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A Distributed Task Allocation Algorithm for UAV Swarm Based on Local Information 4

A Distributed Task Allocation Algorithm for UAV Swarm Based ...

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4th international symposium on Artificial intelligence and Intelligent Manufacturing, AIIM 2024

作者： Zhang, Junfu Liu, Chengjin Liu, Tao Jiang, Shiqi School of Mechanical Engineering Xi Hua University Chengdu China International Joint Institute of Robotics and Intelligent System School of Automation Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9798331541729

This article proposes a distributed task allocation algorithm based on local information for unmanned aerial vehicle (UAV) swarm. A Part of the UAVs which are in the information exchange range participate in the local range auction. The auction members will change due to the movement of the UAVs in the following auctions. Each UAV in the swarm will be allocated a task by multiple rounds of local range auctions. The experimental data clearly demonstrate that the method has superior performance for enabling UAV swarm to complete the allocation of tasks based on local information. © 2024 ieee.

关键词： Consensus algorithm

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automation Using Robots, Machine Learning, and Artificial intelligence to Enhance Production and Quality 2

Automation Using Robots, Machine Learning, and Artificial In...

引用

2nd international Conference computational and Characterization Techniques in Engineering and Sciences, IC3TES 2024

作者： Sajja, Guna Sekhar Reddy Addula, Santosh University of the Cumberlands Department of Information Technology WilliamsburgKY United States

ISBN: (纸本)9798350364699

Automating everyday jobs, expanding production, and improving quality are all examples of how the integration of robotic automation and machine intelligence (AI) is causing activities to change. The purpose of this study is to examine the relationship between artificial intelligence (AI) and robotics, specifically focusing on how machine intelligence (ML) algorithms and AI approaches enable androids to do complicated jobs with greater efficiency and precision. Through the automation of everyday and repetitive tasks, organizations can redirect their workers into activities that are more important and innovative. Automated systems that are driven by artificial intelligence can learn and adapt from data, therefore rebuilding their performance over time and ensuring logical quality. This strategy not only increases productivity but also decreases the number of mistakes and the expenses associated with it. It is important to note that the associations of these gadgets stretch across a variety of fields, including manufacturing, healthcare, management, and departments that interact with consumers. This highlights the potential for wider financial and friendly advantages. A discussion of the difficulties and opportunities associated with the integration of mechanics is presented in the last section of the study. Particular attention is paid to the relevance of ongoing innovation and ethical considerations in the configuration of artificial intelligence and robotics. © 2024 ieee.

关键词： Adversarial machine learning

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Research on Propeller Blades Sanding Degree Recognition Algorithm Based on Improved DeepLabV3+Network 7

Research on Propeller Blades Sanding Degree Recognition Algo...

引用

7th international Conference on Mechatronics, robotics and automation

作者： Cao, Siying Yu, Baocheng Xu, Wenxia Wuhan Inst Technol Coll Artificial Intelligence Coll Comp Sci & Engn Wuhan Peoples R China

ISBN: (纸本)9798350352481;9798350352474

In the overhaul process of airplane propeller blades, the foam filling on the shoulder of the blades needs to be ground off. To achieve automated recognition of the sanding degree of propeller blades, an image semantic segmentation algorithm based on the improved DeepLabV3+ network is proposed. The algorithm adopts a lightweight network, MobileNetV2, to replace the original backbone network of DeepLabv3+, reducing computational complexity. The atrous spatial pyramid pooling (ASPP) feature extraction module is improved by replacing the atrous convolution with depthwise separable convolution and introducing the Convolutional Block Attention Module(CBAM), enhancing the overall training efficiency and segmentation accuracy of the model. Group normalization (GN) is used instead of batch normalization (BN) to further improve model performance. The experimental results on the dataset collected from the propeller blades sanding site show that the proposed algorithm achieves a mean intersection over union (MioU) of 91.40%, a mean pixel accuracy (MPA) of 95.63%, and an inference speed (FPS) of 33.88. Compared to the current mainstream segmentation algorithms such as DeepLabV3+, PSPNet, and HRNet, the improved algorithm demonstrates enhanced segmentation accuracy and inference speed.

关键词： Airplane Propeller blades sanding image Semantic segmentation DeepLabV3+ Lightweight

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Identifying and Implementing Common Tactics to Disrupt IoT Device Functionality Over TCP/IP Stacks 24

Identifying and Implementing Common Tactics to Disrupt IoT D...

