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

共 47828 条记录，以下是651-660 订阅

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A Simple Modal Parameter Identification Method for On-Orbit Satellite with Flexible Appendages 6

A Simple Modal Parameter Identification Method for On-Orbit ...

引用

6th international conference on robotics, Control and Automation engineering, RCAE 2023

作者： Lian, Danxu Yang, Zhen Wang, Ning Yang, Sen School of Astronautics Harbin Institute of Technology Harbin China Shanghai Institute of Spaceflight Control Technology Shanghai China

ISBN: (纸本)9798350315301

Satellites generally have flexible appendages such as solar arrays, and the vibration frequency and damping ratio of the flexible appendages are critical for satellite attitude control system design, but accurate modal parameters cannot be obtained by ground experiments and finite element analysis. In this paper, the relationship between open-loop and closed-loop modal parameters is derived by analyzing the dynamic equations of flexible attitude when the satellite attitude is in the earth-pointing state. A more simple modal parameters identification method based on OKID and ERA algorithm is proposed, and the effectiveness of the method is demonstrated by numerical simulations. © 2023 IEEE.

关键词： Numerical methods

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Assist-Arm Configuration Analysis for Dual-Arm Space Robots in Capture Task 7

Assist-Arm Configuration Analysis for Dual-Arm Space Robots ...

引用

7th international conference on Intelligent robotics and Control engineering, IRCE 2024

作者： Zhang, Long Chinese Academy of Sciences Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Beijing China

ISBN: (纸本)9798350352399

Space robots will play an important role in orbital missions in the future, and multi-arm space robots have a wider range of applications due to their flexible structure. For a dual-arm space robot performing capture task in the mission-arm and assist-arm mode, this paper firstly from an innovative perspective regards the combination of the assist arm and the actual base as a virtual base with adjustable dynamic parameters, and based on this, establishes the system dynamics model. Thereafter, the maximum contact force and the base attitude disturbance are derived as the indicators which are proved that different configurations of the assist arm can affect. Finally, through the simulation experiments, it can be seen that different configurations of the assist arm will have an impact on the capture task of the mission arm, especially for the base attitude disturbance caused by contact. © 2024 IEEE.

关键词： Robotic arms

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Systematic Review of GAN for Enhancing Efficiency in AI in Gaming

Systematic Review of GAN for Enhancing Efficiency in AI in G...

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2024 international conference on advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Singh, Jasdeep Islam, Sardar M. N. Tewatia, Manav Garg, Deepanshu Fatima, Neda School of Computer Applications Manav Rachna International Institute of Research and Studies Faridabad India Victoria University Department of Computer Science and Engineering Melbourne Australia School of Engineering and Technology Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (纸本)9798350388800

A thorough analysis of Generative Adversarial Networks (GANs) and how they might be used to improve AI gaming efficiency is presented in this research. Game developers now have never-before-seen possibilities for innovation, immersion, and personalization thanks to GANs, which have become a potent tool for producing realistic data samples across a variety of fields. This review investigates the architecture, training methods, applications, difficulties, and potential future directions of GANs in AI gaming through a thorough analysis of the body of research and publications. The review demonstrates how GANs can transform the game industry by highlighting their many uses in procedural content generation, texture synthesis, character animation, environment design, and content customization. Nevertheless, there are certain difficulties in integrating GANs into AI games, such as computational resources, mode collapse, training instability, and assessment *** well as legal matters. To tackle these obstacles, multidisciplinary research endeavors and cooperation across academic institutions, corporate entities, and regulatory agencies are necessary. Going forward, new avenues for study and developments in GAN technology hold great potential for getting over these roadblocks and opening up fresh possibilities in AI gaming. We can create a future where AI powered gaming experiences change the parameters of interactive entertainment and provide players globally with more engaging, varied, and personalized gaming experiences by utilizing the possibilities of GANs and addressing the related obstacles. © 2024 IEEE.

关键词： Generative adversarial networks

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Tomato Leaf Disease Detection Using Convolution Neural Network

Tomato Leaf Disease Detection Using Convolution Neural Netwo...

