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arXiv 2023年第8期8卷 4094页

作者： Ampountolas, Konstantinos The Automatic Control & Autonomous Systems Laboratory Department of Mechanical Engineering University of Thessaly Volos38334 Greece

This paper develops and investigates a dual unscented Kalman filter (DUKF) for the joint nonlinear state and parameter identification of commercial adaptive cruise control (ACC) systems. Although the core functionality of stock ACC systems, including their proprietary control logic and parameters, is not publicly available, this work considers a car-following scenario with a human-driven vehicle (leader) and an ACC engaged ego vehicle (follower) that employs a constant timeheadway policy (CTHP). The objective of the DUKF is to determine the CTHP parameters of the ACC by using real-time observations of space-gap and relative velocity from the vehicle's onboard sensors. Real-time parameter identification of stock ACC systems is essential for assessing their string stability, large-scale deployment on motorways, and impact on traffic flow and throughput. In this regard, L2and L∞string stability conditions are considered. The observability rank condition for nonlinear systems is adopted to evaluate the ability of the proposed estimation scheme to estimate stock ACC system parameters using empirical data. The proposed filter is evaluated using empirical data collected from the onboard sensors of two 2019 SUV vehicles, namely Hyundai Nexo and SsangYong Rexton, equipped with stock ACC systems;and is compared with batch and recursive least-squares optimization. The set of ACC model parameters obtained from the proposed filter revealed that the commercially implemented ACC system of the considered vehicle (Hyundai Nexo) is neither L2nor L∞string stable. Copyright © 2023, The Authors. All rights reserved.

关键词： Adaptive cruise control

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A Power-Communication Co-Simulation Platform for Real-Time Microgrid Cyber Security Analysis

A Power-Communication Co-Simulation Platform for Real-Time M...

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2024 International Conference on Artificial Intelligence of Things and systems, AIoTSys 2024

作者： Qiu, Dongmeng Liu, Mengxiang Zhang, Xin University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (纸本)9798331507886

The rapid digitalisation process in power system has greatly benefited the large-scale integration of distributed energy resources (DERs), essentially accelerating the progress towards net zero. However, numerous cyber attacks such as false data injection (FDI) attacks are impeding the normal control and operation activities. This paper aims to establish a real-Time microgrid co-simulation platform to fully study the impacts of realistic attack events on micorgrid's control performance. Although there exist a large number of power-communication co-simulation platforms for the power system, they are designed to mainly provide validation services for control and operation algorithms and little attention has been paid to the cyber security. In the established microgrid platform, power and communication parts are simulated in Typhoon Hardware-in-The-loop(HIL) 602+ and EXata Network Simulator, respectively, and a dedicated synchronisation scheme is developed and embedded in Raspberry Pis to achieve master-slave data exchange between them. Based on the advanced cyber libraries provided by EXata, realistic FDI and packet drop attacks are mounted against the mapped communication links within EXata. The results indicate that these cyber attacks can severely influence the microgrid control performance including destabilising system states and specific state deviations, further demonstrating the necessity of deploying effective cyber-resilience enhancement mechanisms. © 2024 IEEE.

关键词： Cyber attacks

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Learning Input Driven Dynamic Bayesian Networks with Measurement Noise 23

Learning Input Driven Dynamic Bayesian Networks with Measure...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Veres, David Li, Ping Kadirkamanathan, Visakan The University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (纸本)9798350374889

Dynamic Bayesian Networks (DBNs) are useful tools for modelling complex systems whose network representations can be elicited a priori or learnt from data. In this paper, a maximum likelihood Doubly-Iterative Expectation Maximization (DI-EM) Algorithm is developed for the identification of grey-box ARMAX state-space model representations of DBNs involving known, noisy measurement processes. The grey-box model incorporates network dependencies among time series variables and exploits time series data of low longitudinal and high cross-sectional dimensions. A network learning procedure is developed using a score-based structure-selection method to find the underlying network of an input-driven dynamical system. By computing a finite data version of the Bayesian Information Criterion (BIC) for small sample sizes, the proposed method's performance is investigated on simulated and real-world data. The algorithm recovers the underlying ground-truth networks of simulated systems under finite data criteria with Jaccard Coefficient values of up to 0.84, and selects structures with improved weighted mean-squared error loss over a baseline black-box model fit on real-world data. © 2024 IEEE.

关键词： Expectation maximization algorithm

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Optimized Spatial Filter Selection for Transfer Learning in Brain-Computer Interface 3

Optimized Spatial Filter Selection for Transfer Learning in ...