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24th ieee international symposium on computational intelligence and Informatics, CINTI 2024

作者： Hajdu, Szabolcs Kis, Gerlinda Boglarka Fekete, Albert Zsombor Kutasi, Dénes Nimród Department of Electrical Engineering Sapientia Hungarian University of Transylvania Targu Mures Romania Tetronic SRL Targu Mures Romania

ISBN: (纸本)9798350353433

The proliferation of Internet of Things (IoT) devices in home automation systems has introduced unprecedented convenience but also raised serious concerns regarding security vulnerabilities [1]. This paper delves into the exploration of common vulnerabilities present in IoT-based home automation systems and proposes easily implemented solutions to disrupt their functionality. Through a comprehensive analysis of popular IoT devices, protocols, and communication channels, we identify key vulnerabilities that expose these systems to potential attacks, encompassing unauthorized access, data breaches, and device manipulation. Our findings contribute to the ongoing discourse on securing IoT devices and systems, emphasizing the need for proactive measures to safeguard user privacy, data integrity, and overall system reliability. By disseminating easily deployable solutions, we intend to foster a safer and more resilient environment for the growing landscape of IoT home automation. © 2024 ieee.

关键词： Home automation IoT System Vulnerabilities Unauthorized Access

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Learning an Efficient Terrain Representation for Haptic Localization of a Legged Robot

Learning an Efficient Terrain Representation for Haptic Loca...

引用

ieee international Conference on robotics and automation (ICRA)

作者： Sojka, Damian Nowicki, Michal R. Skrzypczynski, Piotr Poznan Univ Tech Inst Robot & Machine Intelligence Poznan Poland

ISBN: (纸本)9798350323658

Although haptic sensing has recently been used for legged robot localization in extreme environments where a camera or LiDAR might fail, the problem of efficiently representing the haptic signatures in a learned prior map is still open. This paper introduces an approach to terrain representation for haptic localization inspired by recent trends in machine learning. It combines this approach with the proven Monte Carlo algorithm to obtain an accurate, computation-efficient, and practical method for localizing legged robots under adversarial environmental conditions. We apply the triplet loss concept to learn highly descriptive embeddings in a transformer-based neural network. As the training haptic data are not labeled, the positive and negative examples are discriminated by their geometric locations discovered while training. We demonstrate experimentally that the proposed approach outperforms by a large margin the previous solutions to haptic localization of legged robots concerning the accuracy, inference time, and the amount of data stored in the map. As far as we know, this is the first approach that completely removes the need to use a dense terrain map for accurate haptic localization, thus paving the way to practical applications.

关键词： computational efficiency

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Robot Trust and Self-Confidence Based Role Arbitration Method for Physical Human-Robot Collaboration

Robot Trust and Self-Confidence Based Role Arbitration Metho...

引用

ieee international Conference on robotics and automation (ICRA)

作者： Wang, Qiao Liu, Dikai Carmichael, Marc G. Lin, Chin-Teng Univ Technol Sydney Robot Inst Fac Engn & Informat Technol 81 Broadway Ultimo NSW 2010 Australia Univ Technol Sydney Australian Artificial Intelligence Inst Sch Comp Sci Fac Engn & Informat Technol Ultimo NSW 2010 Australia

ISBN: (纸本)9798350323658

Role arbitration in human-robot collaboration (HRC) is a dynamically changing process that is affected by many factors such as physical workload, environmental changes and trust. In order to address this dynamic process, a trust-based role arbitration method is studied in this research. A computational model of robot trust and self-confidence (TSC) in physical human-robot collaboration (pHRC) is proposed. The TSC model is defined as a function of objective robot and human co-worker performance. A role arbitration method is then proposed based on the TSC model presented. The human-in-the-loop experiments with a collaborative robot are conducted to verify the TSC-based role arbitration method. The results show that the proposed method could achieve superior human-robot combined performance, reduce human co-workers' workload, and improve subjective preference.

关键词： Collaborative robots

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MiniBert24: A Lightweight Transformer-Based Model for Stock Market Movement Prediction 3

MiniBert24: A Lightweight Transformer-Based Model for Stock ...

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3rd ieee international Conference on robotics, automation, Artificial-intelligence and Internet-of-Things, RAAICON 2024

作者： Rodrigues, Gourab Nicholas Nazmul Hossain Mir, Md Mahmud Bhuiyan, Md Shahriar Al Rafi, Md Fuad, K.M. Nafiur Rahman Shahedul Islam, Md. Nur, Kamruddin Washington University of Science and Technology 2900 Eisenhower ave AlexandriaVA22314 United States Bangladesh University of Business and Technology Dhaka Bangladesh Stamford University Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh American International University-Bangladesh Department of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798331534400

The rapid growth of financial markets has necessitated developing more precise and efficient frameworks to predict stock price movements. This paper introduces MiniBert24, a lightweight, transformer-based architecture specifically designed for stock market movement prediction by leveraging news related to finance and past stock price information. The model is tailored for high computational efficiency, combining a reduced number of transformer layers with fewer attention heads while retaining strong predictive power. We utilize a unique dataset incorporating historical headlines from the Reddit WorldNews channel and Dow Jones Industrial Average (DJIA) data, creating a binary classification task to forecast daily stock price movement. MiniBert24 obtains an excellent AUC of 96.7% and an F1-score of 96.43%, outperforming standard transformer models while reducing computational overhead. These results demonstrate the potential of MiniBert24 to serve as a robust tool for market prediction in real-time trading environments. The model's ability to fuse textual data with stock market indicators provides a compelling solution for financial analysts and algorithmic traders seeking efficient, high-performing predictive models. © 2024 ieee.

