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Service Reservation and Pricing for Green Metaverses: A Stackelberg Game Approach

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arXiv 2023年

作者： Huang, Xumin Wu, Yuan Kang, Jiawen Nie, Jiangtian Zhong, Weifeng Kim, Dong In Xie, Shengli School of Automation Guangdong University of Technology Guangzhou510006 China State Key Laboratory of Internet of Things for Smart City University of Macau China Department of Computer and Information Science University of Macau China School of Computer Science and Engineering Nanyang Technological University Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Metaverse enables users to communicate, collaborate and socialize with each other through their digital avatars. Due to the spatio-temporal characteristics, co-located users are served well by performing their software components in a collaborative manner such that a Metaverse service provider (MSP) eliminates redundant data transmission and processing, ultimately reducing the total energy consumption. The energy-efficient service provision is crucial for enabling the green and sustainable Metaverse. In this article, we take an augmented reality (AR) application as an example to achieve this goal. Moreover, we study an economic issue on how the users reserve offloading services from the MSP and how the MSP determines an optimal charging price since each user is rational to decide whether to accept the offloading service by taking into account the monetary cost. A single-leader multi-follower Stackelberg game is formulated between the MSP and users while each user optimizes an offloading probability to minimize the weighted sum of time, energy consumption and monetary cost. Numerical results show that our scheme achieves energy savings and satisfies individual rationality simultaneously compared with the conventional schemes. Finally, we identify and discuss open directions on how several emerging technologies are combined with the sustainable green Metaverse. © 2023, CC BY-NC-SA.

关键词： Energy utilization

Bioinspired Musculoskeletal Model-based Soft Wrist Exoskeleton for Stroke Rehabilitation

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Journal of Bionic engineering 2020年第6期17卷 1163-1174页

作者： Ning Li Tie Yang Yang Yang Peng Yu Xiujuan Xue Xingang Zhao Guoli Song Imad HElhajj Wenxue Wang Ning Xi Lianqing Liu State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Itelligent Manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Bejing 100049China Rehabilitation Center for the Disabled Shenyang 110015China Vision and Robotics Lab Department of Electrical and Computer EngineeringAmerican University of BeirutBeirut 11072020Lebanon Emerging Technologies Instinute Department of Industrial&Manufacturing Systems EngineeringUniversity of Hong KongPokfulamHong Kong 999077China

Exoskeleton robots have demonstrated the potential to rehabilitate stroke ***,poor human-machine physiological coupling causes unexpected damage to human of muscles and ***,inferior humanoid kinematics control would restrict human natural *** to deal with these problems results in bottlenecks and hinders its *** this paper,the simplified muscle model and muscle-liked kinematics model were proposed,based on which a soft wrist exoskeleton was established to realize natural human ***,we simplified the redundant muscular system related to the wrist joint from ten muscles to four,so as to realize the human-robot physiological ***,according to the above human-like musculoskeletal model,the humanoid distributed kinematics control was established to achieve the two DOFs coupling kinematics of the *** results show that the wearer of an exoskeleton could reduce muscle activation and joint force by 43.3%and 35.6%,***,the humanoid motion trajectories similarity of the robot reached 91.5%.Stroke patients could recover 90.3%of natural motion ability to satisfy for most daily *** work provides a fundamental understanding on human-machine physiological coupling and humanoid kinematics control of the exoskeleton robots for reducing the post-stroke complications.

关键词： stroke rehabilitation simplified muscle model soft wrist exoskeleton robot bionic kinematics model

Collaborative Trolley Transportation System with Autonomous Nonholonomic Robots

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arXiv 2023年

作者： Xia, Bingyi Luan, Hao Zhao, Ziqi Gao, Xuheng Xie, Peijia Xiao, Anxing Wang, Jiankun Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Department of Electrical and Computer Engineering The University of Alberta Canada School of Computing National University of Singapore Singapore

— Cooperative object transportation using multiple robots has been intensively studied in the control and robotics literature, but most approaches are either only applicable to omnidirectional robots or lack a complete navigation and decision-making framework that operates in real time. This paper presents an autonomous nonholonomic multi-robot system and an end-to-end hierarchical autonomy framework for collaborative luggage trolley transportation. This framework finds kinematic-feasible paths, computes online motion plans, and provides feedback that enables the multi-robot system to handle long lines of luggage trolleys and navigate obstacles and pedestrians while dealing with multiple inherently complex and coupled constraints. We demonstrate the designed collaborative trolley transportation system through practical transportation tasks, and the experiment results reveal their effectiveness and reliability in complex and dynamic environments. (Video1) Copyright © 2023, The Authors. All rights reserved.

