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Parameter Uncertainty Analysis in Precise Pointing control of Flexible Spacecraft

IFAC-PapersOnLine 2022年第20期55卷 241-246页

作者： János Bezsilla Béla Takarics Bálint Vanek Jian Guo Systems and Control Laboratory ELKH Institute for Computer Science and Control Budapest Hungary Faculty of Aerospace Engineering Delft University of Technology Delft The Netherlands

This article focuses on the validation of a classical PID controller scheme for flexible spacecraft with regards to the effect of parameter uncertainty on system stability and pointing precision. A high-fidelity simulation environment with external disturbances was built in Simulink using a control-oriented model of an Earth-observing satellite with a flexible appendage and on-board microvibration sources in orbit around the planet. Then, a PID control loop was designed with sensor dynamics, time delay behaviour, and a smooth trajectory generator. After declaring the natural frequencies, damping ratio, and rotation angle of the appendage, as well as the propellant tank mass to be uncertain, two worst-case scenarios were identified. Comparing the response of worst-case systems with nominal settings, only a minor drop has been found in the phase margins, with little to no difference in the pointing errors (smaller than ±2 arcsec for both roll and pitch).

关键词： flexible spacecraft nonlinear dynamics attitude tracking precise pointing control PID control parameter uncertainty

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Feature-Enhanced Federated Graph Convolutional Network for Major Depression Disorder Identification

SSRN

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SSRN 2024年

作者： Liu, Chun Shan, Shengchang Ding, Xinshun Wang, Huan Jiao, Zhuqing School of Computer Science and Artificial Intelligence Changzhou University Changzhou213164 China School of Microelectronics and Control Engineering Changzhou University Changzhou213164 China

Background and ObjectiveDirectly training a graph convolutional neural network (GCN) on a multi-site dataset poses a challenge to protecting the privacy of patients with major depressive disorder (MDD). Federated learning (FL) can train a global model without sharing data, but some previous methods failed to take advantage of non-imaging information, such as gender, age, education years, and site ***, we propose a feature-enhanced federated graph convolutional network (F2edGCN) for the effective identification of MDD. Specifically, both linear and non-linear information from the functional connectivity matrix is exacted via different correlation measures. Then a dual graph attention network (DGAT) module is designed to capture complementary information between them. The population graph is constructed based on the similarity of non-image information among individuals and input into the federated graph convolution network (FedGCN) module, which ensures that the local model effectively learns the features generated by *** proposed framework achieves a classification accuracy of 64.12% on 841 subjects from 3 different sites outperforms some state-of-the-art centralized learning and federated learning *** study fully mines the feature information in brain functional connectivity and addresses the nuisance variables in large samples from multi-site dataset while protecting patient privacy. © 2024, The Authors. All rights reserved.

关键词： Convolution

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Task Offloading and Resource Allocation Scheme based on Cooperative Game

Task Offloading and Resource Allocation Scheme based on Coop...

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2022 IEEE International Conference on Artificial Intelligence and computer Applications, ICAICA 2022

作者： Benhong, Zhang Liang, Han Xiang, Bi Hefei University of Technology School of Computer Science and Information Engineering Hefei China Hefei University of Technology Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Anhui Hefei230009 China

ISBN: (数字)9781665499910

ISBN: (纸本)9781665499910

Mobile edge computing plays an irreplaceable role in 5G network and IoT technology. In addition, it is very urgent for MEC to find an effective task offloading decision and resource allocation method. Although there are many methods, they rarely consider the situation that the uneven processing capacity of MEC server leads to excessive task concentration during the process of offloading. In this article, a cooperative game is proposed to optimize the offloading and resource allocation schemes. The cost function is calculated by calculating the time and power consumption of tasks on different devices, and the task priority is determined by the difference between local calculation and MEC server calculation. In addition, the Lagrange multiplier method is used to allocate various resources required by the task. At the same time, the high-priority task is offloaded. On this basis, the task offloading scheme required by each server is obtained through multi-server cooperative game. The simulation results show that compared with the traditional algorithm, this scheme can not only reduce the total system overhead by about 31 % while meeting the task requirements, but also increase the system load rate by about 20 %. It maximizes the use of MEC server resources and balances the load between MEC servers with different computing capabilities. © 2022 IEEE.