关键词： Prediction models

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Cross-Platform Mobile Application Development for Smart Services 22

Cross-Platform Mobile Application Development for Smart Serv...

引用

ieee Joint 22nd international symposium on computational intelligence and Informatics / 8th ieee international Conference on Recent Achievements in Mechatronics, automation, Computer Science and robotics (CINTI-MACRo)

作者： Wheeler, D. Olszewska, J. I. Univ West Scotland Glasgow Scotland

ISBN: (纸本)9798350398823

Current technological advances and the growth of smart applications and services require the development of multi-platform mobile applications. Writing software to run on multiple computing platforms can involve a large amount of duplicate effort. This duplicate effort can take the form of re-implementing the business logic in a different language, re-implementing the user interface or, in many cases, re-implementing both for each platform. There are many cross-platform frameworks to help reduce or eliminate this effort, but they make compromises on performance or user experience. Hence, this paper presents a cross-platform framework whose goal is to reduce this compromise by aiming to minimise platform-specific code, but not eliminate it. Using this method will allow all platforms to have a feature-rich, native interface, with most of the code, including user interface code, contained in a cross-platform library used by all platforms. Hence, this work establishes a framework for developing cross-platform GUI applications, involving the design and development of a common library as well as translation libraries for iOS, Android, and Windows. This developed framework has been successfully applied to the implementation of a real-world application for smart lift services.

关键词： Web Infrastructures and Devices Mobile Web Intelligent Applications Smart City Applications and Services Smart Living Smart Lifts Industry 4.0 and Smart factory Connected intelligence

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VOOM: Robust Visual Object Odometry and Mapping using Hierarchical Landmarks

VOOM: Robust Visual Object Odometry and Mapping using Hierar...

引用

ieee international Conference on robotics and automation (ICRA)

作者： Wang, Yutong Jiang, Chaoyang Chen, Xieyuanli Beijing Inst Technol Sch Mech Engn Beijing Peoples R China Beijing Inst Technol Yangtze Delta Reg Acad Jiaxing Peoples R China Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha Peoples R China

ISBN: (纸本)9798350384581;9798350384574

In recent years, object-oriented simultaneous localization and mapping (SLAM) has attracted increasing attention due to its ability to provide high-level semantic information while maintaining computational efficiency. Some researchers have attempted to enhance localization accuracy by integrating the modeled object residuals into bundle adjustment. However, few have demonstrated better results than feature-based visual SLAM systems, as the generic coarse object models, such as cuboids or ellipsoids, are less accurate than feature points. In this paper, we propose a Visual Object Odometry and Mapping framework VOOM using high-level objects and low-level points as the hierarchical landmarks in a coarse-to-fine manner instead of directly using object residuals in bundle adjustment. Firstly, we introduce an improved observation model and a novel data association method for dual quadrics, employed to represent physical objects. It facilitates the creation of a 3D map that closely reflects reality. Next, we use object information to enhance the data association of feature points and consequently update the map. In the visual object odometry backend, the updated map is employed to further optimize the camera pose and the objects. Meanwhile, local bundle adjustment is performed utilizing the objects and points-based covisibility graphs in our visual object mapping process. Experiments show that VOOM outperforms both object-oriented SLAM and feature points SLAM systems such as ORB-SLAM2 in terms of localization. The implementation of our method is available at https://***/yutongwangBIT/***.

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The origin and primary areas of application of natural language processing 22

The origin and primary areas of application of natural langu...

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作者： Krutilla, Zsolt Kovari, Attila Obuda Univ Doctoral Sch Appl Informat & Appl Math Budapest Hungary Obuda Univ Alba Regia Tech Fac Inst Engn Budapest Hungary

ISBN: (纸本)9798350398823

Natural language processing with computers, i.e. NLP, is increasingly gaining ground in today's IT. The purpose of this study is to analyze the articles published in the NLP topic and to summarize it from several aspects, especially with regards to the development and practical application of the technology. The idea of NLP as a technology was not formulated in the 21st century, but already in the 1950s, relevant research was carried out and theories were created regarding how a machine can become capable of interpreting human speech. Technology has come a long way from simple algorithms to Deep Learning, but it has not yet solved the problem of machine processing of natural language. The research has confirmed the idea that mathematical modelling and models based on statistical probability dominated the field in the early days of the discipline and before the advent of computers. However, despite technological progress, no solution has yet been found to the comprehensive and all-encompassing problem of natural language processing with computers. To determine the future research area of NLP, we need to take into account the text files on which the technology is based. From this perspective, we can focus on the insurance and banking sectors, where a series of administrative processes take place where centralized paper processing is the incoming data set of the process and a database going back several years may be available.

关键词： Natural Language Processing Deep Learning Artificial intelligence

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