关键词： Multipurpose robots

LUCF-Net: Lightweight U-shaped Cascade Fusion Network for Medical Image Segmentation

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arXiv 2024年

作者： Sun, Songkai She, Qingshan Ma, Yuliang Li, Rihui Zhang, Yingchun The School of Automation Hangzhou Dianzi University Zhejiang Hangzhou310018 China International Joint Research Laboratory for Autonomous Robotic Systems Zhejiang Hangzhou China The Center for Cognitive and Brain Sciences The Department of Electrical and Computer Engineering University of Macau China The Department of Biomedical Engineering University of Miami Coral GablesFL United States

In this study, the performance of existing U-shaped neural network architectures was enhanced for medical image segmentation by adding Transformer. Although Transformer architectures are powerful at extracting global information, its ability to capture local information is limited due to its high complexity. To address this challenge, we proposed a new lightweight U-shaped cascade fusion network (LUCF-Net) for medical image segmentation. It utilized an asymmetrical structural design and incorporated both local and global modules to enhance its capacity for local and global modeling. Additionally, a multi-layer cascade fusion decoding network was designed to further bolster the network's information fusion capabilities. Validation results achieved on multi-organ datasets in CT format, cardiac segmentation datasets in MRI format, and dermatology datasets in image format demonstrated that the proposed model outperformed other state-of-the-art methods in handling local-global information, achieving an improvement of 1.54% in Dice coefficient and 2.6 mm in Hausdorff distance on multi-organ segmentation. Furthermore, as a network that combines Convolutional Neural Network and Transformer architectures, it achieves competitive segmentation performance with only 6.93 million parameters and 6.6 gigabytes of floating point operations, without the need of pre-training. In summary, the proposed method demonstrated enhanced performance while retaining a simpler model design compared to other Transformer-based segmentation networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

A Systematic Evaluation of Different Indoor Localization Methods in Robotic Autonomous Luggage Trolley Collection at Airports

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arXiv 2023年

作者： Sun, Zhirui Chen, Weinan Wang, Jiankun Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Guangdong University of Technology Guangzhou China The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong The Department of Electrical and Computer Engineering The University of Alberta Canada

This article addresses the localization problem in robotic autonomous luggage trolley collection at airports and provides a systematic evaluation of different methods to solve it. The robotic autonomous luggage trolley collection is a complex system that involves object detection, localization, motion planning and control, manipulation, etc. Among these components, effective localization is essential for the robot to employ subsequent motion planning and end-effector manipulation because it can provide a correct goal position. In this article, we survey four popular and representative localization methods to achieve object localization in the luggage collection process, including radio frequency identification (RFID), Keypoints, ultrawideband (UWB), and Reflectors. To test their performance, we construct a qualitative evaluation framework with Localization Accuracy, Mobile Power Supplies, Coverage Area, Cost, and Scalability. Besides, we conduct a series of quantitative experiments regarding Localization Accuracy and Success Rate on a real-world robotic autonomous luggage trolley collection system. We further analyze the performance of different localization methods based on experiment results, revealing that the Keypoints method is most suitable for indoor environments to achieve the luggage trolley collection. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement

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arXiv 2024年

作者： Wu, Xu Hou, XianXu Lai, Zhihui Zhou, Jie Zhang, Ya-Nan Pedrycz, Witold Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China The Department of Electrical & Computer Engineering University of Alberta University of Alberta Canada

Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations;(2) loss of texture and color information caused by noise suppression and light enhancement. In this paper, we propose a novel enhancement approach, CodeEnhance, by leveraging quantized priors and image refinement to address these challenges. In particular, we reframe LLIE as learning an image-to-code mapping from low-light images to discrete codebook, which has been learned from high-quality images. To enhance this process, a Semantic Embedding Module (SEM) is introduced to integrate semantic information with low-level features, and a Codebook Shift (CS) mechanism, designed to adapt the pre-learned codebook to better suit the distinct characteristics of our low-light dataset. Additionally, we present an Interactive Feature Transformation (IFT) module to refine texture and color information during image reconstruction, allowing for interactive enhancement based on user preferences. Extensive experiments on both real-world and synthetic benchmarks demonstrate that the incorporation of prior knowledge and controllable information transfer significantly enhances LLIE performance in terms of quality and fidelity. The proposed CodeEnhance exhibits superior robustness to various degradations, including uneven illumination, noise, and color distortion. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

Digital Twin Definition for Detecting Abnormal Perspiration Patterns in High-Stress Professions

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Digital Twin Definition for Detecting Abnormal Perspiration ...

