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Leveraging Intelligent Recommender system as a first step resilience measure - A data-driven supply chain disruption response framework

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arXiv 2024年

作者： Hu, Yang Ghadimi, Pezhman Laboratory for Advanced Manufacturing Simulation and Robotics School of Mechanical & Materials Engineering University College Dublin Dublin 4 Ireland

In light of the Industry 4.0 era, the global pandemic, and wars, interest in deploying digital technologies to increase supply chain resilience (SCRes) is rising. The utilization of recommender systems as a supply chain (SC) resilience measure is neglected, although these systems can enhance SC resilience. To address this problem, this research proposed a data-driven supply chain disruption response framework based on intelligent recommender system techniques. A prototype implementation was conducted to validate the developed framework through a practical use case. Results show that the proposed framework can be implemented as an effective SC disruption mitigation measure in the SCRes response phase and help SC participants better react after the SC disruption. © 2024, CC BY-NC-SA.

关键词： Decision support systems

A survey of the pursuit-evasion problem in swarm intelligence

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Frontiers of Information Technology & Electronic engineering 2023年第8期24卷 1093-1116页

作者： Zhenxin MU Jie PAN Ziye ZHOU Junzhi YU Lu CAO State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China National Innovation Institute of Defense Technology Beijing 100071China

For complex functions to emerge in artificial systems,it is important to understand the intrinsic mechanisms of biological swarm behaviors in *** this paper,we present a comprehensive survey of pursuit–evasion,which is a critical problem in biological ***,we review the problem of pursuit–evasion from three different perspectives:game theory,control theory and artificial intelligence,and bio-inspired *** we provide an overview of the research on pursuit–evasion problems in biological systems and artificial *** summarize predator pursuit behavior and prey evasion behavior as predator–prey ***,we analyze the application of pursuit–evasion in artificial systems from three perspectives,i.e.,strong pursuer group *** evader group,weak pursuer group *** evader group,and equal-ability ***,relevant prospects for future pursuit–evasion challenges are *** survey provides new insights into the design of multi-agent and multi-robot systems to complete complex hunting tasks in uncertain dynamic scenarios.

关键词： Swarm behavior Pursuit-evasion Artificial systems Biological model Collective motion

A User Study Method on Healthy Participants for Assessing an Assistive Wearable Robot Utilising EMG Sensing

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arXiv 2024年

作者： Suulker, Cem Greenway, Alexander Skach, Sophie Farkhatdinov, Ildar Miller, Stuart Charles Althoefer, Kaspar The Centre for Advanced Robotics @ Queen Mary School of Engineering and Materials Science Queen Mary University of London United Kingdom

Hand-wearable robots, specifically exoskeletons, are designed to aid hands in daily activities, playing a crucial role in post-stroke rehabilitation and assisting the elderly. Our contribution to this field is a textile robotic glove with integrated actuators. These actuators, powered by pneumatic pressure, guide the user’s hand to a desired position. Crafted from textile materials, our soft robotic glove prioritizes safety, lightweight construction, and user comfort. Utilizing the ruffles technique, integrated actuators guarantee high performance in blocking force and bending effectiveness. Here, we present a participant study confirming the effectiveness of our robotic device on a healthy participant group, exploiting EMG sensing. © 2024, CC BY.

关键词： Exoskeleton (robotics)

Rehabilitation Training of Spinal Cord Injured Individuals by a Self-Balancing Exoskeleton Robot

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Rehabilitation Training of Spinal Cord Injured Individuals b...

2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Li, Wentao Li, Jinke He, Yong Tian, Dingkui Li, Feng Sun, Jianquan Wu, Xinyu Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Shenzhen518055 China Siat Cuhk Joint Laboratory of Robotics and Intelligent Systems China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen518055 China China Construction Third Bureau First Engineering Mep Co. Ltd. Shenzhen518109 China

ISBN: (纸本)9798350327182

Physical therapy plays an important role in the rehabilitation for spinal cord injured individuals, and exoskeleton can assist with this process. In this paper, a self-balancing exoskeleton robot is introduced, which can provide assist not only in gait training, but also in balance training. In addition, it allows individuals walk with it without other assistive devices. This exoskeleton is designed to have a high degree of compatibility with humans. The structure and control system of the exoskeleton is introduced and the forward and inverse kinematic are solved, which enables the robot to perform various rehabilitation training actions as required. Experiments with a male subject have shown that this exoskeleton achieves the predicted results in gait training and balance training. © 2023 IEEE.

关键词： Inverse kinematics

An Efficient Method for Solving Routing Problems With Energy Constraints Using Reinforcement Learning

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An Efficient Method for Solving Routing Problems With Energy...

International Conference on Ubiquitous Robots (UR)

作者： Haggi Do Hakmo Son Jinwhan Kim Department of Mechanical Engineering Mobile Robotics and Intelligence Laboratory Korea Advanced Institute of Science and Technology Daejeon South Korea

ISBN: (数字)9798350361070

ISBN: (纸本)9798350361087

With the increasing popularity of electric vehicles (EV s), the research field of planning efficient routes for these vehicles is gaining growing attention. As there are a limited number of charging stations for EVs compared to gas stations for fossil fuel vehicles, EV routing requires careful consideration of energy constraints and replenishment. The classical traveling salesperson problem (TSP) and vehicle routing problem (VRP) are known to be NP-hard, which means that the electric vehicle routing problem (EVRP), a similar problem with added energy constraints, is computationally even more challenging. Recently, reinforcement learning (RL) is being suggested as an effective tool that can alleviate the computational burden of challenging problems. This paper presents a RL-based method for solving routing problems with energy constraints. Multi-head attention mechanisms are employed for both the encoder and decoder, and a masking scheme is applied at the decoding phase in order to compute a feasible solution and minimize the energy constraint violation. This method generates an efficient route in which all task nodes are visited while meeting the energy requirements by visiting the charging stations when needed. The performance of the methodology is demonstrated through a Monte Carlo simulation, and the results are discussed and analyzed.

