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Research of performance parameters of virtual switches with OpenFlow support

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Research of performance parameters of virtual switches with ...

2020 International Conference Engineering and Telecommunication, En and T 2020

作者： Ushakova, Margarita Ushakov, Yuriy Cui, Jie Legashev, Leonid Shukhman, Alexander Bolodurin, Aleksandr Orenburg State University Department of Geometry and Computer Science Orenburg Russia School of Computer Science and Technology Anhui University Anhui China Moscow Institute of Physics and Technology Faculty of Radio Engeneering and Cybernetics Moskow Russia

ISBN: (纸本)9781728188294

This paper discusses the problems that arise with different ways of connecting virtual network switches with the OpenFlow mode. A technique for automated research of performance parameters of virtual switches with OpenFlow support is proposed. The study revealed modes in which significant packet loss and delays of more than 100 ms occur. It was found that on different parts of the packet's route, using Open vSwitch in OpenFlow mode inside virtual machines is not optimal. Also, the connection for virtualization systems via Open vSwitch is preferable to Linux Bridge. Exceeding the load beyond the identified limits entails the need to optimize the Open vSwitch and install the DPDK module for low-latency switching or change the load balancing on such nodes. © 2020 IEEE.

关键词： computer operating systems

A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Image Anomaly Detection

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arXiv 2024年

作者： Lin, Yuxuan Chang, Yang Tong, Xuan Yu, Jiawen Liotta, Antonio Huang, Guofan Song, Wei Zeng, Deyu Wu, Zongze Wang, Yan Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200438 China Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai200438 China Faculty of Computer Science Free University of Bozen-Bolzano Bolzano39100 Italy College of Information Technology Shanghai Ocean University Shanghai201306 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China

In the advancement of industrial informatization, unsupervised anomaly detection technology effectively overcomes the scarcity of abnormal samples and significantly enhances the automation and reliability of smart manufacturing. As an important branch, industrial image anomaly detection focuses on automatically identifying visual anomalies in industrial scenarios (such as product surface defects, assembly errors, and equipment appearance anomalies) through computer vision techniques. With the rapid development of Unsupervised industrial Image Anomaly Detection (UIAD), excellent detection performance has been achieved not only in RGB setting but also in 3D and multimodal (RGB and 3D) settings. However, existing surveys primarily focus on UIAD tasks in RGB setting, with little discussion in 3D and multimodal settings. To address this gap, this artical provides a comprehensive review of UIAD tasks in the three modal settings. Specifically, we first introduce the task concept and process of UIAD. We then overview the research on UIAD in three modal settings (RGB, 3D, and multimodal), including datasets and methods, and review multimodal feature fusion strategies in multimodal setting. Finally, we summarize the main challenges faced by UIAD tasks in the three modal settings, and offer insights into future development directions, aiming to provide researchers with a comprehensive reference and offer new perspectives for the advancement of industrial informatization. Corresponding resources are available at https://***/Sunny5250/Awesome-Multi-Setting-UIAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Smart manufacturing

Corrections to “Intelligent Traffic Data Transmission and Sharing Based on Optimal Gradient Adaptive Optimization Algorithm”

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IEEE Transactions on Intelligent Transportation Systems 2024年第3期25卷 3202-3204页

作者： Xing Li Haotian Zhang Yajing Shen Lina Hao Wanfeng Shang School of Electrical Engineering and Intelligentization Dongguan University of Technology Dongguan China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China College of Computer Science and Technology Jilin University Changchun China Department of Biomedical Engineering City University of Hong Kong Hong Kong China School of Mechanical and Engineering Northeastern University Shenyang China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

In [1] , inaccuracies in several critical equations along with their accompanying descriptions appear in the article. Furthermore, some references are missing, and certain analyses of experiments are flawed.

