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检索条件"任意字段=International Conference on Advances in Science, Engineering and Robotics Technology"

共 47670 条记录，以下是461-470 订阅

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A Multi-party Asymmetric Self-play Algorithm and Its Application in Multi-USV Adversarial Game Simulations 24

A Multi-party Asymmetric Self-play Algorithm and Its Applica...

引用

2024 international conference on Machine Learning, Pattern Recognition and Automation engineering, MLPRAE 2024

作者： Rao, Jinjun Wang, Cong Liu, Mei Lei, Jingtao Giernacki, Wojciech Department of Precision Mechanical Engineering School of Mechatronic Engineering and Automation Shanghai University China Institute of Robotics and Machine Intelligence Faculty of Automatic Control Robotics and Electrical Engineering Poznan University of Technology Poland

ISBN: (纸本)9798400709876

Aiming at the problem that the combination of self-play (SP) and deep reinforcement learning (DRL) only involves two-party games and the policy learning of each party is limited, a multi-party asymmetric self-play algorithm (MASP) is proposed. Firstly, by improving the ELO scoring system, the ELO scoring takes into account each party of the unmanned surface vehicle (USV) clusters, and the imbalance of the number of USV clusters, so that the frequency of exchanging strategies of all USV clusters in the confrontation process is equal. Secondly, it ensures that USV clusters have a balanced combat ability, and at the same time ensures that the combat ability of all parties is strong and weak and the gap is not too wide. In addition, the parameters are dynamically set to reduce the update frequency of the policy of the stronger party. The experimental results show that the MASP can make the USV clusters learn more effective policies, have a shorter game time, and obtain higher rewards and ELO scores in the simple 2v2 adversarial game scenario and the three-party game scenario of a warship escort mission. © 2024 ACM.

关键词： Deep reinforcement learning

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Design of a Parallel Wrist Exoskeleton for Rehabilitation Post-Stroke 10

Design of a Parallel Wrist Exoskeleton for Rehabilitation Po...

引用

10th international conference on Automation, robotics, and Applications, ICARA 2024

作者： Valayil, Tony Punnoose Tanev, Tanio K. Institute of Robotics Bulgarian Academy of Sciences Sofia Bulgaria

ISBN: (纸本)9798350394245

A three identical parallel actuated leg exoskeleton having a central fixed leg is designed and controlled to perform wrist flexion and extension and wrist ulnar and radial deviation motions. The motivation behind the development of this robot is that, robot-assisted physical therapies aids in rehabilitation for paralyzed upper limb in stroke-affected patients. The exoskeleton comprises of a base and moving platforms connected independently by legs. The human lower arm acts as the central leg of the exoskeleton to provide the required degrees of freedom. The exoskeleton is attached to the operator's lower arm. As compared to other exoskeletons, this device uses a parallel wrist manipulator which produces spherical motion about a fixed center. In this research, inverse kinematics solution was used for programming to control the parallel wrist for performing rehabilitation task. The majority of the robot parts are manufactured using additive manufacturing technology. © 2024 IEEE.

关键词： 3D printing

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Optimized Audio Classification with Convolutional Neural Network Ensembles

Optimized Audio Classification with Convolutional Neural Net...

引用

2024 IEEE international conference on Recent advances in science and engineering technology, ICRASET 2024

作者： Sudheesh, K.V. Swetha, K.T. Gireesh Babu, C.N. Ravikiran, H.N. Puttegowda, Kiran Sunil Kumar, D.S. Vidyavardhaka College of Engineering Department of Electronics and communication Engineering Karnataka Mysuru India Atme College of Engineering Department of Electronics and communication Engineering Karnataka Mysuru India Bms Institute of Technology and Management Department of Cse Karnataka Bengaluru India Bgs Institute of Technology Adichunchanagiri University Department of Electronics and CommunicationEngineering BG Nagara Mandya India Anatta Innovations Karnataka Bangalore India

ISBN: (纸本)9798350388602

In this work, we present a structured approach to a typical audio classification task, focusing specifically on Music Genre Classification (MGC). We develop and test three different Convolutional Neural Network (CNN) architectures, each containing various layer configurations, including Conv2d, Dropout, and Batch Normalization layers. These variations allow us to analyze the impact of each layer type on the overall model performance, providing insights into their individual contributions to effective genre classification. After that, we apply PROD fusion ensemble technique to combine multiple predicted probability vectors at inference phase to further improve the model performance. By conducting experiments on the best model performing an accuracy of 90.8%, which is a good accuracy and potential for real-life applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Classification of Football Player Actions Using Sensing Data 3