关键词： Monte Carlo methods Reinforcement learning Charging stations Routing Electric vehicles Transformers Decoding

Deformable Tip Mount for Soft Growing Eversion Robots

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arXiv 2024年

作者： Suulker, Cem Skach, Sophie Kaleel, Danyaal Abrar, Taqi Murtaza, Zain Suulker, Dilara Althoefer, Kaspar Centre for Advanced Robotics @ Queen Mary School of Engineering and Materials Science Queen Mary University of London United Kingdom

Here we present a flexible tip mount for eversion (vine) robots. This soft cap allows attaching a payload to an eversion robot while allowing moving through narrow openings, as well as the eversion of protruding objec... 详细信息

关键词： Robots

HandOS: 3D Hand Reconstruction in One Stage

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arXiv 2024年

作者： Chen, Xingyu Song, Zhuheng Jiang, Xiaoke Hu, Yaoqing Yu, Junzhi Zhang, Lei Department of Advanced Manufacturing and Robotics College of Engineering Peking University China China University of Chinese Academy of Sciences China

Existing approaches of hand reconstruction predominantly adhere to a multi-stage framework, encompassing detection, left-right classification, and pose estimation. This paradigm induces redundant computation and cumulative errors. In this work, we propose HandOS, an end-to-end framework for 3D hand reconstruction. Our central motivation lies in leveraging a frozen detector as the foundation while incorporating auxiliary modules for 2D and 3D keypoint estimation. In this manner, we integrate the pose estimation capacity into the detection framework, while at the same time obviating the necessity of using the left-right category as a prerequisite. Specifically, we propose an interactive 2D-3D decoder, where 2D joint semantics is derived from detection cues while 3D representation is lifted from those of 2D joints. Furthermore, hierarchical attention is designed to enable the concurrent modeling of 2D joints, 3D vertices, and camera translation. Consequently, we achieve an end-to-end integration of hand detection, 2D pose estimation, and 3D mesh reconstruction within a one-stage framework, so that the above multi-stage drawbacks are overcome. Meanwhile, the HandOS reaches state-of-the-art performances on public benchmarks, e.g., 5.0 PA-MPJPE on FreiHand and 64.6% PCK@0.05 on HInt-Ego4D. Copyright © 2024, The Authors. All rights reserved.

关键词： 3D reconstruction

An Intelligent Deterministic Scheduling Method for Ultralow Latency Communication in Edge Enabled Industrial Internet of Things

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IEEE Transactions on Industrial Informatics 2023年第2期19卷 1756-1767页

作者： Lu, Yinzhi Yang, Liu Yang, Simon X. Hua, Qiaozhi Sangaiah, Arun Kumar Guo, Tan Yu, Keping School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Advanced Robotics and Intelligent Systems Laboratory School of Engineering University of Guelph GuelphONN1G2W1 Canada Computer School Hubei University of Arts and Science Xiangyang441000 China Future Technology Research Center National Yunlin University of Science and Technology Douliu City64002 Taiwan Graduate School of Science and Engineering Hosei University Tokyo184-8584 Japan Riken Center for Advanced Intelligence Project Riken Tokyo103-0027 Japan

Edge enabled Industrial Internet of Things (IIoT) platform is of great significance to accelerate the development of smart industry. However, with the dramatic increase in real-time IIoT applications, it is a great challenge to support fast response time, low latency, and efficient bandwidth utilization. To address this issue, time sensitive network (TSN) is recently researched to realize low latency communication via deterministic scheduling. To the best of our knowledge, the combinability of multiple flows, which can significantly affect the scheduling performance, has never been systematically analyzed before. In this article, we first analyze the combinability problem. Then, a noncollision theory based deterministic scheduling (NDS) method is proposed to achieve ultralow latency communication for the time-sensitive flows. Moreover, to improve bandwidth utilization, a dynamic queue scheduling (DQS) method is presented for the best-effort flows. Experiment results demonstrate that NDS/DQS can well support deterministic ultralow latency services and guarantee efficient bandwidth utilization. © 2005-2012 IEEE.

关键词： Bandwidth

A ResNet-based approach for accurate radiographic diagnosis of knee osteoarthritis

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CAAI Transactions on Intelligence Technology 2022年第3期7卷 512-521页

作者： Yu Wang Shibo Li Baoliang Zhao Jianwei Zhang Yuanyuan Yang Bing Li School of Mechanical Engineering and Automation Harbin Institute of TechnologyShenzhenChina Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhenChina TAMS Department of InformaticsUniversity of HamburgHamburgGermany

Currently X-ray images are clinically graded by experienced clinicians using the Kellgren and Lawrence(KL)scoring ***,individual scoring is subjective and error *** study proposes an approach for automated knee osteoarthritis classification based on deep neural *** knee X-ray images are first preprocessed with frequency-domain filtering and histogram normalisation,making the trabecular bone texture more obvious and benefiting the subsequent classification ***,a two-step classification strategy is proposed by extracting the joint centre based on the VGG network and classifying osteoarthritis grades based on the ResNet-50 *** addition,a rebalance operation is proposed to deal with the dataset unbalance problem,and a quick search technique is proposed to improve the iterative search efficiency for the joint *** all of these techniques,a classification accuracy of 81.41%is obtained,which is higher compared to the state-of-the-art approaches.

关键词： AMSGrad knee osteoarthritis recognition ResNet VGG