Roedunet International Conference (RoEduNet)

作者： Cristian Vizitiu Ioan Turcin Kevin Alexander Dominey Selver Softic Adrian-Cătălin Dinculescu Vlad I. Bocăneţ Cosmin Dugan Sorin-Aurel Moraru The Space Applications and Technologies Laboratory Institute of Space Science -Subsidiary of INFLPR Magurele Romania Department of Automatics and Information Technology Faculty of Electrical Engineering and Computer Science Transilvania University of Brasov Brasov Romania Department Automation Technology CAMPUS 02 University of Applied Sciences Graz Austria Department IT & Business Informatics CAMPUS 02 University of Applied Sciences Graz Austria Department of Manufacturing Engineering Faculty of Industrial Engineering Robotics and Production Management Technical University of Cluj-Napoca Cluj-Napoca Romania Bucharest University Emergency Hospital Bucharest Romania

ISBN: (数字)9798331540388

ISBN: (纸本)9798331540395

Perspiration is a physiological response in high-stress situations, that also plays a key role in thermoregulation and stress management. Understanding perspiration patterns is used for assessing physiological responses and optimizing human performance especially in professions such as emergency medical services and military operations, as well as for individuals exposed to Isolated Confined Extreme (ICE) environments such as astronauts and space analog mission participants. This paper defines a Digital Twin (DT) model, that will be integrated with an existing physical sweat simulator module (SSM) prototype. Its goal is to simulate human perspiration under high-stress conditions by integrating environmental, physiological, and psychological data. The system architecture is developed via Model-Based Systems engineering (MBSE). The scope of the designed virtual model is to accurately reflect physical perspiration, detect abnormal perspiration patterns, and generate recommendations regarding stress countermeasures. The goal is to influence the behavioral and/or physiological state of the physical twin, thereby creating a dynamic, responsive system. Machine learning (ML) models will be used to identify patterns in the environmental, physiological, and psychological inputs and predict sweat volume, chemical composition, and regional sweat distributions across the body. ML will also be used to detect abnormal perspiration patterns based on input data. The application of generative AI for creating test data from simulation templates is anticipated to facilitate the comparative analysis of two to three distinct profiles, such as astronauts, soldiers, and paramedics or firefighters, using simulated values. The SSM, with its integrated sensors, will serve the dual purpose of validating the model and providing realistic physical simulation.

关键词： Adaptation models Generative AI Psychology Human factors Machine learning Predictive models Physiology Digital twins Modeling Biomedical monitoring

Raspberry Pi-Based Monitoring System for Grid-Connected PV Systems using Deep Learning Technique*

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Raspberry Pi-Based Monitoring System for Grid-Connected PV S...

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Zahra Yahyaoui Wajdi Saadaoui Mansour Hajji Majdi Mansouri Mohamed Nounou Kais Bouzrara Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Department of Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The evolution of embedded systems has demonstrated their reliability as a solution for monitoring and controlling industrial systems, particularly in renewable energy conversion systems like photovoltaic (PV) energy. The increasing adoption of PV systems highlights the critical need for effective fault diagnosis to ensure their reliable operation. In this paper, we present a novel fault diagnosis approach utilizing Long Short-Term Memory (LSTM) networks optimized through Bayesian optimization techniques. Our methodology is implemented on a Raspberry Pi platform, demonstrating the feasibility of deploying sophisticated fault diagnosis algorithms in resource-constrained environments. Through extensive experiments, we demonstrate the effectiveness of our approach to accurately diagnose faults in grid-connected photovoltaic systems, thereby improving the reliability and efficiency of integrated environmental monitoring *** obtained results highlight the potential of combining advanced deep learning techniques with embedded systems to address complex diagnostic challenges, as demonstrated by achieving a 100% accuracy rate.

关键词： Fault diagnosis Deep learning Photovoltaic systems Embedded systems Accuracy Economic indicators Bayes methods Environmental monitoring Long short term memory Optimization

Platoon-Based Distributed Control for Automated Material Handling Systems

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Platoon-Based Distributed Control for Automated Material Han...

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Alessandro Bozzi Jose-Fernando Jimenez Camilo Hernandez-Rodriguez Eliana-Maria Gonzalez-Neira Damien Trentesaux Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genova Italy Laboratory System and Materials for Mechatronics (SYMME) Université Savoie Mont Blanc Annecy France Industrial Engineering Department Pontificia Universidad Javeriana Bogota Columbia Laboratory of Industrial and Human Automation Control Mechanical Engineering and Computer Science (LAMIH) Université Polytechnique Hauts-de-France Valenciennes France

Smart manufacturing systems combine collaborative and autonomous control systems to provide optimal performance and respond to volatile demands. A key component of smart manufacturing is material management, as raw materials, work-in-progress, and finished products must be transported to the right location at the right time. However, during the production phase, it is imperative to establish an optimal traffic control scheme in order to avoid congestion and optimize material flow. This paper proposes a platooning-based control procedure implemented in a virtual agent-based manufacturing environment to increase the material flow efficiency on a manufacturing shop floor. An analysis of the production makespan and an indicator of the level of congestion in the material handling system was conducted in order to determine productivity improvements. An agent-based simulation is used to test the effectiveness of the novel platooning approach versus a traditional basic approach. A multivariate analysis of variance (MANOVA) reveals that the inclusion of a platoon-based procedure improves production performance and reduces congestion risks.

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