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

作者： Murthy, G. N. Keshava Abisha, K. Bhoomika, H.B. Disha, S.R. Pallavi, K.R. Siddaganga Institute of Technology Department of Electronics and Instrumentation Engineering Karnataka Tumakuru India

ISBN: (纸本)9798350388602

Plant diseases cause low agricultural productivity. Plant diseases are challenging for the majority of farmers to control and identify. To reduce future losses, early disease diagnosis is necessary. This study presents a deep learning approach for detecting tomato leaf diseases using Convolutional Neural Networks (CNNs). The proposed method involves preprocessing the tomato leaf images, followed by training the CNN model to classify them into one of ten categories: healthy, yellow leaf curl virus (YLCV), bacterial spot (BS), early blight (EB), leaf mold (LM), spectoria leaf spot (SLS), target spot (TS), two spotted spider mite spot(TSSMS), mosaic virus(MV) and late blight (LB). The data sets are collected from Kaggle. These results demonstrate the effectiveness of the proposed approach in detecting tomato leaf diseases. The automated approach can aid in the early detection and prevention of tomato diseases, which can ultimately help in improving the yield and quality of tomato crops. © 2024 IEEE.

关键词： Fruits

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A comprehensive survey on client selections in federated learning

A comprehensive survey on client selections in federated lea...

引用

international conference on Innovation and Technological advances for Sustainability, ITAS 2023

作者： Gouissem, A. Chkirbene, Z. Hamila, R. College of Computing and Information Technology University of Doha for Science and Technology Qatar Electrical Engineering Qatar University Qatar

ISBN: (纸本)9781032803722

Federated Learning (FL) is a rapidly growing field in machine learning that allows data to be trained across multiple decentralized devices. The selection of clients to participate in the training process is a critical factor for the performance of the overall system. In this survey, we provide a comprehensive overview of the state-of-the-art client selection techniques in FL, including their strengths and limitations, as well as the challenges and open issues that need to be addressed. We cover conventional selection techniques such as random selection where all or partial random of clients is used for the trained. We also cover performance-aware selections and as well as resource-aware selections for resource constrained networks and heterogeneous networks. We also discuss the usage of client selection in model security enhancement. Lastly, we discuss open issues and challenges related to clients selection in dynamic constrained, and heterogeneous networks. © 2025 The Author(s).

关键词： Federated learning

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Research on Self-diagnosis Method of Nickel top-Blowing Furnace Sensor Based on Causal Graph Neural Network 7

Research on Self-diagnosis Method of Nickel top-Blowing Furn...

引用

7th international conference on robotics, Control and Automation engineering, RCAE 2024

作者： Jiang, Dongnian Li, Junkuan Lanzhou University of Technology College of Electrical and Information Engineering Lanzhou China

ISBN: (纸本)9798350355642

As the scale of nickel top-blowing furnace melting systems expands and the complexity of the equipment increases, the maintenance workload for sensors has gradually increased. Manual maintenance approaches consume significant resources and are not suitable for the development of smart sensors. To address this issue, an unsupervised sensor self-diagnosis method is proposed in this paper. Firstly, the Pearson correlation coefficient is introduced to construct a graph network of sensors of the same type, and node embedding is used to form super nodes of sensors. Secondly, transfer entropy is utilized to assess the causal relationships between these super nodes, based on which a super node graph network is constructed. Finally, a Multi-Head Self-Attention Gated Graph Convolutional Network (MCB-GCN) is introduced. This network captures spatial and temporal dependencies of nodes and, with the help of an enhanced fusion module, achieves time-series prediction for sensors. Subsequently, sensor self-diagnosis is achieved through a standardized residual consistency check algorithm. Simulation experiments on the measurement system of a nickel top-blowing furnace demonstrate the effectiveness of the proposed method, in addressing the problem of autonomous sensor diagnostics. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimization of Data Aggregation Techniques in Wireless Sensor Networks

Optimization of Data Aggregation Techniques in Wireless Sens...

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

作者： Ramakrishnan, Kannan Raja, M. Kalaiarasi, K. Balamurugan, M. Sathesh, M. Saveetha Engineering College Department Ece Chennai602105 India Saveetha University Saveetha School of Engineering Department of Ece Chennai602105 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Ece Chennai602105 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Avadi Chennai India

ISBN: (纸本)9798350388602

Data aggregation is an essential factor in the Wireless Sensor Networks (WSNs) since it determines energy consumption and the precision of the data as well as the lifespan of the network. This work describes a new data aggregation algorithm that solves these problems since it reduces unnecessary broadcasts and groups information while still retaining critical details. The EM algorithm has been evaluated through extensive simulation and its application in real-world scenarios proved that it offers a 35% reduction in energy consumption, data accuracy, and network life cycle that is 30% longer than the existing approaches. The ability to work with different types of networks including the dynamic topologies and high data rate also exhibit the benefits of the algorithm. These results can help build better and more effective WSNs in the future as applicable in environment, health and industry sectors. This research presents a viable solution to the issue of data aggregation in WSN thus opening up the possibility of apply in different fields. © 2024 IEEE.

