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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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CDQKL: Consensus-Based Distributed Quantum Kernel Learning 1

CDQKL: Consensus-Based Distributed Quantum Kernel Learning

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1st International Conference on Quantum Communications, Networking, and Computing, QCNC 2024

作者： Ma, Wenxuan Liu, Mengxiang Deng, Ruilong State Key Laboratory of Industrial Control Technology The College of Control Science and Engineering Zhejiang University Hangzhou310027 China University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (纸本)9798350366778

The field of quantum computing has developed rapidly in recent years due to its promising trend of surpassing traditional machine learning in terms of speed and effectiveness. Quantum kernel learning is one of the paradigms of quantum machine learning, but the training of quantum kernel is time consuming. Therefore, this work makes the first attempt to introduce a consensus-based distributed approach to quantum kernel learning - named CDQKL - that only requires to exchange model parameter information between adjacent nodes while avoiding the need of sharing local training data. Through comparative experimental studies, the advantages of CDQKL in classification accuracy and convergence speed are verified. Considering the popularization of quantum computing cloud service and miniaturization of quantum terminals, the CDQKL adapting to this trend is able to play a vital role in data security, which implies the far-reaching significance of this work. Our code is available at https://***/Leisurivan/CDOKL. © 2024 IEEE.

关键词： Quantum computers

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Segmentation of Retina Vessels in 2D OCT-Reconstructed Fundus Images with 3D UNet 26

Segmentation of Retina Vessels in 2D OCT-Reconstructed Fundu...

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26th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Marciniak, Tomasz Stankiewicz, Agnieszka Zaradzki, Przemyslaw Poznan University of Technology Division of Electronic Systems and Signal Processing Institute of Automatic Control and Robotics Department of Control Robotics and Electrical Engineering Poznan Poland

ISBN: (纸本)9798350304985

This paper presents an analysis of the effectiveness of blood vessel segmentation in human retinal images. The segmentation process was carried out for three-dimensional optical coherence tomography (OCT) scans using the author's publicly available CAVRI-C database. A 3D version of the U-Net artificial neural network was used. Various dimensions of the input blocks were investigated to determine their influence on the segmentation process. The effectiveness of the proposed solutions was evaluated using various metrics and the results obtained were compared with the 2D solution, also based on neural networks. The use of the proposed solutions allowed us to improve the segmentation even by 15% (in the case of the F1-score). © 2023 Division of Signal Processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： Optical tomography

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A Review on Defense Mechanism against the Denial of Service and False Data Injection in Cyber-Physical Power systems 6

A Review on Defense Mechanism against the Denial of Service ...

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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Zhou, Zhuoran Zhang, Jinning Zhang, Xin University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom School of Engineering University of Leicester Leicester United Kingdom

ISBN: (纸本)9798350346671

Cyber-physical power system (CPPS), with its bi-directional power and information flows, is considered as the next generation of widely distributed and automated electrical power network. However, CPPS is vulnerable to malicious cyberattacks that can cause significant operational risk and financial loss to stakeholders, such as electricity system operators. The complexity of vulnerabilities, which arises from the heterogeneity and interdependency of physical and cyber domains, will present substantial challenges for the detection and prevention of cyberattacks. This paper provides a comprehensive review of the defense mechanisms in CPPS against coordinated and widespread cyberattacks. The most prevalent cyberattacks in the CPPS are denial-of-service (DoS) and false data injection (FDI) attacks. This paper examines the state-of-the-art prevention, detection, and mitigation technologies for these major mainstreams and coordinated cyberattacks. In particular, the advantages and disadvantages of popular cyberattack prevention sciences such as Game Theory, Optimization, control Theory, and Data-driven method are discussed. It reveals that cyberattack detection techniques have evolved from traditional state estimation to artificial intelligence-based methods. Finally, the challenges and opportunities for future research on cybersecurity in CPPS are identified. © 2023 IEEE.

关键词： Game theory

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Decision Making For Multi-Robot Fixture Planning Using Multi-Agent Reinforcement Learning

TechRxiv

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TechRxiv 2024年

作者： Canzini, Ethan Auledas-Noguera, Marc Pope, Simon Tiwari, Ashutosh Department of Automatic Control & Systems Engineering University of Sheffield United Kingdom Airbus Robotics

Within the realm of flexible manufacturing, fixture layout planning allows manufacturers to rapidly deploy optimal fixturing plans that can reduce surface deformation that leads to crack propagation in components during manufacturing tasks. The role of fixture layout planning has evolved from being performed by experienced engineers to computational methods due to the number of possible configurations for components. Current optimisation methods commonly fall into sub-optimal positions due to the existence of local optima, with data-driven machine learning techniques relying on costly to collect labelled training data. In this paper, we present a framework for multiagent reinforcement learning with team decision theory to find optimal fixturing plans for manufacturing tasks. We demonstrate our approach on two representative aerospace components with complex geometries across a set of drilling tasks, illustrating the capabilities of our method;we will compare this against state of the art methods to showcase our method’s improvement at finding optimal fixturing plans with 3 times the improvement in deformation control within tolerance bounds. © 2024, CC BY.

关键词： Reinforcement learning

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Counting assistant through multi-sensor fusion for inventory monitoring 27

Counting assistant through multi-sensor fusion for inventory...