关键词： Adaptive systems Data communication Optimization Intelligent transportation systems

Constrained Generative Sampling of 6-DoF Grasps

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arXiv 2023年

作者： Lundell, Jens Verdoja, Francesco Le, Tran Nguyen Mousavian, Arsalan Fox, Dieter Kyrki, Ville Intelligent Robotics Group Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Finland KTH Royal Institute of Technology Sweden NVIDIA Corporation United States Paul G. Allen School of Computer Science & Engineering University of Washington Seattle United States

Most state-of-the-art data-driven grasp sampling methods propose stable and collision-free grasps uniformly on the target object. For bin-picking, executing any of those reachable grasps is sufficient. However, for completing specific tasks, such as squeezing out liquid from a bottle, we want the grasp to be on a specific part of the object’s body while avoiding other locations, such as the cap. This work presents a generative grasp sampling network, VCGS, capable of constrained 6-Degrees of Freedom (DoF) grasp sampling. In addition, we also curate a new dataset designed to train and evaluate methods for constrained grasping. The new dataset, called CONG, consists of over 14 million training samples of synthetically rendered point clouds and grasps at random target areas on 2889 objects. VCGS is benchmarked against GraspNet, a state-of-the-art unconstrained grasp sampler, in simulation and on a real robot. The results demonstrate that VCGS achieves a 10–15% higher grasp success rate than the baseline while being 2–3 times as sample efficient. Supplementary material is available on our project website. Copyright © 2023, The Authors. All rights reserved.

关键词： Bottles

Non-Invasive Brain-computer Interfaces: a New Perspective on the Assessment and Classification of Individuals with Methamphetamine Addiction

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SN Comprehensive Clinical Medicine 2023年第1期5卷 1-12页

作者： Zhan, Gege Su, Haolong Wang, Pengchao Wang, Junkongshuai Jiang, Haifeng Zhang, Lihua Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China Shanghai Mental Center Shanghai Jiao Tong University School of Medicine Shanghai China CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT) Chinese Academy of Sciences Shanghai China Ji Hua Laboratory Foshan Guangdong Province China Yiwu Research Institute of Fudan University Yiwu City China

Methamphetamine addiction is a brain disease that causes abnormalities in the structure and function of the brain. EEG, a common signal acquired based on the noninvasive brain-computer interface, can reflect the altered brain activity associated with methamphetamine addiction. EEG-based analysis methods provide a perspective to explore the neural mechanisms of methamphetamine addiction and the effects on brain activity. This paper reports the results of a review of EEG-based assessment and classification of methamphetamine addiction. Current methods commonly used in EEG-based methamphetamine addiction research include traditional resting-state EEG analysis, brain network analysis, and analysis of event-related potentials. A small number of studies have classified methamphetamine addiction and healthy individuals based on resting state EEG features or event-related potentials. EEG is one of the common tools used to examine the effects of methamphetamine on brain function. In follow-up studies, new methods for analyzing resting-state EEG and event-related potentials may help to investigate the neural mechanisms of methamphetamine addiction.

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Few-Shot Medical Image Segmentation with High-Fidelity Prototypes

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arXiv 2024年

作者： Tang, Song Yan, Shaxu Qi, Xiaozhi Gao, Jianxin Ye, Mao Zhang, Jianwei Zhu, Xiatian IMI Group School of Health Sciences and Engineering University of Shanghai for Science and Technology Shanghai China TAMS Group Department of Informatics Universität Hamburg Hamburg Germany School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Surrey Institute for People-Centred Artificial Intelligence Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China

Few-shot Semantic Segmentation (FSS) aims to adapt a pretrained model to new classes with as few as a single labelled training sample per class. Despite the prototype based approaches have achieved substantial success, existing models are limited to the imaging scenarios with considerably distinct objects and not highly complex background, e.g., natural images. This makes such models suboptimal for medical imaging with both conditions invalid. To address this problem, we propose a novel Detail Self-refined Prototype Network (DSPNet) to constructing high-fidelity prototypes representing the object foreground and the background more comprehensively. Specifically, to construct global semantics while maintaining the captured detail semantics, we learn the foreground prototypes by modelling the multi-modal structures with clustering and then fusing each in a channel-wise manner. Considering that the background often has no apparent semantic relation in the spatial dimensions, we integrate channel-specific structural information under sparse channel-aware regulation. Extensive experiments on three challenging medical image benchmarks show the superiority of DSPNet over previous state-of-the-art methods. The code and data are available at https://***/tntek/DSPNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

A Clustering-Based Support Vector Classifier for Dynamic Time-Linkage Optimization

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A Clustering-Based Support Vector Classifier for Dynamic Tim...

IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Meng Gao Xiao-Fang Liu Zhi-Hui Zhan Jun Zhang Institute of Robotics and Automatic Information Systems College of Artificial Intelligence Nankai University Tianjin China School of Computer Science and Engineering South China University of Technology Guangzhou China Hanyang University Ansan South Korea Nankai University Tianjin China

Dynamic time-linkage optimization problems (DTPs) bring challenges to existing evolutionary algorithms due to the influence of a current decision in the future. Existing methods usually model the rewards of a current decision in the future for prediction. However, these methods often present low prediction accuracy due to the lack of sufficient training data. In addition, they often require a long computational time. To address these issues, the problem of predicting rewards is converted into a simpler binary classification problem, which evaluates whether a current solution can bring positive or negative influence in the future. This paper proposes a clustering-based support vector classifier for solution evaluation. In the proposed method, the density of the time-linkage property is detected first. Historical data are divided using k-means clustering so as to train a support vector classifier for solution evaluation. Good solutions are selected to generate a final decision solution using a crossover operator. Integrating the clustering-based support vector classifier into particle swarm optimization, a new method named CSVC-PSO is put forward. Multiple instances are constructed using a recent DTP test suite with different types of time-linkage patterns and density. Experimental results demonstrate that the proposed CSVC-PSO outperforms state-of-the-art algorithms on most instances using a shorter time.

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Analysis and development of smart production and distribution line system in smart grid based on optimization techniques involving digital twin

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Measurement: Sensors 2024年 34卷

作者： Arumugam, Thangaraja Kamble, Nitin Kundlik Guntreddi, Venkataramana Vishnu Sakravarthy, N. Shanthi, S. Ponnusamy, Sivakumar Business School Vellore Institute of Technology Tamilnadu Chennai India Department of Robotics and Automation Engineering D Y Patil College of Engineering Akurdi Maharashtra Pune411044 India Department of Electrical Telecommunication and Computer Engineering School of Engineering and Applied Sciences Kampala International University Isakha Uganda Department of Mechanical Engineering Sri Eshwar College of Engineering Tamilnadu Coimbatore India School of Computer Science & Engineering Presidency University Karnataka Bengaluru560064 India Department of Computer Science and Engineering K.S.R. College of Engineering Tiruchengode TamilNadu Namakkal India

The term Digital Twin (DT) is defined as the virtual demonstration of an object that is represented through real-time datasets. DT is done through artificial intelligence to enhance decision-making techniques. DT includes the process of simulation, amalgamation, observation, analysis, and conservation. The DT is simply the exact reproduction of the physical structures. DT is used in the identification and evaluation of problems through real-time analysis. It is important to have prior analysis and evaluation of the object before existing in the real world. These digital twins help in the manufacturing and implementation of the production line system. DT includes the production line with the station division and the hours needed for the operating conditions for the assembly process. The systems are integrated to reduce the overall cost parameter. The physical simulation model is employed to obtain higher performance with reduced cost. An artificial neural network with a genetic algorithm is used for the optimization process to achieve a production line system using digital twins. © 2024 The Authors

关键词： Genetic algorithms

RS-ModCubes: Self-reconfigurable, Scalable, Modular Cubic Robots for Underwater Multitasking

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arXiv 2024年

作者： Zheng, Jiaxi Dai, Guangmin He, Botao Mu, Zhaoyang Meng, Zhaochen Zhang, Tianyi Zhi, Weiming Fan, Dixia Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Zhejiang University Zhejiang Hangzhou310027 China Key Laboratory of Coastal Environment and Resources of Zhejiang Province School of Engineering Westlake University Zhejiang Hangzhou310030 China Department of Computer Science University of Maryland College ParkMD20742 United States Institute of Advanced Technology Westlake Institute for Advanced Study Zhejiang Hangzhou310024 China

This paper introduces a reconfigurable underwater robot system, RS-ModCubes, which allows scalable multi-robot configuration. An RS-ModCubes system consists of multiple ModCube modules, that can travel underwater with 6 DoFs and assemble with each other into a larger structure with onboard electromagnets. This system is designed to support diverse underwater applications through modularity and reconfigurability, eliminating the need to customize mechanical designs for a specific task. We present a modeling framework tailored for such reconfigurable robot systems, with hydrodynamics integrated based on Monte Carlo approximation. A model-based feedforward Proportional Derivative (PD) controller serves as the baseline for control. Inspired by dexterous manipulation, we evaluated the robot’s maximum task wrench space and power efficiency, compared against four commercial underwater robots. RS-ModCubes is validated via both real-world experiments and simulations, including individual and multi-module trajectory tracking and hovering docking. We open-source the design and code to facilitate future research: https://***/***/. © 2024, CC BY.

关键词： Machine design