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3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： A A Mohamed, Mohamed Giles, Joshua Arvaneh, Mahnaz University of Sheffield Automatic control and Systems Engineering Department Sheffield United Kingdom

ISBN: (纸本)9798350378009

The Common Spatial Patterns (CSP) algorithm has shown great efficacy in extracting features for Brain-Computer Interfaces (BCIs), particularly in motor imagery BCIs. However, CSP performs poorly when dealing with limited labeled data, which leads to long calibration time at the beginning of each session. To overcome this challenge, we leverage source data, i.e., labeled data from other subjects, to transfer knowledge across subjects. This work proposes a novel approach termed Scaled and Warped CSP-based Transfer Learning (SW-CSP-TL). This method aligns source data with the temporal and amplitude structure of target data before computing CSP filters, ensuring optimization for capturing discriminative features relevant to the target domain. We evaluate the proposed SW-CSP-TL algorithm using the publicly available dataset 2a from BCI Competition IV and compare its performance with that of the classical CSP algorithm and the transfer learning CSP algorithm (CSP-TL) without amplitude scaling and temporal warping. Our algorithm exhibits superior performance particularly when the training data size is relatively small. Results demonstrate that for 5 and 15 training trials per class from the target session, SW-CSP-TL outperformed the classical CSP by an average of 10%, and 8%, respectively. © 2024 IEEE.

关键词： Transfer learning

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A Lightweight Target Following Architecture for Indoor Mobile Robot 25

A Lightweight Target Following Architecture for Indoor Mobil...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Wang, Fuli Hutabarat, Windo Tiwari, Ashutosh The University Of Sheffield Department Of Automatic Control And Systems Engineering Sheffield United Kingdom

ISBN: (纸本)9798350340266

Manufacturing efficiency and transport operations are being significantly improved by mobile robots. As the implementation of a configurable, lightweight, and stateof-the-art robotic system is required for current manufacturing sectors to maximize the use of mobile robots, this paper presents a software architecture comprised of perception and action systems for human following in real time. The perception system uses the YOLOv8 computer vision model to identify and estimate human pose. Additionally, an algorithm is developed using the 3D information from a stereo camera to determine which target and whether to follow. Once the perception system perceives the desired target information, the action system can control and coordinate the robot using the ROS. Experimental results from the physical robot demonstrate the feasibility of the system architecture. © 2024 IEEE.

关键词： Mobile robots

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Creating and Studying a Methodology for Calculating a Resistive Voltage Divider for Use in Electronic Voltage Transformers 7

Creating and Studying a Methodology for Calculating a Resist...

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7th International Scientific and Technical Conference Relay Protection and Automation, RPA 2024

作者： Yablokov, A.A. Panashchatenko, A.V. Gotovkina, E.E. Maltsev, R.A. Ivanovo State Power Engineering University Electric Power Systems Automatic Control Department Electric Systems Department Ivanovo Russia

ISBN: (纸本)9798331501105

In 2017 the Russian government approved a programme, Russian Federation Digital Economy, the main goal of which is to create an ecosystem with large scale use of digital format data in all fields of social and economic activity [1]. The electric power industry is being transformed in accordance with the concept, Digital Transformation 2030, developed by Russian Power Grids PLC [2]. Pursuant to this concept, electric substations under construction should be digital. At present, Russia has a number of operating digital substations (DSS) (Medvedevskaya SS, UVAT SS, Desna SS, Tobol SS [3]-[5], while many DSSs are at the design or construction stage. The creation of new substations, and also reconstruction of already operating ones, requires solving the problems of operating conventional electromagnetic current and voltage transformers. Electromagnetic voltage transformers (VT) are energy-inefficient, explosion - and flammability- hazardous equipment (due to their requiring additional charging with low consumption by micro-processing devices), with large weight and dimensions (an NKF-100 type transformer weighing 840 kg). © 2024 IEEE.

关键词： Distribution transformers

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Pneumonia Detection from X-ray Images Using Convolutional Neural Networks 24

Pneumonia Detection from X-ray Images Using Convolutional Ne...