关键词： Mobile edge computing

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Application of Hybrid Variable Admittance Force Tracking and Fixed-time Position control for Robot Interaction Tasks

Application of Hybrid Variable Admittance Force Tracking and...

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International Conference on control, Mechatronics and Automation (ICCMA)

作者： Hong Zhan Chao Zeng Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China Department of Computer Science University of Liverpool Liverpool UK

ISBN: (数字)9798331517519

ISBN: (纸本)9798331517526

This paper applies the proposed hybrid force and position control method to the physical robot system with interaction tasks to further improve our previous study. In the control scheme, the variable stiffness based on proportional integral derivative(PID) admittance control is adopted for interaction force tracking and the radial basis function neural network(RBFNN) based fixed-time control is designed to ensure position tracking. We have performed interaction tasks based on a Baxter robot for drawing on the plane and slope plane with different expected interaction forces and position trajectories. The experiment results indicate that the method performs well in terms of interaction force and trajectory tracking.

关键词： Mechatronics Automation Trajectory tracking Force Dynamics Position control Trajectory Robots Admittance control Convergence

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Internet of things-based automation design and organizational innovation of manufacturing enterprises

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Materials Today: Proceedings 2022年 56卷 1769-1775页

作者： Babu, T. Roopa, H. Shukla, Arvind Kumar Stalin David, D. Jayadatta, S. Rajesh, A.S. Department of Instrumentation and Control Engineering St. Joseph's College of Engineering Chennai India Department of Information Science and Engineering Bangalore Institute of Technology Bangalore India Department of Computer Application IFTM University Moradabad India Department of CSE IFET College of Engineering Villupuram India Department of Management Studies KLE's IMSR BVB Campus Hubli India Department of Mechanical Engineering JSS S&T University Mysore India

The Internet of Things (IoT) foresaw a transparent network between the real and virtual worlds. IoT offers a promising potential to develop strong market and industrial applications. The export-oriented economy of industrial manufacturers relies on product and service creativity to retain a competitive advantage. The IoT-based applications of innovative manufacturing companies (IOTs), where most companies are involved and engaged in many of the case IoT-based applications, is motivated by computing capacity, networking, cost reduction and electronic components. A common motive is the Internet of Things applications and the improvement of after-sales service activities. The suggested Linear Product Design Regression (LPDR) algorithm classifies business product innovation design based on consumer input to support business development. Businesses, companies and the technology industry: the inhibitors hinder the development of the four IT applications. IoT offers one of the latest innovations in manufacturing to allow us to meet key challenges. The product design and development engineering are engaged in utilizing the Internet of Things (IoT) to create comprehensive policies and policy creation for manufacturing *** support business development. Businesses, companies and the technology industry: the inhibitors hinder the development of the four IT applications. To allow us to meet key challenges and to create comprehensive policies and policy creation for manufacturing companies. © 2021

关键词： Product design

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Discrete control algorithms for systems with big dead time for control101 MATLAB toolbox

Discrete control algorithms for systems with big dead time f...

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Cybernetics & Informatics (K&I)

作者： Ruth Bars Gyula Max J. Anthony Rossiter László Keviczky Department of Automation and Applied Informatics Budapest University of Technology and Economics Budapest Hungary School of Electrical and Electronic Engineering University of Sheffield Sheffield UK Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9798331541811

ISBN: (纸本)9798331541828

The first course of control is under a critical review. Both the teaching material covered and the teaching methods require new considerations. Introducing interactivity in the education process makes the learning more successful and enjoyable. MATLAB provides an effective environment for learning and applying different disciplines. control101 is a new MATLAB toolbox under development which provides tools for interactive learning of control disciplines. This paper presents the framework for teaching discrete control algorithms applied for processes containing large dead times.

关键词： Visualization Systematics PI control Reviews Education Laboratories Process control Prediction algorithms PD control MATLAB

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A Concept Drift Tolerant Abnormal Detection Method for Network Traffic

A Concept Drift Tolerant Abnormal Detection Method for Netwo...

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International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC

作者： Xu Wang Li Han Shimin Sun Guowei Xu School of computer science and engineering Tianjin University of Technology National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China School of software Tiangong University Tianjin China

we propose a cutting-edge solution that leverages passive adaptive methods based on ensemble learning to effectively detect anomalous traffic in data streams. Our approach tackles the issue of concept drift by integrating various deep learning models, each offering a unique perspective to learn data features. By doing so, our solution can accurately capture the distribution of data concepts and propose weight layers to make the model adaptable to changes caused by concept drift. In a series of experiments conducted on public datasets, our model demonstrated remarkable effectiveness in detecting anomalies, achieving accuracy rates of over 97% after adapting to concept drift.

