufacturing parameters and a geometrical correction model to compensate printing deviations, the printing accuracy of the pebbles was enhanced. The resulting pebbles had a diameter of approximately 1 mm, a sphericity of 1.01, a uniformly distributed pore structure, a phase purity > 95%, and a satisfying relative density reaching 91.39%. The average crush load 25.7 ± 0.3 N, surpassed that achieved by previously reported methods. Furthermore, the thermal conductivity was enhanced compared to that of solid-phase synthesized Li4SiO4 powder, facilitating the tritium release process for fusion applications. This study may serve as a guidance for designing appropriate ceramic breeders, providing a cost-effective approach applicable to produce other high-precision ceramic components with complex compositions. © 2024 Elsevier Ltd

关键词： Lithium compounds

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CTNet:A Convolutional Transformer Network for Color Image Steganalysis

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Journal of Computer Science & technology 2025年第2期40卷 413-427页

作者： Kang-Kang Wei Wei-Qi Luo Shun-Quan Tan Ji-Wu Huang School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen UniversityGuangzhou 510006China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen UniversityShenzhen 518060China Shenzhen Key Laboratory of Media Security Shenzhen UniversityShenzhen 518060China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518129China

Compared with convolutional neural network(CNN),Transformer can obtain global receptive field features more effectively and has recently achieved great success in natural language processing and computer *** to the particularity of steganography,however,almost all existing steganalytic networks just employ CNN with local receptive fields to detect embedding *** this paper,we propose a novel convolutional Transformer network for color image ***,we firstly obtain various image residuals for each color channel of an input image in the pre-processing *** capture more comprehensive steganalytic features,the truncated residuals after channel concatenation will pass through a feature extraction module composed of a CNN group and a Transformer *** CNN group aims to extract local receptive fields features,while the Transformer group with multi-head self-attention as the key tries to extract global steganalytic ***,we employ a global covariance pooling(GCP)and two fully-connected(FC)layers with dropout for *** comparative experiments demonstrate that the proposed method can significantly improve the detection performances in color image steganalysis and achieve state-of-the-art *** the proposed method is originally designed for color images,it can also obtain competitive results for grayscale images compared with the current best *** addition,we provide numerous ablation studies to verify the rationality of the proposed network architecture.

关键词： steganalysis steganography convolutional neural network Transformer color image

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Classification of Camellia oleifera using a dual recognition strategy based on deep learning

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Multimedia Tools and Applications 2025年第13期84卷 12219-12240页

作者： Meng, Zhichao Du, Xiaoqiang Yao, Xiaohua He, Leiying Lin, Lepeng School of Mechanical Engineering Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Key Laboratory of Transplanting Equipment and Technology Hangzhou310018 China Provincial Key Laboratory of Agricultural Intelligent Sensing and Robotics Zhejiang Province Hangzhou310018 China Research Institute of Subtropical Forestry Chinese Academy of Forestry Hangzhou311400 China School of Youjia Intelligent Manufacturing Hangzhou Vocational & Technical College Hangzhou310018 China

With the rapid growth of the Camellia oleifera industry, counterfeit seedlings have surfaced in the market. Authenticating the genuineness of Camellia oleifera species and cultivars has emerged as a substantial challenge. Present methods for discerning fake Camellia oleifera cultivars depend on manual expertise, which is both time-consuming and labor-intensive. Current deep learning-based crop classification methods can solely be used to classify training objectives and are unable to distinguish between authentic and counterfeit objectives. In light of this situation, it is crucial to establish an efficient recognition strategy to accurately recognize Camellia oleifera species and cultivars and thereby eliminate the distribution of fake seedlings in the market. Firstly, the image datasets of the foreside and backside of Camellia oleifera leaves are constructed in this study. The classification is accomplished using the EfficientNet model, and a dual recognition strategy is employed to verify fake Camellia oleifera. The experiments demonstrate that EfficientNet B4 achieves the best performance among the EfficientNet models. Furthermore, to further enhance the model's performance, the EfficientNet B4 model incorporates the Mish activation function, Multi-class Focal Loss, and SENet V2. The Improved EfficientNet B4 achieves an average T-acc of 97.5%, which is 0.89% higher than the original EfficientNet B4 for the training target classification. Additionally, for the non-training target, the Improved EfficientNet B4 achieves an average T-acc of 93.9%, surpassing the original EfficientNet B4 by 2.22%. The improved model is then compared with 11 other convolutional neural networks to highlight its superior performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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A Review of Small-Sample Target Detection Research 14th

A Review of Small-Sample Target Detection Research

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14th International Workshop of Advanced Manufacturing and Automation, IWAMA 2024

作者： Zhou, Lin Wang, Chen Yang, Shuai Huang, Peng College of Mechanical Engineering Hubei University of Automotive Technology Shiyan442002 China China Academy of Engineering Science and Technology Shiyan Industrial Technology Research Institute Shiyan442000 China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai200072 China

ISBN: (纸本)9789819626243

Deep learning-based methods for detecting targets in images have advanced significantly in recent years and shown to have tremendous application potential across various domains. But in real-world applications, these methods are frequently constrained by the lack of labelled samples, particularly in situations with small sample sizes. As a result, it has become a crucial area of research to determine how to carry out target detection efficiently. This paper conducts a thorough analysis of image target detection technology under small sample conditions. It does this by methodically elaborating on the definition of the problem of small-sample image target detection and by thoroughly discussing a number of small-sample learning approaches, the most prominent of which are data enhancement-based, migration learning-based, and meta-learning-based. By discussing various algorithms and the fundamental idea behind the process, the benefits and drawbacks of the current approaches to small-sample detection are made evident. Sample identification issues. Lastly, the technological challenges of the current small sample target identification and the direction of future improvement are explored. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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