关键词： Data aggregation

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Smart Attendance Management System Using Face Recognition 1

Smart Attendance Management System Using Face Recognition

引用

1st international conference on advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Rawat, Shivam Goyal, Shweta Rawat, Nikita Graphic Era Hill University Dept. Computer Science & Engineering Dehradun India Graphic Era deemed to be University Dept. of Electrical Engineering Dehradun India

ISBN: (纸本)9798331518578

Managing attendance in educational institutions is often a time-consuming and error-prone task, with traditional methods like roll calls or sign-in sheets being inefficient and susceptible to proxy attendance. This paper presents a Smart Attendance Management System that leverages facial recognition technology to automate the attendance process. By using cameras to capture images of students, the system recognizes their faces and marks their attendance in real time. Developed in Python, the system employs the K-Nearest Neighbours (KNN) algorithm for accurate face recognition. Once attendance is recorded, it is stored in a CSV file and can be visualized using the flask web application. The proposed system significantly reduces manual effort, ensures accuracy, and operates efficiently in real-time environments. © 2025 IEEE.

关键词： Nearest neighbor search

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Prediction of pulmonary arterial hypertension from HRCT chest in post-COVID patients using double attention U-NeT model 5th

Prediction of pulmonary arterial hypertension from HRCT ches...

引用

5th international conference on advances in AI for Biomedical Instrumentation, Electronics and Computing, ICABEC 2023

作者： Nazir, Azra Naaz, Roohie Qureshi, Shaima Nazir, Nidha School of Computer Science and Engineering VIT Bhopal University Bhopal Indore Highway MP India Department of Computer Science and Engineering National Institute of Technology J&K Srinagar India Department of Radiology State Medical Hospital J&K Srinagar India

ISBN: (纸本)9781032642987

There is an unusual spike in the number of cardiac attacks POST COVID- 19 worldwide, leading to sudden deaths. The virus’s effect on the heart can be due to direct viral invasion, systemic inflammation, increased clotting, and hypoxia. This has led to an increase in the frequency of Pulmonary arterial hypertension (PAH) cases. PAH affects the arteries in the lungs and the right side of the heart and can be life- threatening. The change in the dimensions of the pulmonary artery in PAH is diagnosed from a CT scan. With no cure for the disease, it is essential to diagnose the condition to provide special care to the patient. In this paper, a double attention U-Net model with attention blocks in its down-sampling and up-sampling paths is employed to extract the variation in the CT of a high-risk patient. The experimental results show that the proposed methodology has 93.8% classification accuracy, which is a 6.8% improvement compared to the VGG16 model. © 2024 The Author(s).

关键词： Computerized tomography

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Targetless LiDAR-Camera Auto Calibration (TLAC): Leveraging Foundation Models for Enhanced Accuracy and Efficiency 15

Targetless LiDAR-Camera Auto Calibration (TLAC): Leveraging ...

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15th international conference on Information and Communication technology Convergence, ICTC 2024

作者： Lee, Dongjin Kang, Jungyu Han, Seung-Jun Min, Kyoung-Wook Park, Cheong Hee ETRI Cnu Daejeon Korea Republic of ETRI Daejeon Korea Republic of Dept. of Computer Science and Engineering Daejeon Korea Republic of

ISBN: (纸本)9798350364637

In this paper, we introduce a novel target-less LiDAR-Camera Auto Calibration (TLAC) method, leveraging foundation models to achieve high-precision alignment without the need for physical calibration targets. Our approach utilizes advanced processing techniques for both point cloud data (PCD) and images, incorporating voxel downsampling, normal estimation, and clustering for PCDs, along with semantic segmentation and depth estimation for images. We validate our method using the widely recognized KITTI odometry benchmark dataset, specifically focusing on sequence 00 for a comparative analysis with existing techniques. Our experimental results demonstrate significant improvements in calibration accuracy, showcasing the potential of our method to facilitate enhanced sensor fusion for autonomous vehicles and robotics. This study not only advances the field of sensor calibration but also highlights the importance of integrating diverse data processing techniques for improved environmental perception. © 2024 IEEE.

关键词： Semantic Segmentation

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