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Enabling IoT Continuous Connectivity in Smart Spaces 22

Enabling IoT Continuous Connectivity in Smart Spaces

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22nd International Symposium on Parallel and Distributed Computing, ISPDC 2023

作者： Andreou, Andreas Mavromoustakis, Constandinos X. Batalla, Jordi Mongay Dobre, Ciprian Markakis, Evangelos Mastorakis, George University of Nicosia University of Nicosia Research Foundation Departement of Computer Science Nicosia Cyprus Warsaw University of Technology Warsaw Poland University Politehnica of Bucharest National Institute for Research and Development in Informatics Automatic Control and Computers Bucharest Romania Hellenic Mediterranean University Department of Electrical and Computer Engineering Heraklion Greece Hellenic Mediterranean University Department of Management Science and Technology Agios Nikolaos Greece

ISBN: (纸本)9798350341270

Smart spaces are a rapidly emerging concept in technology. They result from the convergence of various novel technologies, such as the Internet of Things, Machine Learning and Artificial Intelligence, which allow for greater levels of automation and control within physical environments. The devices which are connected to the IoT network are equipped with sensors to acquire and exchange data. As a result, the IoT has transformed how we live, work, and play. However, the deployment in smart spaces is not always the best due to the issues arising from network node positioning. Therefore, we are investigating solutions to this problem with a novel approach which utilises Voronoi diagrams in conjunction with the algorithmic genetic technique. First, the initial positions of the IoT nodes will be determined by simulating a homogeneous Poisson point process in the smart space environment. Then, after dividing the area into the Voronoi cells, the genetic algorithm will optimise the position towards achieving full network coverage within the smart space. Experimental results prove the 100% network coverage within the specified area. © 2023 IEEE.

关键词： Genetic algorithms

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Out-of-distribution Object Detection through Bayesian Uncertainty Estimation

arXiv

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arXiv 2023年

作者： Zhang, Tianhao Wang, Shenglin Bouaynaya, Nidhal Calinescu, Radu Mihaylova, Lyudmila Automatic Control and Systems Engineering The University of Sheffield United Kingdom Department of Electrical and Computer Engineering Rowan University United States Department of Computer Science University of York United Kingdom

The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many practical applications, out-of-distribution (OOD) instances are inevitable and usually lead to uncertainty in the results. In this paper, we propose a novel, intuitive, and scalable probabilistic object detection method for OOD detection. Unlike other uncertainty-modeling methods that either require huge computational costs to infer the weight distributions or rely on model training through synthetic outlier data, our method is able to distinguish between in-distribution (ID) data and OOD data via weight parameter sampling from proposed Gaussian distributions based on pre-trained networks. We demonstrate that our Bayesian object detector can achieve satisfactory OOD identification performance by reducing the FPR95 score by up to 8.19% and increasing the AUROC score by up to 13.94% when trained on BDD100k and VOC datasets as the ID datasets and evaluated on COCO2017 dataset as the OOD dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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Reinforcement learning-enhanced dynamic multi-objective evolutionary algorithm for cooperative shovel scheduling in open-pit mining

Reinforcement learning-enhanced dynamic multi-objective evol...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Zihao Li, Changhe Yang, Shengxiang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Artificial Intelligence Anhui University of Science & Technology Hefei232001 China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

ISBN: (纸本)9798350368604

Aiming at the ore blending problem in the open-pit mining scenario, the optimization objective is to minimize the grade variance of the sliding window of the ore flow, and a cooperative shovel scheduling model for real-time ore blending is established. A reinforcement learning-enhanced dynamic multi- objective evolutionary algorithm is proposed, where a Q-learning operator selection mechanism is introduced to reuse the information from the previous environment for tracking the optimal solution in the dynamic environment. Experimental results show that the proposed method can effectively control the grade fluctuation of the ore flow and dynamically respond to external events, and can find a better solution in real time compared with the traditional operator selection algorithm. © 2024 IEEE.

关键词： Open pit mining

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