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24th International Conference on control systems and Computer Science, CSCS 2023

作者： Dediu, Viorel Udrea, Andreea Faculty of Automatic Control and Computer Science Univesity Politehnica of Bucharest Department of Automatic Control and Systems Engineering Bucharest Romania

ISBN: (纸本)9798350313390

Pneumonia is one of the top causes of death in Romania and early detection of this disease improves the recovery chances and shortens the length of hospitalization. In this work, we develop a solution for automatic pneumonia detection based on convolutional neural networks. Four network models are investigated. They are trained on 4.163 images from a public dataset and tested on 530 images. The best results are obtained by one of the proposed models conducting to a sensitivity of 98.72%, an accuracy of 89.81%, and ROC 93.46%. Thus, this research proposes a lightweight screening tool that can help triaging the patients with pneumonia. © 2023 IEEE.

关键词： Convolution

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Developing a Novel MATLAB control Toolbox for Encouraging Student Engagement 6

Developing a Novel MATLAB Control Toolbox for Encouraging St...

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6th Experiment at International Conference, exp.at 2023

作者： Rossiter, John Anthony University of Sheffield Department of Automatic Control and Systems Engineering United Kingdom

ISBN: (纸本)9798350314403

The numerous toolboxes within MATLAB [12] to aid student engagement and learning largely require deep understanding of coding, as well as the associated engineering. This paper discusses some work in progress which is focussing on developing files and resources for a control 101 course [17] which are both engaging and also have much lower pedestals of understanding before use. The intention is focus on attracting student interest and communicating core concepts before students need to begin the longer process of deep learning. Thus this toolbox fills a large gap in the current provision. © 2023 IEEE.

关键词： Students

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Utilization of Uniform Circular Array for Estimating RF Source Location in Real-time

Utilization of Uniform Circular Array for Estimating RF Sour...

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2024 International Conference on control, Automation and Diagnosis, ICCAD 2024

作者： Alsayegh, Hamad Almuhaihi, Mohammad University of Sheffield Automatic Control and Systems Engineering Department United Kingdom University of Michigan Aerospace Engineering Department United States

ISBN: (纸本)9798350361025

This Radio Frequency (RF) source location estimation scheme utilizes angles of arrival of a signal source acquired from the Multiple Signal Classification (MUSIC) algorithm. In reliance on that as a Maximum Likelihood estimation, the obtained angles are augmented to an adaptive probability distribution function (PDF), which is then employed to estimate the location of the RF emitter by feeding the PDF to an Extended Kalman Filter (EKF). Numerous use cases and theoretical works have been presented in the literature. Some of these papers mention applications regarding implementing DoA algorithms in dynamic platforms like in [9], [10], [11], [12], and [13] mostly all of them use Uniform Linear Array (ULA), four of them working with Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT), and one with MUSIC Direction of Arrival (DoA) method. In [14], MUSIC was used with a Uniform Circular Array (UCA). However, less coupling has been incorporated with the covariance of measurement error. In this paper, the dynamic 2D DoA MUSIC algorithm used in a platform, applied together with an EKF estimation and coupled with measurement covariance in a simulation environment to precisely locate an RF emitter and provide high-resolution Line-of-Sight (LOS). © 2024 IEEE.

关键词： Multiple signal classification

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A Machine-Learning Approach for Prognosis of Oscillating Water Column Wave Generators 27

A Machine-Learning Approach for Prognosis of Oscillating Wat...

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27th International Conference on Circuits, systems, Communications and Computers, CSCC 2023

作者： Garrido, Izaskun Lecube, Jon Mzoughi, Fares Aboutalebi, Pavam Ahmad, Irfan Cayuela, Salvador Garrido, Ailtor University of the Basque Country-UPV/EHU Automatic Control Group Acg Department of Automatic Control and Systems Engineering Bilbao Spain

ISBN: (纸本)9798350337594

Wave excitations cause structural vibrations on the Oscillating Water Columns (OWC) lowering the generated power and reducing the life expectancy. The problem of generator deterioration has been considered for the Mutriku MOWC plant and a machine learning-based approach for prognosis and fault characterization has been proposed. In particular, the use of k-Nearest Neighbors (kNN) models for predicting the time to failure of OWC generators has been proposed. The analysis is based on data collected from sensors that measure various operational parameters of the turbines. The results demonstrate that the proposed kNN model is an excellent choice for reducing maintenance costs by enabling scheduling months in advance. The model's high accuracy in predicting generator failures allows for timely and cost-effective maintenance, preventing costly breakdowns and improving turbine efficiency. These results highlight the potential of machine learning-based approaches for addressing maintenance challenges in the energy sector and underscore the importance of proactive strategies in reducing operational costs and maximizing energy production. © 2023 IEEE.

关键词： Cost effectiveness

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