Classification of Football Player Actions Using Sensing Data

引用

3rd international conference on Image Processing and robotics, ICIPRoB 2024

作者： Hirasawa, Yukito Kinoshita, Yuichiro Kamarudin, Latifah Munirah Zakaria, Ammar University of Yamanashi Department of Computer Science and Engineering Yamanashi Japan Universiti Malaysia Perlis Faculty of Electronic Engineering and Technology Perlis Malaysia

ISBN: (纸本)9798350374766

This study classifies the actions of football players using sensing data acquired from wearable sensors attached to players and the ball. More than 800 sensing data with the labels of five types of player actions were created as a dataset. The neural networks were trained using 19 input items created by considering time-series variations in player and ball locations. The trained neural network model demonstrated a classification accuracy of 84.0%. The model successfully obtained sufficient accuracy for all types of *** results demonstrate that the sensing data and created input items can be effectively utilized for classifying the actions of football player. © 2024 IEEE.

关键词： Wearable sensors

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Crop Recommendation Systems using Machine Learning Algorithms

Crop Recommendation Systems using Machine Learning Algorithm...

引用

2023 international conference on Recent advances in science and engineering technology, ICRASET 2023

作者： Gokila Brindha, P. ReenaSri, S. Dhanushree, U. Sivadhanu, K. Kongu Engineering College Department of Computer Technology-UG Erode Perundurai India

ISBN: (纸本)9798350306927

Agriculture is the foundation of all the countries. Due to the decreased size of a farming parkland, it has become a most important issue in picking the maximum fitting crop based on current factors in a particular field. The difficulty of young farmers in India to estimate the ideal crop based on their needs is one of the most significant problems they face. This problem arises due to the ecological factors like rainfall, humidity, temperature etc. Machine learning (ML) a branch of AI enables computer to be trained based on the experience being clearly programmed. The goal of ML is to create computer programmes so as to access the data and exploit it to learn for themselves. The crop recommender assistant will suggest the proper crop considering the parameters such as NPK nutrients, humidity, temperature along with pH values with the assistance of Machine Learning algorithms. © 2023 IEEE.

关键词： Crops

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Dynamic Obstacle Avoidance of An Omnidirectional USV

Dynamic Obstacle Avoidance of An Omnidirectional USV

引用

2024 IEEE international conference on Real-Time Computing and robotics, RCAR 2024

作者： Wang, Ziyou Zhang, Lianxin Lin, Yang Qian, Huihuan The Chinese University of Hong Kong School of Science and Engineering Shenzhen China The Chinese University of Hong Kong Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798350372601

This paper addresses the significance of dynamic collision avoidance for robots operating in environments with numerous obstacles. We propose a path planning algorithm based on RRT for omnidirectional unmanned surface vessels (USV) to navigate around static and dynamic obstacles. Our algorithm employs linear regression to predict future positions of dynamic obstacles based on their current states, including positions and velocities. These predicted positions are then incorporated into the RRT* path planning process. The algorithm aims to achieve a prediction after a fixed period, ensuring real-time collision avoidance. In addition, simulations explore various map and prediction times to evaluate the algorithm's performance, ultimately determining that a prediction time of 0.5 s is optimal. This experiment underscores the algorithm's capability to navigate and respond to complex environments effectively. © 2024 IEEE.

关键词： Motion planning

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Hybrid Edge-Cloud Approach for Renewable Energy Management Using Deep Learning with Predictive Analytics

Hybrid Edge-Cloud Approach for Renewable Energy Management U...

引用

2024 IEEE international conference on Recent advances in science and engineering technology, ICRASET 2024

作者： Rekha, V. Sharmila, N. Komala, M. Pavithra, G.S. Mallikarjunaswamy, S. Naveen, K.B. Shilpa, M. Sheela, S. Department of Computer Science and Engineering Bangalore India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Computer Science and Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Bgs Institute of Technology Adichunchanagiri University Dept of Electronics and Electrical Engineering BG Nagara India Bengaluru Institute of Technology Dept of Information Science and Engineering Bengaluru India Global Academy of Technology Dept of Computer Sceinec and Engineering Bengaluru India