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27th International Conference on Automation and Computing, ICAC 2022

作者： Lin, Ruofan Tiwari, Ashutosh Oyekan, John The University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

A large number of parts are used in the production activities of the assembly plant. The quantity and quality of these parts in the process of transportation, distribution, use, and recycling in the factory have an impact on production. With large amount of parts and tools, how to provide the right types and quantity of parts at the time and right location becomes the key to business success. The traditional method uses manpower which is costly and time-delayed. Previous single sensor monitoring has its own limitations. For example, weight recognition is hard to determine when multiple type items are mixed. Visual identification is susceptible to problems such as occlusion and difficulty in identifying dense areas and every item needs to be tagged by using RFID. Therefore, this paper introduces the multi-sensor method, which uses a mathematical approach to fuse weight and visual data to predict different types and quantities of parts in small containers. This study also provides a case study, simulated classification and counting of Aircraft General Standard(AGS) parts, which are mainly fasteners. They are small in size, expensive, and difficult to identify by a single sensor. Fusion results show up to 80% in average accuracy which is an improvement over single vision(73%) and weight detection up to 50%. © 2022 IEEE.

关键词： Aircraft

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Multi-Task Learning for Time Series Forecasting Using NARMAX-LSTM 27

Multi-Task Learning for Time Series Forecasting Using NARMAX...

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27th International Conference on Automation and Computing, ICAC 2022

作者： Nikentari, Nerfita Wei, Hua-Liang University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

Traditional time series forecasting models based on neural networks are usually designed for a single task, which ignores the source of knowledge or information from other related tasks. On the other hand, multitask learning (MTL) offers a new perspective where one model is not only for one forecasting case or task but several tasks at the same time. The MTL model proposed in this study is based on the Nonlinear AutoRegressive Moving Average with eXogenous input (NARMAX) model and Long-Short Term Memory (LSTM) Neural Networks. The NARMAX-LSTM model is tested and validated using three different time horizons and different cases/tasks. The model forecasting performance is compared with that of three other linear and nonlinear models. The comparison results show that the multi-task NARMAX-LSTM model does not only have better prediction performance but also avoid suffering from negative transfer and overfitting. © 2022 IEEE.

关键词： Forecasting

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Efficient Mask Attention-Based NARMAX (MAB-NARMAX) Model Identification 27

Efficient Mask Attention-Based NARMAX (MAB-NARMAX) Model Ide...

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27th International Conference on Automation and Computing, ICAC 2022

作者： Sun, Yiming Wei, Hua-Liang University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

Model structure selection is crucial in system identification and data-driven modelling. Many spurious candidate variables can influence the determination of model structures due to the lack of prior knowledge of the system of interest. The commonly used method is to test as many possible models as possible and select a set of best models. This study proposes a novel mask attention-based NARMRAX (MAB-NARMAX) modelling method for nonlinear dynamic system identification. The mask attention mechanism comes from the widely used neural network Transformer to reduce the dependency of the features and neurons. The performance of the proposed method is tested on three simulation datasets. Results show that the proposed MAB-NARMAX modelling framework has convincing multistep-ahead prediction performance for nonlinear system identification in that it can produce the precious model structure. Even when the data are polluted with high noise (resulting in low SNR), the proposed method can still generate reliable system models compared with the state-of-the-art machine learning methods, e.g., LASSO and LSTM. © 2022 IEEE.

关键词： Signal to noise ratio

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Machine Learning for Medical and Healthcare Data Analysis and Modelling: Case Studies and Performance Comparisons of Different Methods 27

Machine Learning for Medical and Healthcare Data Analysis an...

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27th International Conference on Automation and Computing, ICAC 2022

作者： Sun, Bo Wei, Hua-Liang University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

In smart healthcare, binary classification is one of the most important tasks for disease or clinical outcome prediction. Machine learning (ML) methods have great potential to discover knowledge from data. While there are a wide range of ML methods that are potentially useful, the selection, design and implementation of an appropriate method from a variety of ML models for specific prediction tasks is still quite challenging in many medical and healthcare data modelling tasks. This paper investigates, evaluate and compares nine typical representative ML methods using four clinical and healthcare datasets. The main attention is paid to the strengths and weaknesses of these methods and provide suggestions to researchers in related fields. Results demonstrate that ensemble learning (such as Adaboost and random forest) outperform the other approaches in prediction accuracy and interpretability. © 2022 IEEE.

关键词： Adaptive boosting

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A Hybrid 1DCNN-SVM Model for Bearing Fault Recognition and Classification 27

A Hybrid 1DCNN-SVM Model for Bearing Fault Recognition and C...

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27th International Conference on Automation and Computing, ICAC 2022

作者： Wang, Guoliang Wei, Hua-Liang University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

Rolling bearings play a significant role in the operation of rotating machinery. Finding bearing faults in the early stage can not only keep machinery running safely but also avoid economic loss. Traditional machine learning (ML) and deep learning (DL) are two important classes of fault diagnosis methods. The process of feature extraction is significant for ML based methods and many signal processing techniques have been used in this process. However, when dealing with massive and complex signals, features extracted by these techniques may not fully describe the characteristics of faults. Convolutional neural network (CNN) models are applied in many areas, including image identification and classification, pattern recognition, and speech processing. In this paper, a novel hybrid model by combining one dimensional CNN (1DCNN) and support vector machine (SVM) is proposed for bearing fault diagnosis and classification, which is called 1DCNN-SVM model or method. The first part of the model, 1DCNN, is used to extract features of raw data, whereas the second part, SVM, is employed to carry out the fault recognition and classification tasks. The evaluation of the proposed method is operated based on a real dataset. The experiment results show that the fault diagnosis accuracy of proposed method based on the hybrid model is highly improved compared to the traditional SVM based methods. © 2022 IEEE.

关键词： Failure analysis

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