ISBN: (纸本)9798350388602

The integration of renewable energy sources into smart grids requires advanced management systems to optimize energy production, storage, and distribution. Conventional methods like Static Energy Allocation (SEA) and Rule-Based Control (RBC) struggle with real-time adaptability, leading to inefficiencies in handling the variable nature of renewable energy. SEA and RBC lack the predictive capabilities needed to effectively manage dynamic energy generation, resulting in increased energy losses and operational *** paper proposes a Hybrid Edge-Cloud Renewable Energy Management System (HECREMS) using Deep Learning with Predictive Analytics (DLPA). HECREMS integrates edge computing for real-time data processing with cloud computing for enhanced predictive analytics, providing a robust framework for efficient energy management. The proposed system leverages the low latency of edge devices and the computational power of the cloud to improve energy forecasting and decision-making. HECREMS outperforms traditional SEA and RBC methods, achieving a 0.25% improvement in energy utilization efficiency, a 0.20% reduction in operational costs, and a 0.30% increase in system responsiveness. By addressing the drawbacks of conventional approaches, HECREMS demonstrates significant advancements in the efficient and sustainable management of renewable energy within smart grids. © 2024 IEEE.

关键词： Cloud analytics

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An Adaptive Golden Jackal Optimization Algorithm for Mobile Robotic Path Planning

An Adaptive Golden Jackal Optimization Algorithm for Mobile ...

引用

2024 international conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Chen, Siwen Zheng, Xiujuan Shanghai United Imaging Healthcare Co. Ltd. Shanghai China Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and Technology Wuhan China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Given the shortcomings of the traditional Golden Jackal Optimization (GJO) algorithm, including limited accuracy and slow convergence speed in solving mobile robot path planning problems, an improved adaptive Golden Jackal Optimization (IAGJO) method is proposed. Firstly, a nonlinear adaptive energy strategy is presented to adjust the energy decay pattern, balancing global and local search. Secondly, an enhanced position update mechanism based on Cauchy and Gaussian mutation increases population diversity and guide the search based on optimal individuals, thereby facilitating efficient exploration of unknown regions and avoiding local optima. Finally, the IAGJO algorithm is applied to mobile robot path planning (MRPP), demonstrating that the IAGJO achieves shorter path lengths and higher search efficiency, exhibiting significant advantages over existing methods. © 2025 SPIE.

关键词： Optimization algorithms

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Design of Universal Integrated Robot Joint and Its Applications in Multiple Fields 2

Design of Universal Integrated Robot Joint and Its Applicati...

引用

2nd international conference on Automation, robotics and Computer engineering, ICARCE 2023

作者： Ren, Hongxi Qiang, Ligang Zhang, Kangjun Song, Dingan Xiao, Taokang Li, Li Guizhou Aerospace Control Technology Co. Ltd Aerospace Jiangnan Group Co. Ltd Guiyang China

ISBN: (纸本)9798350308341

This article mainly focuses on the design and prototype development of a universal integrated joint, and analyzes the design of the joint and the motor used. By verifying and optimizing weak materials such as enameled wires, heat shrink tubes, and lead wires, and applying them to the entire product, core components such as motors and rotary transformers can have a longer lifespan in complex environments. At the same time, universal interfaces are designed for joints to ensure that integrated joints can be widely used in various fields such as rehabilitation, medical exoskeletons, and nuclear industrial robots. Finally, practical use cases are provided in the article. © 2023 IEEE.

关键词： Industrial robots

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Design of Visualization Simulation Software for Collaborative Formation Control of Mobile Robot Cluster 4

Design of Visualization Simulation Software for Collaborativ...

引用

4th international conference on Artificial Intelligence, robotics, and Communication, ICAIRC 2024

作者： Liu, Haiming Luo, Jiaxiang Zhang, Mei Chen, An South China University of Technology School of Automation Science and Engineering Guangzhou China

ISBN: (纸本)9798331531225

Simulation tools are important auxiliary tools for scientific research. Design method of a practical and convenient simulation software for visual simulation of collaborative formation control for mobile robot cluster was discussed in this paper. The simulation software was designed in two main parts: human-computer interaction and visualization display of algorithm simulation. The software was implemented based on Qt development platform and some key methods of design and implementation involved were detailed. The simulation results of this software under different formation control algorithms are shown to demonstrate its practicality and flexibility in applications. © 2024 IEEE.

关键词： Mobile applications

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